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The rate of living in miceVaanholt, Lobke Maria

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The Rate of Living in Mice:Impacts of activity and temperature on

energy metabolism and longevity

The research reported in this thesis was carried out at the Behavioural Biology Group atthe University of Groningen, The Netherlands and at the Rowett Institute in Aberdeen,Scotland. All studies were approved by the Ethical Committee of the University ofGroningen (DEC 2777(-1), DEC 3039(-1), DEC 3128 and DEC 4184A). Production ofthis thesis was partly funded by the University of Groningen and the research school ofBehavioural and Cognitive Neurosciences (BCN). Additional financial support camefrom UNO Roestvaststaal BV in Zevenaar and Harlan Netherlands BV in Horst.

Cover: Lobke Vaanholt and Dick Visser

Lay-out and figures: Dick Visser

Photos: Lobke Vaanholt and Kristin Schubert

Printed by: Ponsen en Looijen b.v., Wageningen

ISBN: 9789036729444ISBN: 9789036729451 (electronic version)

RIJKSUNIVERSITEIT GRONINGEN

The Rate of Living in Mice:Impacts of activity and temperature on

energy metabolism and longevity

PROEFSCHRIFT

ter verkrijging van het doctoraat in deWiskunde en Natuurwetenschappenaan de Rijksuniversiteit Groningen

op gezag van de Rector Magnificus, dr. F. Zwarts,in het openbaar te verdedigen op

vrijdag 23 maart 2007om 16.15 uur

door

Lobke Maria Vaanholt

geboren op 20 februari 1979te Enschede

Promotores: Prof. dr. S. DaanProf. dr. G.H. Visser

Beoordelingscommissie: Prof. dr. A.J.W. ScheurinkProf. dr. J.R. SpeakmanProf. dr. R.J.G. Westendorp

Chapter 1 General introduction 7

Part I – ACTIVITY & METABOLISM

Chapter 2 Wheel-running activity and energy metabolism in relation to ambient 19temperature in mice selected for high wheel-running activity

Chapter 3 Behavioural and physiological responses to increased foraging effort 35in male mice

Chapter 4 Plasma adiponectin is increased in mice selectively bred for 57high wheel-running activity, but not by wheel running per se

Chapter 5 Responses in energy balance to high-fat feeding in mice selectively-bred 71for high wheel-running activity

Part II – METABOLISM & AGEING

Chapter 6 Life span, body composition, and metabolism in mice selected for 91high wheel-running activity and their random-bred controls

Chapter 7 Protein synthesis and antioxidant capacity in ageing mice: 111effects of long-term voluntary exercise

Chapter 8 Ageing under cold conditions: effects on body composition, 127metabolism and longevity

Chapter 9 Protein synthesis and antioxidant capacity in ageing mice: 147effects of life-long cold exposure

Chapter 10 General Perspective 161

References 177

List of abbreviations 193

Nederlandse samenvatting – Dutch summary 195

Addresses of co-authors 203

Dankwoord – Acknowledgements 205

Contents

General introduction

Chapter1

“Death is the only certainty you have in life”

In evolutionary biology, ageing is usually defined as a persistent decline in the age-specific fitness components of an organism due to internal physiological deteriora-tion. This definition integrates effects on reproduction and survival. Gerontologistssimply define ageing as an increase in the likelihood that an individual will die in acertain time interval. As we age, intracellular processes degenerate and ultimatelyfail. This can lead to age-related diseases, such as cardiovascular disease, Parkin-son’s disease etc., and ultimately to death. There has been much speculation on therole of energy metabolism in the causation of these processes. This has led to theformation of several intriguing theories which attribute the causation of death ulti-mately to the very motor of life itself; the rate of living theory (Pearl, 1928; Rubner,1908) and free radical theory of ageing (Harman, 1956). This idea was summarized by Murray (1926) in the statement:

‘If aliveness is measured by the velocity of chemical activity (heat production) an organismmay in this sense be said to dig its own grave. The more abundant its manifestations of life,

the greater will be its rate of senescence’.

The primary aim of this thesis was to investigate the relationship between ageingand metabolic rate. In two large-scale experiments I manipulated the energy expen-diture of a group of animals by either increasing their physical activity or exposingthem to cold. Survival curves were created for different experimental groups and Ilooked at changes that occurred in several physiological parameters that might beinvolved in ageing to explain differences that occurred in life span (Part II:Metabolism & Ageing). In addition, I explored the behavioural and physiologicalconsequences of changes in energy balance in mice that had been selectively bredfor high levels of spontaneous physical activity (Part I: Activity & Metabolism).

ACTIVITY & METABOLISM

Life history theoryThe evolution of life histories has been explained by the presence of limitedresources that results in trade-offs between survival (maintenance of the body) andreproduction (Stearns, 2000). In times of plenty, resources can be allocated togrowth and reproduction, but when resources are scarce, energy has to be allocatedto enable survival of the individual and future success. In many species the repro-ductive season is tuned to coincide with the peak in food availability. When food isscarce, reproduction ceases and energy is allocated to increase the chance of sur-vival into the next year. There is a large variation in the way animals deal with suchtrade-offs. When there is a genetic basis for these decisions, natural selectionfavours life-history traits that result in a higher fitness. The main environmental

Chapter 18

factors influencing the available resources for endothermic animals are environmen-tal temperature and food availability.

In part I of this thesis we investigated the effects of low ambient temperaturesor food availability on metabolism and the amount of voluntary activity mice werewilling to engage. We used mice that had been selected by T. Garland Jr. for highwheel-running activity and their random-bred controls. Detailed description of theselection protocol and the main characteristics of these mice is provided in box 1.1.The amount of wheel-running activity was the selection criteria. After 31 genera-tions of selection the mice ran approximately 2,7 times as much as control animals(Rhodes et al., 2005). With the selection for wheel-running activity other traits havebeen co-selected (i.e. small body size) and much research has been undertaken touncover these co-selected traits.

In chapter 2 we investigate mice exposed to various ambient temperatures. Wemeasured wheel-running activity and metabolic rate simultaneously to determinewhether high-activity mice have evolved to have a lower running economy andwhether they would more likely use heat generated by activity to substitute forthermoregulatory heat at low ambient temperatures than control mice do. In chap-ter 3 we manipulated food availability using a system in which animals had to runin a running wheel for a set number of revolutions to obtain a food pellets. Thisapproach was used to study effects of food availability on physiological and behav-ioural responses in control and selected mice. Previous studies in rats by T. Adageshowed that rats with low spontaneous levels of wheel-running activity have moredifficulties to cope with a workload schedule than rats with high spontaneous levelsof wheel-running activity. Similar effects were expected between control and high-activity mice.

Exercise & obesityObesity is becoming an increasingly prevalent health problem in affluent societies.It is often associated with metabolic derangements such as impaired glucose toler-ance, insulin resistance, high blood pressure, dyslipidemia, and abdominal obesity.When these metabolic abnormalities are displayed in concert (often referred to asthe “metabolic syndrome”), they entail a high risk of developing into life-threaten-ing conditions such as cardiovascular disease and diabetes mellitus type 2 (forreview see (Carroll and Dudfield, 2004; Moller and Kaufman, 2005)). Increaseddietary fat intake in combination with a sedentary existence are factors precipitatingthe development of obesity and the associated metabolic syndrome. Adipose tissueproduces several hormones, such as leptin and adiponectin, that are important forenergy homeostasis. Levels of these hormones are associated with metabolic riskfactors. Adiponectin levels are decreased and leptin levels increased in obese com-pared with lean subjects (Park et al., 2004).

Mice that have been selected for high wheel-running activity (for detaileddescription see box 1.1.) have decreased levels of leptin even when correcting forfat mass (Girard et al., 2006). Leptin is produced by adipose tissue and informs the

General introduction 9

body about its available fat stores and is involved in regulating food intake. Selectedmice have a high food intake to cover the increased costs of wheel-running activity(Swallow et al., 2001), and lowering levels of leptin may be an adaptation toincrease food intake and maintain energy balance.

High-activity mice have a lean phenotype (Dumke et al., 2001; Swallow et al.,1999) and adiponectin levels are thus expected to be increased in high-activitymice. This together with low levels of leptin might make high-activity mice lessprone to develop metabolic derangements on a high-fat diet and would make thesemice a suitable model to study the metabolic syndrome.In chapter 4 we measured hormone levels of leptin, adiponectin and corticosteronein aging male mice selected for high wheel-running activity and their random-bredcontrols. We studied correlations between the hormones and body composition. Inchapter 5 we describe a study in which we exposed selected males and females to ahigh fat diet. Body composition, food efficiency, energy metabolism and glucose tol-erance were tested to determine whether high-activity mice responded differently toa high-fat diet than controls.

METABOLISM & AGEING

“Rate of living” and “free radical” theory of ageingInstinctively we know that things (cars, machines) break down faster if you usethem more often and more intensively. The same might be applicable to animal(and human) life. This notion that the rate of energy turnover determines the rateof breakdown is known as the “rate of living” theory (Pearl, 1928). In 1908, Rubnernoted that food intake per gram decreased with increasing life span among fivedomestic animals (guinea pig, cat, dog, cow and horse). He calculated the energyintake per gram per life span (life-time energy potential, LEP) and found that thevariation in LEP between species was small (1,5 fold), although the variation inbody mass was very large. Including data for men the variation in life-time energyexpenditure was slightly larger, but still only 5-fold. He concluded that mass-specif-ic energy metabolism times the maximal lifespan was a constant (Rubner, 1908).Energy metabolism might thus be the factor that determines our life span. In 1928Pearl postulated the “rate of living theory” that states that there is an inverse rela-tionship between energy expenditure and life span (Pearl, 1928). An extensive bodyof evidence exists that is consistent with this theory. Comparative studies haveshown that energy expenditure tends to show an inverse relationship with bodysize and longevity when compared across mammalian or bird species (Ku et al.,1993; Speakman, 2005a). Also evidence from intra-specific studies show evidencefor the rate of living theory. Increasing ambient temperature (and thereby energyexpenditure) decreased life span of nematodes proportionally (Van Voorhies andWard, 1999). Honey bees that were forced to fly with extra loads had decreased lifespans (Wolf and Schmid-Hempel, 1989), and flies prohibited to fly (thereby

Chapter 110

decreasing metabolic rate) had increased life spans (Yan and Sohal, 2000). Broodsize increases in kestrels resulted in increased energy turnover and a subsequentdecrease in the survival of parents that had enlarged broods (Daan et al., 1996). Inhibernating hamsters survival was higher than in hamsters that did not hibernate(Lyman et al., 1981). A moderate increase of the level of basal metabolism of youngadult rats adapted to hypergravity compared to controls in normal gravity wasaccompanied by a roughly similar increase in the rate of organ aging and reductionof survival (Economos et al., 1982). In contrast there are also numerous studiesthat showed no relationship or a positive relationship between energy expenditureand life span (in mammals (Holloszy and Smith, 1987; Holloszy and Smith, 1986;Navarro et al., 2004; Speakman and Selman, 2003; Speakman et al., 2004)), andcomparative studies show that for a certain body mass birds expend up to 4 timesmore energy than a mammal, and live longer (Speakman, 2005b). Another line ofevidence comes from experiments on calorically restricted animals. Caloric restric-tion (CR; decreasing energy intake) is widely recognized as the only (non-genetic)manipulation that increases mean and maximum life span in mammals (first shownby (McCay et al., 1935)). In 1977 Sacher proposed that CR extended life span bydecreasing metabolic rate. A study by Masoro et al. found that following the initia-tion of CR there was a brief period of reduced food intake per gram body mass, butthis was followed by a lifetime where the intake per gram body mass was higher inCR rats than ad-libitum fed rats (Masoro et al., 1982). In a study where mass-spe-cific 24-h metabolic rates were measured mass-specific (based on lean mass) meta-bolic rates were reduced upon the initiation of CR, but increased to levels higherthan ad-libitum fed animals later on (McCarter and Palmer, 1992). Similar resultswere shown in rhesus monkeys (Ramsey et al., 1996). These studies disagree with arole for metabolic rate in the life extending effect of CR. Interpretation of theresults is confounded because metabolic rate is usually normalized for body mass orlean mass, whereas the relative sizes of organs are not the same for animals that areCR or fed ad libitum (Greenberg, 1999b; Greenberg and Boozer, 2000).

A related theory of ageing was suggested by Harman in 1956 known as the “freeradical theory” (Harman, 1956). This theory specifies the reason why there shouldbe a direct link between energy metabolism and the rate of ageing. Free radicals orradical oxygen species (ROS) are produced as by-products of normal oxidativephosphorylation, and can cause damage to macromolecules which may result inmalfunction and eventually cell death (for review see (Beckman and Ames, 1998)).The body has evolved defense systems against these radicals in the form of antioxi-dant enzymes (e.g. superoxide dismutase, catalase and gluthione peroxidase) thatscavenge ROS and transform them into less toxic products. A small amount of ROSescape conversion. If damage to macromolecules has occurred the processes ofDNA repair and protein synthesis can repair most of this damage. Despite thesedefense systems a small amount of damage still occurs and this accumulates withage resulting in malfunction of cells and eventually death (see Figure 1.1. for agraphical representation of the process). When energy expenditure (and oxidative


phosphorylation) increases, the production of ROS will also increase. This wouldexplain the relationship between ageing and metabolism proposed by the rate of liv-ing theory.

The relationship between oxidative phosphorylation and ROS production is notlinear. Oxidative phosphorylation takes place on the inner membrane of mitochon-dria as a result of the transport of electrons over the membrane (electron transportchain; ETC). The ETC consists of 4 complexes. NADH and FADH2 that have beenformed in the tricarboxylic acid cycle (TCA) donate their electrons to subsequentlycomplex I or II which are then passed on to ubiquinone (Q). Q moves across themembrane to complex III and the electrons are passed on to cytochrome C thatmoves on to complex IV where the electrons are accepted by molecular oxygen andcombined with protons to form water (for a more detailed description see (Brand,2000)). During this process protons are pumped across the membrane into theinner membrane space and a proton motive force builds up. When oxidative phos-phorylation is coupled these protons are pumped back to the matrix via an ATP-asepump resulting in the phosphorylation of ADP to ATP (ATP synthesis).

Chapter 112

METABOLISM

ROSPRODUCTION

DNA, LIPIDPROTEIN DAMAGE

AGEING

repair

uncoupling

antioxidant enzymes

ambienttemperature

activity

Figure 1.1. Schematic representation of the relationship between metabolism and ageing. Whenmetabolism (=O2 consumption) increases reactive oxygen species (ROS) are produced that cancause damage to DNA, lipids and proteins which may result in ageing. Several defence mecha-nisms can slow down this process (in dark grey). First of all uncoupling of the electron transportchain from ATP production decreases the amount of ROS produced at a certain metabolic rate.Second, once ROS are produced they can be scavenged before they cause damage by antioxidantenzymes, such as superoxide dismutase, catalase and glutathione peroxidase. Third, oxidativedamage to macromolecules can be repaired, i.e., DNA repair mechanisms, protein turnover. Onecan study these processes by experimentally increasing metabolic rate by decreasing ambienttemperature or increasing activity.

Free radicals are generated during oxidative phosphorylation when an oxygenmolecule promiscuously reacts with one of the transported electrons before itreaches complex IV. This can for instance occur when the supply of ADP is limitedthereby blocking up the system. Agents that increase respiration rate and therebylower proton motive force (i.e., ATP synthesis) thus lower the rate of ROS produc-tion. ROS production is thus not linearly related to the rate of electron transport.The flow of electrons in the ETC is usually tightly coupled to the production of ATP,and it does not occur unless the phosphorylation of ADP can proceed. This pre-vents a waste of energy, because high-energy electrons do not flow unless ATP canbe produced. If electron flow is uncoupled from the phosphorylation of ADP therewould be no production of ATP, and the energy of the electrons would be wasted asheat. Uncoupling agents abolish the link between oxidation and phosphoryalation,allowing electron transport to proceed without coupled ATP synthesis, therebyincreasing the respiration rate and lowering ROS production (Brand, 2000).Therefore, metabolic rate and free radical production are not necessarily linearlyrelated.

Many studies support the importance of antioxidants, oxidative stress and repairof oxidative damage for the ageing process. For instance, the importance of antioxi-dants enzymes is clear from studies with over-expression or knocking out of theseenzymes. Overexpression of catalase and superoxide dismutase in Drosophilamelanogaster increased median and maximum lifespan up to 30% (Orr and Sohal,1994; Sohal et al., 1995), and mice lacking manganese superoxide dismutase diedwithin 10 days (Li et al., 1995), whereas administration of superoxide dismutase-catalase mimetics increased lifespan up to three times in mice (Melov et al., 2001).In CR animals life span extending effects have also been attributed to differences inoxidative stress. Increased antioxidant enzyme activity, DNA repair and proteinsynthesis, and decreased numbers of oxidatively damaged molecules have beenshown in CR animals (for reviews see (Gredilla and Barja, 2005; Tavernarakis andDriscoll, 2002; Yu, 1996)).

Whereas the “free radical” theory has gained much support in recent years, therate of living theory has been discarded as invalid by many researchers based oninter-specific comparisons and the lack of effects on (or increases in) energy metab-olism in CR animals. This is remarkable since the free radical theory of ageing isitself the main theory postulating the mechanism connecting energy turnover andageing. As argued by Speakman (2002; 2005a) the reasons to dispute the theorymay not always be valid, because the arguments that are used to test the theory arefraught with problems. Firstly, maximum life span is not a good measure of ageing.Maximal life span is determined by a single point in every data base and is highlyaffected by the sample size used and also by the conditions in which animals arehoused (i.e. laboratory or natural conditions). Secondly, basal metabolic rates havebeen used in most studies to estimate life-time energy potential. Basal metabolicrate is the metabolism of an animal when fasting and resting at thermo-neutraltemperatures and contributes only 40% to the total daily energy expenditure. The


latter is a better measure of metabolism. Using a single measure of metabolic ratein the life time of an animal might not be sufficient to make an accurate estimate oflife-time energy potential. Thirdly, testing for consistency in life-time energy expen-diture per gram of tissue by inter-specific comparisons between birds and mammalsis not the best way to test the rate of living theory and inter-specific comparisonsare complicated by the fact that animals from different species may reflect adaptiveor genetic differences in free-radical production or differences in defence and repairmechanisms. Therefore, intra-specific comparisons are more convincing when look-ing at associations between energy expenditure and ageing. A fourth argumentrelates to the scaling of energy expenditure to body mass. Greenberg has shownthat in cases where no relation was found between life-time energy expenditure pergram body mass and life span, a relationship does exist when one calculates theenergy expenditure for certain metabolically active organs and relate this to lifespan (Greenberg, 1999). Life-time energy expenditure per gram dry lean body massinstead of total body mass might be a better measure to test the rate of living theo-ry since this contains the tissue that is metabolically most active. A stronger corre-lation between energy metabolism and dry lean mass is usually found then betweenbody mass and energy expenditure.

In studies on energy expenditure and life span almost never the body composi-tion and energy turnover are followed throughout life. In order to resolve some ofthe confusion in this area we carried out two large scale experiments. We manipu-lated energy metabolism by either increasing activity through selection (chapter 6)or by decreasing environmental temperature (chapter 8) and looked at the relation-ship between energy metabolism and survival in intra-specific comparison. Miceselectively bred for high wheel-running activity were used to investigate the effectsof increased voluntarily exercise. In the cold experiment, c57Bl/6J mice were usedthat were subjected to 10°C compared to 22°C in control mice. An additional groupthat was exposed to cold early in life was added. This for the first time tests onebasic implicit proposition in the rate of living and free radical theories: that theeffects of energy turnover are cumulative. Energy turnover increase in youth shouldstill have and effect in old age. In both experiments we paid specific attention to theeffects of age and experimental manipulation (i.e. cold or activity) on two systemsthat are involved in defending the body against ROS, the antioxidant defence sys-tem and protein turnover (chapter 7 and chapter 9).

Chapter 114


BOX 1.1: Mice selected for high wheel-running activity: selection proce-dure and main characteristics

Selection procedures were carried out by Theodore Garland Jr. et al. at the University ofWisconsin, Madison, USA and first described in 1998 by Swallow et al. (1998).

Selection procedure:Progenitors were 112 male and 112 female Hsd:ICR house mice (Mus domesticus)obtained from Harlan Sprague Dawley (USA) which are genetically heterogeneous(generation -2). These mice were randomly paired and their offspring (generation -1)was then randomly assigned to 1 of 8 lines so that every line contained 10 pairs ofmice. These pairs were again randomly paired within each line. In the next generation(generation 0) selection for high wheel-running activity started. In 4 of the lines (con-trol lines: 1, 2, 4 and 5) mice were paired randomly and at least 1 male and 1 femalefrom each family were chosen as breeders (control lines). In the other lines (selected:3, 6, 7 and 8) the highest running female and male from each family were chosen asbreeders. Three extra female and male (second highest runners from highest runningfamilies; never 2 from the same family) were chosen to ensure 10 families per genera-tion. Animals were paired at approximately 10 weeks of age and males were removed15-18 days after pairing (gestation length: 19 days). All offspring of selection familieswere kept and 2 males and 2 females from each control family. Selection took place at6-8 weeks of age, when mice were monitored for voluntary wheel running for 6 consec-utive days (wheel circumference= 1.12 m). The average number of wheel revolutionson day 5 and 6 were used as selection trait. In April of 2002 80 breeding pairs (10 perline) of generation 31 of selection were shipped to the Zoological laboratory in Harenand a new breeding colony was started at these facilities without further selection pre-venting sibling mating. Offspring these mice were used in the experiments described inthis thesis.

Main characteristics:The selection method has resulted in mice that run longer distances (more revolutions)per day, but not more time per day. High-activity mice thus run at higher velocities(Swallow et al., 1998). In addition, mice run more intermittently and in shorter bouts(Girard et al. 2001), probably to lower costs of running (Rezende et al., 2006). At gener-ation 31 (the generation of the mice obtained by us) high-activity mice ran approxi-mately 2,7 times more than control mice (Rhodes et al., 2005). On average female micerun more than males, but the selection procedure affected wheel-running activity in asimilar way in both males and females (Morgan et al., 2003). High-activity mice arealso hyperactive in their cages when deprived from wheels (Rhodes et al., 2001). Bodymass at maturity is decreased in high-activity mice and food intake is increased com-pared to control mice (Dumke et al., 2001; Swallow et al., 2001). The reduction in bodymass is mainly caused by a reduction in fat mass (Dumke et al., 2001; Swallow et al.,1999). Reproductive output is similar in control and high-activity mice (Girard et al.,2002). Control and high-activity mice do not differ with respect to their thermoregula-tory mechanism (Rhodes et al., 2000). No differences in open field activity or defeca-tion have been found (Bronikowski et al., 2001). Leptin levels are decreased (evenwhen correcting for fat content) in females (Girard et al., 2006), and corticosterone lev-els are elevated in high-activity mice relative to controls (specifically in females)(Girard and Garland, Jr., 2002; Malisch et al., 2006).

ACTIVITY & METABOLISM

PartI

18

Wheel-running activity and energy metabolismin relation to ambient temperature in miceselected for high wheel-running activity

Lobke M. Vaanholt, Theodore Garland Jr., Serge Daan, G. Henk Visser

Journal of Comparative Physiology B, 177(1); 109-118

AbstractInterrelationships between ambient temperature, activity, and energy metab-olism were explored in mice that had been selectively bred for high sponta-neous wheel-running activity and their random-bred controls. Animals wereexposed to three different ambient temperatures (10, 20 and 30°C) andwheel-running activity and metabolic rate were measured simultaneously.Wheel-running activity was decreased at low ambient temperatures in allanimals and was increased in selected animals compared to controls at 20and 30°C. Resting metabolic rate (RMR) and daily energy expenditure(DEE) decreased with increasing ambient temperature. RMR did not differbetween control and selected mice, but mass-specific DEE was increased inselected mice. The cost of activity (measured as the slope of the relationshipbetween metabolic rate and running speed) was similar at all ambient tem-peratures and in control and selected mice. Heat generated by runningapparently did not substitute for heat necessary for thermoregulation. Theoverall estimate of running costs was 1.2 kJ km-1 for control mice andselected mice.

Chapter2

INTRODUCTION

Homeothermic animals maintain a rather constant body temperature over a wideambient temperature range. At low ambient temperature resting homeotherms ele-vate metabolic levels to compensate for elevated heat loss, while at high ambienttemperatures metabolic rates should be low to avoid hyperthermia (Mount, 1966;Tieleman et al., 2002). This temperature dependence of metabolic rate becomesmore complicated when animals exhibit high locomotor activity, which is known tobe energetically expensive (Taylor et al., 1970). In the cold, high levels of activitymay be favourable if activity-related metabolic costs can be used for temperatureregulation. The excess heat produced by activity might theoretically substitute forshivering thermogenesis during rest. In principle, if substitution takes place, thenthe cost of locomotion, formally measured as the energy turnover during activityminus the energy turnover during inactivity, will be reduced at low temperature. Ifno substitution takes place, then the costs for activity will be added to those forthermoregulation (addition).

The empirical literature is ambiguous on this issue. Several studies demonstratesubstitution (in White crowned sparrows, Zonotrichia leucophrys gambelii (Paladinoand King, 1984), potoroo, Potorous tridactylus (Baudinette et al., 1993) deer mice,Peromyscus maniculatus (Chappell et al., 2004), rat, Rattus novegicus (Arnold et al.,1986; Makinen et al., 1996)), but there are also results consistent with addition(Kowari, Dasyroides byrnei (MacMillen and Dawson, 1986), Chipmunk, Eutamiasmerriami (Wunder, 1970) patas monkey, Erythrocebus patas (Mahoney, 1980)). Thisdiscrepancy among studies may well be related to different conditions. Activity may,for instance, simultaneously lead to reduced insulation and increased heat loss insituations where animals huddle or use bedding material while resting and couldtherefore mask substitutive effects of activity. If substitution occurs, this would leadto low net costs of activity at low temperatures and thereby should lead us toexpect increased activity in the cold.

We decided to exploit mice specifically selected for high activity to test thehypothesis of substitutive metabolic rate in this species. Swallow et al. (1998) haveselected mice for high spontaneous wheel-running activity during many generations(for selection procedure see (Swallow et al., 1998)), which make these animals prof-itable to further explore interrelationships between ambient temperature, activity,and energy metabolism. Animals were bred under ambient temperatures of approx-imately 22°C. The intensity of spontaneous wheel-running activity has increasedover generations and reached an apparent plateau around generation 16(Bronikowski et al., 2001). In addition, selected animals have become smaller andleaner (Girard et al., 2006; Swallow et al., 2001), thereby diminishing whole-animalcosts of running in these mice (Rezende et al., 2006). Smaller animals also havelarger surface-to-volume ratios, which could make them more susceptible to heatloss at low ambient temperatures. During the selection process for high sponta-neous wheel-running activity, animals seemed to exhibit annual cycles regarding

Chapter 220

their spontaneous wheel-running activity (Bronikowski et al., 2001) which might beattributed to variations in ambient temperature. In order to evaluate the effects ofgenetically increased activity in the selected mice, we studied animals from controland selected lines at various ambient temperatures and recorded their wheel-run-ning activity, body temperature, resting metabolic rate and daily energy expenditure.

MATERIAL AND METHODS

Animals & housingHouse mice (Mus domesticus) that had been selected for high wheel-running activityand their random bred controls were obtained from the lab of Prof. Dr. T. GarlandJr, Riverside, CA, USA. Originally, eight lines of mice consisting of 10 pairs eachhad been created, four in which mice were randomly bred and four in which micewere selected for high wheel-running activity. Selection took place at 6-8 weeks ofage during a 6 day trial on wheel running (1.12 m circumference). The most active-ly running female and male within each family were chosen as breeders for the nextgeneration, without allowing sibling matings.

Eighty breeding pairs (10 per line) from generation 31 of selection were sent tothe Zoological Laboratory in Haren (NL) to start a breeding colony without furtherselection. In the present study, 16 male mice (8 control and 8 selected) at the age of6-8 weeks were used from one of the control (lab designation is line 2) and one ofthe selection lines (line 7). The mice were individually housed in cages equippedwith running wheels (Macrolon type II cages (15x30x15cm); UNO RoestvaststaalBV, Zevenaar, NL; adapted to fit in a wheel running with a diameter of 14 cm) andwood shavings as bedding two weeks prior to the experiments. The mice were on a12:12 light-dark cycle (lights on at 8:00 CET) and food (Standard lab chow RMB-H(2181), HopeFarms B.V., Woerden, NL ) and water were provided ad libitum.

Experimental protocolAt the start of the experiment animals were randomly divided into two groups

(each consisting of 4 controls and 4 selected animals) and housed in two separatetemperature-controlled rooms. The mice stayed in these rooms throughout theexperiment. All animals were exposed to three ambient temperatures (10, 20 and30°C) over a time course of three weeks. Each week ambient temperature wasincreased or decreased by 10 degrees, starting at 10°C in room 1 and at 30°C inroom 2. Wheel-running activity was recorded on a PC-based event recording system(ERS) with 2-min resolution. Body weight was measured every day at 12 pm.

At noon on day 6 of each stay at a set ambient temperature animals were putwith their home cage in a respirometry chamber (25x35x25cm), in the same roomas they were housed. Oxygen consumption (V

.O2, l h-1) and carbon dioxide (V

. CO2,

l h-1) production was then recorded for each individual for 24 h by indirectcalorimetry. Our 8-channel open circuit system has been described earlier by

Ambient temperature and wheel-running activity 21

Oklejewicz et al. (1997). In brief, oxygen and carbon dioxide concentration of driedinlet and outlet air (drier: molecular sieve 3 Å, Merck) from each chamber wasmeasured with a paramagnetic oxygen analyzer (Servomex Xentra 4100) and car-don dioxide by an infrared gas analyzer (Servomex 1440). The system recorded thedifferentials in oxygen and carbon dioxide between dried reference air and dried airfrom the metabolic chambers. Oxygen and carbon dioxide analyzers were calibratedwith two gas mixtures with known amount of O2 and CO2 prior to each measure-ment. Flow rate of inlet air was measured with a mass-flow controller (Type 5850Brooks) and set at 30 liter per hour. Of the respiration air a subsample was passedat a rate of 6 l h-1 through the drying system and subsequently through the gas ana-lyzers. Ambient temperature in the chamber and cage were measured simultane-ously. Data were collected every 10 minutes for each animal and automaticallystored on a computer. Oxygen consumption was calculated according the equation2 of Hill (1972) to correct for volume changes with respiratory quotient below 1and expressed in standard temperature and pressure. The respirometric chambersfitted the complete home cage of the animals. Animals therefore did not need to behandled and had access to their own running wheel throughout the measurements.Water and food were provided ad libitum. Wheel-running activity was also measuredthroughout the respirometry measurement using the ERS.

Body temperature was measured with a rectal probe (NTC type C, Ahlborn,Holzkirchen, Germany) immediately after the respirometry measurement. Bodyweight was also measured at this time. After these measurements the ambient tem-perature in the rooms was changed.

Data analysisContinuous recordings of wheel-running activity were available for day 3-5 in eachcondition, just prior to the respirometry. These data were used for further analysis,excluding days 1 and 2 after the temperature transition. Average wheel-runningactivity per day (distance run), time spent running and average running speed werecalculated for each temperature. In addition, maximum wheel-running activity pertemperature was calculated over the same days by determining the maximumamount run in a 2-min interval. The same variables of wheel-running activity weredetermined for the 24-h interval in the respirometer. Wheel-running recordingsduring this time were not available for all animals and sample size for controls andselected mice were, respectively, 5 and 3 at 10°C, 6 and 6 at 20°C and 5 and 6 at30°C.

Metabolic rate (MR, kJ h-1) was calculated using the equation MR= (16.18 xV.

O2) + (5.02 x V.

CO2 ) (Romijn and Lokhorst, 1961). Instead of using a fixed gasexchange conversion factor this versatile equation enabled the calculation of heatproduction of different nutritional states (see also (Gessaman and Nagy, 1988).Resting metabolic rate (RMR, kJ h-1) was defined as the lowest (running) meanmetabolic rate recorded over half an hour anywhere during the 24 h measurement.The average metabolic rate over 24 h was used to calculate daily energy expenditure

Chapter 222

(DEE, kJ d-1). The body weight measured before and after the respirometrymeasurement was averaged and used to calculated mass-specific RMR and DEE (inkJ g-1 d-1).

Independent t-tests were used to screen for differences between animals housedin the two separate rooms. No significant differences were found and data fromboth rooms were pooled for further analysis. For all traits, two-way repeated meas-ures ANOVA were performed with a factor group (ctrl vs. selection), temperature(10 vs. 20 vs. 30°C) and group x temperature using SAS 9.1 (PROC MIXED). Bodymass is known to have a strong influence on metabolic rate and analysis of restingmetabolic rate (RMR) and daily energy expenditure (DEE) were done using modelswith or without body mass as a covariate. In addition, we were interested in therelationship between parameters of wheel-running activity (distance run, time run,average running speed and maximal running speed) and DEE, and these parameterswere added as an additional covariate to body mass in the model one at a time toexplore these relationships. Data was normally distributed and thus not trans-formed before analysis. When the ANOVA showed significant effects post hoc t-tests were performed. Significance was assumed at p≤ 0.05. All tests were two-tailed.

To determine the relationship between running speed (V, km h-1) and heat pro-duction (HP, kJ h-1) at the different ambient temperatures, average wheel-runningactivity (while in the respirometer) and HP of most mice (for sample size see


heat

pro

duct

ion

(kJ

h-1)

0

1

2

3

4

5

126 180

400

800

1200

1600

runn

ing

whe

el a

ctiv

ity (r

ev h

-1)

mouse 242

external time (h)

Figure 2.1. Simultaneous measurements of RWA (white dots) and HP (grey dots) for a mouserepresentative of the group (10-min. averages) from 4 hours prior to the dark phase to 4 hoursafter the dark phase (black bar) at 30°C. At the flow rate employed a 30 min. time lag isdetectable in our respirometry system and therefore data on HP were corrected with 30 min. todetermine the relationship between heat production and running speed (black dots).

above) were calculated in 30-min. bins during the dark phase. We only used datafrom the dark phase (12 h) because mice are nocturnal and wheel-running activityis mainly limited to the dark phase (24 time points per mouse). We accounted for a30 min. lag in the HP measurements caused by the low air flow rate through therespirometry system (see Figure 2.1). At each temperature and for both groups wecalculated the average running speed and heat production per 30-min bin. The rela-tionship between running speed and heat production for all groups was plotted inFigure 2.3. Using ANCOVA models we explored effects of group and temperatureon the relationship between running speed and metabolic rate.

RESULTS

Body mass, food intake and wheel-running activityTable 2.1 shows the effects of ambient temperature on body mass, food intake andseveral measures of wheel-running activity in control and selected mice. We foundno differences between control and selected mice in average body mass or foodintake. Ambient temperature had no effect on body mass, but food intake was sig-nificantly higher at low ambient temperatures (10 and 20°C).

Chapter 224

Table 2.1. Body mass, food intake and wheel-running activity of control and selected mice at vari-ous ambient temperatures.

Group Ambient temperatures P-values for repeatedmeasures ANOVA

10°C 20°C 30°C Group Temp

Body mass (g) Control 28.7±1.5 28.2±1.6 28.0±2.2 0.151 0.085Selected 27.2±2.2 26.8±1.9 26.6±2.3

Food intake (g d-1) Control 8.4±2.3 8.0±2.8 4.4±0.5 0.190 0.001Selected 9.3±2.4 9.1±1.4 5.6±0.6

Distance run (km d-1) Control 7.1±4.1 10.7±3.7 8.8±3.2 0.024 0.003Selected 9.6±3.0 14.1±5.1 13.1±3.4

Time spent running (h d-1) Control 6.1±2.4 7.7±2.1 7.8±1.8 0.048 0.001Selected 7.5±1.1 9.1±1.0 9.5±2.3

Average speed (km h-1) Control 1.1±0.3 1.4±0.2 1.1±0.2 0.092 0.005Selected 1.3±0.2 1.6±0.5 1.4±0.3

Maximum speed (km h-1) Control 2.3±0.4 2.5±0.4 2.3±0.3 0.048 0.092Selected 2.5±0.4 2.7±0.6 2.9±0.2

Mean ± sd are given for several variables, for control and selected mice at 3 different ambient temperatures seper-ately. Two-way repeated measures ANOVA were performed on all variables with group as a between subjects factorand temperature (temp) and group x temp (GxT) as within subjects factors. P-values for effects of age and group aregiven in the table and are bold when the effect was statistically significant (p<0.05). No significant interactioneffects between group and age were found (p>0.1), and p-values are therefore not shown in the table. Sample sizewas 8 in both groups, except for the measures of wheel-running activity where data of one mouse in the selectedgroup were missing.

As expected, selected mice had significantly higher wheel-running activity(expressed as time spent running or distance run) than control mice (see table 2.1).Ambient temperature significantly affected the distance run per day, running timeper day and running speed. In both groups, wheel-running activity was significantlydecreased at 10°C compared with 20°C. Maximum running speed was also signifi-cantly higher in selected mice than in control mice, but ambient temperature didnot influence maximal running speed. Body mass was never a significant covariatein the models, indicating that body mass had no statistically detectable effects onfood intake or any measure of wheel-running activity. This is most likely due tosmall variance in the body mass of the mice used for the experiments.

Metabolism and body temperatureAnimals were put in respirometry chambers for 24 h at different ambient tempera-tures to measure resting metabolic rate and daily energy expenditure in control andselected mice at these temperatures (see Figure 2.2). RMR was similar in controland selected mice and significantly decreased with increasing ambient temperature(see Table 2.2 for statistical analyses). Body mass was a significant predictor ofRMR in the model , but did not influence the effects of group and temperature onRMR . DEE also did not significantly differ between control and selected mice anddecreased with increasing ambient temperature. When body mass was included inthe model as a covariate it significantly contributed to the explained variance inDEE and the group effect became significant, with a higher DEE in selected micecompared to controls. Post-hoc comparison showed that DEE was significantly dif-ferent between lines at 30 and 20°C, but not 10°C. We were interested in howwheel-running activity as measured during the respirometry measurement con-tributes to the explained variance in DEE, and included activity variables into the


0

1

2

3

4

heat

pro

duct

ion

(kJ

g-1d-1

)

10 20 30ambient temperature (°C)

CTRL RMRCTRL DEESEL RMRSEL DEE

**

Figure 2.2. Mass-specific resting metabolic rate (RMR) and daily energy expenditure (DEE) inmice selected for high wheel-running activity (SEL) and their random bred controls (CTRL) atvarious ambient temperatures. Values represent mean±SD. Asterisks (*) show at which tempera-tures DEE significantly differed between control and selected mice (p<0.05).

model with body mass, one at a time. All variables were positively related to DEE.Only distance run and time spent running significantly contributed to the variancein DEE in these models They fully accounted for the group effect, but not for tem-perature. The effect of ambient temperature remained significant in these models.

Body temperature at the different ambient temperatures was measured at themoment when animals came out of the respirometry chambers. In control micebody temperature was on average 36.7±0.5, 37.3±0.8 and 37.3±0.4 (mean±sd) at10, 20 and 30°C, respectively, and in selected mice it was 36.7±0.4, 37.8±0.5 and37.3±0.4. Body temperature did not differ significantly between control and select-ed mice and decreased with ambient temperature in both groups (see Table 2.2).

Cost of transportEstimates of the incremental cost of transport (COT, kJ km-1) are generally derivedfrom the slope of the regression of heat production and running speed. The rela-tionship between HP and running speed (V) for control and selected mice in thepresent study is shown in Figure 2.3 (see also Table 2.3). This figure plots the

Chapter 226

Table 2.2. Results for repeated measures ANOVA on metabolic measurements and body tempe-rature.

Variable n Group Temperature Covariated.f. F p d.f. F p p

RMR 16 1,14 0.6 0.441 2,28 408.3 <0.001 noneRMR 16 1,14 0.1 0.851 2,27 457.3 <0.001 Body mass 0.009

DEE 16 1,14 3.0 0.103 2,28 208.7 <0.001 noneDEE 16 1,14 7.9 0.014 2,27 203.7 <0.001 Body mass 0.017DEE 15 1,13 0.1 0.935 2,10 167.8 <0.001 Body mass 0.179

Distance 0.005DEE 15 1,13 0.1 0.764 2,10 115.7 <0.001 Body mass 0.103

Time 0.042DEE 15 1,13 0.2 0.679 2,10 107.5 <0.001 Body mass 0.556

Speed 0.059DEE 15 1,13 0.1 0.971 2,10 117.2 <0.001 Body mass 0.968

Max speed 0.074

Body temp.16 1,14 0.9 0.356 2,28 11.9 <0.001 none

Repeated measures ANOVA were performed on all variables with group as a between subjects factor and temper-ature and group x temperature (GxT) as within subjects factors. In addition, where appropriate body mass andwheel-running activity variables were added into the model as covariates. Degrees of freedom (d.f.), F and p-val-ues for each factor are given in the table. P-values are bold when the effect was statistically significant (p≤ 0.05).No significant interaction effects between group and age were shown and p-values are therefore not shown in thetable. Sample size was 8 in both groups, except for the measures of wheel-running activity where data of onemouse in the selected group were missing.

interindividual average metabolic rate for each 30-min bin of running speed at eachof the three temperatures. The figure clearly shows that at each temperature, themetabolic rates of both lines were distributed around the same positive regressionwith speed. The highest speeds were more often observed in the selected line.There was a thermal gradient, with higher metabolism at lower temperature, but ateach temperature the slope appeared to be similar. We tested for effects of tempera-ture and group in an ANCOVA model with HP as the dependent variable and run-ning speed as a covariate, where we looked at effects of group (selection vs. con-


Table 2.3. Effect of temperature and group on linear regressions between running speed (km h-1)and metabolic rate (kJ h-1).

Temperature Slope Intercept R2

CONTROL 10°C 1.25±0.14 3.64±0.05 0.7920°C 1.30±0.08 2.41±0.06 0.9230°C 1.31±0.13 1.71±0.07 0.83

SELECTED 10°C 1.16±0.18 3.61±0.10 0.6820°C 1.08±0.11 2.72±0.10 0.8230°C 1.06±0.11 1.99±0.11 0.82

Using linear regression, slopes and intercepts of the relationship between running speed and metabolic rate (Figure2.3) were determined (without body mass as a covariate). Slopes and intercepts for all separate groups are shown asmean±sem. All regressions were highly significantly different from zero (p<0.001).

heat

pro

duct

ion

(kJ

h-1)

0

1

2

3

4

0.0 0.5 1.0running speed (km h-1)

5

1.5

30°C

20°C

10°C

Figure 2.3. Heat production (HP, kJ h-1) of control and selected mice during voluntary running asa function of running speed (V, km h-1). Each symbol represents average running speeds andmetabolic rates of mice at that temperature for each half hour of the dark phase. Control mice arein white and selected mice in black. Circles represent 10°C, triangles represent 20°C and squaresrepresent 30°C. The solid lines are the regression lines for the different ambient temperatures(see text).

trol), temperature, and their interactions with running speed. Temperature stronglyaffected the relationship between HP and running speed (F2,51=676.5, p<0.001).This supports the visual inspection of Figure 2.3, with obviously different inter-cepts (HP at zero running) at the ambient temperatures measured. There was nointeraction effect between temperature and running speed, supporting similarslopes of all relationships (Slope=1.19, 95% CI: 1.09-1.29). Hence the incrementalcosts of running were equal at all temperatures measured. Group did not signifi-cantly affect the regression (F1,44=3.61, p=0.064). The slope of the regressionbetween running speed and HP was slightly lower in selected mice (see Table 2.3),but not significantly so. Costs of running were thus similar in both groups. Eventhough body mass is known to affect COT, body mass did not contribute signifi-cantly to the explained variance in HP. Again this is probably caused by small vari-ance in mass. The relationship between HP and body mass was positive in the mod-els used, though. The only factor that significantly influenced the relationshipbetween HP and running speed was thus ambient temperature. The solid lines inFigure 2.3 show the regressions for the three ambient temperatures measured withboth groups combined and without taken body mass taken into account. The equa-tions for these regression lines are: at 10°C: HP= 1.16 V + 3.63, at 20°C: HP=1.20V + 2.55 and at 30°C: HP=1.20 V + 1.82 (p<0.001 for all regressions).

DISCUSSION

We explored effects of ambient temperature on wheel-running activity, body tem-perature and metabolic rate in mice that had been selected for wheel-running activi-ty for 31 generations and their random bred controls.

We expected that at low ambient temperatures the heat generated by activitymight (partially) substitute thermostatic metabolic rate and therefore mice mightrun more in the cold. At high ambient temperatures animals were expected toreduce their activity to prevent hyperthermia, as has been shown in humans(Cheuvront and Haymes, 2001) and birds (Davies, 1982; Spinu et al., 2003).Ambient temperature did indeed significantly affect wheel-running activity, butopposite to the prediction on the basis of themogenetic substitution, wheel-runningactivity (distance run, time spent and average running speed) was decreased byapproximately 60% in control as well as selected mice at low ambient temperature(10°C). As expected, selected mice ran a longer distance (+42%), more time(+22%) and at faster speeds (+19%) than control mice did. This differencebetween control and selected mice was no longer significant at low ambient tem-perature. The mice have been selected at ambient temperatures of approximately22°C and at 10°C thermoregulatory costs might be too high for mice to maintainhigh levels of activity. Indeed, mice at 10°C had body temperatures decreased byapproximately 0.6°C which could reflect difficulties to maintain constant body tem-perature. Lowering of body temperature may also be a strategy to lower costs for

Chapter 228

thermoregulation while resting. Body temperature was measured once in the mid-dle of the light phase (rest phase) and the variation assessed between ambient tem-peratures may just reflect ambient temperatures at rest and may not have persistedwhile running.

One could speculate that there is a restraint on running at low ambient temper-atures (due to slower muscle contraction). Maximal running speeds, however, didnot significantly vary between temperatures in this study. A study in deer mice like-wise provided no evidence for effects of temperature on wheel-running activity(Chappell et al., 2004).

No differences in body temperatures were observed between control and select-ed mice at any of the ambient temperatures measured, which is in agreement withprevious measurements of body temperature in mice of the same strain at an ambi-ent temperature of 22°C (Rhodes et al., 2000). Regulation of body temperature atrest thus appears unchanged in mice selected for high wheel-running activity andthere does not appear to be a difference in thermoregulatory capacity, at least at thetemperatures studied here. Nonetheless, we can not exclude differences betweenthe lines in shivering or non-shivering thermogenesis. Moreover, mice from select-ed lines show elevated heat shock protein 72 expression in the triceps surae muscle(Belter et al., 2004).

As expected for a small endotherm, energy expenditure decreased with increas-ing ambient temperature. RMR increased 1.6-fold from 20 to 10°C and 1.8-foldfrom 30 to 20°C. DEE was also affected by ambient temperature with a 1.4-foldincrease from 20 to 10°C and a 1.5-fold increase from 30 to 20°C. These results aresimilar to values found in a study in deer mice (Chappell et al., 2004) housed at 3,10 and 25°C. Wheel-running activity (distance run and running time) was positive-ly correlated with the simultaneously measured DEE. In concurrence with anincrease in wheel-running activity, mass-specific DEE was significantly increased inselected mice compared with controls. RMR did not differ between control andselected mice, even though there are differences in body composition between them(Swallow et al., 2005; Swallow et al., 2001). Apparently, the costs for thermoregula-tion and maintenance of the body are similar in control and selected mice. Giventhat RMR did not differ between the groups and the group difference in DEE disap-peared when correcting for variables of wheel-running activity, the difference inDEE between groups can be fully attributed to energy spent on activity.

Total energy spent on activity was thus higher in the selected mice. This doesnot imply that there were differences in the costs per unit distance between thegroups (COT). At all ambient temperatures COT were approximately 1.2 kJ km-1

(at an average body mass of 27.6 g), which is comparable to the COT 1.19 kJ km-1

obtained by forced locomotion on a treadmill by Taylor et al. (for a 21 g housemouse) (Taylor et al., 1970). COT is related to body mass, with higher costs oftransport at higher body mass. In our study as well as previous work by Chappell etal. (2004) and Rezende et al. (2006), body mass was not a statistically significantpredictor of COT. The incremental cost of terrestrial locomotion in relation to body


mass can be estimated using the allometry given by Taylor et al. in 1982: COT(kJ km-1)= 10.7* mass (kg)0.684 (Taylor et al., 1982), and predicts a slope of 0.92 kJkm-1 for a 27.6 g animal. This is lower than the value we found for these mice. Themice measured by Taylor were forced to run on a treadmill. Animals on treadmillsare forced to run at specific speeds, whereas voluntary running mice choose theirpreferred speed. This might render a different relationship between running speedand metabolic cost. A previous study on male selected mice at 22°C estimated aCOT of 1.29 kJ km-1 (when using a conversion factor of 20.1 J ml-1 O2) (Rezende etal., 2006), which is very similar to the value of 1.2 kJ km-1 we obtained. The slightdifference may easily be attributed to the different wheels used (plastic wheels witha 7 cm. radius in our study compared to metal wheels with a 18 cm. radius in thestudy by Rezende et al. (2006). The study by Rezende et al. demonstrated thatwhole-body COT during voluntary wheel running was significantly lower in theselected lines, when combining analysis of males and females (Rezende et al.,2006). When body mass and/or maximal speed were added as a covariate the differ-ence disappeared. These factors apparently caused the line difference. Similar to ourstudy, analyzing males alone did not render a significant effect of selection on COT.

The novel result in our study is that COT was unaffected by ambient tempera-ture. With decreasing ambient temperature the intercept of the relationshipbetween metabolic rate of running speed did increase, indicating increased costs atrest at lower temperatures, as is also reflected in an increase in RMR. Heat generat-ed by running apparently did not substitute for thermoregulation costs at low ambi-ent temperature in our mice (Figure 2.3). At all ambient temperatures the slope ofthe relationship between metabolic rate and running speed was statistically indis-tinguishable. Contradictory evidence exists for other species of homeotherms,showing either addition or substitution of activity-generated heat for thermoregula-tory heat at low ambient temperatures. Table 2.4 summarizes the results for twostudies on birds and several on various mammals. We have listed whether heat gen-erated by activity was additive or substitutive and at which temperature substitu-tion first occurred. The two studies on birds indicate partial or complete substitu-tion of exercise-generated heat production for thermoregulatory costs usually atlow ambient temperatures and additive at moderately cold ambient temperature(Paladino and King, 1984; Pohl and West, 1973). In mammals the results are morescattered with cases of total, partial and no substitution (see Table 2.4). The ambi-ent temperatures used vary widely amongst these studies. In our study the ambienttemperatures applied might not have been extreme enough to show substitution ofactivity generated heat for thermoregulatory heat. However, there is no theoreticalbasis to assume that substitution should exclusively occur at very low ambient tem-peratures. At all ambient temperatures below the lower critical temperature substi-tution could occur to a certain degree. Also, when partial substitution occurs, theseeffects may be masked by differences in heat loss under resting or active conditions.For example, when an animal leaves a well-insulated resting place to become active,thermoregulatory costs may well simultaneously shoot up due to increased surface

Chapter 230


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area and reduced insulation, and thus counteract substitutive effects of activity. Inthis case the net effect on costs of transport may not be different and partial substi-tution would not be noticed. In our animals housed in their home cage with bed-ding during the measurements, these effects may have been more pronounced thanin other studies. At 10°C mice were less active and may have chosen to use shiver-ing thermogenesis while well-insulated and curled up in their nest instead of usingheat generated by wheel-running activity to offset increased heat loss (animals didnot have nesting material, but did built small nests using wood shavings).Interestingly, in selected and control mice the cost of running was found to be simi-lar and in both groups heat generated by activity could not substitute for heat nec-essary for thermoregulation at the lowest ambient temperature measured.

In summary, mice that have been selected for high voluntary wheel-runningactivity had increased mass-specific daily energy expenditure, but did not differfrom control mice with respect to resting metabolic rate. Wheel-running activitydecreased at low ambient temperature (10°C) in both selected and non-selectedmice and was unchanged at high ambient temperature (30°C) compared to controltemperature (20°C). The cost of transport was similar between the lines. It was alsoindistinguishable between the ambient temperatures measured, indicating that theenergy spent on activity was additive and did not substitute for heat necessary forthermoregulation.

AcknowledgementsThe authors thank Laura Ross, Edwin Alserda, Mark Doornbos and Els Van der Zee for helpwith the experimental procedures. All procedures concerning animal care and treatmentwere in accordance with the regulations of the ethical committee for use of experimentalanimals of the University of Groningen (DEC nr. 3039). TG was supported by US NSF grantIBN-0212567.

Chapter 232


Behavioural and physiological responses toincreased foraging effort in male mice

Lobke M. Vaanholt, Berber De Jong, Theodore Garland Jr.,Serge Daan, G. Henk Visser

Journal of Experimental Biology, In Revision

AbstractFree-living animals must forage for food and hence may face energetic con-straints imposed by their natural environmental conditions (e.g., ambienttemperature, food availability). Simulating the variation in such constraints,we have experimentally manipulated the rate of work (wheel running) micemust do to obtain their food, and studied the ensuing behavioural and phys-iological responses. This was done in mice selectively-bred for high sponta-neous wheel running and their randomly-bred controls to vary the amountof baseline wheel-running activity. We first determined the maximum work-load for each individual. The maximum workload animals could engage inwas ~23 km d-1 in both control and activity-selected mice, and was notassociated with baseline wheel-running activity. We then kept mice at 90%of their individual maximum and measured several physiological and behav-ioural traits. At this high workload, mice increased wheel-running activityfrom an average of 10 to 20 km d-1, and decreased food intake and bodymass by approximately 20%. Mass-specific resting metabolic rate stronglydecreased from 1.43 to 0.98 kJ g-1 d-1, whereas daily energy expenditureslightly increased from 2.09 to 2.25 kJ g-1 d-1. Costs of running decreasedfrom 2.3 to 1.6 kJ km-1 between baseline and workload conditions. At highworkloads, animals were in a negative energy balance, resulting in a sharpreduction in fat mass as well as a slight decrease in dry lean mass. In addi-tion, corticosterone levels increased, and body temperature was extremelylow in some animals at high workloads. When challenged to work for foodmice thus showed several physiological and behavioural adaptations.

Chapter3

INTRODUCTION

Free-living animals need to forage for food and they may face energetic constraintsrelated to their natural environmental conditions (Speakman et al., 2003a), such aslow ambient temperature and limited food availability. The main energetic costs foran endothermic and homeothermic animal with a large surface-to-volume ratio,such as a mouse, are of thermoregulatory nature (rather than those related to costsof locomotion (Carbone, 2005; Garland, 1983; Goszczynski, 1986)). Mice furtherneed energy for maintenance of the body and for foraging activity. Excess energycan be used for non-essential physical activity (e.g., play behaviour), stored as fat orinvested in growth and/or reproduction. When food is scarce, mice must investmore time (and energy) in foraging, and they may face constraints on the energyavailable for behaviour and maintenance functions other than foraging. They thenneed a physiological strategy to reallocate their limited energy. Fat reserves mayprovide energy for a short time (Bronson, 1987; Day and Bartness, 2001), but whenfood availability is low for extended periods animals must reallocate energy to sys-tems that need it most from functions that are less crucial for survival. Reducingbody mass and/or mass-specific resting metabolic rate is one strategy to reduceenergetic demands (Deerenberg et al., 1998; Rezende et al., 2006; Speakman andSelman, 2003). Perrigo and colleagues have shown reduced investment in reproduc-tion by female mice challenged to work for food (Perrigo, 1987; Perrigo andBronson, 1985). Experiments by Adage et al. (2002) have shown that rats chal-lenged to work for food undergo numerous physiological changes, including areduction in body mass, blood glucose, and insulin levels, accompanied by increasesin insulin sensitivity, ACTH, and corticosterone level.

In these rats there was large inter-individual variation in the amount of wheelrunning rats could perform. The ability to maintain body mass during the workingperiod could be predicted from the individual spontaneous wheel-running activity.This raises the intriguing question of whether spontaneous locomotor activityreflects the physiological capacity of individuals. To address this question, we haveexploited the existence of replicate mouse lines that have been selectively-bred forhigh voluntary wheel-running activity (Swallow et al., 1998). We investigated theeffects of an increase in foraging effort on behaviour, energy metabolism, body tem-perature, and body composition in both the selected lines and their random-bredcontrol lines. Animals were housed in specialized cages with a running wheel andfood dispenser. A steering computer controlled food rationing as determined byrunning-wheel activity. With this paradigm, as pioneered by Perrigo (1985; 1983),we could experimentally vary the wheel-running activity required to obtain a pelletof food. This is intended to mimic variations in the work animals would need to doto secure a living in nature under varying food availability. Unlike caloric restric-tion, this protocol more carefully mimics the natural conditions animals face. Thepresent study had two aims: firstly, to investigate physiological and behaviouralresponses to high workloads and secondly, to investigate whether mice with a high

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spontaneous level of wheel running would respond differently to the exposed chal-lenge.


Animals & housingOutbred Hsd:ICR mice (Mus domesticus) selected for high wheel-running activityover 31 generations and their random bred controls were obtained from TheodoreGarland Jr. (for selection procedure see ((Swallow et al., 1998), see also (Garland,Jr., 2003)), and a breeding colony (without further selection) was started at theZoological Laboratory in Haren, Netherlands. Sixteen male mice, 8 from one of thecontrol lines (C; lab designation is line 2) and 8 from one of the selected lines (S;line 7) were used in the experiments. At 4–5 weeks of age, mice were housed indi-vidually in cages (30x30x40 cm) equipped with a plastic running wheel (Ø 14,5 cm,code 0131, Savic®, Belgium). They were maintained on a 12:12 light-dark cycle(lights on at 8:00 CET). Food (standard rodent chow RMB-H (2181), with a grossenergy content of 16.2 kJ g-1, HopeFarms, Woerden, NL) and water were providedad libitum. Spontaneous wheel-running activity was recorded automatically by a PC-based event recording system (ERS) and stored in 2-min bins. Body mass and foodintake were determined throughout the whole experiment at 11:00 each day. Whenthe animals worked for food, pellets (0.045 g per pellet) that were not eaten wereremoved, counted, and deducted from the total number of pellets the mice received.However, small, crumbled and wasted pieces of food (orts) were not removed, andhence represent an uncontrolled, but probably minor, source of error variance; see(Koteja et al., 2003). All procedures concerning animal care and treatment were inaccordance with the regulations of the ethical committee for the use of experimen-tal animals of the University of Groningen (License DEC 3039(-1)).

Experiment 1: Individual maximum workloadAll mice were kept for 30–40 days under ad libitum food conditions. At 8–9 weeks ofage, food was removed and the running wheel was connected to a food dispenser(Med Associates Pellet dispensor ENV-203, Sandown Scientific, Hampton, UnitedKingdom) that released a food pellet (45 mg precision food pellets with a grossenergy content of 13.4 kJ g-1, Sandown Chemicals, Surrey, UK) at a set number ofrevolutions (General Electric Series 3 Programmable Controller). The number ofrevolutions per pellet was established for each mouse by dividing its average spon-taneous daily wheel-running activity over the previous week (= baseline wheel run-ning) by 150. When running at baseline a mouse would thus receive 6.8 g of food(150 x 0.045), which is similar to the amount of food a mouse on ad libitum foodwould eat. On average mice had to run 218 (s.d. 54) revolutions per pellet at base-line level. All animals were kept at this level for two days, then the number of revo-lutions was increased by 15% of baseline every two days until the animal reached

Physiological adaptations to hard work 37

its maximum wheel-running activity. This maximum was defined as when a mousestarted decreasing its wheel-running activity (running mean over three days) forthree consecutive days. After the maximum was established, animals stayed in thesame cages with a running wheel and received ad libitum food to allow recovery.

Experiment 2: Behavioural and physiological consequences of high workloadBecause we did not show any statistically significant differences in the response toworkload between control (C) and activity-selected (S) mice in experiment 1 (seeResults section), animals from both groups were pooled in experiment 2. Theseanimals will be referred to as Workload mice (n=16).

The workload mice were allowed to recover from experiment 1 for at least fourweeks prior to the start of the experiment 2. Again food was taken away and therunning wheels were connected to food dispensers via the computer system on dayzero (t=0). Animals had to work at baseline level for two days and then over a peri-od of 14 days the workload was increased by equal steps every two days until theworkload had increased to 90% of the individual maximal wheel-running activityestablished in Experiment 1. Mice were kept at this level for 10 days and then ter-minated.

To test whether the Workload mice had sufficiently recovered from experiment 1and to enable comparisons of body composition an extra control group was used.Mice in this control group were housed in standard cages with a running wheel(15x30x15cm, Macrolon Type II long, UNO Roestvaststaal BV, Zevenaar, NL) whenthey were 4-5 weeks old, and received ad libitum food (standard rodent chow RMB-H (2181), HopeFarms, Woerden, NL) throughout the experiment. The group con-sisted of three mice from the C line and four from the S line. This group will bereferred to as Ad-lib mice (n=7).

METABOLIC MEASUREMENTS

In the Workload mice body temperature, daily energy expenditure (DEE, using thedoubly-labeled water technique, DLW), and resting metabolic rate (RMR, indirectcalorimetry) were determined twice, once during baseline (day -4 to 0) and onceduring workload (day 19 to 23, at 90% of maximal workload). In the Ad-lib group,DEE and RMR were determined once (at the same age as the working mice duringthe second measurements).

The protocol for the measurements was as follows. First, mice were weighed ona balance to the nearest 0.1 g and body temperature was measured at 11:00 using arectal probe inserted to a depth of approximately 10 mm (+ 0.1°C, NTC type C,Ahlborn, Holzkirchen, Germany). Thereafter we injected the animal with about 0.1 gdoubly labeled water (2H and 18O concentrations of the mixture 37.6% and 58.7%,respectively) allowing an equilibration period of 1 hr. The precise dose was quanti-fied by weighing the syringe before and after administration to the nearest 0.0001 g.After puncturing the end of the tail, an “initial” blood sample was collected andstored in three glass capillary tubes each filled with about 15 µl blood. These

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capillaries were immediately flame-sealed with a propane torch for later analysis.Thereafter the mouse was returned to its cage. After 48 h a “final” blood samplewas collected as described before, and the animal was weighed again. We collectedblood samples of four sentinel mice from our breeding colony which had not beeninjected with DLW, to assess the natural abundances of 2H and 18O in the bodywater pools of the animals. Throughout these measurements the Workload micewere working for their food at 90% of their previously observed maximum(Experiment 1), and the Ad-lib mice had access to a running wheel.

The next day at 12:00, animals were transferred to an 8-channel respirometrysystem to determine RMR. Mice were put in flow-through boxes (15x10x10 cm)connected to an open-flow respirometry system where oxygen consumption (V

.O2,

l h-1) and carbon dioxide production (V.

CO2, l h-1) was measured simultaneouslywith ambient temperature and activity for 24 h, as described by Oklejewicz et al.(1997). In brief, oxygen and carbon dioxide concentration of dried inlet and outletair (drier: molecular sieve 3 Å, Merck, Damstadt, Germany) from each chamber wasmeasured with a paramagnetic oxygen analyzer (Servomex Xentra 4100, Crow-borough, United Kingdom) and carbon dioxide by an infrared gas analyzer(Servomex 1440), respectively. The system recorded the differentials in oxygen andcarbon dioxide between dried reference air and dried air from the metabolic cages.Flow rate of inlet air was set at 20 l h-1 and measured with a mass-flow controller(Type 5850 Brooks, Rijswijk, Netherlands). Data were collected every 10 minutesand automatically stored on a computer. Animals from the Workload groupsreceived ~3 g of food (based on their food intake at that moment) and a piece ofapple while in the respirometer. Animals from the other group (Ad-lib mice) had adlibitum food and a piece of apple.

Metabolic rate (MR, kJ h-1) was calculated using the following equation: MR =(16.18 x V

.O2) + (5.02 x V

.CO2) (Romijn and Lokhorst, 1961). RMR was defined

as the lowest value of metabolic rate in half-hour running means. RMR in thisstudy thus represents the lowest metabolic rate of animal at room temperature(22°C).

MASS SPECTROMETRY

The determinations of the 2H/1H and 18O/16O isotope ratios of the blood sampleswere performed at the Centre for Isotope Research employing the methodsdescribed in detail by Visser and Schekkerman (1999) using a SIRA 10 isotope ratiomass spectrometer. In brief, each capillary was microdistilled in a vacuum line. The18O/16O isotope ratios were measured in CO2 gas, which was allowed to equili-brate with the water sample for 48 h at 25°C. The 2H/1H ratios were assessed fromH2 gas, which was produced after passing the water sample over a hot uraniumoven. With each batch of samples, we analysed a sample of the diluted dose, and atleast three internal laboratory water standards with different enrichments. Thesestandards were also stored in flame-sealed capillaries and were calibrated againstIAEA standards. All isotope analyses were run in triplicate.


The rate of CO2 production (rCO2, moles d-1) for each animal was calculatedwith Speakman's (1997) equation:

rCO2 = N/2.078 * (ko – kd) – 0.0062 * N *kd

where N represents the size of the body water pool (moles), ko (1 d-1) and kd (1 d-1)represent the fractional turnover rates of 18O and 2H, respectively, which were cal-culated using the age-specific background concentrations, and the individual-specif-ic initial and final 18O and 2H concentrations. The value for the amount of bodywater for each animal was obtained from the carcass analyses. The amounts of bodywater of the animals at baseline conditions were calculated from the body water vsbody mass relationship of the 7 control animals. Finally, the rate of CO2 productionwas converted to energy expenditure assuming a molar volume of 22.4 l mol-1 andan energetic equivalent per l CO2 based on RQ measurements in our respirometrysetup (on average 22 kJ l-1 CO2,(Gessaman and Nagy, 1988)).

BODY COMPOSITION

After the respirometry measurement all animals were euthanized with CO2 fol-lowed by decapitation, and organs were dissected out and weighed to the nearest0.0001 g. All tissues were stored at -20°C until further analysis. Dry and dry leanorgan masses were determined by drying organs to constant mass at 103°C fol-lowed by fat extraction with petroleum ether (Boom BV, Meppel, NL) in a soxhletapparatus.

HORMONES

Blood samples were taken from the Workload mice from the tail tip during baseline(day –5) and workload (day 18) at 10:00 (one hour prior to daily weighing).Behaviour of the mice was noted prior to sampling, and all mice were at rest.Animals were not aneasthesized and samples were collected within 90 seconds ofinitial disturbance. Blood was collected in Eppendorf tubes with EDTA as anticoag-ulant and kept on ice until it was centrifuged at 2600 rpm at 4°C. The supernatantwas collected and stored at –80°C. Corticosterone levels were determined using RIA(Linco Research, Nucli Lab, The Netherlands).

Data analysisStatistical analysis was performed using SPSS for windows (version 14.0). Forexperiment 1, we applied repeated measures ANOVA with line (C vs. S) asbetween-subjects factor and treatment (baseline vs. workload) as within-subjectsfactor. For experiment 2, paired t-tests were used to test for differences betweenbaseline and workload conditions within the Workload animals and independent t-tests were used to test for differences between Ad-lib and Workload animals. Alltests were two-tailed and significance was set at p≤ 0.05.

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RESULTS

Experiment 1: Maximum workload Table 3.1 shows values of wheel-running activity, body mass, and absolute andmass-specific food intake in the workload mice during baseline and at maximumworkload. We applied repeated-measures ANOVA to investigate differencesbetween the workload and baseline conditions (within-subjects factor), andbetween the lines (C vs. S; between-subjects factor). Overall, wheel-running activitydid not differ statistically between C and S mice (Table 3.1, no effect of line).However, as illustrated in Figure 3.1, post-hoc t-tests showed that spontaneouswheel-running activity under baseline conditions was significantly higher in S mice(14.7 km d-1, see Table 3.1) than in C mice (11.5 km d1; p=0.05). Body mass andfood intake did not differ between C and S mice (Table 3.1).

When challenged to work for food, all mice increased wheel-running activity(Figure 3.1). The maximum level of running did not differ statistically between Cand S mice, and was on average 23.3 km d-1 in both groups (Table 3.1). This maxi-mum level was independent of the spontaneous baseline wheel-running activity ofthe individual mice, as shown in Figure 3.1 (Pearson's r = 0.3, 2-tailed p = 0.26).At the maximal level of wheel running, body mass had decreased by approximately16% and absolute food intake by 20% (significant effect of treatment; see Table3.1). Mass-specific food intake did not differ between baseline and workload condi-tion (no effect of treatment, Table 3.1). No significant interaction effects were seenbetween line and treatment. C and S mice thus responded similarly to the workloadschedule, and both groups showed a similar increase in wheel-running activity andsimilar decreases in body mass.


Table 3.1. Experiment 1; Effects of maximal workload on main characteristics in control (C) andactivity-selected mice (S).

Baseline Maximal workload P-values for repeatedmeasures ANOVA

C (n=8) S (n=8) C (n=8) S (n=8) d.f. Line Treat

Wheel-running activity 11.5±1.2 14.7±0.8 23.2±1.4 23.4±1.4 1,14 0.29 <0.001(km d-1)Body mass (g) 30.9±0.5 30.6±0.5 26.0±0.3 25.5±0.5 1,14 0.82 <0.001Food intake (g d-1) 5.7±0.1 6.0±0.2 4.6±0.3 4.6±0.2 1,14 0.44 <0.001Food intake 0.46±0.01 0.43±0.01 0.40±0.02 0.41±0.02 1,14 0.56 0.02(g g BM0.75- d-1)Mass-specific food intake 0.20±0.01 0.18±0.01 0.18±0.01 0.18±0.01 1,14 0.38 0.43(g g-1 d-1)

Wheel-running activity, body mass, and food intake during baseline and at maximal workload in activity-selectedmice and random-bred controls. Values given are mean±sem. Repeated-measures ANOVA with line (C vs. S) asbetween-subjects factor and treatment (baseline vs. workload) as within-subjects factor were performed. Inter-actions were never significant and are therefore not shown in the table. Significant results are shown in bold.BM=body mass, n=sample size, d.f. = degrees of freedom.

Figure 3.2 shows the circadian pattern of wheel-running activity during baselineand workload. Under baseline conditions, mice mainly ran in the dark phase. Asmall peak in wheel-running activity after 11:00 (time of daily measurements) canbe observed, probably due to disturbance. When challenged to work for food, theperiod of running was extended and mice started running more during the lightphase. It appears that the mice shifted the onset of activity towards the time atwhich daily measurements took place.

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max

. whe

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m d

-1)

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155

20

25

Figure 3.1. Relationship between spontaneous wheel-running activity (RWA BL) and maximumwheel-running activity (RWA MX) in control mice (C, open circles) and mice selectively-bred forhigh wheel-running activity (S, closed circles). Linear regression gave the following equations:combining both groups: RWA MX = 0.35 RWA BL + 20.1 (R2=0.09, n.s.); for C mice: RWA MX= –0.22 RWA BL +27.6 (R2=0.02, n.s.), and for S mice: RWA MX = 0.84 RWA BL +15.2(R2=0.47, n.s.).

whe

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m h

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0.0

0.5

1.0

1.5

2.0

2.5

208 8clock time (h)1810 12 14 16 22 0 2 4 6

Figure 3.2. Circadian pattern of wheel-running activity in control (C; open symbols) and activi-ty–selected mice (S; closed symbols) running spontaneously (circles) or running for food (trian-gles). Each symbol plots the mean distance ran in hourly bins (e.g., bin 12 = from 12:00 till13:00). Vertical bars are inter-individual s.e.m. The black bar on top represents the dark phase.

Experiment 2: Behavioural and physiological consequences of high workloadExperiment 1 showed no differences in wheel-running activity, body mass or foodintake between C and S mice under the high workload conditions. In Experiment 2we therefore pooled data from both groups (Workload mice, n=16) to study theeffects of workload on behavioural and physiological traits. Effects of workloadwere investigated by comparing the baseline condition (ad libitum food) to the highworkload condition (wheels attached to food dispensor) within these mice (usingpaired t-tests). For comparison of body composition, however, an additional controlgroup of 7 age-matched animals housed with a wheel and ad libitum food was added(Ad-lib group). This extra control group also enabled us to determine whether theWorkload mice had sufficiently recovered from Experiment 1 before the start ofExperiment 2.

DEVELOPMENT OF BODY MASS, FOOD INTAKE, AND WHEEL-RUNNING ACTIVITY AT

SUB-MAXIMAL WORKLOAD

For daily measurements (body mass, food intake, and wheel-running activity) wecalculated a baseline and workload value that was the average over one week (seeTable 3.2). For the baseline condition, this was the week prior to the start of thetraining, and for the workload the week started when the animals were on a maxi-mal workload for 2 days.

To determine whether the animals had recovered sufficiently from Experiment1, we first compared baseline data (Workload group) to data on animals in the Ad-lib


Table 3.2. Experiment 2; Main characteristics of Ad-lib animals, and Workload animals at baselineor workload conditions.

Ad-lib animals (n=7) Workload animals (n=16)

Baseline Workload

Wheel-running activity (km d-1) 7.7±1.3 10.2±0.9b 20.2±1.5Body mass (g) 34.2±0.8 34.6±0.5b 28.2±0.5Food intake (g d-1) 4.3±0.4 6.4±0.2a,b 4.0±0.2Mass-specific food intake (g g-1 d-1) 0.13±0.01 0.19±0.01a,b 0.14±0.01RMR (kJ d-1) 49.5±1.9 49.3±1.2b 27.4±1.8Mass-specific RMR (kJ g-1 d-1) 1.45±0.05 1.43±0.03b 0.98±0.05DEE (kJ d-1) 62.6±2.9 72.3±1.7a,b 60.0±1.7Mass-specific DEE (kJ g-1 d-1) 1.83±0.10 2.09±0.04a,b 2.25±0.07Body temperature (°C) - 36.6±0.4b 35.4±0.8Corticosterone (x103 ng ml-1) - 15±5b 222±47

Average(±sem) wheel-running activity, body mass, food intake, resting metabolic rate (RMR), daily energy expendi-ture (DEE), body temperature, and corticosterone level are shown for workload animals under baseline and work-load conditions and for ad libitum fed mice. Values are mean±sem. One control animal died during the respirometrymeasurements and data on RMR thus were not available. a Indicates a significant difference between Ad-lib andWorkload mice at baseline (independent t-test, p<0.05) and b indicates a significant difference within the Workloadgroup between baseline and workload conditions (paired t-test, p<0.05).

group of the same age using independent t-tests (see Table 3.2). Ad-lib andWorkload mice under baseline conditions did not systematically differ in body massor wheel-running activity (see Figure 3.3, triangles, and Table 3.2). Food intake wasslightly lower in Ad-lib mice than in Workload mice (4.3 vs. 6.3 g d-1). These resultsindicated that mice had recovered sufficiently from the preliminary workload exper-iment and subsequently the new workload scheme was started.

Figure 3.3 shows the changes in body mass and food intake that occurred in theWorkload mice when put on a workload schedule. On day 0 wheels were attached tothe food dispensers and the foraging effort was increased over 14 days up to 90% ofthe previously observed maximum for each mouse (training period). Mice werekept at this level for 10 days (workload period). Wheel-running activity showed aslight decrease just before the start of the training, which can probably be attrib-uted to the manipulations done at this time (doubly-labeled water injections).Wheel-running activity increased steadily during the training period and reached aplateau of approximately 20 km d-1 at the highest workload (90% of maximumworkload). In the Workload mice, body mass decreased significantly with approxi-mately 20% from 34.6 to 28.4 g, and both absolute and mass-specific food intakedecreased by approximately 30% at 90% workload compared to baseline. Wheel-running activity approximately doubled at high workload in the Workload mice (seeFigure 3.3 and Table 3.2).

We calculated the average time spent running by adding up all the 2-min inter-vals in which running occurred per day, and the maximum speed the mice ran (maxdistance covered per 2-min interval). This was done during baseline and workloadto determine which strategy animals used to increase their wheel-running activity.During baseline, time spent running was 5.9 h (s.d.1.8), but this almost doubled to11.5 h (s.d. 2.0) during workload. Max running speeds were 4.7 km h-1 (s.d. 0.8)and 6.3 km h-1 (s.d. 0.5) in baseline and workload phases, respectively (paired t-test; p<0.001 for both). Mice thus increased both time spent running (+94%) andmaximum running speed (+34%).

Multiple regression analysis showed that food intake was significantly, positivelypredicted by both body mass and wheel-running activity at baseline (Multipleregression: R2=0.49, p=0.012; body mass, p=0.018, wheel-running activity=0.067), as well as during the high workload experiment (Multiple regression:R2=0.58, p=0.004; body mass, p=0.0012, wheel-running activity=0.002).

METABOLIC RATE

Metabolic rate of the Workload animals was measured under baseline and workloadconditions (Table 3.2). First, we compared resting metabolic rate (RMR) and dailyenergy expenditure (DEE) between Ad-lib animals and Workload animals at baseline(see Table 3.2). No significant differences were found for RMR, but DEE was signif-icantly lower in the Ad-lib fed mice, which might be due to the slightly smallercages they were housed in. Second, we compared RMR and DEE under baseline andworkload conditions within the Workload group. At 90% of maximum workload,

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mice decreased RMR by approximately 50%, from an average 49.3 kJ d-1 to 27.4 kJd-1. The reduction in mass-specific RMR was about 1/3, from 1.43 to 0.98 kJ g-1 d-1.Both differences were statistically significant. Workload also influenced absoluteand mass-specific DEE. Absolute DEE decreased on average from 72.3 to 60.0 kJ d-1

at high workload, but mass-specific DEE slightly increased from 2.09 to 2.25 kJ d-1.Both differences were statistically significant (Table 3.2). Looking at individual vari-ation, all mice except one individual exhibited a decrease in DEE during workload(whole-animal values).


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Figure 3.3. Development of wheel-running activity, body mass, and food intake during trainingand at a workload of 90% from the maximal capacity in Workload animals (C & S groups pooled).Spontaneous wheel-running activity is shown for the 2 weeks prior to the training period (day 0).Circles show the development of the different variables during the experiment in the Workloadanimals and triangles represent average values for mice in the Ad-lib group.

We estimated the cost of activity (ACT, in kJ d-1) by deducting RMR from DEE(ACT was 23.0 and 32.6 kJ d-1 at baseline and workload respectively), and dividedthis by the amount of wheel running to estimate the costs per km. Costs of runningwere 2.3 kJ km-1 (s.d., 1.6) and 1.6 kJ km-1 (s.d., 0.3) at baseline and workload,respectively. This difference was significant (paired t-test, 2-tailed, p=0.026).

It is well known that metabolic rates (RMR and DEE) are positively associatedwith body mass, and under baseline conditions this relationship was obvious in allmice, based on bivariate relationships (open symbols in Figure 3.4A, Table 3.3).However, when working for food there was no longer a statistically significant rela-tionship between body mass and metabolic rates (closed symbols in Figure 3.4A,Table 3.3). We also performed multiple regression analyses with body mass andwheel-running activity as independent predictors of RMR or DEE. At baseline, themodels including both body mass and wheel-running activity was significant(R2=0.48, p=0.015), but only body mass (p=0.007) and not wheel-running activity(p=0.148) significantly predicted RMR. The same was true for the relationshipwith DEE (R2=0.43, p=0.025; body mass; p=0.008, wheel-running activity,p=0.785). Body mass alone explained more of the variation in RMR and DEE thanmodels that included wheel-running activity (see Table 3.3).

At high workload, metabolic rates were better predicted by the amount ofwheel-running activity than by body mass (see Figure 3.4B and Table 3.3). Multipleregressions for DEE or RMR with body mass and wheel-running activity were notsignificant (RMR: R2=0.25, p=0.182; body mass; p=0.709, wheel-running activity,

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A B

10 20 30wheel-running activity (km d-1)

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Figure 3.4. Relationship between body mass and metabolic rates (A) and between wheel-run-ning activity and metabolic rates (B) at baseline (open symbols) and workload (closed symbols)conditions in Workload animals. Triangles represent the resting metabolic rate (RMR) and circlesrepresent daily energy expenditure (DEE). Regression lines for all relationships are drawn. Forequations of the regression lines, R-square, and p-values see Table 3.4. Results of multiple regres-sions are presented in the text.

p=0.071 and for DEE: R2=0.25, p=0.158; body mass; p=0.241, wheel-runningactivity, p=0.073). As shown in Table 3.3, wheel-running activity alone did signifi-cantly predict DEE (p=0.005), and approached significance for predicting RMR(p=0.065). RMR was negatively related to wheel-running activity, while DEE waspositively related to wheel-running activity at workload. The animals that ran themost thus decreased their RMR the most, while increasing DEE. RMR and DEE atbaseline did not relate to RMR and DEE at workload.

ENERGY BALANCE

Figure 3.5 shows the energy budget of Workload mice at baseline and workload cal-culated over the days when DEE was measured in these mice. The figure shows thevarious components of the energy budget; gross energy intake (GEI), metabolisableenergy intake (MEI), and daily energy expenditure (DEE) divided into resting meta-bolic rate (RMR) and energy spent on activity (ACT). GEI was calculated on thebasis of the measured food intake and was 97.4 and 53.6 kJ d-1 in mice under base-line and workload conditions respectively (see M&M, for gross energy content ofthe food). Animals are not 100% efficient in metabolising their food and the actualamount of energy animals take out of their food can only be calculated when diges-tive efficiency and the amount of energy lost in the urine has been measured aswell. Previous studies have shown a digestive efficiency of 79.1% in ad libitum fedmice, including loss of energy in urine (Hambly and Speakman, 2005). Under theassumption that workload did not alter digestive efficiency, MEI at baseline andworkload was estimated using a digestive efficiency of 79.1%. Based on these valueswe can see whether animals were in a positive or negative energy balance. It is clear


Table 3.3. Linear regressions of metabolic rates (RMR and DEE) on body mass or on wheel-run-ning activity in workload mice.

Linear regression Slope Intercept R2 p

BaselineBody mass vs. RMR 1.52 -3.3 0.38 0.011Body mass vs. DEE 2.48 -13.5 0.50 0.002Wheel running vs. RMR –0.35 52.9 0.06 0.370Wheel running vs. DEE 0.61 66.2 0.09 0.260

WorkloadBody mass vs. RMR 0.13 23.8 0.01 0.390Body mass vs. DEE 0.36 50.0 0.05 0.790Wheel running vs. RMR –0.56 38.8 0.24 0.065Wheel running vs. DEE 1.10 37.7 0.41 0.005

Slope, intercept, R2, and p-values are given in the table. Multiple regressions showed that at baseline RMR and DEEwere better predicted by body mass alone and at workload by wheel-running activity alone. Results of multipleregressions with both body mass and wheel-running activity as independent predictors of RMR or DEE are describedin the text. See also Figure 3.4.

from this picture that at high workload the proportion of energy used for RMR wasstrongly decreased and the energy available for activity had increased. At high work-loads there was a negative energy budget of -17.7 kJ d-1 (or -0.74 kJ g-1 d-1) and theextra energy needed was obtained by reducing body mass by 0.8 grams on average.During baseline the energy budget was positive, +4.7 kJ d-1 (or +0.15 kJ g-1 d-1),and animals gained 1.0 gram body mass over the course of the measurements. Evenafter assuming an unlikely digestive efficiency of 100% in the workload animals, theenergy budget would still be negative (-6.4 kJ).

BODY COMPOSITION

We compared data from the animals in the Workload group with the animals in theAd-lib group using independent t-tests to investigate the effects of workload onbody composition (see Table 3.4). Body mass, total dry lean, and fat content werestrongly decreased in animals in the Workload group. Fat content decreased themost, by 70%, from 3.1 to 0.9 g. Dry lean organ masses were significantlydecreased in all organs of working animals compared to Ad-lib animals, except for

Chapter 348

MEI77.0

A

baseline

W20.4

RMR49.3

ACT23.0

GEI

DEE

absolute (kJ d-1)

MEI42.4

workload

W11.2

RMR27.4

ACT32.6

GEIDEE MEI

2.2

B

baseline

W0.6

RMR1.43

ACT0.66

GEI

DEE

mass-specific (kJ g-1 d-1)

MEI1.5

workload

W0.4

RMR0.98

ACT1.28

GEI

DEE

–0.7

4+0.

15

–17.

7

+4.

7

Figure 3.5. Energy budget of Workload mice during baseline and workload conditions. Panel Ashows the absolute values and B the mass-specific values. To determine the energy balance weused measures of RMR and DEE. Energy for activity (ACT) was calculated by deducting RMRfrom DEE. In addition, the gross energy intake (GEI) was calculated on basis of the absolute foodintake during the doubly labelled water measurements. Metabolisable energy intake (MEI) wasthen calculated from GEI assuming that digestive efficiency together with energy lost in the urinewas 79.1 % (Hambly and Speakman, 2005). The white bars represent the energetic value of thefood that is not metabolised (GEI - MEI = Waste, W). The numbers in the bars represent theamount of energy (either in kJ d-1 or in kJ g-1 d-1) spent on each part of the energy budget. Thebracket shows the surplus energy available to the animals for growth.

the brain, stomach, and lungs that showed no difference, and the intestines thatshowed a significant increase in dry lean mass. Fat content also decreased signifi-cantly in most organs (except for the heart), with the largest decrease in skin (81%) and the lowest in the brain (10%).

We also calculated mass-specific organ masses to be able to correct for effects ofbody mass on body composition (data not shown). In these analyses, total fat con-tent and fat content of all organs (except for heart) still showed a significantdecrease. Total mass-specific dry lean mass did not differ between Ad-lib andWorkload animals anymore; dry lean mass did significantly decrease in liver, kidney,skin and the remainder of the carcass, but it increased significantly in brain, stom-ach, intestine, and lung.

The total fat content of the mice could be negatively predicted by the amount ofwheel-running activity at workload (r=-0.67, p=0.006).


Table 3.4. Body composition of Ad-libitum fed mice and mice working for food.

Variable (g) Ad-lib animals Workload animals % difference Independent t-test(n=7) (n=15) t p

Body mass 31.4±0.9 25.9±0.4 –17 –6.8 <0.001Dry lean mass 8.1±0.2 6.6±0.1 –19 –9.9 <0.001Fat content 3.13±0.35 0.94±0.09 –70 –8.2 <0.001Dl heart 0.04±0.001 0.03±0.001 –29 –4.8 <0.001Dl liver 0.48±0.03 0.30±0.02 –37 –5.0 <0.001Dl kidney 0.13±0.01 0.10±0.001 –29 –4.0 0.001Dl brain 0.08±0.001 0.08±0.001 –3 –1.3 0.23Dl stomach 0.04±0.001 0.04±0.001 +5 0.9 0.36Dl intestines 0.29±0.01 0.34±0.01 +18 4.5 <0.001Dl lung 0.04±0.001 0.04±0.001 +3 0.6 0.59Dl skin 1.50±0.04 1.26±0.02 –16 –6.0 <0.001Dl rest 5.48±0.12 4.35±0.05 –21 –10.6 <0.001Fat heart 0.006±0.001 0.005±0.001 –18 –1.1 0.30Fat liver 0.048±0.009 0.027±0.003 –43 –2.8 0.011Fat kidney 0.026±0.003 0.006±0.001 –75 –7.7 <0.001Fat brain 0.047±0.001 0.043±0.001 –10 –3.7 0.002Fat stomach 0.008±0.001 0.005±0.001 –36 –4.2 <0.001Fat intestines 0.070±0.010 0.039±0.002 –44 –4.2 <0.001Fat lung 0.008±0.001 0.004±0.001 –51 –6.7 <0.001Fat skin 0.85±0.120 0.16±0.028 –81 –7.6 <0.001Fat rest 2.07±0.240 0.65±0.071 –69 –7.4 <0.001

Mean(±sem) total dry lean mass, fat content and the dry lean (dl) mass and fat mass of separate organs are shownfor Workload and Ad-lib mice. One mouse died during the second respirometry measurement in the Workload group.% difference shows the change in mass between Ad-lib and Workload animals. Independent t-tests were performed totest for differences between groups and results are shown in the table.

BODY TEMPERATURE & PLASMA CORTICOSTERONE

Body temperature of the Workload animals was measured in the light phase underbaseline and workload conditions (see Table 3.2). Three out of 16 mice under work-load conditions had extremely low body temperatures at the time of measurement(32.2, 32.5 and 26.8°C), but no significant differences were found within the work-load mice between baseline or workload conditions. Plasma corticosterone levelswere strongly affected by treatment. At high workload, corticosterone levels wereapproximately 15-times increased (see Table 3.2). Body temperature or plasma cor-ticosterone did not correlate with wheel-running activity.

DISCUSSION

Challenging mice to work for food to mimic low food availability resulted in severalphysiological and behavioural changes that may be adaptative. All animals increasedwheel-running activity by approximately 100%. This was mainly accomplished byspending more time running (including during the light phase), but running speedalso increased. A shift in activity patterns towards the day in response to workloadwas shown before in Mus musculus (Perrigo, 1987). The increase in wheel-runningactivity was not sufficient to maintain adequate food intake, and body massdecreased (Figure 3.3).

A detailed look at the body composition of the workload mice showed that thereduction in body mass was mainly caused by a reduction in fat mass. Total fat con-tent was reduced by ~70% in Workload mice compared to mice in the Ad-lib group.Fat content of all organs (except for the heart) reduced significantly, with the mostpronounced decreases in subcutaneous and intra-peritoneal fat and the smallestdecrease in the brain. Similarly, dry lean mass of the brain was not significantlyreduced; mass-specific dry lean mass of the brain even increased in the workloadgroup. The brain is very important for the central regulation of bodily functions andis apparently protected in times of scarcity. A similar result was found in food-restricted rats, where brain mass was unaffected, but heart, kidney, and liver massdecreased (Greenberg and Boozer, 2000). Total mass-specific dry lean mass wassimilar in Ad-lib and Workload mice, but the distribution of dry lean mass over thebody did change under high workload conditions. In liver, kidney, skin, and theremainder of the carcass, mass-specific dry lean mass was decreased, whereas it wasincreased in lung, stomach, and intestine. The increase in intestine mass and stom-ach mass could indicate that animals increased their digestive efficiency underworkload conditions. This would enable them to get more energy from a gram offood. In addition, mice could have ingested their faeces (coprophagy) to increasetheir food efficiency even more. Further studies would be necessary to test thesehypotheses. The strong reduction in fat content without a major change in dry leanmass is in agreement with observations by Perrigo and Bronson (1983) in pre-pubertal female mice. In their study, fat depots remained undiminished or above

Chapter 350

control levels over a wide range of forced activity, even when accompanied by amoderate decrease in food intake, but at the maximum requirement of 225 revolu-tions per pellet (comparable to our conditions) females accumulated less body fatthan ad libitum fed animals. Studies on food restriction in sedentary mice show con-trasting results on body composition changes with greater use of fat mass than drylean mass (Greenberg and Boozer, 2000), defense of fat mass and reduction of drylean mass (Hambly and Speakman, 2005), or no differential use of the differentcomponents (Selman et al., 2005).

Corticosterone levels were increased at high workload and comparable to thevalues reported in response to restraint stress in male mice of this strain (Malisch etal., 2006). Baseline values were slightly lower than the ones reported in that study.We did not show a relationship between wheel-running activity (over 24h) and cor-ticosterone or body temperature. Wheel-running activity in the 10–20 min. prior tomeasurements has been shown to correlate positively with both body temperature(Rhodes et al., 2000) and plasma corticosterone (Girard and Garland, Jr., 2002) inthese lines of mice.

Unexpectedly, selective breeding for high spontaneous wheel-running activitydid not affect the response to a workload challenge, at least based on the two ofeight total lines (see Swallow et al., 1998) that we studied here. Control (C) andactivity-selected (S) mice did not differ with respect to their maximum wheel-run-ning activity on a high workload (~23 km d-1; Table 3.1) and both groups showedsimilar decreases in food intake and body mass at the maximum workload. Also,spontaneous wheel-running activity at baseline did not predict wheel-running activ-ity at workload (Figure 3.1). These results are in contrast to a similar study in rats(Adage et al., 2002). Based on measurements of spontaneous wheel-running activitythey divided female Wistar rats from a single population into high or low sponta-neous runners. They found that animals with high baseline running activity copedbetter on a workload schedule than rats with low spontaneous levels of wheel-run-ning activity, and the former could also increase their wheel-running activity more.The rats with low spontaneous levels of activity markedly decreased in body mass,whereas rats that had high levels of spontaneous wheel running maintained bodymass at the same workload level. The discrepancy between our study and the studyof Adage et al. (2002) may represent differences between mice and rats in the regu-lation of wheel-running activity and body mass, and also may depend on differencesin motivation to run. The rats were of similar age (3–4 months) to our mice, so agewas probably not a factor.

Resting metabolic rates and, to a lesser extent, daily energy expenditure showeda strong reduction under workload conditions (~50%), an effect that has beenshown in several studies manipulating workload; in birds (Bautista et al., 1998;Deerenberg et al., 1998; Wiersma and Verhulst, 2005), hamsters (Day and Bartness,2001), and mice (Perrigo, 1987), for summary see Table 4 in Wiersma and Verhulst(2005). In another study, an increase in daily energy expenditure has been shown(Wiersma et al., 2005), but that study used a variable rather, rather than the fixed


reward ratio we used in this study. With increasing wheel-running activity, restingmetabolic rate decreased and daily energy expenditure increased, but daily energyexpenditure was lower under workload conditions than when animals were runningspontaneously at a lower level. In principle, mice had unlimited access to food, butthey stopped foraging at a point where their food intake was lower than the foodintake of animals that had immediate access to food. Instead of increasing theirfood intake, animals compensated for the increased costs of activity by decreasingRMR. This may indicate the presence of constraints that prevent animals fromincreasing their activity further (see also Garland, 2003; Rhodes et al., 2005). First,the capacity for sustained, endurance-type activity can be a limiting factor. Second,time can be a limiting factor, and animals did extend their activity into the lightphase on the workload (Figure 3.2), thus leaving less time to rest and sleep. All ani-mals need to sleep to survive (Everson, 1995), and this may have limited the timemice had left to run. However, levels of running were much lower during the daythan during the night, and animals only spent ~12 hours continuously running athigh workloads, which would seem to leave enough time for rest. Third, digestiveconstraints could limit the intake of extra food. Total food intake was reduced athigh workload compared to the baseline condition, and it is thus not likely thatdigestive constraints were at work in our mice. Moreover, when cold-exposed, thesemice can increase their food intake by much greater amounts (Koteja et al., 2001)than were ever exhibited in the present study. Another possible constraint is meta-bolic. When we looked at mass-specific metabolic rates, RMR was reduced in miceat high workload, but DEE was slightly increased. Several lines of evidence indicatethat maximum metabolic rates are limited by the intrinsic physiology of the animal(Drent and Daan, 1980; Speakman and Krol, 2005). When animals reach this maxi-mum level they can no longer increase their activity (energy expenditure) to obtainmore food. The maximum sustainable level of energy expenditure in laboratorymice subjected to forced exercise (Mus musculus) has been measured at 2.94 kJ g-1

d-1; see Table 2 in (Hammond and Diamond, 1997). With a daily energy expendi-ture of 2.25 kJ g-1 d-1, our mice were not yet at this highest sustainable level.Interestingly, measurements with the same lines of mice used here showed thatanimals exposed to cold with wheel access had an average daily energy expenditureof approximately 3.2 kJ g-1 d-1 at 10°C (Vaanholt et al., 2007) and animals exposedto severe cold without wheel access attained values as high as 5.7 kJ g-1 d-1 at -5°C(Koteja et al., 2001).

Whatever the constraints on increasing activity further may have been, the micein our study "chose" to compensate for the experimentally manipulated increase inenergy expended on activity by reducing RMR. The mice that ran the most showedthe greatest decrease in RMR. But how could they have accomplished this? First,reducing body mass reduces whole-animal RMR (Deerenberg et al., 1998; Speak-man and Selman, 2003). However, the reduction in RMR observed in the presentstudy was much greater than expected based on changes in body mass alone, and atthe high workload body mass did not significantly correlate with RMR. As proposed

Chapter 352

by Rezende et al. (2006), in the lines of mice selectively-bred for high running, low-ering of body mass may be a way to keep whole-animal energy costs of activity rela-tively low while selective breeding causes total running distance to increase.Similarly, in animals forced to work for food, lowering body mass may be a way todecrease costs of running and/or maintenance costs. Indeed, when we calculatedthe energy spent per km at baseline and workload condition, a reduction in whole-animal running costs of approximately 35% was found. The cost of transport (COT)estimated here (2.3 kJ km-1) is much higher than that reported previously ~1.2 kJkm-1 for these mice (Koteja et al., 1999; Rezende et al., 2006; Vaanholt et al., 2006).This discrepancy occurs because in this study we did not calculate COT based onthe slope of the regression between running speed and energy expenditure, butinstead made a crude estimate of COT by dividing ACT by the amount of wheelrunning.

Animals also could have saved energy by reducing behaviours other than wheel-running activity, such as grooming or exploration, or they may have compensatedby saving on maintenance processes. It has, for instance, been shown that zebrafinches in energetically demanding situations refrain from mounting an immuno-logical response to a novel challenge (Deerenberg et al., 1997) and that they investless in regrowing feathers (Wiersma and Verhulst, 2005). Further research is neces-sary to determine whether similar effects may have occurred in our mice.Hypothermia, as we saw in several mice, and that has been reported in previousexperiments manipulating foraging effort (Perrigo and Bronson, 1983) and in food-restricted animals (birds (Daan et al., 1989), and mice (Gelegen et al., 2006)), mayalso have contributed to the strong reduction in RMR. In the present study, micewere housed at 22°C, which is well below the lower critical temperature of mice;thermoregulatory costs could have been lowered even more by substituting ther-moregulatory heat production for heat generated by activity. However, a previousstudy of these mice did not show substitution of thermoregulatory heat for heatgenerated by voluntary activity (Vaanholt et al., 2007). Lowering body temperaturecan be beneficial to save energy, but lowering body temperature may also impose atrade-off. When body temperature gets below the optimal temperature for enzymat-ic activity, protein turnover and/or cellular turnover in general decelerates, causingreduced repair of cellular damage or a reduction immunological defense (Deeren-berg et al., 1997). This will make animals more vulnerable and may reduce their lifespan. In addition, reduced body temperature may lower locomotor performance(Bennett, 1990) and impair various other physiological rate processes.

Despite the strong reduction in RMR, the mice in our study were in a negativeenergy balance (Figure 3.5) and had to burn fat to meet their energetic require-ments. Under baseline conditions mice were in a positive energy balance andgained body mass. Similarly, in humans prolonged imbalances in the energy budget(positive energy balance) have resulted in a strong increase of the prevalence ofobesity. Dietary restriction alone generally only leads to weight loss during the peri-od of restriction, and may mainly result in a reduction in lean mass, instead of fat


mass; moreover, the lost weight is usually regained afterwards (in mice (Hamblyand Speakman, 2005) and humans (Stiegler and Cunliffe, 2006)). The main reasonfor this is that animals have evolved ways to compensate during periods of foodscarcity, including reducing resting metabolic rate and increasing digestive efficien-cy, or reducing activity. Dietary restriction together with exercise is the most advo-cated treatment for obesity. We showed in our working mice that combining bothfactors was indeed very effective in reducing fat mass. Because animals had not yetreached a new equilibrium in their energy balance, we do not know whether thisloss of fat mass would be sustained over longer periods. Westerterp (2001) hasshown that ‘novice’ trainees for the half-marathon lose body mass and concomitant-ly lower night-time metabolism (per gram fat-free mass), similar to what we saw inour mice.

In summary, challenging mice to work for food resulted in several physiologicalchanges. Mice readily increased wheel-running activity when they had to work forfood, but they did not maintain food intake, and body mass subsequently decreased(mainly by a reduction in fat mass). Animals compensated for the increased ener-getic requirements by decreasing resting metabolic rate. The physiological respons-es were independent of inter-individual variation in spontaneous wheel-runningactivity, but wheel-running at the high workload was negatively related to RMR.The more they ran, the lower their RMR became. DEE showed an opposite relation-ship.

AcknowledgementsThe authors thank Gerard Overkamp for expert technical assistance with the workloadequipment and Berthe Verstappen for performing the isotope analyses. Peter Meerlo enabledus to do the corticosterone measurements. We thank Kristin Schubert for comments on ear-lier versions of the manuscript. TG was supported by U.S. National Science Foundationgrant IOB-0543429. S.D. was supported by the European 6th framework Integrated ProgramEUCLOCK.

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55

Plasma adiponectin is increased in miceselectively bred for high wheel-running activity,but not by wheel running per se

Lobke M. Vaanholt, Peter Meerlo, Theodore Garland Jr., G. Henk Visser,Gertjan van Dijk

Hormone and Metabolic Research, In Press

AbstractMice selectively bred for high wheel-running activity (S) have decreased fatcontent compared to mice from randomly bred control (C) lines. Weexplored whether this difference was associated with alterations in levels ofcirculating hormones involved in regulation of food intake and energy bal-ance, and whether alterations were caused by the presence of a runningwheel. Plasma levels of leptin, adiponectin, and corticosterone as well asbody composition were analyzed in male S mice housed with (+) and with-out (-) access to running wheels at ages of 10 and 18 months. These levelswere compared to those found in C+ mice. Plasma corticosterone did notdiffer among groups. While plasma leptin levels tended to be lower in S+mice as compared to S- or C+ mice, these differences were largely attributa-ble to differences in fat content. Adiponectin levels were increased in S mice(+60%) compared to C mice, irrespective of wheel access. High levels ofthis hormone may be a trait co-segregated in mice bred for high wheel-run-ning activity.

Chapter4

INTRODUCTION

It is generally accepted that moderate physical activity has a positive influence onhealth and life expectancy (Holloszy, 1988; Navarro et al., 2004). From a clinicalstandpoint, the most beneficial effect of regular exercise is that it prevents or antag-onizes increased adiposity and the adverse health risks (e.g. cardiovascular diseaseand metabolic syndrome, type-2 diabetes mellitus) that are associated with it(Holloszy, 1988; Novelli et al., 2004), for review see (Carroll and Dudfield, 2004).Indeed, physical activity promotes the breakdown of triglyceride stores inside adi-pose tissue and muscle, which in turn contributes to increased whole-body insulinaction and improvement of tissue perfusion (Boden, 1997).

Besides having a direct effect on metabolic and vascular processes, it has beenpointed out that physical activity could also affect endocrine activity, which in turncould influence body adiposity (McMurray and Hackney, 2005). In this respect, adi-pose tissue has received considerable attention because it secretes the adipocytehormones leptin and adiponectin. These hormones are extremely important in thelong-term maintenance of energy balance and fuel homeostasis (Caro et al., 1996;Halaas et al., 1995; McMurray and Hackney, 2005; Ryan et al., 2003) and thus have astrong influence on sustainable health. Circulating levels of leptin have been shownto correlate highly with indices of fat content in many species (Park et al., 2004),and as such may signal the available amount of body fat to the brain (Halaas et al.,1995). In this way, leptin regulates appetite and metabolism in a coordinated fash-ion (Caro et al., 1996; Van Dijk et al., 1999). Adiponectin, on the other hand, doesnot correlate positively with indices of fat content, but was found to be negativelycorrelated with fat content in humans (Cnop et al., 2003; Park et al., 2004; Ryan etal., 2003) or be unrelated (Ferguson et al., 2004). Adiponectin has been implicatedto stimulate fat oxidation in metabolically active tissue (Berg et al., 2002; Bruce etal., 2005; Fruebis et al., 2001; Yamauchi et al., 2002) and peripheral insulin sensitivi-ty (Baratta et al., 2004; Schondorf et al., 2005; Yamauchi et al., 2001). Circulatingadiponectin levels are reduced and leptin levels are enhanced in obese humans com-pared with lean individuals (Arita et al., 1999; Havel, 2001). This might contributeto the insulin resistance that is observed in obese subjects (Gil-Campos et al., 2004).

Human studies on the interaction between physical activity and endocrine activ-ity of adipose tissue are not conclusive, and outcomes may depend on the intensityof the exercise paradigm and the exercise capacity of subjects (Ferguson et al., 2004;Jurimae et al., 2005; Zoladz et al., 2005). In laboratory animals, however, leptin con-centrations were found to be decreased in life-long voluntary exercising rats at 23months of age (Novelli et al., 2004) and in hamsters that had been exercising volun-tarily for 31 days (Coutinho et al., 2006), which may be consistent with a loweringof triglyceride stores in exercising animals. Mice selectively bred for high physicalactivity (Swallow et al., 1998) show a decreased body fat (Swallow et al., 2001) anda decreased leptin concentration in females at 3 month of age (Girard I., Rezende,E. L., and Garland, T., Jr., unpublished observation). The reduction in circulating

Chapter 458

leptin level, however, was greater than could be explained by the reduced fat massalone. To further investigate the relation between physical activity, circulatingadipocyte hormones, and body composition, the present study assessed these rela-tionships in selectively bred high-activity male mice with chronic access to runningwheels, and compared these effects to those found in their random-bred controls atdifferent ages. Because relatively low plasma leptin levels in the high activity-select-ed females were associated with increased plasma corticosterone levels (Girard andGarland, Jr., 2002; Malisch et al., 2006) plasma levels of corticosterone were alsoassessed in the present study. To investigate the effects of wheel-running activity persé on adipose and adrenal hormones and body composition, above-mentioned rela-tions were also investigated in selectively-bred mice without the presence of a run-ning wheel. These comparisons may shed light on the nature of changes that occurin the regulation of adipose and adrenal hormones; i.e., whether they are caused byhigh activity per se, or whether it is a trait that has genetically co-segregated withselection for high wheel-running activity.


Animals & housingThe progenitors for the original selection experiment (Swallow et al., 1998) were112 pairs of outbred, genetically variable Hsd:ICR mice obtained from Harlan-Sprague-Dawley in Indianapolis, Indiana, USA. After initial generations of randommating, the selection procedure then employed 8 separate lines, 4 in which breed-ers were chosen randomly within each line (control or C lines) and 4 in which thehighest-running males and females from each family were used as breeders (pre-venting sib-matings, selected or S lines). At generation 31, 80 breeding pairs, repre-senting all 8 lines, were shipped by air to the animal facility of the Biological Centerin Haren, and a breeding colony was started. Male offspring from all 8 lines wereused in the experiment described below. After weaning, mice were housed withtheir littermates until the age of 5 months when all animals were individuallyhoused with or without a running wheel for the rest of their lives (Macrolon TypeII, UNO Roestvaststaal BV, Zevenaar, NL; adapted to fit in a plastic running wheelwith a 7 cm radius and 1 cm spacing between bars). Wood shavings were used asbedding material and all animals got a wooden stick. The animals had ad libitum food(Standard lab chow 2181, Hopefarms B.V., Woerden, NL) and water and were undera 12:12 light-dark cycle (lights on at 8:00). All procedures concerning animal careand treatment were in accordance with the regulations of the ethical committee forthe use of experimental animals of the University of Groningen (DEC 2777(-1)).

Experimental proceduresThree experimental groups were created; group 1) Control mice housed with a run-ning wheel (C+), group 2) Selected mice with a running wheel (S+), and group 3)

Adiponectin in mice bred for high activity 59

Selected mice without a running wheel (S-). Logistical constraints precluded inclu-sion of a fourth group, i.e., C- mice. For both C and S groups, mice from all fourlines were represented, although not in equal proportions. Animals of two differentages were used, mice in the 10 month age group were 312±7 days old (mean±SD)and mice in the 18 month group were 559±10 days old. Every mouse was weighedonce per month and left undisturbed except for the weekly cage cleaning. In theweek prior to killing food intake was monitored over 3 consecutive days. In addi-tion, wheel-running activity was assessed in the S+ and C+ groups over two weeksprior to sacrifice with a PC-based event recording system (ERS). Data on wheel-running activity was not available for all animals due to computer problems.

At different ages (10 and 18 months) 7–8 mice per cohort were briefly anaesthe-sized with CO2 then killed by decapitation in the middle of the light phase. Micewere not fasted prior to blood sampling and all mice were asleep when they weretaken for blood sampling. Trunk blood was collected in pre-chilled tubes with anti-coagulant (EDTA) within 90 seconds from initial disturbance to the final drop ofblood. Samples were spun down at 2600 g for 15 minutes at 4°C. Plasma was col-lected and stored at –80°C until later analysis for hormone levels. Corticosterone,leptin, and adiponectin levels were determined in duplicate with commercialradioimmunoassay kits (Linco Research, Nucli lab, The Netherlands).

After blood collection animals were dissected and fresh mass of different organs(heart, liver, kidney, lung, stomach, intestines, skin) and the remainder of the carcasswere weighed to the nearest 0.0001 g. The fat content of all animals was determinedby drying (ISO 6496-1983(E)) all separate tissues at 103°C followed by fat extractionusing petroleum ether (Boom BV, Meppel, Netherlands) and subsequent drying.

Data analysisTo test for effects of treatment and/or age we applied ANCOVA models in theMIXED procedure in SAS for Windows (version 9.1). Group, age, and the group xage interaction were fixed effects. The main interest of this study was to determineeffects of selective breeding and effects of the presence of a running wheel; there-fore, a priori, we tested for differences between group 1 and group 2 (C+ versusS+), and between group 2 and group 3 (S+ versus S-), by adding contrasts to themodel. Because we did not maintain a C- group and because sample sizes per groupx age combination were relatively small (see Table 4.1), we did not include line as arandom effect nested within linetype. Covariates were included as appropriate. Forinstance, leptin and adiponectin are produced by fat cells and have been shown tocorrelate with total fat content. Therefore, fat content was added into the model asa covariate for both hormone levels. Data were log10-transformed as necessary toimprove normality and linearity of relations with covariates. Based on previousfindings, one-tailed tests could be used for some traits; however, for simplicity two-tailed p-values are given for all variables. The significance level of p≤ 0.05 was used.Pearson correlations were used to explore relations between traits of interest, con-sidering each group separately.

Chapter 460

RESULTS

Body composition, food intake, and wheel-running activityTable 4.1 shows the main characteristics of the three experimental groups. Asexpected from previous studies, selected mice with a running wheel (S+) tended tohave lower body mass (F1,40=17.4, p<0.001) and higher food intake than controlmice with wheels (C+) (not significant: F1,40=3.72, 2-tailed p=0.061). Food intakealso did not differ significantly between S+ and S- mice, but body mass of S+ micewas lower than S- mice (F1,40=5.4, p=0.025). Adding body mass to the model as acovariate did not alter these effects for food intake.

Absolute fat content was approximately 50% lower in S+ mice compared to C+and S- mice (F1,40=12.2, p=0.001 and F1,40=11.3, p=0.002, respectively) andabsolute dry lean mass was higher in controls than both selected groups(F1,40=12.1, p<0.001). Both fat and dry lean mass strongly correlated with totalwet body mass (see Figure 4.1) and therefore body mass was added to ANOVAmodels as a covariate. This analysis showed that fat mass was significantly lower inS+ mice compared to S- mice (F1,39=6.1, p=0.02) and that age did not affect fatmass. Dry lean mass did not differ significantly anymore between the groups orwith age once wet body mass was included as a covariate.


Table 4.1. Main characteristics of mice from control lines housed with a running wheel (C+) andof mice from selected lines housed either with (S+) or without a running wheel (S-).

Age C+ S+ S-

Body mass (g) 10 42.5a (1.8) 36.7 (2.1) 39.6b (2.4)18 44.4 (2.8) 33.4 (1.4) 39.5 (1.6)

Food intake (g d-1) 10 4.3 (0.5) 6.8 (1.5) 5.3 (0.7)18 6.2 (0.3) 6.4 (0.6) 5.9 (0.6)

Fat mass (g) 10 8.8a (1.2) 5.3 (1.9) 10.7b (1.7)18 10.5 (2.3) 4.7 (0.7) 8.9 (1.7)

Dry lean mass (g) 10 9.4a (0.2) 8.3 (0.2) 8.3 (0.3)18 9.7 (0.3) 8.1 (0.4) 8.8 (0.2)

Wheel-running activity (km d-1) 10c 14.0 (2.2) 18.2 (4.5) - -18 7.4 (3.1) 9.4 (1.3) - -

Values are simple means (SEM). Two-way ANOVA was used to test for differences between groups and with age(months). Data were log-transformed as necessary to improve normality. a Indicates a significant difference between C+ and S+ mice (p<0.05), b indicates a significant difference betweenS+ and S- mice (p<0.05), c indicates a significant effect of age (p<0.05). Sample size was 8 per group, but in the S+and S- groups it was 7 at 18 months.

Wheel-running activity assessed over the two weeks prior to sacrifice was calcu-lated in km per day. As expected, wheel-running activity was higher in the selectedmice and decreased with age. On average selected mice ran approximately 30%more than controls at both ages and wheel-running activity decreased approximate-ly 50% in both groups between 10 and 18 months. The group effect on wheel-run-ning activity was not significant, but the age effect was (F1,21=4.6, p=0.043).

Hormone concentrationsFigure 4.2 shows leptin, adiponectin, and corticosterone levels in plasma for thedifferent experimental groups and Table 4.2 gives an overview of the statisticalanalysis. Leptin levels were slightly lower in S+ mice compared with C+ and S-mice. This effect of group was statistically significant when comparing S+ with S-mice. No effect of age on leptin levels was shown. When total fat mass was addedto the model as a covariate, no significant effects of age or group remained and totalbody fat was a significant predictors of leptin levels in the model.

Adiponectin levels were increased by approximately 60% in both groups ofselected mice compared with control mice at both ages (10 and 18 months) anddecreased significantly with age. This group effect remained when fat content wasadded to the model as a covariate. Fat content was not a significant predictor ofadiponectin levels in the model. Basal corticosterone levels did not significantly dif-fer between control and selected mice with or without a wheel (See Figure 4.2 andTable 4.2) and did not change with age.

CorrelationsAs shown in Table 4.3, body mass and fat content were negative predictors ofwheel-running activity in control mice. In selected mice, a similar trend was visible

Chapter 462

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Figure 4.2. Leptin, adiponectin, and corti-costerone concentrations in C+ (white bars),S+ (dark grey bars) and S- (light grey bars)mice. Values represent simple means ± SEM.Sample size is 8 in C+ mice at all ages. In S+and S- mice the sample size was 8 and 7 at10 and 18 months respectively.

Table 4.2. Results of two-way nested ANCOVA of leptin, adiponectin, and corticosterone plasmalevels in control (C) and selected mice (S) housed with (+) or without (-) a running wheel.

p p p pVariable N Age C+ vs S+ S+ vs S- Covariate Covariate

Leptin (ng ml-1) 45 0.346 0.091 0.029 noneLeptin (ng ml-1) 45 0.154 0.154 0.641 FAT 0.001

Adiponectin (ng ml-1) 46 0.035 0.001 0.673 noneAdiponectin (ng ml-1) 46 0.037 0.003 0.939 FAT 0.534

Corticosterone (ng ml-1) 46 0.309 0.203 0.805 none

Two-way ANOVA were performed with age, group, and groupxage as fixed factors. A priori we tested for differencesbetween C+ and S+ or S+ and S- mice. All data were log-transformed to improve normality. P-values of age effectsand group (C+ vs. S+ and S+ vs. S-) are given in the table. No significant interaction effects were found and there-fore p-values for interaction effects (agexgroup) are not shown. Fat mass was added into the model as a covariate forleptin and adiponectin. Total sample size (N) and degrees of freedom (d.f.) are given for all groups. Sample size was8 per group, except for S+ and S- group at 18 months where n was 7. One leptin plasma sample gave a leptin con-centration of 0 ng ml-1, so this sample was not used in the analyses (S+ group at 10 months).

but probably due to the small sample size (n=9), the correlations were not signifi-cant. Overall, lighter and leaner animals ran more than heavier and fatter animals.Food intake did not correlate with any of the measures of body composition. Plasmaleptin levels positively correlated with fat in all groups and correlated with dry leanmass and body mass in C+ and S- mice, but not S- mice. Leptin did not correlatewith food intake or wheel-running activity. Plasma adiponectin and corticosteronedid not correlate with any of the measures of body composition nor with wheel-running activity and food intake in any of the groups. No significant correlationsbetween the different hormones measured were found in selected mice, but in con-trol mice corticosterone significantly correlated with adiponectin. Figure 4.3 showsthe relationships between hormones and two measures of body composition, totalfat, and dry lean mass.

DISCUSSION

Mice selectively bred for high wheel-running activity (S) generally have decreasedbody mass and fat content compared to control (C) mice (Dumke et al., 2001;Swallow et al., 2001). We explored whether these differences are associated withalterations in plasma levels of leptin, adiponectin, and corticosterone, and whetherthey depend on age, the actual levels of physical activity or other traits. Consistentwith our expectations was the observation that wheel-running activity of mice was

Chapter 464

Table 4.3. Pearson correlations between body composition, wheel-running activity, food intake,and hormones

Body Mass Dry Lean Mass Fat Mass Running Adiponectin Leptin

C+Wheel-running activity –0.69** –0.68** –0.60*Adiponectin –0.07 –0.14 –0.04 0.28Leptin 0.70** 0.56* 0.81** –0.28 0.04Corticosterone 0.15 0.25 –0.09 –0.21 –0.52* –0.08S+Wheel–running activity –0.46 –0.06 –0.61#Adiponectin –0.18 –0.20 –0.48# 0.43Leptin 0.53 0.01 0.79** –0.40 –0.20Corticosterone 0.22 –0.03 0.17 –0.40 –0.15 0.01S–Adiponectin 0.27 0.06 0.38Leptin 0.83** 0.61* 0.85** 0.10Corticosterone 0.10 –0.02 0.25 –0.07 0.03

* denotes p<0.05; ** denotes p<0.01, # denotes p>0.05 and p<0.1 (all unadjusted for multiple comparisons). Foodintake did not correlate significantly with any of the other traits and results are not shown in the table.

a negative predictor of body fat content, and fat content in turn correlated positivelywith circulating levels of leptin. The levels of leptin were better predicted by fatcontent than by physical activity, which suggests that the leptin-reducing effects ofphysical activity may be mediated primarily through an effect on body fat content.In contrast to previous studies on these mice, at younger ages (Koteja et al., 1999;Swallow et al., 2001), we did not find a positive relationship between wheel-run-ning activity and food intake in control or activity-selected mice. Food intake was


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Figure 4.3. Correlations between hormones (leptin, adiponectin, and corticosterone) and bodycomposition (fat mass and dry lean mass). Linear regressions were performed to examine signifi-cant relations. White dots represent C+ mice, black dots represent S+ mice and grey dots repre-sent S- mice. Regression lines are shown in graphs when relations were significant (dotted linefor C+, solid line for S+, dashed line for S-).

increased in S+ mice compared to C+ mice in the present study, and sample sizesmay have been too small to show effects of wheel running on food intake at theindividual level.

Leptin levels were found to be significantly decreased in S+ mice compared toS- mice (not corrected for fat mass), but not compared to C+ mice. The latter is atodds with an unpublished observations of decreased plasma leptin levels in youngfemale S mice compared to C mice, even after correction for fat mass (Girard I.,Rezende, E. L., and Garland, T., Jr., unpublished observation). The difference in out-come between this study and the one mentioned above may reflect a partial sex-dependent difference in the regulation of leptin levels. Indeed, women are known tohave higher leptin levels than men with a similar fat content (Havel, 2001; Hickeyet al., 1996). Leptin levels are known to decrease in voluntarily exercising rodentscompared to sedentary controls (rats; (Novelli et al., 2004) and hamsters;(Coutinhoet al., 2006)) and this was also shown in the present study when comparing S+ andS- mice. Obviously differences in activity were largest between the sedentaryhoused S- mice and the S+ mice than between the S+ and C+ mice and this mightexplain why leptin levels were not decreased in the S+ mice compared to C+ mice.

The most important finding in the present study was the observation that plas-ma adiponectin levels were significantly increased in S mice compared to the levelsfound in their random-bred controls. This increase was found in S mice irrespectiveof the availability of running wheels and occurred in all of the separate selectionlines (results not shown), which suggests that this increase is a genetically co-seg-regated trait with selection for increased wheel-running activity, instead of beingmediated via increased physical activity per se. At present, we do not know whetherthis increased adiponectin is of high or low molecular weight (active or inactiveform; (Fruebis et al., 2001)), or whether it is a cause of altered release or clearance.Future work is necessary to study this and to determine whether the increasedadiponectin levels in activity-selected mice are associated with increased insulinsensitiviy. Also, if the increased levels of adiponectin are responsible for their lowfat content, does this or the high adiponectin levels protect them against diet-induced obesity?

There a number of reasons to suggest that increased circulating adiponectin lev-els might contribute to the different phenotypes seen in S mice compared to Cmice. For example, Fruebis et al (Fruebis et al., 2001) found that chronic adminis-tration of gAcrp30 (i.e., adiponectin) caused weight loss in mice despite the factthat food consumption was unaffected. This is a phenotype which appears homolo-gous to the one found in selected animals in this and previous studies (Swallow etal., 1999; Swallow et al., 2001). Fruebis et al. attributed the effect of adiponectin onbody mass to increased fat oxidation, specifically in liver and muscle (Fruebis et al.,2001), and this was confirmed in subsequent studies (Berg et al., 2002; Bruce et al.,2005; Yamauchi et al., 2002). Although not directly assessed in the present study,we recently observed a decrease in the respiratory quotient (RQ) in S mice com-pared to C mice measured over a period of 24 hours in non-fasted animals (see

Chapter 466

Chapter 5), which indeed indicates higher levels of fat oxidation in selected mice. Itmight be speculated that an increased capacity to down-grade lipids in muscular tis-sue contributes the increased physical activity displayed by activity-selected mice.The effects of adiponectin on fat oxidation are believed to arise through stimulationof AMP-activated protein kinase (AMPK) (Berg et al., 2001; Yamauchi et al., 2002).Zhang et al. have shown increased levels of phosphorylated AMPK in aorta of maleactivity-selected mice compared with controls (Zhang et al., 2006), which is consis-tent with the observed elevated levels of adiponectin in this study. At present we donot know whether there is a positive relationship between adiponectin and AMPKat the individual level in the activity-selected mice.

Previous studies have shown an increase in resting corticosterone levels in 2-month old male and female S mice (Girard and Garland, Jr., 2002; Malisch et al.,2006), without a change in the absolute corticosterone concentration in response to40 min. restraint stress (Malisch et al., 2006). In the present study we investigatedwhether these differences were present in older animals. No statistically significantdifferences were found in corticosterone levels between C and S males at 10 or 18months of age. Glucocorticoids increase in response to exercise (Droste et al., 2003)and can affect leptin and adiponectin concentrations (Droste et al., 2003; Fallo et al.,2004; Van Dijk et al., 1997). Nevertheless, we did not show any relations betweenwheel-running activity, plasma adiponectin or leptin and corticosterone levels inC+ and S- mice. In S+ mice, however, a negative relationship between corticos-terone and adiponectin was found.

In conclusion, mice from lines that have been selectively bred for high wheel-running activity over many generations show several endocrine alterations. Maleactivity-selected animals have higher adiponectin levels than their randomly-bredcontrols, independent of the presence of a running wheel. Leptin and corticosteronelevels were unchanged in selected mice of 10 months and older, although a decreasein leptin and an increase in corticosterone were shown previously in youngerfemales. These effects might be driven mainly by differences in wheel-runningactivity between the groups at young ages. Selected mice have been shown to beleaner than control mice (Dumke et al., 2001; Swallow et al., 2001) and togetherwith the observed increase in adiponectin concentration this might render the miceless prone to develop insulin resistance and other aspects of the metabolic syn-drome. This would make them a suitable model to study whether “physical activitygenes” exist, and whether these could influence mechanisms underlying pronenessfor diet-induced obesity and related diseases.

AcknowledgementsThe authors thank Gerard Overkamp for expert technical assistance and Berber de Jong forhelp with the experimental procedures. Jan Bruggink is thanked for performing analyticalprocedures. This work was supported primarily by a Career Development Grant from theDutch Diabetes Association (to GvDijk). Additional funding was provided by grants fromthe U.S. National Science Foundation to T.G., most recently IOB-0543429.


Chapter 468

BOX 4.1: Leptin, adiponectin and corticosterone in cold-exposed mice

Hormone levels of leptin, adiponectin and corticosterone were determined in plasma ofmale C57BL mice exposed to 10°C (CC) or 22°C throughout life (WW), and in miceexposed to 10°C till the age of 15 months and at 22°C afterwards (CW; for a detaileddescription of the experimental protocol see Chapter 8). Trunk blood was collected intubes with anticoagulant (EDTA) at 3, 11, 19 and 27 months of age, centrifuged at 4°Cfor 15 min at 2600 g. Then plasma was collected and stored at –80°C for later hormoneanalyses (with RIA, Linco kits). Plasma samples at 27 months of age were not analyzedfor leptin and adiponectin levels. Corticosterone measurements at 19 months were leftout of statistical analysis: results were unreliable because the cold mice were probablydisturbed prior to the measurements resulting in very high corticosterone values at thisage in these mice.

Figure 4.4 shows the results. Both, levels of leptin and adiponectin were decreasedin mice exposed to the CC compared to WW mice (Two-way ANOVA with group andage as fixed factors (excluding CW mice); Effect of group: Leptin; F1,48=41.8,p<0.001, Adiponectin; F1,47=7.8, p<0.01). Age did not affect adiponectin levels signif-icantly but leptin levels were significantly affected by age (Effect of age: Leptin;F2,48=21.4, p<0.001) and there was a significant interaction between group and age(GroupxAge interaction: Leptin; F2,48=10.1, p<0.001). Both hormones, leptin andadiponectin are produced by adipose tissue and specifically leptin is known to correlatestrongly with fat content. Therefore, we also applied ANCOVA models with fat as acovariate. In these models there was still a significant effect of group on leptin levels(F1,47=14.9, p<0.001), but adiponectin was no longer significantly different betweengroups. In both cases fat content was significantly correlated to the hormone levels(p<0.001 and p=0.01 respectively for leptin and adiponectin). There was no longer asignificant effect of age on leptin levels, but the group x age interaction remained sig-nificant (F2,47=5.5, p<0.01), indicating that leptin levels increased more with age inthe WW mice than in the CC mice. We also tested for differences in leptin andadiponectin levels between CW, CC and WW mice using one-way ANOVA with a factorgroup. No significant effect of group on adiponectin level was found, but leptin did dif-fer significantly between groups (p<0.05), and the CW and WW mice had significantlyhigher levels of leptin relative to CC mice (see figure 4.4). Corticosteron levels did notdiffer between groups and were not affected by age.

These results agree with several other studies (Puerta et al., 2002; Imai et al.,2006), and may indicate a role for leptin and adiponectin in the extensive metabolicadaptations to cold.


Figure 4.4. Leptin, adiponectin, and corticosterone concentrations in WW (white bars), CC(dark grey bars) and CW (light grey bars) mice. Values represent simple means ± SEM.Sample size is 5-8 in all groups.

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Responses in energy balance to high-fat feedingin mice selectively-bred for highwheel-running activity

Lobke M. Vaanholt, Izabella Jonas, Mark Doornbos, Kristin A. Schubert,Csaba Nyakas, Theodore Garland Jr., G. Henk Visser, Gertjan van Dijk

AbstractObesity is becoming an increasingly prevalent health problem among indi-viduals in affluent societies, and can lead to life-threatening conditions likecardiovascular disease and type 2 diabetes mellitus. Increased dietary fatintake in combination with a sedentary existence are precipitating factors forthe development of obesity and the associated metabolic syndrome.Whereas most animal studies have investigated effects of exercise using“forced” activity protocols, in the present study we investigated the role of“unforced” physical activity in the interaction between dietary fat on regula-tion of energy balance and fuel homeostasis. This was done using selective-ly-bred mice which display a high level of voluntary wheel-running activityrelative to control lines. Control and selected males and females were fedwith a standard lab chow or an iso-caloric 60% fat diet. All fat-fed mice rap-idly increased in body mass and decreased food intake, with the exception ofthe selected females. Selected females did not develop diet-induced obesityand even increased their food intake on the fat diet. In addition, they hadimproved glucose tolerance on the standard diet compared to the othergroups, but this difference was lost upon feeding the fat diet. Adiponectinwas increased in selected mice (specifically in males) compared to controlmice on the fat diet. Leptin and insulin levels increased on the fat diet incontrol females and both groups of males, but not in selected females. Inconclusion, selected females fed a high-fat diet did not develop diet-inducedobesity as the other groups did, but they did become slighty glucose intoler-ant relative to selected mice on standard chow. This effect could not beexplained by differences in adiponectin levels and may be related to a failureto upregulate leptin. (Female) mice selected for high spontaneous wheel-running activity are an attractive model to further investigate mechanismsinvolved in the metabolic syndrome and associated type 2 diabetes mellitus.

Chapter5

INTRODUCTION

Obesity is becoming an increasingly prevalent health problem among individuals inaffluent societies, because it is often associated with metabolic derangements suchas impaired glucose tolerance, insulin resistance, high blood pressure, dyslipidemia,and abdominal obesity. When these metabolic abnormalities are displayed in con-cert (often referred to as the “metabolic syndrome”), they have a high risk of devel-oping into life-threatening conditions such as cardiovascular disease and diabetesmellitus type 2 (for review see (Carroll and Dudfield, 2004; Moller and Kaufman,2005)). While the mechanisms underlying this epidemic are largely unknown, thereis consensus that increased dietary fat intake in combination with a sedentary exis-tence are precipitating factors (WHO, Carroll and Dudfield, 2004).

A major part of the current knowledge on the aetiology of obesity and the meta-bolic syndrome has come from studies in which rodents are subjected to a high-fatdiet (Storlien et al., 1986; Surwit et al., 1995; Ahren and Scheurink, 1998; Lin et al.,2000; Winzell and Ahren, 2004). In particular the use of selectively-bred and genet-ically-engineered rodents which display diet-induced obesity or obesity-resistancehas been insightful to unravel the underlying mechanisms (for reviews see Tschopand Heiman, 2001; Carroll et al., 2004). Few animal studies, however, have address-ed whether and how physical activity could be involved in preventing dietary fat-induced obesity and associated metabolic derangements. In part, this is due to themethodological problems that arise when animals are “forced” to be active. Forexample, increased physical activity induced by forced treadmill running, waterimmersion etc. (Pellizzon et al., 2002; Jen et al., 2003) are perceived as psychologi-cal stress (Chennaoui et al., 2002). A way around these potential pitfalls is to inves-tigate interactions between “unforced” physical activity, dietary fat intakes andenergy balance in mice selectively bred for high voluntary wheel running relative tocontrol lines (for selection procedure see (Swallow et al. 1998). Mice selected onincreased wheel-running activity are leaner than their respective controls, and haverelatively low circulating leptin levels (Girard et al. unpublished results). Even with-out the presence of a running wheel, these selected animals display increased loco-motor activity (Vaanholt et al. 2006), and therefore may represent a relevant modelfor the study of increased spontaneous activity in the development of high-fat feed-ing induced obesity and metabolic derangements.

In the present study female and male mice selected for high wheel-runningactivity and their random-bred controls were either exposed to a high-fat diet orremained feeding chow to test the hypothesis that selected mice are resistant todeveloping obesity and other indicators of the metabolic syndrome, such asimpaired glucose tolerance and reduced lipid oxidation. Food intake and bodyweights were assessed, and total and resting metabolic rates were determinedGlucose tolerance was assessed by repeated blood sampling after intra-peritonealglucose injection. Finally, plasma levels of several metabolic hormones were deter-mined and body composition analysis was performed. The results indeed point to a

Chapter 572

major contribution of spontaneous activity in the prevention of high-fat inducedobesity.


Animals & housingEighty breeding pairs of Hsd:ICR mice (Mus domesticus) selected on high wheel-run-ning activity for 31 generations and their random bred controls were obtained fromT. Garland Jr, Riverside, CA (For a detailed description of the selection proceduresee (Swallow et al., 1998). From these founder mice, separate breeding lines werecontinued at our facilities in Haren without further selection for wheel-runningactivity. In the original selection protocol eight lines of mice were created (4 select-ed and 4 control). We used mice from one control line and one selection line in thepresent study (lab designated lines 2 and 7 respectively). Forty eight mice (25 maleand 23 female) at the age of 5 months were used of the fourth generation of off-spring (without selection), of which half belonged to the control line (C) and theother half tot the selected line (S). All mice were individually housed in standardcages (Macrolon Type II, UNO Roestvaststaal BV, Zevenaar, NL) in the same roomwith an ambient temperature of 22±1°C. At the start of the experiment all animalshad ad libitum food (Standard lab chow RMH-B 2181, HopeFarms BV, Woerden,NL) and water; they were on a 12:12 light-dark cycle (lights on at 8:00). Woodshavings and EnviroDry® were used as bedding material. At the start of the experi-ments, they were 20 weeks of age.

Food intake and body mass were measured daily (except for the weekends).After 4 weeks, half of the mice were put on a high-fat diet containing 60% ofweight as fat (see Table 5.1 for the composition of the diets), which was equicaloric

High-fat feeding in mice bred for high activity 73

Table 5.1. Composition of the diets.

Standard chow diet (RMH-B) - HC High fat diet HFContent (g kg-1) Energy (%) Content (g kg-1) Energy (%)

Protein 228 23 200 20.4Crude (lab chow) 228 69Added casein 131Fat (saturated fat) 55 (0.7) 14 260 (94.3) 60.2Lab chow fat 55 17Added corn oil 74Added beef tallow 169Carbohydrates 625 63 190 19.4Lab chow polysaccharides 600 182Simple sugars 25 8

Energy 16.1 kJ g-1 16.3 kJ g-1

to the standard lab chow. Food intake and body mass were measured daily until themice had been on the fat diet for 9 weeks.

Glucose tolerance testIn weeks 10 and 11 relative to the start of high fat feeding, all animals underwent aglucose tolerance test. Specifically, animals were food deprived for 6 hours (startingat lights on), and injected intra-peritoneally with 0.01 ml per gram body mass of a10% glucose solution at noon (±1 hr). Drops of blood were collected via tail clip-ping just before glucose injection and at 20, 40 and 60 minutes following glucoseinjection. Blood glucose levels were determined immediately using a hand-held glu-cose analyzer (OneTouch Ultra, LifeScan). The glucose tolerance test was repeated4 weeks later in the selected female animals. This time the mice received a dosethat was comparable to the average dose that the control mice received before (0.35ml of 20% glucose solution), and an additional blood sample was taken 120 minafter the injection.

RespirometryIn week 11-13 animals were moved into respirometric chambers to determine oxy-gen consumption (V

. O2, l h-1) and carbon dioxide production (V

. CO2, l h-1) by

indirect calorimetry for 48 hours. Eight animals could be measured simultaneously.Oxygen and carbon dioxide concentration of dried inlet and outlet air (drier: molec-ular sieve 3 Å, Merck) from each chamber was measured with a paramagnetic oxy-gen analyzer (Servomex Xentra 4100) and cardon dioxide by an infrared gas analyz-er (Servomex 1440). The system recorded the differentials in oxygen and carbondioxide between dried reference air and dried air from the metabolic chambers.Flow rate of inlet air was measured with a mass-flow controller (Type 5850Brooks). Ambient temperature in the chamber and cage, as well as activity (withpassive infra-red detectors) were measured simultaneously. Samples were collectedevery 10 minutes for each animal and automatically stored on a computer. Toreduce novel cage stress, the respirometric chambers (45x25x30 cm) were adaptedto accommodate the home cage of the animal. Animals therefore did not need to behandled and stayed in their home cage during the entire measurements. Animalswere measured at an ambient temperature of 22°C and food (standard chow or fat)and water were provided ad libitum.

Heat production (HP, kJ h-1) was calculated using the following equation: HP=(16.18 x V

. O2) + (5.02 x V

. CO2 ) (Romijn and Lokhorst, 1961). Resting metabolic

rate (RMR, kJ h-1) was defined as the lowest value of heat production calculated asthe running mean over half an hour and was calculated for the first and second dayin the respirometer separately. Maximal heat production was also calculated as therunning mean over half an hour. In addition, the average heat production (dailyenergy expenditure: DEE, kJ d-1), respiratory quotient (RQ= V

. CO2/V

. O2) and PIR

(passive infrared) activity were calculated for both consecutive days.

Chapter 574

Wheel-running activityAfter the respirometry measurements, all mice were put in cages with a plastic run-ning wheel with a 7 cm radius for a two-week period. Animals had never beenexposed to running wheels prior to this time. The running wheels were attached toa PC-based event recording system, and total wheel-running activity was logged in2 minute bins over a three week period.

Body composition & Metabolic hormonesFollowing wheel-running activity measurements, animals were again housed seden-tary for an additional month, and then animals were anaesthesised with CO2 fol-lowed by decapitation. Trunk blood was collected in tubes with EDTA as anti-coag-ulant, and organs and specific fat pads (i.e. retroperitoneal fat, gonadal fat, subcuta-neous fat) were dissected out and weighed to the nearest 0.0001 g. All tissues werestored at –20°C until further analyses. Blood samples were centrifuged at 2600 g for15 min at 4ºC, plasma was then collected and stored at –80°C for later analysis withRIA (Linco). Dry and dry lean organ masses were determined by drying organs toconstant weight at 103°C (ISO 6496-1983(E)) followed by fat extraction withpetroleum ether (Boom BV, Meppel, NL) in a soxhlet apparatus.

Data analysesBecause males and females are known to differ with respect to their regulation ofmetabolic hormones, first we tested for effects of sex on all variables with an inde-pendent samples t-test and then tested the effects of diet and group were testedseparately for males and females. To test for effects of diet and group we used GLMmodels in SPSS (version 12). Group, diet and group x diet were added as fixed fac-tors. Where appropriate, covariates (i.e. body mass) were used in the models. Post-hoc independent t-tests were used to compare groups and diets. For several vari-ables repeated measures ANOVA was applied . Group, time (repeated factor) andgroup x time were used as fixed factors in these models. The significance level wasset at p≤ 0.05 and all tests were two-tailed.

RESULTS

Changes in food intake and body weight Body weights were significantly higher in males than females (t-test, p<0.001), butfood intake was similar. Selected females had significantly lower body weight andincreased food intake compared to controls when feeding chow. (ANOVAF3,22=14.3, p<0.001 and F3,22=7.7, p=0.01 respectively). In males of selected andcontrol lines, there were no differences in body weight when feeding chow, but foodintake was significantly increased in selected males compared to control males(F3,24=13.9, p<0.01).


Figure 5.1 shows the development of body weight and food intake for male andfemale control and selected mice after the mice had been put on a high-fat diet. Inanimals on the high-fat diet, we tested for differences in body weight or food intakebetween the weeks prior to and after the food manipulation using a repeated meas-ures ANOVA. In female mice, the fat diet significantly influenced body weight(F1,10=11.36, p<0.01), and there was a significant interaction between group anddiet (F1,10=7.5, p<0.05), indicating that control and selected mice responded dif-ferently to the fat diet. As seen in Figure 5.1, female control mice increased bodyweigh on the fat diet, whereas selected females did not change their body weight.Food intake was not significantly affected by diet in females overall. In males, bodyweight significantly increased (F1,11=10.2, p<0.01) and food intake significantlydecreased (F1,11=17.1, p<0.01) on the fat diet, but there was no group x diet inter-action. Control and selected males thus responded to the fat diet with similarchanges in body weight and food intake.

Closer inspection of the day-by-day changes in body weight and the associatedfood intakes revealed differences in food efficiency (FE= weight gained/gross ener-gy consumed) between the control and selected females (Figure 5.2). Comparisons

Chapter 576

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Figure 5.1. Development of body mass (top graphs) and food intake (bottom graphs) in maleand female control (circles) or selected mice (triangles). Running means over three consecutivedays are shown. At day 0 animals were put on a fat diet or remained on standard lab chow.

between diets can easily be made without corrections since the high fat and thechow diet used in the present study are equicaloric. At several categories of bodyweight changes from one day to the next, control females had a lower food intakeon the fat diet than on the chow diet. Selected females, however, increased foodintake on the fat diet (interaction effect: F3,19=4.4, p<0.05). Both groups of malesshowed a decrease in food intake on the fat diet compared to standard chow(F3,18=6.6, p<0.05).

Glucose toleranceResults of the intraperitoneal glucose injection on blood glucose levels are shown inFigure 5.3. At all time points males had significantly higher glucose levels thanfemales (t-test, p<0.01). In females, repeated measures ANOVA of the model intotal revealed very strong trends of diet (F1,18=5.4, p=0.051), group (F1,18=3.6,p=0.075), and groupxdiet (F1,18=4.3, p=0.056) on glucose level. Post-hoc analysisrevealed that at t40 selected females on a standard chow diet had lower blood glu-cose levels than control mice (p=0.073, p=0.005 and p=0.052 at T20, T40 and T60respectively), and also compared to selected mice on a fat diet (p=0.071, p=0.008and p=0.053 at T20, T40 and T60 respectively). No significant differences in glu-cose levels were found between control mice and selected mice on the high-fat diet(posthoc t-test: p>0.1). Glucose tolerance was analysed by comparing group and


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Figure 5.2. Changes in daily food intake (g d-1) underlying body mass changes (g d-1) in controland selected mice fed standard or fat diet. Average food intake during the first month on the dietwas calculated per animal for various categories of weight changes, by taken the average of thefood intakes measured on days where body mass changes were smaller than –1.0, between –1.0and –0.25, between –0.25 and 0.25 (constant body mass), between 0.25 and 1.0 or larger than 1.0g (on two consecutive days). Two-way ANOVA was used to look at effects of group, diet andgroup x diet. a represents statistical differences between control and selected mice, b representssignificant effects of diet, and c represents a significant interaction effect (group x diet) (p<0.05).

diet effects on Area-Under-the-Curve of the different glucose levels relative to thoseobserved at baseline. Glucose tolerance decreased strongly in selected mice, (area-under-the-curve increased from 5.1 (s.d. 3.0) to 10.5 (s.d. 6.2) respectively on thefat diet), but this effect was not significant (post-hoc t-test; p=0.089). In controlsthere was no effect of the diet on glucose tolerance (area-under-the-curve; 8.4 (s.d.3.1) and 8.4 (s.d. 4.1) respectively). In female selected mice the glucose tolerancetest was repeated two weeks later, when the mice were injected with a higher doseof glucose (similar to the average dose control mice received previously). No signif-icant differences were found between this test and the first one (data not shown).

In males, no differences in blood glucose were found between the groups oneither diet in response to the glucose injection (Two-way repeated measuresANOVA, p>0.05 for group, diet and dietxgroup interaction; see Figure 5.3, rightframe). The area-under-the-curve was slightly increased in selected male mice onthe fat diet compared to selected males on standard chow (=9.6 (s.d. 6.3) and 12.8(s.d. 6.3) respectively), but this difference was not significant (p>0.1). Also glucosetolerance did not differ from that of control male mice (area-under-the-curve was10.9 (s.d. 6.4) and 8.1 (s.d. 7.7) on standard chow and fat diet, respectively).

Energy expenditureAll animals were put in a respirometric chamber for 48 hours to determine restingmetabolic rate (RMR), daily energy expenditure (DEE), spontaneous activity andRQ. Values were highly repeatable measured on the two consecutive days. Datafrom day 2 were used in the analysis (results summarized in Table 5.2 and 5.3). Infemales, there were no effects of group or diet on RMR. However, when body masswas added to the model as a covariate selected females had a significantly higher

Chapter 578

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Figure 5.3. IP glucose tolerance test results. Blood glucose levels measured 20, 40 and 60 min-utes after an injection with a 20% glucose solution in control (circles) and selected (triangles)mice on a fat diet (black) or standard lab chow (white). Values given are mean±sem.

RMR than control females. DEE was also significantly increased in selected femalescompared to controls (with or without body mass as a covariate). In selectedfemales, the fat diet increased DEE; as shown by a significant interaction effectbetween group and diet. Results for maximal heat production were similar to thosefor DEE, with higher HP in selected females compared to controls and the highestlevels in selected females on a fat diet. Selected females were far more active thancontrols in the respirometer, which is in agreement with their higher DEE. Whenfeeding the high fat diet, control females slightly reduced spontaneous activity. Incontrast, selected females further increased spontaneous activity when feeding thehigh fat diet to appr. 700% relative to controls.

In males, there was no effect of group on RMR, but diet significantly increasedRMR in both control and selected males. DEE was increased in selected mice com-pared to controls, and both control and selected males showed an increase in DEEon a fat diet compared with mice on a standard diet. As in females, maximal HPwas higher in selected males compared to controls, but in males there was no inter-action effect between group and diet. Also, activity did not differ significantlybetween control and selected male mice.

A respiratory quotient (RQ) around 1 indicates that mice mainly utilize glucose,whereas at RQ values around 0.7 mice are mainly utilizing fat. As expected, RQ waslower in all mice on the high-fat diet. In addition to this, in females (not males) asignificant effect of group and an interaction between diet and group was shown.


Table 5.2. Metabolic rates of mice selected for high wheel-running activity and their random-bred controls on a standard and fat diet.

Control SelectionStandard chow Fat chow Standard chow Fat chow

Females:RMR (kJ d-1) 35.8 (4.1) 38.6 (2.2) 36.4 (1.7) 40.2 (1.4)DEE (kJ d-1) 47.8 (4.6) 50.9 (2.5) 59.0 (3.5) 70.5 (3.2)Maximum HP (kJ d-1) 74.3 (9.7) 72.8 (5.1) 84.1 (6.7) 113.8 (6.3)RQ 0.93 (0.01) 0.76 (0.03) 0.87 (0.01) 0.76 (0.02)Activity (number d-1) 1249 (254) 1047 (155) 5845 (1705) 7643 (1211)

Males:RMR (kJ d-1) 38.7 (1.7) 41.9 (1.7) 38.2 (2.0) 46.0 (0.6)DEE (kJ d-1) 51.1 (2.3) 54.1 (1.6) 59.9 (7.1) 67.6 (3.3)Maximum HP (kJ d-1) 72.5 (5.5) 73.5 (1.6) 86.1 (7.4) 103.9 (10.4)RQ 0.90 (0.01) 0.75 (0.01) 0.88 (0.01) 0.75 (0.01)Activity (number d-1) 784 (238) 1109 (147) 1115 (151) 3453 (1681)

Mean and (SEM) are given for metabolic rates, RQ and activity measured on day 2 of the respirometric measure-ments. N= 6 per group, with the exception of control females on a standard diet (n=4), control males on a standarddiet (n=5) and control males on a fat diet (n=7).

Post-hoc analyses revealed that selected females on standard chow had lower RQvalues than controls on normal chow (t-test, p<0.05), whereas RQ values did notdiffer between control and selected females on a fat diet. RQ was similar betweencontrol and selected males on a fat diet.

Wheel-running activityTo test whether animals differed with respect to their wheel-running activity as pre-dicted by their selection history, animals were exposed to running wheels with a 7cm radius after the respirometry measurements and wheel-running activity wasmeasured for three weeks. Average wheel-running activity was 9.4±1.6 and 10.8±1.7 kilometers per day in control and selected mice, respectively. Overall, therewere no significant effect of group and diet on wheel-running activity. Initially (dur-ing the first week of measurements) wheel-running activity was significantlyincreased in selected animals compared with controls (t-test, p<0.05), but this dif-ference disappeared later.

Chapter 580

Table 5.3. Respirometry, results for two-way ANOVA

Group Diet Groupxdiet Covariate

Variable n d.f. F p F p F p p

FemalesRMR (kJ d-1) 22 3,21 0.2 0.684 1.6 0.224 0.0 0.853

22 4,21 9.5 0.007 0.0 0.899 1.0 0.322 BW 0.001DEE (kJ d-1) 22 3,21 9.0 0.008 3.6 0.074 3.2 0.091

22 4,21 33.9 <0.001 0.8 0.372 10.3 0.005 BW 0.001Maximum HP (kJ d-1) 22 3,21 12.6 0.002 3.9 0.065 4.8 0.042

22 4,21 40.8 <0.001 1.0 0.330 14.1 0.002 BW 0.001RQ 22 4,21 11.5 0.003 195.4 <0.001 7.3 0.015Activity (number d-1) 22 4,21 16.2 0.001 0.3 0.574 0.5 0.482

MalesRMR (kJ d-1) 24 3,23 1.3 0.373 11.4 0.003 1.0 0.173

24 4,23 2.1 0.161 7.1 0.015 2.3 0.143 BW 0.036DEE (kJ d-1) 24 3,23 10.4 0.004 4.9 0.039 2.3 0.144

24 4,23 9.8 0.006 6.4 0.020 2.4 0.137 BW 0.233Maximum HP (kJ d-1) 24 3,23 10.2 0.005 1.9 0.186 1.5 0.236

24 4,23 9.5 0.006 2.6 0.120 1.5 0.234 BW 0.340RQ 24 3,23 0.4 0.553 180.7 <0.001 1.2 0.278Activity (number d-1) 24 3,23 2.4 0.136 2.4 0.137 1.4 0.255

Data recorded on day 2 of the respirometric measurement were analyzed with a two-way ANOVA with group, dietand groupxdiet (GxD) as fixed factors for females and males separately. Body weight (BW) is known to have a stronginfluence on metabolic rate and was added into the model as a covariate (Cov.) for these variables. Significant effectsare shown in bold (p<0.05).

Body compositionSeperate fat pads were collected and weighed to determine the distribution of fatdeposition throughout the body (see Table 5.4, and see Table 5.5 for statisticalanalysis). In control female mice, but not selected females, the amount of fatincreased in all fat pads (except for the fat in the organs) when put on a fat diet. Onthe fat diet, control females increased their total fat content approximately by 40%whereas total fat content only increased by 5% in selected females. The distributionof fat (% of total fat) over the different fat pads was similar between the control andselected mice on standard chow and also between selected females on standard andfat chow. In control females on a fat diet, however, this distribution changed com-pared to control females on standard chow. The para-uterine fat pad representedapproximately 2% of the total fat content on the standard diet and this increased to14% on the fat diet. The intramuscular fat content decreased from 61 to 50 %between the standard and fat diet. The other fat pads remained similar.

In both control and selected males, there was an increase of approximately 25%in the total amount of fat and in the separate fat pads (except for organ fat; see


Table 5.4. Fat content in mice selected for high wheel-running activity and their random-bredcontrols on a standard and fat diet.

Control SelectedMass (g) Standard chow Fat chow Standard chow Fat chow

FemalesBody mass 33.9 (2.0) 43.6 (2.8) 27.3 (2.8) 29.2 (1.1)Dry lean mass 7.2 (0.4) 7.4 (0.3) 6.3 (0.3) 6.2 (0.2)Total fat 21.3 (1.3) 29.4 (2.2) 16.6 (1.7) 17.5 (0.5)

Para-uterine fat 0.42 (0.12) 4.03 (1.19) 0.13 (0.08) 0.21 (0.05)Retroperitoneal fat 0.10 (0.02) 0.48 (0.12) 0.04 (0.02) 0.09 (0.01)Subcutaneous fat 3.4 (0.2) 6.0 (0.7) 2.5 (0.6) 2.5 (0.2)Intramuscular fat 12.9 (0.8) 14.6 (0.6) 10.1 (0.9) 10.9 (0.3)Organ fat 4.5 (0.3) 4.3 (0.3) 3.9 (0.2) 3.7 (0.1)

MalesBody mass 39.4 (1.8) 43.5 (1.5) 37.1 (1.9) 43.0 (3.2)Dry lean mass 8.2 (0.2) 8.3 (0.2) 7.7 (0.3) 7.9 (0.2)Total fat 28.0 (1.7) 33.9 (1.9) 25.7 (2.0) 33.5 (3.5)

Epididimal fat 0.71 (0.11) 1.23 (0.21) 0.66 (0.19) 1.85 (0.40)Retroperitoneal fat 0.22 (0.06) 0.45 (0.14) 0.24 (0.08) 0.76 (0.20)Subcutaneous fat 4.6 (0.5) 6.4 (0.7) 3.5 (0.5) 6.2 (1.1)Intramuscular fat 17.7 (1.0) 21.0 (0.8) 16.5 (1.2) 20.0 (1.9)Organ fat 4.8 (0.2) 4.8 (0.2) 4.8 (0.1) 4.7 (0.2)

Mean and (SEM) are given in the table. Values given for the separate fat pads include the water content. N= 6 pergroup, with the exception of control and selected females on a standard diet (n=4 and n=5 respectively), controlmales on a standard diet (n=5) and control males on a fat diet (n=7).

Table 5.4 and 5.5) on the fat diet. The distribution of fat over the fat pads was simi-lar between both groups when they were on standard chow, but on the fat diet theselected males stored more fat in the intraperitoneal and epididimal fat pads com-pared to controls (Table 5.4 and 5.5).

Metabolic hormonesPlasma samples collected at decapitation were analysed for the concentration of sev-eral metabolic hormones, and the results are shown in Figure 5.4 (i.e., adiponectin,insulin, and leptin) and Figure 5.5 (i.e., T3 and T4). When feeding chow, plasma lev-els in females were generally higher of adiponectin, and lower of leptin and insulinthan in males, but no differences were observed between control and selected mice.

On the fat diet, plasma insulin levels increased in control females, an effect notobserved in selected females (Two-way ANOVA: Group; F1,16=4.8, p<0.05), diet;F2,16=3.9, p=0.065, group x diet; F2,16=1.6, p>0.1). In males, insulin levels weresimilar in control and selected mice and slightly increased on the fat diet in bothgroups. This effect of diet was not significant. In both females and males a signifi-cant effect of diet on leptin levels was shown (females: F2,16=6.9, p<0.05; males:

Chapter 582

Table 5.5. Body composition, results for two-way ANOVA

Group Diet Group x Diet CovariateVariable N d.f. F p F p F p p

FemalesBody mass 21 3,20 20.2 <0.001 6.1 0.024 2.7 0.117Dry lean mass 21 3,20 11.3 0.004 0.1 0.737 0.2 0.640Total fat 21 4,20 8.4 0.010 8.3 0.011 4.9 0.042 DL 0.026

Para-uterine fat 21 4,20 6.7 0.020 6.8 0.019 6.3 0.023 DL 0.561Retroperitoneal fat 21 4,20 3.8 0.069 7.4 0.015 4.5 0.048 DL 0.663Subcutaneous fat 21 4,20 5.8 0.028 5.8 0.029 5.2 0.036 DL 0.189Intramuscular fat 21 4,20 8.3 0.011 9.3 0.008 0.1 0.711 DL <0.001Organ fat 21 4,20 0.1 0.749 2.4 0.134 0.4 0.528 DL <0.001

MalesBody mass 24 3,23 0.4 0.535 5.1 0.036 0.2 0.662Dry lean mass 24 3,23 3.8 0.064 0.5 0.469 0.0 0.851Total fat 24 4,23 1.4 0.239 10.1 0.004 0.1 0.725 DL <0.001

Epididimal fat 24 4,23 6.3 0.021 11.5 0.003 2.1 0.165 DL 0.005Retroperitoneal fat 24 4,23 4.3 0.050 6.6 0.019 1.0 0.312 DL 0.037Subcutaneous fat 24 4,23 0.3 0.611 9.2 0.007 0.3 0.602 DL 0.002Intramuscular fat 24 4,23 0.8 0.377 9.7 0.006 0.1 0.872 DL <0.001Organ fat 24 4,23 0.7 0.416 0.6 0.453 0.1 0.881 DL 0.03

Data on body composition were analyzed with a two-way ANOVA with group, diet and groupxdiet as fixed factorsfor females and males separately. Dry lean mass was added as a covariate when testing for differences in fat massand p-values are given in the table. Significant effects are shown in bold (p≤ 0.05).

F2,21=8.0, p=0.01). Post-hoc tests showed that in control mice, but not in selectedmice, leptin was significantly increased on the fat diet compared to the control diet.In addition, to the effect of diet, in females a significant effect of group (F1,16=6.7,p=0.02) and a significant interaction between group and diet was shown(F2,16=5.3, p<0.05). This indicates that the control mice increased their leptin lev-els more than the selected females did on the fat diet, which is also apparent inFigure 5.4. Leptin highly correlated with fat content, though, and when total fatcontent was added to the model as a covariate no significant effects of group, dietor group x diet remained. When feeding the high-fat diet, plasma adiponectin lev-els were significantly increased in selected males relative to those feeding chow,(Two-way ANOVA: group: F1,19=2.6, p=0.12 and diet; F1,19=5.3, p=0.032,groupxdiet; F2,19=9.8, p=0.006, and post-hoc t-test: p=0.021), and a strong trend


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Figure 5.4. Plasma adiponectin, insulin and leptin levels of control and selected, male (rightgraph) and female (left graph) mice. Blood samples for determining hormone levels were takenwhen the animals had been on a fat diet (dark grey bars) or standard diet (light grey bars) forover eight months.

towards increased levels was observed in females (post-hoc t-test: p=0.078). Theseeffects of high fat feeding were absent in control males and females. When fat wasadded to the model as a covariate the differences between control and selected micebecame more pronounced: controls showed a slight decrease in relative adiponectinlevels (not significant), whereas selected mice still had increased adiponectin levelson the fat diet.

Figure 5.5 shows plasma levels of T3 and T4. Plasma T3 levels were similar incontrol and selected females on both diets. In selected males on standard chow T3levels were decreased compared to control males on standard chow. On the fat diet,however, plasma T3 levels increased significantly in selected males (post-hoc t-test,p<0.05), but not in control males (Two-way ANOVA: group; F1,19=6.8, p=0.017and diet; F1,19=4.1, p=0.057, groupxdiet; F1,19=3.5, p=0.07). In selected femalesand males, plasma T4 levels were decreased compared to controls on standardchow, and T4 levels only tended to increased on the fat diet. In males a similar pat-tern emerged. Selected males had increased T4 levels on the fat diet compared tomice on standard chow, whereas control males had similar levels of T4 on bothdiets. These effects reached statistical significance (post-hoc t-tests: p=0.079 andp=0.052; Two-way ANOVA, group; F1,16=3.9, p=0.067 and diet; F1,16=0.3,p=0.58, groupxdiet; F1,16=2.8, p=0.112). When selected animals received fat foodthe T4 levels increased up to a similar level as that of controls.

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Figure 5.5. Plasma T3 and T4 levels of control and selected, male (right graph) and female (leftgraph) mice. Blood samples for determining hormone levels were taken when the animals hadbeen on a fat diet (dark grey bars) or standard diet (light grey bars) for over eight months.

DISCUSSION

It has been reported that feeding a high-fat diet increases the risk to attract obesityand symptoms of the “metabolic symptoms” in a number of species including man(Carroll and Dudfield, 2004; Stiegler and Cunliffe, 2006) and mice (Storlien et al.,1986; Surwit et al., 1995; Ahren and Scheurink, 1998; Lin et al., 2000; Winzell andAhren, 2004). The present study investigated whether and how such an effect maybe influenced by physical activity. For this purpose, we used a line of mice selective-ly bred for high-wheel runnning activity and randomly bred controls. Selectedfemales in the present study did not develop increased adiposity on a high-fat diet,whereas control female and male mice markedly increased their body mass with13% and 5% respectively. This increase in body mass was mainly due to an increaseof adipose tissue mass which appeared to be equally distributed over all adipose tis-sue depots in the body. Interestingly, selected males did show some increased fatstorage when feeding a high fat diet, but this was almost exclusively due toenlarged visceral depots to an extent that was even more pronounced than found incontrol males switched to a high fat diet. Important for consideration of these find-ings are the observations that spontaneous activity by infra-red detection wasincreased in selected – in particular female - mice relative to controls, and these dif-ferences were even more amplified in animals feeding the high fat diet. At the endof the study, the mice were subjected to running wheels for two weeks for charac-terization, but observed differences in spontaneous activity were not reflected bydifferences in wheel running activity among selected and control mice. This couldbe due to ageing, since previous studies indicated that differences between wheelrunning activity in selected and control mice were strongest at weaning and reduceover time (Morgan et al., 2003; Bronikowski et al., 2006). Since we wanted to avoidpotential training-effects of wheel running at weaning, we do not know which ani-mals in the present study would meet the standard breeding criteria of belonging tothe highest running animal within each litter of his generation.

In line with the generally higher level of spontaneous activity in selected micewas the finding that these animals had increased daily energy expenditure (DEE)compared to control mice when corrected for body weight. Again, this differencewas most pronounced in females as they also showed the largest differences inoverall spontaneous activity. Infra-red locomotion detection allowed us to dissociateresting metabolic rate (RMR) from DEE. These analyses revealed that selectedfemales, but not selected males had a higher RMR than control animals. Thus,both RMR and spontaneous activity-related metabolic rate (by other defined asnon-exercise activity thermogenesis; Levine, 2004) contributed to the overallincreased DEE in selected females relative to controls. In addition to the effect ofselection, in males the fat diet increased RMR and DEE further. Despite theseincreases in metabolism, both groups of males still increased body mass to a similarextent on the fat diet. In females, RMR and DEE were not increased on the fat diet,which might explain the extra weight gain in control females compared to males.


Differences in activity (and metabolism) were more pronounced in females, andthis is probably the mechanism that render selected females resistant to high-fatdiet induced weight gain. In agreement with the higher metabolic rate in selectedmice, mainly the males had increased levels of thyroid hormones (T3 and T4) whenfed a high-fat diet.

Despite the fact that selected females had lower body weight than controls, foodintake of the latter was lower than that of selected females, particularly if this is cal-culated per gram body weight. No differences in food intake were observed in themales. Food intake usually decreases when animals are put on a fat diet, becausefood efficiency is elevated with elevated dietary fat content (Winzell and Ahren,2004; Morens et al., 2005). Indeed, in control females and both groups of males wefound an decrease in food intake and increase in food efficiency on the fat diet.When food efficiency is high, the amount of energy an animal obtains per gram offood is higher and therefore food intake should be reduced to prevent increases inbody mass. In males and control females the decrease in food intake was not suffi-cient to prevent weight gain on the fat diet. In the selected females, these normalresponses of high fat diet were totally absent, and food efficiency was even lower onthe fat diet and a constant body mass was maintained. Thus, an increased metabolicrate in combination with a low food efficiency in selected females resulted in themaintenance of constant body mass on a fat diet in these mice. Apparently theselected females cope with the fat diet different than control mice and males do.Selected females did not store extra fat when given a high-fat diet. They couldaccomplish this by increasing mass-specific DEE. Fat was thus burned instead ofstored. On standard chow, selected mice also burned more fat as was shown by adecreased RQ in selected females compared to control females.

Female selected mice were resistant to developing obesity on a fat diet, but werethey resistant to developing glucose intolerance on a fat diet as well? The activeselected females did have a higher glucose tolerance on standard diet compared tocontrols. However, on the fat diet glucose tolerance was similar to control females.Compared to selected females on standard chow glucose tolerance was actuallyimpaired in selected females on the fat diet, whereas in none of the other groupsglucose tolerance was affected by the fat diet.

Selected females responded differently to the fat diet compared to the othergroups in our study and also compared to what has been shown from experimentson other mouse strains (Surwit et al., 1995; Ahren and Scheurink, 1998; Winzelland Ahren, 2004). Identifying the factors involved here, may shed light on themechanisms involved in the regulation of energy homeostasis. We measured theplasma levels of several hormones involved in the regulation of nutrient metabo-lism. Adiponectin is a metabolic hormone secreted by adipose tissue and has animportant role in improving insulin sensitivity (Yamauchi et al., 2001; Baratta et al.,2004; Schondorf et al., 2005) by stimulating oxidation of lipids in liver and muscle(Fruebis et al., 2001; Berg et al., 2002; Yamauchi et al., 2002; Bruce et al., 2005).Previously we found that male selected mice had higher basal plasma adiponectin

Chapter 586

levels throughout life, even when they were feeding chow (Vaanholt et al., 2006). Inthis study, we did not show an increase in adiponectin levels in selected mice on astandard chow diet, although on the fat diet there appeared to be an increase inboth selected males and females. As opposed to the animals in the present studythat were 4th generation offspring of either control or selected lines (without fur-ther selection for wheel-running activity), the animals in the previous study weredirect offspring from females and males that were choosen as breeders based ontheir high wheel-running activity (i.e., belonging to the highest runners per litter).Thus, it might be possible that hyperadiponectemia while feeding standard chow isonly found in animals fit those criteria, while in others it may only become evidentwhen feeding a high-fat diet.

The improved glucose tolerance in selected female mice on the standard diet inthe present study can thus not be explained directly by increased levels ofadiponectin, but may be related to the decreased fat content in these animals. Onthe fat diet, selected females had increased plasma adiponectin levels and this mayhave suppressed plasma glucose levels through sensitization of the liver to circulat-ing insulin. Basal plasma glucose levels did not differ between between control orselected animals (in males and females), though, and insulin, which promotes glu-cose disposal was increased on the fat diet in all groups, but not in the activity-selected females. Similarly to what we show here in all groups, except for theselected females, previous studies have also shown increases in insulin and leptin,involved in the regulation of food intake and fuel homeostasis, in response to high-fat feeding in rats (Iossa et al., 2003; Woods et al., 2004). Perhaps a failure toupregulate leptin in the selected females on the fat diet was crucial in the develop-ment of glucose intolerance relative to selected females on standard chow, sinceleptin has been shown in other studies to be important for insulin action (Shi et al.,1998).

In conclusion, female mice that had been selected for high wheel-running activi-ty over 31 generations responded differently to high-fat feeding than selected malesand random-bred control mice. On standard chow selected females were capable ofburning more fat, and they could clear glucose from the bloodstream faster thancontrol mice on standard chow. Interestingly, when given a high-fat diet the select-ed females did not develop diet-induced obesity as was seen in the other groups,but they did become more glucose intolerant. The selected female mice might provean important model to investigate resistance to high fat diet-induced obesity.Selected males on the other hand did develop in fact a higher level of viseral obesitythan control mice. This unexpected finding might be an adaptive strategy since fatdeposition in central place in the body (i.e., visceral fat) in stead of storing it inother parts might be an economical adjustment to the more active lifestyle of theselected mice.


METABOLISM & AGEING

PartII

Life span, body composition, and metabolismin mice selected for high wheel-running activityand their random-bred controls

Lobke M. Vaanholt, Serge Daan,Theodore Garland Jr., G. Henk Visser

AbstractThe rate of living hypothesis proposes that an increased rate of energyturnover leads to shorter life, such that the product of mass-specific energyexpenditure and life span remains unaffected. Increasing the daily rate ofenergy expenditure should decrease life span. We tested this hypothesis inmales from lines of house mice that had been selectively bred for high vol-untary wheel running. We compared three groups of mice: Selected lineshoused with access to a running wheel (S+), Selected lines housed withoutaccess to a wheel (S-), and Control lines (not bred for high wheel-runningactivity) housed with wheel access (C+). Median life spans were similar inS+ and S- (735 and 725 d respectively), but both were significantly shorterthan C+ (826 d). As expected S+ ran more than C+, although the differ-ence diminished at later ages and was no longer statistically significant by 20months of age. Subgroups were used for determination of energy turnoverand of body composition at four different ages (2, 10, 18, 26 months).Resting energy metabolism was established by indirect calorimetry and over-all daily energy expenditure by the Doubly Labelled Water method. Dailyfood consumption and energy expenditure on a mass-specific basis weregreatest in S+ (+~30%), as would be expected, and were similar in C+ andS- mice. Results were similar for mass-specific values based on wet bodymass and values based on fat-free dry body mass or on organ (heart, kidney,liver and brain combined) mass. Reduced longevity in the S+ group com-pared to the C+ mice is consistent with the rate of living hypothesis, but thesimilarity of life spans of S+ and S- mice is not. Since reduced longevity inthe S strains does not require running activity, there may be other factorsthan energy expenditure that are instrumental in causing this reduction.

Chapter6

INTRODUCTION

The “Rate of Living” theory proposed by Pearl in 1928 states that an increased rateof energy metabolism increases the rate of ageing and shortens life (Pearl, 1928).The idea was derived from a study by Rubner who showed in 1908 that the life-time energy potential (mass-specific energy metabolism times maximum life span)was remarkably size independent in animals over a wide range of body masses(Rubner, 1908). The energy that an animal spends per gram body mass in its lifetime was similar for a guinea pig and horse. In Rubner’s study humans were anexception. Humans spend more than predicted for their body mass, but even whenincluding humans the variation in life-time energy potential was still much smallerthan the observed variation in body mass. Thus, the original argument for the rateof living theory was based on interspecific allometric comparison. In comparingspecies we look at the product of evolution. Natural selection has acted on bodymass, on energy metabolism and on life span in all species. It has been pointed outthat in interspecific allometry rates of energy turnover tend tot increase with a massexponent of ca. 0.7, while life span (and other time measures) increase with a massexponent of 0.3 (Kozlowski and Weiner, 1996; Daan and Tinbergen, 1997). Theirproduct (i.e., Life-time Energy Potential, LEP) is then proportional to mass to theexponent 0.3+0.7=1, and thus mass-specific LEP scales to the body mass to thepower 0, i.e., is independent of body mass. This interspecific proportionality clearlydoes not prove that one causes the other.

The rate of living theory obviously can not be tested by comparing species. Itneeds to be tested within species. Several experimental tests (Holloszy et al., 1985;Holloszy and Smith, 1986; Navarro et al., 2004; Speakman et al., 2004) have failedto support the hypothesis in rats and mice. Speakman (Speakman et al., 2002;Speakman, 2005a) has identified a number of problems associated with such tests.Firstly, maximum life span is not a valid measure of ageing, because it is measuredin a single individual of the population and depends on the sample size used. Usingthe age reached by a group of animals (e.g. the oldest 5-10%) would provide a morereliable measure. Secondly, a single measurement of basal metabolic rate is usuallyused to calculate the total energy spent in a lifetime. Basal metabolic rate repre-sents the lowest metabolic rate of an animal in rest at thermo-neutral temperaturesand only comprises a small part (~40% depending on the environmental condi-tions) of the total energy budget. A better estimate of life-time energy expenditurewould be total daily energy expenditure under standard housing conditions meas-ured at different ages throughout life. A third problem arises when energy expendi-ture is expressed per gram body mass, as is done in the interspecific allometric scal-ing analysis. Not all components of body mass contribute equally to the metabolicrate. Greenberg (1999; 2000) has pointed out that in an ideal test of the rate of liv-ing theory one should express energy turnover rates not with respect to the wholebody but with respect to the most relevant tissues, i.e., the most metabolicallyactive organs (heart, liver, kidney, brain).

Chapter 692

We have heeded the warnings from these considerations in the present study. Toaddress the question of the consequences of elevated energy turnover rates on lifespan we exploited strains that had been selectively bred for high wheel-runningactivity (S+ and S- mice, see (Swallow et al., 1998)). We studied the relationshipsbetween energy metabolism and life span in these mice housed with or without arunning wheel, and compared them to randomly-bred mice housed with wheels(C+). Recently, another study has been published on the life spans of mice fromthese same strains (Bronikowski et al., 2006). This study demonstrated interestingconsequences of selection of activity on patterns of body mass and food intake thatare suggestive of differences in energy turnover. No direct metabolic rate measure-ments were included in this paper. Also, the significant differences in median lifespan between strains claimed in this paper are based on small sample sizes (n=20per strain and sex combination) and on underestimation of the standard errors (seediscussion). In our study we aimed to avoid this problem by using n=100 pergroup, of which a subgroup of 60 animals was left undisturbed throughout life togenerate reliable survival curves. In a smaller subgroup of the population (n=40)body composition and metabolic rate were measured at various ages to obtain reli-able estimates of the life-time energy potential (McCarter et al., 1985).


Animals & housingHsD:ICR mice (Mus domesticus) selected for running wheel activity over 31 genera-tions and their controls were used in these experiments. For a detailed descriptionof the selection procedure we refer to Swallow et al, 1998 (Swallow et al., 1998). Inshort, 112 outbred Hsd:ICR house mice were obtained form Harlan SpraqueDawley (Indianaplois, IN, USA). These mice were randomly divided in 8 separatepopulations with 10 pairs per line, four lines were selected on increased runningwheel activity and four were randomly bred as control lines. In each generationmice at the age of 6–8 weeks were tested in a running wheel (with a diameter of 30cm) for 6 consecutive days after which pairs were formed on basis of their runningwheel activity on day 5 and 6 of the test. The male and female of a family with thehighest spontaneous wheel-running activity were chosen as breeders and the micewere paired randomly within each line preventing sibling matings. In the controllines the breeders were randomly selected. At generation 31 selected mice ranapproximately 2,5 times more than control mice (Rhodes et al., 2005). Eightybreeding pairs (10 per line) from the lab of Theodore Garland Jr (University ofCalifornia, Riverside, USA) were bred at the Zoological Laboratory in Haren.Animals were bred randomly within each line preventing sibling matings. Malemice, born between 31 July 2002 and 27 January 2003, from the first generation ofoffspring were used in the experiments described below. After weaning, mice werehoused with their littermates till the age of 5 months when all animals were

Living fast, dying young? 93

individually housed with or without a running wheel for the rest of their lives(Macrolon Type II, UNO Roestvaststaal BV, Zevenaar, NL; adapted to fit in a plasticrunning wheel with a diameter of 14 cm). Animals had ad libitum food (StandardRodent Chow RMB-H (2181), HopeFarms, Woerden, NL) and water and were on a12:12 light-dark cycle (lights on at 8:00).

Experimental protocolThree experimental groups were created: Group 1: control mice housed with a run-ning wheel (C+); Group 2: selected mice with a running wheel (S+); and group 3:selected mice without a running wheel (S-). All groups consisted of 100 animals ofwhich 40 mice were used to assess food intake, energy expenditure and body com-position at different ages (test mice) and 60 animals were left undisturbed to deter-mine the timing of spontaneous death (life span mice). Before the start of the experi-ments mice were randomly assigned to one of the two subroups. Every month bodymass of all animals was measured on the same day. Due to differences in agesbetween the animals these data had to be sorted by age later on. The body massdata was categorized in 30 day blocks, starting at 15 days of age. Wheel-runningactivity was measured throughout life in the ‘life span mice’ with an event record-ing system (ERS). The system counts events (wheel revolutions) in 2-min bins andstores the counts in file. Every mouse was measured for a period of approximately30 days at a time every other month due to limitations in the technical set up (max.64 channels). This yielded data on the development of wheel-running activity withage from each individual mouse housed with a running wheel in the life span sub-groups.

In the test mice we measured food intake and metabolic rate at various ages (2,10, 18 and 26 months). Daily energy expenditure (kJ d-1) was determined using thedoubly labeled water technique. Before each trial, the mouse was weighed on a bal-ance to the nearest 0.1 g. Thereafter it was injected with about 0.1 g doubly labeledwater (2H and 18O concentrations of the mixture 37.6% and 58.7%, respectively)allowing an equilibration period of 1 hour. The dose was quantified by weighing thesyringe before and after administration to the nearest 0.0001 g. After puncturingthe end of the tail, an “initial” blood sample was collected and stored in 3 glass cap-illary tubes each filled with about 15 µl blood. These capillaries were immediatelyflame-sealed with a propane torch. Thereafter the mouse was put back in theirhome cage. After 48 hours the animal was weighed again and a “final” blood samplewas collected as described before. Per sampling period, we collected blood samplesof 4 mice which had not been injected with DLW, to assess the natural abundancesof 2H and 18O in the body water pools of the animals.

After the final blood sample was taken, animals were put in a clean cage withweighed food. Three days later the remaining food was weighed again. To be able tocorrect for the humidity of the air in the room a known amount of food was placedwith the mice in the room that was also weighed three days later. A subsample ofthe food was taken and dried to constant weight in an oven at 103°C for 4 h (ISO

Chapter 694

6496-1983(E)) to be able to determine the dry mass of the food and make compar-isons between different time points.

After measuring food intake mice were moved to our respirometry room and putin flow-through cages where oxygen consumption (V

. O2, l h-1) and carbon dioxide

production (V.

CO2, l h-1) was measured (described previously by Oklejewicz et al.,1997 (Oklejewicz et al., 1997)) simultaneously with ambient temperature and activ-ity. Oxygen and carbon dioxide concentration of dried inlet and outlet air (drier:molecular sieve 3 Å, Merck) from each chamber was measured with a paramagneticoxygen analyzer (Servomex Xentra 4100) and by an infrared carbon dioxide gasanalyzer (Servomex 1440), respectively. The system recorded the differentials inoxygen and carbon dioxide between dried reference air and dried air from the meta-bolic cages. Flow rate of inlet air was measured with a mass-flow controller (Type5850 Brooks). Data were collected every 10 minutes and automatically stored on acomputer. All mice were measured for 24h at an ambient temperature of 22°C.

Metabolic rate (MR, kJ h-1) was calculated using the following equation: MR =16.18 x V

. O2 + 5.02 x V

. CO2 (Hill, 1972). Resting metabolic rate (RMR, kJ h-1)

was defined as the lowest value of metabolic rate calculated in half-hour runningmeans. After the respirometry measurements, animals were weighed and sacrificedusing CO2 gas followed by decapitation. Trunk blood was collected in tubes withanticoagulant (EDTA or heparin) for later hormone analyses (results are publishedelsewhere, Chapter 4). Heart, liver, kidneys, hind limb muscles, brown adipose tis-sue, white adipose tissue, intestines, stomach, lung, brain, testis and skin were dis-sected out and weighed to 0,1 mg. Subsamples of heart, liver, kidney, muscle,brown adipose tissue (BAT) and white adipose tissue (WAT) were immediatelyfrozen at –80°C. The gut fill of stomach and intestines was removed and the sam-ples were weighed again. Tissues were stored at –20°C until the water and fat con-tent was determined. Water content was determined by drying for 4 hours to con-stant weight in an oven at 103°C following ISO protocol (ISO 6496-1983(E)). Fatwas extracted using a soxhlet and petroleumether. Following fat extraction, sampleswere dried to constant mass at 103°C. Dry lean masses of the organs of which sub-samples were taken, were calculated using the left over pieces with the assumptionthat the subsamples taken contained similar amounts of fat and water. In the esti-mation of the dry lean mass of the remainder of the carcass we assumed that themuscle taken out consisted of protein and that BAT and WAT was all fat.

Mass spectrometryThe determinations of the 2H/1H and 18O/16O isotope ratios of the blood sampleswere performed at the Centre for Isotope Research employing the methodsdescribed in detail by Visser and Schekkerman (1999) using a SIRA 10 isotope ratiomass spectrometer. In brief, each capillary was microdistilled in a vacuum line. The18O/16O isotope ratios were measured in CO2 gas, which was allowed to equili-brate with the water sample for 48 h at 25ºC. The 2H/1H ratios were assessed fromH2 gas, which was produced after passing the water sample over a hot uranium


oven. With each batch of samples, we analysed a sample of the diluted dose, and atleast three internal laboratory water standards with different enrichments. Thesestandards were also stored in flame-sealed capillaries and were calibrated againstIAEA standards. All isotope analyses were run in triplicate. The rate of CO2 pro-duction (rCO2, moles d-1) for each animal was calculated with Speakman's (1997)equation:

rCO2 = N/2.078 * (ko - kd) - 0.0062 * N *kd

where N represents the size of the body water pool (moles), ko (1 d-1) and kd (1 d-

1) represent the fractional turnover rates of 18O and 2H, respectively, which werecalculated using the age-specific background concentrations, and the individual-specific initial and final 18O and 2H concentrations. The value for the amount ofbody water for each animal was obtained from the carcass analyses. Finally, the rateof CO2 production was converted to energy expenditure assuming a molar volumeof 22.4 l mol-1 and an energetic equivalent per l CO2 based on RQ measurements inour respirometry setup (on average 22 kJ l-1 CO2, (Gessaman and Nagy, 1988)).

Statistical analysisResults are reported as means ± SEM unless stated otherwise. To test for effects ofgroup and/or age we applied ANCOVA models in the MIXED procedure in SAS forWindows (version 9.1). Group and age were added as fixed factors in the model.Because we used four replicated control and selected lines in our experiment, weapplied nested ANCOVA models in the analyses of these animals, where replicatelines nested within group (group(line))was added as a random effect. In the testanimals the different lines were not distributed evenly and random effects were notadded to the models. Covariates were added to the models where appropriate (i.e.body mass for measures of body composition and metabolic rate). Data were log10-transformed or squared (see text) when necessary to attain a normal distribution ofthe data. The significance level was set at p≤ 0.05, and all tests were two-tailed.

RESULTS

Development of body mass and wheel-running activityBody mass was measured every month in all animals. Figure 6.1A shows the devel-opment of body mass in C and S mice housed with or without a running wheel.Already at weaning, body mass was reduced in S mice compared to C mice and thisdifference was maintained nearly throughout life in the two groups with runningwheels (S+, C+) (2 months: ANOVA F1,8=9.4, p=0.016). When S mice werehoused without a running wheel they did increase their body mass and fromapproximately 8 months of age S- mice had significantly higher body mass com-pared to S+ mice (F1,8=7.4, p=0.024).

Chapter 696

Wheel-running activity was measured in each group for one month at a time,every two months (Figure 6.1B). For statistical analysis values were averaged overperiods of two months. As expected, wheel-running activity was higher in S+ micethan in C+ mice (5–20 months, p<0.05). Wheel running decreased with age inboth groups – a pattern that is common to most animals (Aschoff, 1962). S+decreased their wheel-running activity slightly faster with age, and from approxi-mately 600 days (20 months) of age no significant difference in wheel running waspresent anymore between C+ and S+ mice (see Figure 6.1B).

To evaluate relationships between body mass and wheel-running activity we cal-culated for each mouse the average body mass and wheel-running activity at young(0–250 d), middle-aged (251–500 d) and old age (501–750 d). The relationshipbetween body mass and wheel-running activity for middle-aged mice is shown inFigure 6.2. There appears to be no relationship in S+, and a negative association inC+ mice. Indeed at all ages there was a significant negative association betweenbody mass and wheel-running activity in C+ mice (young: r=-0.49, p<0.05; mid-dle-aged: r=–0.53, p<0.001; old: r=–0.50, p<0.001), but not in S+ mice.


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Figure 6.1. Development of body mass (A) and wheel-running activity (B) in mice selectivelybred for high-wheel running activity (S+, black circles) and randomly-bred controls (C+, whitecircles) housed with wheels. Grey circles represent activity-selected mice housed without a run-ning wheel (S-). * in Figure 1B denotes a significant difference between the groups at this age(p<0.05).

Survival Figure 6.3 shows the survival curves for the three experimental groups (A) and thefinite mortality rates (B). Finite mortality rates (FMR) over intervals of 100 dayswere calculated using the following formula: FMR = 1- Ne/Nb (see (Krebs, 1994)),where Ne is the number of animals left over at the end of the interval, and Nb isthe number of animals at the start of the interval. Table 6.1 presents different meas-ures of longevity for different groups: the mean age at death, median (50%), 90%percentile and maximum age reached.

Activity selected mice showed similar survival throughout life in the groupswith and without running wheel (Figure 6.3A). Mortality early in life was slightlyhigher in the selected groups compared with the controls (Figure 6.3B), resulting ina lower median age at death in the selected mice. We tested for differences betweengroups using the Life Tables in the survival analysis of SPSS for windows (version14.0) which uses the Wilcoxon (Gehan) test to compare survival distributionsbetween groups. Overall no significant group effect was found (p=0.057), but pair-wise comparisons showed that mortality was higher in S+ and S- mice compared toC+ mice (p=0.046 and p=0.032 respectively).

MetabolismAverage food intake over one week was measured once at the age of 668 (s.d., 70)days in all mice in the life span subgroup alive at that time. Average food intake was4.7 g d-1 (s.d., 1.1; n=44) in C+, 5.3 g d-1 (s.d., 0.8; n=32) in S+ and 4.2 g d-1

(s.d., 1.0; n=30) in S- mice. Effects of group were tested using a one-way ANCOVAwith group(line) as a random effect and age as a covariate testing a priori for differ-ences between C+ vs. S+ and S+ vs. S-. Food intake was significantly increased inS+ mice compared to both C+ and S- mice (F1,9=8.7, p<0.05 and F1,9=7.8,

Chapter 698

body

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5 15wheel-running activity (km d-1)

10

C+ miceS+ mice

Figure 6.2. Relationship between wheel-running activity and body mass in control (C+) andactivity-selected mice (S+).

p<0.05 respectively). Food intake was not statistically associated with body mass orwheel-running activity.

At 2, 10, 18 and 26 months of age food intake, metabolic rate and body compo-sition were measured in a subgroup of S+, C+ and S- test mice. At 2 monthsgroups present, S+ and C+, did not yet have their running wheels. Hence, S+ andS- were still in the same treatment. These results were tested separately from therest with independent t-tests.


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Figure 6.3. Effect of selection for activity on mice survival (A) and mortality rates (B; see text forformula). White circles represent randomly-bred mice housed with wheels (C+), black circlesrepresent selectively-bred mice housed with wheels (S+), and grey circles are activity-selectedmice housed without a running wheel(S-).

Table 6.1. Survival data.

Group n Mean SE Median SE 90% Maximum

C+ 60 787 23 826 24 965 1090S+ 60 704 32 735 28 979 1099S- 60 711 29 725 54 997 1098

Mean, median 90 percentile and maximum survival in control mice housed with wheels (C+), activity-selected micewith wheels (S+) and activity-selected mice without wheels (S-). All values are given in days. SE = standard error

Figure 6.4 shows the food intake, resting metabolic rate (RMR) and daily energyexpenditure (DEE) for S+, C+ and S- mice measured at different ages. At 2 monthsof age there was a significant increase in food intake in S+ mice compared to C+mice (t-test, p<0.011). We tested for differences between the groups at 10, 18 and26 months using two-way ANOVA with age, group and agexgroup as fixed factors.Here we did not add line effects nested within group to the model, because in somegroups of the test animals not all lines were represented or the lines were not rep-resented evenly. Body mass was entered to the models as a covariate where appro-

Chapter 6100

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Figure 6.4. Absolute (left graphs) and mass-specific (left graphs) food intake, resting metabolicrate and daily energy expenditure in activity selected mice housed (S+, black circles) or random-ly-bred controls (C+, white circles) housed with wheels at 4 different ages (2, 10, 18 and 26months). Grey circles represent activity-selected mice at 10, 18 and 26 months of age that werehoused without wheels (S-).

priate (for measures of metabolic rate and body composition). At ages beyond 2months no significant difference was found in food intake.

RMR was similar in S+, C+ and S- mice around 47.5 kJ d-1 (s.d., 7.0). Indeed,there was no significant contribution to the explained variance from either age orgroup. Mass-specific RMR was maximal at the youngest age class (2 months).Absolute and mass-specific DEE was significantly increased in S+ mice comparedto both S- and C+ mice throughout life (F2,56=7.6, p=0.001). On average DEE (kJd-1) was 59.0 (s.d., 7.7), 69.5 (s.d., 11.9) and 61.2 (s.d., 7.6) in C+, S+ and S- micerespectively. With age, DEE significantly decreased (F2,56=4.7, p=0.013) and therewas a significant interaction between group and age (F4,56=3.3, p=0.018).

Body compositionResults on body composition are summarized in Table 6.2, and the results of statis-tical analysis on these data are shown in Table 6.3. Main differences betweengroups were found for dry lean mass, fat mass, heart, lung and skin mass. Dry leanmass was increased in S+ mice compared with S- mice, but did not differ from C+mice. Fat mass was lowest in S+ mice, but differed significantly only from S- mice.Heart and lung mass were increased and skin mass was decreased in the runningmice (S+, C+) compared to the sedentary mice (S-).

Age significantly affected fat free mass, fat mass, heart, kidney, skin and remain-der of the carcass. Fat free mass, heart, kidney mass significantly increased between10 and 26 months, and skin and fat mass significantly decreased with age.

Life-time energy potentialTraditionally, the life-time energy potential (LEP, kJ) has been estimated based onmeasurements of resting metabolic rate and maximum life-span (Rubner, 1908).Life is, however, not passed solely in the resting state, and the life-time energypotential should equal DEE times life span. RMR might be used as an estimator ofDEE, but only if DEE and RMR have a fixed ratio. In most animals that is not thecase for obvious reasons. Differences in activity will result in a higher DEE, but notRMR, as we showed in our mice (Figure 6.4). RMR was on average 83, 68 or 87 %of DEE in C+, S+ and S- mice respectively. Maximum life span represents only asingle event in the colony and is therefore subject to a large variance and highlydependent on the sample size that is used. Using the 90% mortality yields a morereliable measure of life span (see also (Speakman et al., 2002)).

Taking these considerations into account we estimated LEP based on measure-ments on DEE (measured with doubly-labelled water) and the age at which 90% ofthe animals had died. To incorporate changes that occur in DEE with age, we calcu-lated the average DEE per group based on measurements at 4 ages throughout life(2, 10, 18 and 26 months, see Table 6.4). The resulting LEP values were 56908,67075 and 60977 kJ in C+, S+ and S- mice respectively. On a whole-animal basisLEP is highest in the S+ group. We return to a possible approach to significancetesting below.


Chapter 6102

Tabl

e 6.

2.Bo

dy c

ompo

siti

on in

con

trol

(C

) an

d se

lect

ed m

ice

(S)

hous

ed w

ith

(-)

or w

itho

ut (

+)

a ru

nnin

g w

heel

.

Age

(m

onth

s)2

1018

26

Gro

upC

+S+

C+

S+S-

C+

S+S-

C+

S+S-

N8

88

88

87

78

57

BM (

g)32

.9±

1.1

30.5

±1.

242

.5±

1.8

36.7

±2.

139

.6±

2.4

44.4

±2.

833

.4±

1.4

39.5

±1.

639

.3±

2.1

36.7

±1.

839

.0±

2.0

FFM

(g)

29.5

±0.

828

.1±

1.1

33.6

±1.

031

.4±

0.7

28.9

±0.

933

.9±

1.2

28.7

±1.

030

.6±

0.7

34.4

±1.

333

.3±

0.9

31.0

±0.

5D

ry le

an (

g)8.

0±0.

27.

6±0.

39.

4±0.

28.

3±0.

28.

3±0.

39.

7±0.

38.

1±0.

48.

8±0.

28.

9±0.

38.

6±0.

38.

5±0.

4Fa

t (g)

3.4±

0.4

2.4±

0.2

8.8±

1.2

5.3±

1.9

10.7

±1.

710

.5±

2.3

4.7±

0.7

8.9±

1.7

4.9±

1.2

3.4±

1.2

8.0±

1.7

Hea

rt (

g)0.

18±

0.01

0.19

±0.

010.

24±

0.01

0.23

±0.

010.

22±

0.01

0.29

±0.

020.

28±

0.03

0.25

±0.

010.

28±

0.02

0.26

±0.

020.

24±

0.01

Live

r (g)

1.94

±0.

081.

67±

0.08

2.16

±0.

041.

90±

0.14

1.77

±0.

112.

71±

0.32

1.87

±0.

112.

08±

0.05

2.36

±0.

281.

81±

0.14

2.26

±0.

36K

idne

y (g

)0.

68±

0.04

0.71

±0.

040.

82±

0.03

0.74

±0.

030.

72±

0.04

0.95

±0.

060.

77±

0.05

0.93

±0.

050.

87±

0.04

1.03

±0.

270.

73±

0.05

Brai

n (g

)0.

51±

0.01

0.50

±0.

010.

55±

0.01

0.55

±0.

010.

55±

0.01

0.52

±0.

010.

54±

0.02

0.55

±0.

020.

56±

0.01

0.54

±0.

020.

55±

0.00

Stom

ach

(g)

0.24

±0.

030.

22±

0.04

0.32

±0.

100.

29±

0.07

0.27

±0.

050.

32±

0.09

0.32

±0.

140.

40±

0.21

0.41

±0.

270.

37±

0.14

0.32

±0.

05In

test

ines

(g)

2.19

±0.

362.

07±

0.28

2.59

±0.

272.

27±

0.34

2.14

±0.

302.

47±

0.37

2.08

±0.

312.

09±

0.22

1.96

±0.

372.

43±

0.47

2.50

±0.

26Lu

ng (

g)0.

50±

0.17

0.54

±0.

130.

39±

0.05

0.32

±0.

070.

30±

0.11

0.33

±0.

040.

34±

0.14

0.29

±0.

030.

42±

0.09

0.30

±0.

100.

35±

0.09

Skin

(g)

4.32

±0.

504.

08±

0.56

5.92

±1.

144.

98±

1.43

5.76

±1.

265.

94±

2.04

4.02

±0.

595.

08±

0.99

4.73

±1.

524.

15±

0.75

5.28

±1.

33Re

st (

g20

.4±

1.7

18.6

±2.

426

.9±

4.1

22.9

±4.

025

.4±

4.7

28.2

±5.

421

.4±

2.9

25.7

±3.

123

.8±

2.7

22.3

±3.

023

.3±

3.3

Valu

es g

iven

are

mea

n ±

sem

. At

two

mon

ths

of a

ge a

ll an

imal

s w

ere

still

hou

sed

with

out

a ru

nnin

g w

heel

and

bod

y co

mpo

sitio

n of

S+

and

S- i

s as

sum

ed t

o be

sim

ilar.

BM=

Bod

ym

ass,

FFM

= F

at fr

ee m

ass.

On the basis of interspecific comparisons Rubner concluded that LEP expressedper gram body mass is rather invariant across species (Rubner, 1908). This led to thepremise of the rate of living theory that states that there is a negative relationshipbetween mass-specific metabolism and life span (Pearl, 1928). LEP per gram bodymass (LEPBW) was 1431, 1956 and 1642 kJ g-1 in C+, S+ and S- mice respectively.When expressed per gram body mass S+ mice thus still spend most energy in theirlife-time. This is due to the fact that S+ mice do have shorter life spans (Table 6.1)as predicted from the measurement of daily energy expenditure (Table 6.4).

Total body mass contains water and fat that are not metabolically active. Wetherefore also calculated LEP per gram dry lean mass; LEPDL was 6335, 8248, 7349kJ g-1 respectively. Dry lean body mass still includes matter such as skeleton andskin that is metabolically rather inactive. Greenberg has therefore proposed that weshould go one step further and express LEP relative to the metabolically most activeorgans: the heart, liver, kidney and brain (Greenberg, 1999). Therefore, we calculat-ed the sum of the dry lean weight of the heart, liver, kidney and brain (Organ mass,OM) and then calculated the LEP per gram of organ mass (LEPOM): 64255, 87463and 78228 kJ g-1 in C+, S+ and S- mice, respectively. LEPOM is still highest in theS+ mice and the coefficient of variance is 15%. Table 6.4 provides a summary ofthese results.


Table 6.3. Statistical analysis on body composition data.

Variable (g) Group Age Covariate N df F p F p p

Body mass 66 2,57 6.8 0.002 0.3 – noneFat free mass 66 2,56 13.8 0.001 7.4 0.001 BM 0.001Dry lean mass 66 2,56 6.2 0.004 2.3 – BM 0.001Fat 66 2,56 13.8 0.001 7.4 0.001 BM 0.001

Heart 66 2,56 2.5 0.04 6.8 0.002 BM 0.06Liver 66 2,56 1.4 – 2.7 – BM 0.001Kidney 66 2,56 1.1 – 3.2 0.05 BM –Brain 66 2,56 0.4 – 1.2 – BM –Stomach 66 2,56 0.3 – 2.3 – BM 0.005Intestines 66 2,56 0.8 – 1.0 – BM 0.001Lung 66 2,56 3.4 0.04 0.9 – BM –Skin 66 2,56 3.5 0.04 7.5 0.001 BM 0.001Rest 66 2,56 0.5 – 14.2 0.001 BM 0.001

Two–way ANCOVA were performed with group, age and groupxage as fixed factors. Body mass (BM) was entered tothe models as a covariate to correct for effects of body mass on variables of body composition. Data for all groups atthe ages of 10,18, and 26 months were analyzed. No significant interactions (groupxage) were found, except forintestine mass (F4,56=6.1, p=0.001), and data are therefore not shown in the table. – represents non–significanteffects (p>0.05). Significant effects are shown in bold (p<0.05).

The problem with these comparisons is obviously that we have only a singlemeasure for life span in each group. We can thus not readily test for differencesbetween groups in average LEP. We do however have individual values for total andmass-specific (per dry lean and wet mass) DEE in each group. We can test thegroup averages against each other, both before and after multiplying all individualdata with the groups life span. The basic data as well as the results of testing aresupplied in Table 6.4. We applied two-way ANOVA with a factor group, age andgroupxage. Afterwards we estimated LEP for each individual, by multiplying themass-specific value of DEE with the life span (90%) of its group; this was 965, 979and 997 for C+, S+ and S- mice respectively. S+ mice had significantly increasedDEE and LEP when calculated per gram body mass, dry lean mass or organ mass,and these effects were necessarily equal when testing DEE or LEP (p<0.001).Compared to S- mice, S+ mice thus spend more energy per gram body mass, drylean mass or organ mass, but had similar life span.

DISCUSSION

We compared metabolic rates and life span of mice selectively-bred for high wheel-running activity and their randomly-bred controls. As expected, spontaneouswheel-running activity was increased in activity-selected mice and exercise declined

Chapter 6104

Table 6.4. Life-time energy potential.

C+ S+ S- CV (%) Sign.

Body mass (g) 39.8 34.3 37.1Total dry lean mass (g) 9.0 8.1 8.3Organ mass (dry lean, g) 0.89 0.77 0.78DEE (kJ d-1) 59.0 68.5 61.2 0.001DEEBM (kJ g-1 d-1) 1.5 2.0 1.6 0.001DEEDL (kJ g-1 d-1) 6.6 8.4 7.4 0.001DEEOM (kJ g-1 d-1) 67 89 78 0.001Max. Life span (90%, days) 965 979 997LEP (kJ) 56908 67075 60977 8 0.002LEPBM (kJ g-1) 1431 1956 1642 16 0.001LEPDL (kJ g-1) 6335 8248 7349 13 0.001LEPOM (kJ g-1) 64255 87463 78228 15 0.001

Life-time energy potential (LEP; kJ) is the product of energy expenditure and life span and was calculated using aver-age daily energy expenditure (DEE, kJ d-1) measured at 4 ages (2, 10, 18 and 26 months) throughout life in the testgroup, and maximum life span (90 percentile) measured in the life span animals. In addition, LEP (kJ g-1) was cor-rected for various measures of body composition measured at the same ages: BM; body mass, DL; dry lean mass,OM; organ mass (sum of dry lean heart, liver, kidney and brain mass). CV represents the coefficient of variation cal-culated over the three groups (s.d. divided by mean x100%). Sign. shows the p-values for the two-way ANOVA per-formed to look at differences between the groups (see text for detailed description). n.s. is not significant (p>0.05).

with age. Between 5 and 20 months of age wheel-running activity was significantlyhigher in activity-selected mice, but afterwards levels of running dropped to similarlevels in selected and control mice. These results are similar to those found in pre-vious studies in these mice (Morgan et al., 2003; Bronikowski et al., 2006). Theycorrespond to the general pattern of decline in spontaneous activity with age whichhas been known in rodents for nearly a century (Richter, 1922; Aschoff, 1962).Despite the fact that wheel-running activity was only increased up to 20 months ofage, daily energy expenditure was increased throughout life in selected mice housedwith running wheels. On average daily energy expenditure was increased with 14%(or 29% mass-specific DEE) compared to sedentary selected mice and controls witha running wheel. How did this effect life span?

Activity-selected mice had a median life span approximately 730 days, apparentlyindependent of the presence of a running wheel. This is approximately 100 daysshorter than controls (with a running wheel). These differences are attributable pri-marily to increased early mortality in the activity-selected mice. Neither median normaximum life span differed significantly between the groups. The results disagreewith a previous experiment on longevity in activity-selected mice from generation 16of the same strains of mice (we used mice from generation 31) (Bronikowski et al.,2006). This study yielded a different conclusion on life span. It states that ‘medianlife expectancy differed significantly between selection and control mice within bothfemales and males (standard errors varied between one and two days) based on thenonoverlap of the 95% confidence intervals.’ (Bronikowski et al, loc cit p.1497). Thestandard errors of the median life span in the paper vary from 1.3 to 1.7 days(Bronikowski et al. loc cit. table 1), which is an order of magnitude smaller than thevalues we find (24 to 54 days, see table 1). This difference is in spite of the factsthat survival curves are visually nearly identical, and that our data are based onn=60 per group, the Bronikowski data essentially on n=20. We have recalculatedthe median life span from the control active female life span data (Bronikowski et al.loc.cit. Fig 2A) using SPSS (version 14, Kaplan-Meijer survival analysis). This yields799 days, s.e.m. 54.8 days, compared to the Bronikowski figure of 801 days, s.e.m.1.7 days. We thus remain unconvinced that the difference between conclusions inthe two studies is due to a change between generations 16 and 31.

Our results also contrast with some other studies in rats and mice that showedbeneficial effects of exercise (absence or presence of a running wheel) on life span(Holloszy and Smith, 1987; Navarro et al., 2004). Exercise increased median, butnot maximal survival in these studies. Holloszy has suggested that exercise maybring about this rectangularization of the survival curves by counteracting deleteri-ous effects of a sedentary life combined with overeating, making it possible formore of the animals to attain old age without slowing primary ageing (Holloszy,1988). In rats under caloric restriction, not only median but also maximal life spanis increased (McCay et al., 1935). If animals exercise during caloric restriction, thisdoes not influence the survival curve and exercise thus does not have an extrabeneficial effect (Holloszy and Schechtman, 1991). In our activity-selected mice


food intake has increased during the selection protocol, and sedentary mice (S-)have greater body weight than mice housed with wheels. S- however did not experi-ence deleterious effects of their sedentary life style, and had similar life spans to S+mice. This may be related to the fact that selected mice are more active per se. In acage without the presence of a running wheel, we observed that activity of selectedmice is also increased compared to control mice (infra red measurements inrespirometer, data not shown). It is also possible that traits that have been co-selected during the selection process may play a role in this.

Negative effects of increased work on life span, as expected on the basis of therate of living theory, have been shown in several species (house flies (Sohal andBuchan, 1981), honey bees (Wolf and Schmid-Hempel, 1989), kestrels (Daan et al.,1996)). In agreement with this, comparing control and selected mice housed with arunning wheel, the selected mice spent more energy and had decreased life spans.However, when comparing selected mice with or without a running wheel, a differ-ence was found in energy metabolism but not in life span. These results indicatethat the differences in life span between the groups may not be directly related totheir metabolic rates, and thus contradict the rate of living theory. The differenceswe showed in life span between control and selected mice may be caused by anybehavioural or physiological traits that differ between these mice. For instance,young selected females have increased corticosterone levels (Malisch et al., 2006)(not old males; see Chapter 4), and it has been shown in rats that animals withhigh corticosterone levels have significantly shorter life spans (Cavigelli andMcClintock, 2003).

There has been much debate over the optimal way to test the rate of living theo-ry (Lynn and Wallwork, 1992; Greenberg, 1999; Speakman et al., 2002). On thebasis of results from experiments on caloric restriction, some authors have discard-ed the rate of living theory (McCarter et al., 1985; Masoro, 1996). Caloricallyrestricted rodents have greater median and maximum life span, but have similarmass-specific food intakes (Masoro et al., 1982) and mass-specific 24h metabolicrates are similar to ad libitum fed animals (McCarter et al., 1985; McCarter andMcGee, 1989). Mass specific energy turnover disregards however that the metabolicrate is far from uniform in the body. Greenberg and Boozer (2000) have argued thatone should express energy metabolism per unit of mass of the metabolically mostactive organs (heart, liver, kidney and brain), and that this organ-specific metabolicrate does decrease in calorically restricted animals (Greenberg, 1999), such thatthe results are consistent with the rate of living theory. In the present study weeliminated these problems involved.

For the first time we accurately measured body composition as well as metabolicrates throughout life to obtain good estimates of LEP. Mass-specific life-time energyexpenditure (LEPBW) is traditionally calculated to make inter-specific comparisonsof LEP (Rubner, 1908). LEPBW was 1431, 1956 and 1642 kJ g-1 Live-1 in C+, S+and S- mice respectively. Based on these values we would discard the rate of livingtheory because LEP is not a constant as proposed by Rubner (1908). Also when cal-

Chapter 6106

culating LEP relative to dry lean mass (LEPDL) or the mass of the metabolicallyactivity organs (LEPOM), S+ mice still have a higher LEP than C+ and S+ mice. Inthe calculations we do not differentiate between metabolic organs (heart, liver, kid-ney and brain) in energy expenditure, whereas Greenberg based his estimates onthe different metabolic rates per organ (Greenberg, 1999).

Complex mechanisms exist that protect the body from reactive oxygen species(ROS) that are inevitably produced during normal oxidative metabolism. TheseROS have been shown to cause damage to macromolecules and could eventuallylead to cell death (Beckman and Ames, 1998). The body is protected against theseROS among others by antioxidant enzymes. Antioxidants enzymes scavenge ROSbefore they can cause damage. Up-regulating antioxidant production protects thebody against oxidative stress and potentially slows ageing. The antioxidantsenzymes superoxide dismutase (SOD) and glutathione peroxidase (GPx) have beenmeasured at various ages in the test mice (Chapter 7), but as in the study ofBronikowski et al. (2002),no differences in antioxidant levels were found betweenactivity-selected and control mice. A positive relationship between SOD activity andmetabolic rate was found, but the increase in SOD activity with metabolic rate wasnot high enough to increase SOD activity of selected mice above the levels meas-ured in controls. Given the increased metabolic rate, and presumably ROS produc-tion, in selected mice, this would leave the selected mice more susceptible to oxida-tive damage. When the protection against ROS (enzyme activity per kJ) was calcu-lated no differences were found between control and selected mice.

Another important mechanism protecting animals against the accumulation ofdamaged proteins is protein turnover. Protein turnover involves the removal (break-down) and replacement (synthesis) of inactive or oxidized proteins in the cell.Protein turnover therefore plays a potentially vital role in ageing (Sohal, 2002;Ryazanov and Nefsky, 2002; Yarasheski, 2003). In young selected mice an increasein protein synthesis in muscle, but not liver was found relative to control mice(both housed with a running wheel, Chapter 7), but at later ages there were no dif-ferences in protein synthesis rates. At old age, when oxidative stress starts causingproblems, selected mice were thus not better protected against the accumulation ofdamaged proteins.

In summary, mice bred selectively for high wheel-running activity have increasedlevels of wheel-running up to 20 months of age, which results in an increase inabsolute metabolic rate of 14%. Life span was subsequently reduced by approxi-mately 100 days compared to randomly-bred mice housed with a running wheel,but they did not differ from activity-selected mice housed without a running wheel.The resultant life-time energy expenditure (LEPBM) in activity-selected micehoused with wheels was increased with 27% compared to the two other groups.These results are not consistent with the rate of living theory and suggest thatphysiological and/or behavioural adaptations rather than differences in metabolicrate underlie the differences seen in life span between control and selected mice.


AcknowledgementsWe thank Saskia Helder for taking excellent care of the animals, and Gerard Overkamp fortechnical assistance. Berthe Verstappen performed the isotope analyses. We also thank PeterMeerlo, Kristin Schubert, Alinde Wallinga, Mark Doornbos and Berber De Jong for their helpat various stages in the project. S.D. is supported by EUCLOCK (EC 6th framework).

Chapter 6108

109

Protein synthesis and antioxidant capacity inageing mice: effects of long-termvoluntary exercise

Lobke M. Vaanholt, Gerald E. Lobley, Theodore Garland Jr, John R.Speakman, G. Henk Visser

Physiological and Biochemical Zoology, In review

AbstractReactive oxygen species (ROS) are produced as by-products of aerobicmetabolism and can cause damage to macromolecules (DNA, lipids and pro-teins) thereby contributing to ageing. Antioxidants scavenge ROS and lowertheir concentrations. In addition, oxidative damage caused to macromole-cules can be repaired or replaced via protein turnover. Exercise increasesmetabolism and ROS production but also elevates protein turnover. The bal-ance between these two responses may underlie the effect of physical activi-ty on longevity. Effects of life-long exercise on antioxidant enzyme activitiesand fractional synthesis rates (FSR) were examined in heart, liver and mus-cle of mice selectively bred for high wheel-running activity at various ages(2-26 months). FSR decreased with age and were increased in muscle of inyoung, not old, activity-selected mice. FSR did not differ between controland activity-selected mice in liver. Enzyme activity of superoxide dismutaseand glutathione perioxidase also decreased with age, and showed a peak at10 months of age in liver. Selection for wheel-running activity did not affectantioxidant enzyme activity. Daily energy expenditure correlated positivelywith antioxidant levels in liver of control and activity-selected mice. Thiscould indicate that increases in ROS production with raised metabolic rateresult in up-regulation of antioxidant enzymes.

Chapter7

INTRODUCTION

Reactive oxygen species (ROS), such as the superoxide anion (O2•-), hydrogen per-oxide (H2O2) and the hydroxyl radical (•OH), are produced as by-products of aero-bic metabolism in mitochondria and can cause damage to DNA, lipids and proteins(Beckman and Ames, 1998; Davies et al., 1982; Mecocci et al., 1999; Tyler, 1975).This damage to macromolecules can accumulate with age (Barja, 2004b) and maycontribute to senescence and degenerative diseases associated with ageing (e.g. car-diovascular disorders, Parkinson’s disease) (McEwen et al., 2005; Melov et al., 1999;Wallace, 2005). An elaborate defence system consisting of endogenous antioxidantenzymes such as catalase (CAT), superoxide dismutase (SOD), glutathione peroxi-dase (GPx), and numerous non-enzymatic antioxidants, including vitamins A, Eand C, glutathione (GSH), ubiquinone, melatonin and flavonoids, exist to scavengeROS and thereby prevent deleterious effects (Beckman and Ames, 1998). A smallamount of the ROS produced escape conversion and can still damage macromole-cules. As a second line of defence, damaged macromolecules can be replaced, asoccurs by protein turnover that involves the removal (breakdown) and replacement(synthesis) of inactive or oxidized proteins in the cell. Protein turnover thereforeplays a potentially vital role in ageing (Ryazanov and Nefsky, 2002; Sohal, 2002;Yarasheski, 2003).

Relationships between antioxidant enzyme activity, protein turnover and metab-olism have been studied by experimentally increasing metabolic rate, e.g. by increas-ing physical activity. Although some results are contradictory, it is widely acceptedthat regular physical activity leads to an increase in both ROS production and activ-ities of antioxidant enzymes, especially in muscle (reviewed in (Ji, 1999)). If theincrease in antioxidant defences in response to exercise is greater than the increasein ROS production, this would lead to a better protection against oxidative damage.Exercise generally also has a stimulatory effect on protein synthesis rate, specifical-ly in skeletal muscle, in rats (Hayase and Yokogoshi, 1992; Hernandez et al., 2000;Katzeff et al., 1994; Mosoni et al., 1995), and humans (Biolo et al., 1995; Chesley etal., 1992; Phillips et al., 1997; Rennie et al., 1981; Sheffield-Moore et al., 2004; Shortet al., 2004). Maintaining high levels of antioxidant enzyme activity together withhigh rates of protein synthesis in old age would diminish the accumulation of dam-aged proteins and increase cell survival. For example, protein turnover and antioxi-dant enzyme activity have been shown to be increased in calorically-restricted ani-mals, a nutritional condition that increases life span in several species, includingmice (Weindruch et al., 1986) and rats (McCay et al., 1935); for recent review see(Masoro, 2005). Therefore, ageing may be ameliorated by mechanisms, such asexercise, that elevate protein turnover and/or antioxidant enzyme activity.

Mice from lines selectively-bred for high wheel-running activity for 31 genera-tions and their random-bred control lines (Swallow et al., 1998) were used to studyeffects of life-long voluntary exercise on antioxidant enzyme activities and proteinturnover. Although the effects of exercise on antioxidant systems and protein syn-

Chapter 7112

thesis are often compared between young and old subjects the influence of exercisethroughout life on oxidant systems and the relationship between metabolic rate andantioxidant activity on an individual level are not well studied. The main aim of thecurrent study was to test whether long-term exercise, that increases metabolic rate,induces compensatory changes in antioxidant enzyme activities and protein synthe-sis rates. In addition, we studied combined effects of age and exercise on both sys-tems and we explored the relationship between energy metabolism and antioxidantenzyme activity.


Animals & housingMale mice, originally of the Hsd:ICR strain (Mus domesticus), from lines that hadbeen selected for high wheel-running activity for 31 generations and their randombred controls were used (Swallow et al., 1998). Eighty breeding pairs of mice fromthe lab of Prof. T. Garland Jr. were used as the basis for a colony at the University ofGroningen. In the original selection protocol (Swallow et al., 1998) eight separatelines were created (4 control and 4 selected) by breeding randomly (control) orselecting the most actively running (in revolutions per day) male and female of eachfamily for breeding (selected lines). Mice from each of the eight lines were used inthe experiments.

Mice were housed with three litter mates from weaning until they were fivemonths old, after which they were housed individually (Macrolon type II longcages, UNO Roestvaststaal BV, Zevenaar, NL) with wood shavings as bedding mate-rial and a running wheel (cages were adapted to fit a plastic running wheel with a 7cm radius: (Vaanholt et al., 2006)). Food (standard rodent chow RMH-B (2181),HopeFarms, Woerden, NL) and water were provided ad libitum, and animals were ona 12:12 light:dark cycle. Two experimental groups were created: Control mice (C+)and Selected mice (S+) both housed with a running wheel from the age of fivemonths. In the 2-month old group, animals were thus not yet housed with a runningwheel. Metabolic rates of all animals were measured prior to sacrifice at four ages: 2,10, 18, and 26 months. All procedures concerning animal care and treatment werein accordance with the regulations of the ethical committee for the use of experi-mental animals of the University of Groningen (License DEC 2777(-1) and 4184A).

Tissue collectionAt each age, five to eight mice per group were briefly anaesthesized with CO2 andthen killed by decapitation. Animals were dissected and biopsies of hind limb mus-cle (only at 2 and 26 months), liver and heart were immediately frozen in liquidnitrogen and stored at –80°C for antioxidant enzyme measurements.

Protein synthesis was assessed only in 2 and 26 month old animals. The 26month old individuals were the same animals used for the antioxidant measure-

Antioxidants and protein synthesis in high-activity mice 113

ments, but for the 2-month old group different animals were used (n=8 per group).For logistic reasons, this group only contained one control (lab designation is line2) and one selected line (line 7). Food intake and body mass were measured for twoconsecutive days prior to the harvesting of tissues for protein synthesis measure-ments. Protein synthesis was measured using the large-dose method as describedby Garlick et al. (1980). Mice were given an intra-peritoneal injection of 150 mM2H5-phenylalanine (1.5 ml per 100 g animal). After 15 minutes the mice were euth-anized using CO2, followed by decapitation. Trunk blood was collected in pre-chilled tubes with heparin as anti-coagulant. Blood samples were centrifuged at2600 g at 4°C for 15 min, and the plasma was collected and stored at –80°C untilanalysis. Liver and hind-leg muscle were rapidly removed, weighed to 4 decimalplaces, rinsed in ice-cold saline, frozen in liquid nitrogen, and stored at –80°C untilanalysis. Exact times (nearest second) of injection and freezing of tissues wererecorded.

Protein synthesisFree and protein-bound enrichments of phenylalanine in liver and muscle tissueswere quantified as described by Wester et al. (2004). Approximately 300 mg offrozen tissue was homogenised on ice in 3 ml 7% (w/v) sulphosalicylic acid (SSA).Free phenylalanine was separated from protein-bound phenylalanine by centrifuga-tion at 1000 g at 4°C for 15 minutes and the supernatant retained. The pellet wasthen washed three times with 3 ml 7% sulphosalicylic acid to remove free pheny-lalanine. The initial supernatant fraction (free pool) was passed through a 0.4 mlcolumn of Dowex AG 50W-X8 (100-200 mesh) and the resin rinsed with 2x3.5 mlwater before the phenylalanine was eluted with 2 ml 2M NH4OH and 1 ml water.The eluate was freeze-dried and stored at –20°C for later analysis. Half of thewashed pellet (protein-bound pool) was transferred to a 8 ml screw-topped Pyrexhydrolysis tube and solubilised in 1 ml 0.5 ml 0.3 M NaOH for 30 minutes. A fewphenol crystals were added and the sample was hydrolysed by adding 7 ml 4M HCland heating on a dri-block at 110°C for 18 hours. Hydrolysates were dried undervacuum, resuspended in 1 ml 0,5 M sodium citrate (pH 6.2), and stored at -80°Cuntil later analysis.

For the plasma samples 150 µl was treated with 150 µl 15% SSA, centrifuged at1000 g at 4°C for 10 minutes and 150 ml of the supernatant was passed through a0.2 ml column of Dowex AG 50W-X8. Elution conditions and subsequent treat-ments were similar to the tissue free pool samples.

Stable isotope enrichments of the tissue and plasma free pool were measured bygas chromatography mass spectrometry (GC/MS) after conversion to the tertiary-butyldimethylsilyl (TBDMS) derivatives (Calder and Smith, 1988). In the hydrol-ysed samples (protein-bound pool, low enrichment) phenylalanine was convertedto phenylethylamine by enzymatic decarboxylation prior to forming the TBDMSderivative. This was separated by capillary column gas chromatography, and enrich-ments obtained from electron impact ionization selective ion monitoring (EI-SIM)

Chapter 7114

mass spectrometry was used to detect the TBDMS derivate of phenylethylamine(see Calder et al., 1992 and Slater et al., 1995).

The fractional synthesis rate (FSR, % d-1) was calculated using the followingequation: FSR = 100*(BP/FP)*1440/t, where BP is the bound pool of phenylala-nine in mole percent excess (MPE), FP is the MPE of the free pool of phenylalaninemeasured in either plasma or tissue, and t is the time (min) between injection ofphenylalanine and freezing of the tissue in minutes. The ratio between FP meas-ured in plasma and liver or muscle was calculated. The ratio was 1.02±0.03 and1.00±0.03 (mean±sd) in muscle and liver respectively. Neither ratio was signifi-cantly different from unity. From these data we concluded that the injected pheny-lalanine effectively equilibrated between tissue and plasma and remained so untiltime of death. In consequence, the FSR reported represent the FSR calculated basedon plasma free phenylalanine as representative of the precursor pool.

Antioxidant enzyme activities & protein contentPrior to enzyme activity determinations, tissue samples were homogenized by soni-cation in 20 volumes of ice cold 50 mM phosphate buffer. Following centrifugation(25 min at 3000 g), the supernatant fraction was collected, divided over severaltubes and stored at –80°C for enzyme activity and protein measurements.

Total superoxide dismutase (SOD) activity was determined at 25°C by the inhi-bition of the auto-oxidation of pyrogallol by SOD in the supernatant, following themethod of Marklund and Marklund (1974). The reaction was followed spectropho-tometrically at 420 nm in the following reaction mixture: 50 mM Tris-DTPA buffer,15 µl supernatant and 15 µl pyrogallol in a total volume of 800 µl. Each triplicatemeasurement was preceded by a blank, containing only pyrogallol in Tris-DTPAbuffer. One unit of SOD was defined as the amount of enzyme causing 50% inhibi-tion of pyrogallol auto-oxidation.

Glutathione peroxidase (GPx) activity was determined at 25°C via the oxidationof NADPH in the presence of reduced glutathione (GSH) and H2O2 (combining theassays of Paglia and Valentine (1967), and Lawrence and Burk (1976). The follow-ing reaction mixture was used: 4.28 mM sodium azide (to block catalase activity),1.07 mM EDTA, 4.286 mM GSH, 0.214 mM NADPH, 1 U ml-1 GR in ice cold 50mM phosphate buffer. 25 µl H2O2 and 25 µl sample were added to the reaction mix-ture. Reactions were followed spectrophotometrically at 340 nm in a total volumeof 700 µl. To correct for spontaneous oxidation reactions independent of GPx,blanks without H2O2 were measured and subtracted from the assay values. Oneunit of GPx was defined as the amount of enzyme that oxidized 1 µmol of NADPHper minute in the presence of reduced glutathione. Protein content of the super-natant fraction was determined using a Bradford assay (Quick start Bradford pro-tein essay kit 2; Biorad Laboratories B.V., Veenendaal, NL).


Indirect calorimetryPrior to killing, resting metabolic rates (RMR) and daily energy expenditure (DEE)were measured for each animal (the same individuals for which antioxidant enzymeactivity was measured) using an eight-channel indirect calorimetry system(described previously in (Oklejewicz et al., 1997; Vaanholt et al., 2006)). The micewere put in flowthrough chambers where oxygen consumption (V

. O2, l h-1) and

carbon dioxide production (V.

CO2, l h-1) was measured simultaneously with ambi-ent temperature and activity (passive infrared detectors). Oxygen and carbon diox-ide concentrations of dried inlet and outlet air (drier: molecular sieve 3 Å, Merck)from each chamber were measured with a paramagnetic oxygen analyzer (ServomexXentra 4100) and carbon dioxide by an infrared gas analyzer (Servomex 1440). Thesystem recorded the differentials in oxygen and carbon dioxide between dried refer-ence air and dried outlet air from the metabolic cages. The flow rate of inlet air wasmeasured with a mass-flow controller (Type 5850 Brooks). Computerised data werecollected every 10 minutes. All mice were measured for 24h at an ambient tempera-ture of 22°C. Oxygen consumption was calculated according the equation 2 of Hill(1972) to correct for volume changes with respiratory quotient below 1 andexpressed in standard temperature and pressure. Metabolic rate (MR, kJ h-1) wasestimated using the following equation: MR= 16.18 x V

. O2 + 5.02 x V

. CO2

(Romijn and Lokhorst, 1961). Resting metabolic rate (RMR, kJ d-1) was defined asthe lowest value of metabolic rate calculated from cumulative means every 30 min(Vaanholt et al., 2006). Daily energy expenditure (DEE, kJ d-1) was calculated as theaverage metabolic rate during the entire 24-h measurement period.

Statistical analysisResults are reported as means ± SEM. To test for effects of treatment and/or agewe applied ANOVA models in the MIXED procedure in SAS for Windows (version9.1). Group, age, and group x age were added as fixed factors. Because we used fourreplicated control and selected lines in our experiment, we applied nested ANOVAmodels in the analyses of these animals, where replicate lines nested within groupand line(nested within group) x age (i.e., an interaction term) were added as ran-dom effects. These random effects of line were not added when testing for differ-ences in protein synthesis, because not all lines were represented in the 2-monthold animals used for these measurements. Where appropriate, covariates wereadded into the models (e.g., metabolic rates, food intake). Adjusted means were cal-culated using the least squares means command in SAS MIXED. The outlier testdescribed by Cook and Weisberg (1999) was used to test for outlying data points.One significant outlier was identified when testing protein synthesis in muscle (seeTable 7.2). This data point was subsequently removed before final statistical analy-sis. Data on antioxidant enzymes were log10-transformed as necessary to improvenormality. The significance level was set at p≤ 0.05, and all tests were two-tailed.

Chapter 7116

RESULTS

Body mass, food intake and fractional synthesis rates At both ages control mice had greater body mass than activity-selected mice andbody mass increased with age in both groups (see Table 7.1). Interestingly, foodintake did not differ between groups or with age.FSR decreased by approximately 15% with age in liver and muscle of both groups.This effect was significant in liver but not muscle (Table 7.1). In liver, no significanteffects of group (selection vs. control) nor a group x age interaction was found. Inmuscle, the group effect reached significance (p=0.053) and post-hoc tests (Tukey)showed that at 2 months of age FSR was significantly higher in muscle of activity-selected mice relative to controls (p<0.05), but no difference between groups wasfound at 26 months of age. Also, no interaction between group and age was found.Food intake can influence protein synthesis and therefore we added food intake tothe models as a covariate. In none of the models was food intake a significantcovariate.

Antioxidant enzyme activity & metabolismThe age-related development of antioxidant enzyme activities in the liver (SOD andGPx) and heart (SOD) are shown in Figures 7.1 and 7.2, respectively. In the liver,antioxidant enzyme activities (GPx and SOD) varied considerably with age (seeTable 7.2) and were highest at 10 months of age (post-hoc Tukey; at 10 monthsSOD activity was significantly different compared to 2 and 26 months; no differ-


Table 7.1. Effects of selection for high locomotor activity activity on body mass, food intake, andfractional synthesis rates (FSR) in liver and muscle.

2 months 26 months p-values

Variable name Control Selected Control Selected Group Age

n 8 8 8 5Age (d) 73±2 72±5 781±11 781±11Body mass (g) 33.9±1.5 30.0±1.5 41.4±1.5 37.9±1.9 0.026 <0.001Food intake (g d-1) 4.1±0.4 4.0±0.4 3.8±0.4 3.0±0.5 0.21 0.57

FSR Liver (% d-1) 69.3±2.6 69.6±2.6 56.7±2.6 63.4±3.3 0.23 0.003

FSR Muscle (% d-1) 5.1±0.5 7.1±0.5 4.9±0.5 5.2±0.6 0.053 0.065

Results for two-way ANOVA’s with a factor group, age and group x age are given in addition to least square (adjust-ed) means±SE for all groups. For analysis of food intake body mass was added to the model as a covariate. See textregarding analyses of FSR with food intake as a covariate. No significant interactions (groupxage) were found(p>0.1), and p-values are therefore not shown in the table. Bold values represent significant results (p≤ 0.05). Foodintake data for two mice were missing. n=sample size.

Chapter 7118

GPx

act

ivity

(µm

ol N

AD

PH m

g-1 p

rote

in)

0.0

0.4

0.8

1.2

2age (month)

controlselected

10 18 26

SO

D a

ctiv

ity(U

mg-1

pro

tein

)

0

200

300

100

1.6

400

Figure 7.1. Superoxide dismutase (SOD; top graph) and glutathione peroxidase (GPx; bottomgraph) activity in liver of mice selected for high wheel-running activity and their random-bredcontrols at different ages. One unit of SOD was defined as the amount of enzyme that causes50% inhibition of pyrogallol auto-oxidation. One unit of GPx is defined as the amount of enzymethat oxidizes 1 µmol of NADPH per minute in the presence of reduced glutathione.

SO

D a

ctiv

ity (U

mg-1

pro

tein

)

0

40

2age (month)

controlselected

10 18 26

80

120

Figure 7.2. SOD enzyme activity in heart of mice selected for high wheel-running activity andtheir random-bred controls at different ages. One unit of SOD was defined as the amount ofenzyme that causes 50% inhibition of pyrogallol auto-oxidation.

ences between the other ages, p<0.05). An effect of age on SOD activity in hearttissue was also found (Table 7.2). A similar pattern as seen in liver with a peak at10 months can be seen in control mice (see Figure 7.2), however, post-hoc testsonly showed a significant difference between SOD activity at 2 and 26 months(Tukey; p<0.05), indicating a decrease in SOD activity with age in heart. Selectionfor high wheel-running activity did not affect antioxidant activities in liver or heart(Table 7.2). SOD activity in muscle from control and selected animals was meas-ured only at 2 and 26 months. There was no effect of either age or selection foractivity (Figure 7.3, Table 7.2).

Resting metabolic rate (RMR, kJ g-1 d-1) and daily energy expenditure (DEE, kJg-1 d-1) were measured in all animals at all ages. Overall, mass-specific RMR was


SO

D a

ctiv

ity (U

mg-1

pro

tein

)

0

20

2age (month)

controlselected

26

30

10

40

50

Figure 7.3. SOD enzyme activity in muscle of mice selected for high wheel-running activity andtheir random-bred controls at different ages. One unit of SOD was defined as the amount ofenzyme that causes 50% inhibition of pyrogallol auto-oxidation.

Table 7.2. Nested ANOVA on effects of group and age on antioxidant enzyme activities.

Group Age Group x Age

Trait N d.f. F p d.f. F p F p

SOD Liver 57 1,6 0.02 0.89 3,11 5.9 0.012 1.4 0.29GPX Liver 55 1,6 0.26 0.63 3,10 5.6 0.016 0.4 0.78SOD Heart 57 1,6 0.11 0.75 3,11 3.7 0.047 1.2 0.35SOD Muscle 29 1,2 0.05 0.85 1,2 0.5 0.56 1.1 0.42

Nested ANOVA models were performed in the MIXED procedure of SAS for Windows (version 9.1). Group, age andgroup x age were added as fixed factors and replicate lines nested within group and line(nested within group) x agewere added as random effects to correct for line effects. To test for relationships between resting metabolic rate(RMR, kJ d-1) or daily energy expenditure (DEE, kJ d-1) and enzyme activity both were added to the model as covari-ates separately (data not shown in Table, see text). Sample sizes were: 2 months, n=8; 10 months, n=8 and n=5 incontrol and selected mice respectively; 18 months; n=8 and n=7, 26 months; n=8 and n=5. In 2 mice sample vol-ume of liver was too small to measure both SOD and GPx enzyme activity, reducing the sample size for these meas-urements. N represents the total sample size and d.f. the degrees of freedom. Significant effects are in bold format(p≤ 0.05).

1.20±0.26 (mean±sd) and 1.31±0.25 in control and selected mice respectively.Selected mice thus had a slightly higher RMR (+9%), but this difference was notsignificant (Two-way nested ANCOVA with body mass as covariate). Mass-specificDEE was on average 1.53±0.31 and 1.82±0.35 in control and selected mice respec-tively (+19%). Differences in daily energy expenditure were slightly larger at youngages, but overall no significant effect of group on DEE was shown (two-way nestedANCOVA).

RMR and DEE were added into the models as covariate to test whether theyaffected antioxidant enzyme activity. In liver, but not heart, DEE significantly pre-dicted both SOD (p=0.008) and GPx (p=0.037) enzyme activity. RMR also signifi-cantly predicted SOD (p=0.044), but not GPx (p=0.056) activity). Including RMRor DEE into the models did not alter the outcome. Figure 7.4 shows the relation-ship between DEE and hepatic SOD activity of control (left panel) and activity-selected (right panel) mice of all ages. In both groups a positive relationshipbetween daily energy expenditure and antioxidant enzyme activity was found at allages. Similar results were found for the relationship between resting metabolic rateand both antioxidant enzymes, and between DEE and GPx activity in liver (data notshown). In heart and muscle metabolic rate did not predict SOD activity.

We calculated the protection against ROS for each individual mouse by dividinghepatic SOD enzyme activity by daily energy expenditure (protection in U mg-1 pro-tein kJ-1 d-1) and plotted it against daily energy expenditure to see whether micewith a high metabolic rate were better protected against ROS (See Figure 7.5). Nosignificant relationship was shown between protection and metabolic rate. Averageprotection of control and selected mice was 4.6±1.4 and 4.5±1.2 U mg protein-1 kJ-1

respectively and these values did not differ significantly.

Chapter 7120

300

200

300

400

500

100

SO

D a

ctiv

ity (U

mg-1

pro

tein

)

40 50 80dailt energy expenditure (kJ d-1)

60 70

CONTROL SELECTED

30 40 50 8060 70

2 month10 month18 month26 month

Figure 7.4. Relationship between daily energy expenditure and SOD activity in liver of control(left panel) and selected mice (right panel) at various ages.

DISCUSSION

In liver, age had a strong effect on protein FSR in both control and selected mice.On average FSR decreased with 16%. In muscle, selected animals had a steepdecrease in FSR with age (36%), but control mice only showed a small decline(4%). The literature on this subject is ambiguous. Most studies have shown an age-related decrease in muscle FSR (Dorrens and Rennie, 2003; Lewis et al., 1985;Rattan, 1996) but other reports claim no difference (Sheffield-Moore et al., 2005;Volpi et al., 2001). Discrepancies between studies can have many possible explana-tions, such as tissue (muscle type) used, sex, diet and activity level of subjects. Acommon finding is an increase in muscle protein synthesis in response to exercise(Chesley et al., 1992; Hayase and Yokogoshi, 1992; Hernandez et al., 2000; Short etal., 2004). As expected, an increase in muscle FSR was found in 2-month old activi-ty-selected mice. At this age the activity of the mice was not controlled as bothgroups without a running wheel and no record of activity was attempted. In a simi-lar study, however, where animals were housed in a cage with a locked runningwheel, overall activity recorded by passive infrared sensors was found to beincreased by 130% in selected mice at 2 months of age (unpublished data).Therefore, the activity levels were probably also increased in the current 2-monthold mice. This is supported by the observed increase seen in muscle FSR. Severalstudies have shown that exercising at old ages still induces an increase in FSR(Sheffield-Moore et al., 2004; Short et al., 2004; Yarasheski et al., 1993). At 26months of age we did not find a difference in muscle FSR between control andactivity-selected mice. Mice selected for wheel-running activity have been shown to


300

2

4pr

otec

tion

(U m

g-1 p

rote

in k

J-1)

40 50 80dailt energy expenditure (kJ d-1)

60 70

controlselected

6

8 9.4

Figure 7.5. Protection against ROS (SOD enzyme activity in liver per kJ energy expenditure) as afunction of daily energy expenditure in control (white circles) and selected (black circles) mice.Linear regression showed no significant relationship between protection and energy expenditure.The arrow indicates an outlier that was left out of analysis.

run approximately 2.5 times more per day than control mice at 6-8 weeks of age(Morgan et al., 2003), but these differences disappear over time. Indeed, afterapproximately 60 weeks of age no differences exist in wheel-running activity(Bronikowski et al., 2006; Morgan et al., 2003). Thus, in old animals (26 months)wheel-running activity probably no longer differed between selected and controlmice and this would explain why protein synthesis was not different between thegroups at an old age. These findings would also suggest that prior exercise historyhas no long-term effect on muscle FSR. A more intensive workout at old age isprobably necessary to evoke increases in protein synthesis rates. In liver, selectionfor activity did not affect FSR, but FSR did change with age. Previous studies havereported both increased (Mosoni et al., 1995) or decreased (Hayase and Yokogoshi,1992) hepatic protein synthesis in response to exercise and the effects remainunclear. Another important factor in determining the extent of the effect of exerciseon protein synthesis might be age. Lewis et al. (1985) studied the effects of caloricrestriction on whole-body protein turnover in rats at different ages and found themost pronounced effect of caloric restriction on FSR at 12 months of age. At thisage caloric restriction increased FSR by 45% whereas at 24 months the increase wasonly 6% and at 2 months there was a 2% decrease. Measurements at intermediateages are thus important to give insights into the effects of exercise on FSR through-out life.

In addition to effects FSR, age affected antioxidant enzyme activity of SOD andGPx in mouse liver and heart. In liver, peak enzyme activity occurred at 10 monthsof age with a subsequent decline, while heart SOD decline in enzyme activitybetween 2 and 26 months. There was no effect of age on SOD activity in muscle,although with the sampling protocol used (only measuring at 2 and 26 months ofage) intermediate changes may have been missed. Conflicting data exist on theeffects of age on antioxidant enzyme activities and comparisons across studies arecomplicated by the use of different species, ages selected and organs studied. Moststudies have measured antioxidant enzyme activity at only two ages and foundincreased, decreased or no differences in antioxidant enzyme activities in varioustissues (Gunduz et al., 2004; Kakarla et al., 2005; Rao et al., 1990; Sohal et al., 1990).Antioxidant enzyme activity has also been measured in C57BL/6J mice at 3, 11, 19and 27 months of age and a similar peak in SOD activity in heart and liver wasfound at 11 months of age (see Chapter 9). In rats, SOD and CAT enzyme activityin the brain at five ages also followed a similar pattern in antioxidant enzyme activi-ty as shown here, with a peak in activity at 12 months of age (Tsay et al., 2000).These results highlight the importance of measuring antioxidant enzyme activitiesat various ages. By doing this, discrepancies between existing data might beresolved.

There were no significant effects of exercise (selection for activity) on antioxi-dant enzyme activities in liver, heart or muscle. This is in contrast to a study byGunduz et al. (2004) comparing antioxidant enzyme activity in rats that underwentone-year forced swimming exercise and sedentary controls. In this study, exercising

Chapter 7122

rats had increased antioxidant enzyme activity in heart and liver relative to seden-tary controls (Gunduz et al., 2004). Animals in our study exercised voluntarily andthe difference in activity between the groups may not have been large enough toobserve effects on antioxidant enzyme activities. In agreement with this, Selman etal. (2002) compared antioxidant enzyme activity in heart, liver and muscle ofsedentary and voluntarily exercising voles and also observed no differences.Another study on voluntary exercising male rats has shown that GPx and CATactivities was not altered, while SOD activity increased in response to exercise(Yamamoto et al., 2002). In voluntary exercising female rats, increases in both SODand CAT in liver of young (three-month) and old (twelve-month) were found(Kakarla et al., 2005). Female rats voluntarily undergo up to 10-fold more wheel-running activity than males (Yamamoto et al., 2002), which might explain the dis-crepancy between studies. In agreement with this, in the activity-selected strainused here, females run approximately 25% more than males (Morgan et al., 2003),and hepatic antioxidant mRNA expression (SOD2 and CAT) at 20-months of agewas different for the females selected for activity compared with controls, but notfor males (Bronikowski et al., 2002).

Metabolic rate positively correlated with liver antioxidant activity in both con-trol and selected mice at all ages (Figure 7.4). This indicates that individual micewith a higher metabolic rate protected themselves against increased ROS produc-tion by increasing antioxidant enzyme activity. Without measures of ROS thishypothesis is unproven and the relationship between antioxidant enzyme activityand metabolic rate can also be explained by another, equally tenable, hypothesis;the disposable soma theory (Kirkwood and Shanley, 2005), This theory suggeststhat protecting the soma is costly hence it is traded off against other effects. Ifindeed the costs of protection are high, increasing protection levels could cause adirect metabolic cost. The difference in metabolic rate between control and selectedmice was small, and no significant difference in antioxidant enzyme activity wasfound between the groups. The small difference in metabolic rate may relate to theobservation that mice selected for high wheel-running activity have increased run-ning economy on a whole-animal basis, i.e. they spent less energy per distance trav-elled (Rezende et al., 2006). However, this effect was mainly seen in females andanother study in male mice housed with wheels at 10, 20 or 30°C showed no differ-ences in costs of transport between control and selected male mice (Vaanholt et al.,2007). In this study there was a significant increase in daily energy expenditure inactivity-selected mice (Vaanholt et al., 2007). The measurements on daily energyexpenditure in the current study were done in the respirometer (without wheels)and not under normal housing conditions (with wheels), which may have resultedin an underestimation of the daily energy expenditure in the home cage. If so, thismay indicate that selected mice did not compensate for their higher metabolismwith increased antioxidant enzyme activity. At present, we do not know whetherthe antioxidant system was affected in activity-selected mice in other tissues thanthe ones measured.


Several studies have reported increased mortality or reduced life span followingincreased workload (energy turnover), e.g. hamsters Mesocricetus brandti (Lyman etal., 1981), kestrels Falco tinnunculus (Daan et al., 1996), honey bees Apis mellifera(Wolf and Schmid-Hempel, 1989), house flies Musca domestica (Yan and Sohal,2000) and nematodes Caenorhabditis elegans (Van Voorhies and Ward, 1999). This isconsistent with the “rate of living theory” (Pearl, 1928; Rubner, 1908) derived frominter-species comparisons, that postulates total energy turnover of homeothermsdetermines life span. Comparisons between species are not always straightforward(Perez-Campo et al., 1998; Tolmasoff et al., 1980), however, because several taxa,such as birds (Barja, 1998) and bats (Brunet-Rossinni, 2004), combine high meta-bolic rates with long life spans. Also, these comparative studies are complicated bythe co-variation of traits with body mass and the lack of independence of the datadue to a shared phylogenetic history (Speakman, 2005a). Several studies haveshown that exercise has a beneficial effect on life span (Bronikowski et al., 2006;Holloszy, 1988; Navarro et al., 2004) and the beneficial effects of exercise on proteinturnover rates and/or antioxidant enzyme activities might underlie this effect. Up-regulation of these systems would diminish the damage caused by ROS (that areproduced in parallel with metabolic rate under certain conditions) and could there-by increase life span. In practice, we did not show any major effects of long-termvoluntary exercise on either parameter. A correlation between metabolic rate andantioxidant enzyme activities was found, indicating that mice with a higher meta-bolic rate had more protection against ROS. Assuming that the amount of ROS pro-duced increased linearly with increasing metabolism (with a gradient of 1 and inter-cept of 0), the net result would be a similar protection against oxidative stress inanimals with a high or low metabolic rate. Indeed, as shown in Figure 7.5, the pro-tection (enzyme activity per kJ) was similar at all metabolic rates. Selected mice hadslightly higher metabolism without a change in antioxidant enzyme activity, whichmay have left them more vulnerable for damage caused by ROS. We calculated theprotection by antioxidants per kJ energy expenditure and no differences in protec-tion between the groups were shown.

In summary, age strongly affected antioxidant enzyme activity, which showed apeak at 10 months of age in liver and a decline with age in heart. Protein synthesisrates also decreased with age in liver, and to a lesser extent in muscle. DEE andRMR correlated with antioxidant activity in the liver, showing that individual micewith high metabolic rates protect themselves against the increased ROS productionby increased scavenging. Despite a small difference in metabolic rate, there was nodifference in antioxidant enzyme activity between control and activity-selected linesof mice. Long-term voluntary exercise thus did not result in compensatory changesin antioxidant enzyme activities or protein synthesis rates.

Chapter 7124

AcknowledgementsWe thank Suzan Anderson and David Bremner for performing the analysis of fractional syn-thesis rates and Annemieke Meijer for performing the antioxidant enzyme activity measure-ments. Serge Daan is thanked for commenting on earlier manuscripts. L.M. Vaanholt was arecipient of an EU Marie Curie Training Site award (Mass School) to the Rowett ResearchInstitute, Aberdeen, Scotland. TG was supported by US NSF grant IBN-0212567.


Ageing under cold conditions: effects on bodycomposition, metabolism and longevity

Lobke M. Vaanholt, Serge Daan, G. Henk. Visser

Journal of Gerontology: Biological Sciences, In review.

AbstractThe “Rate of Living” hypothesis proposes that variations in the rate of mass-specific energy expenditure are causally involved in senescence related mor-tality. Thus, increasing the daily rate of energy expenditure should decreaselife span. We tested a prediction from the hypothesis by exposing mice tolow ambient temperature, thereby aiming to increase energy expenditure.We compared groups of 60 mice each, housed at 22°C (warm, WW) and at10°C (cold, CC) throughout their life. Precipitated death in the cold groupwould be consistent with the rate of living hypothesis but also with thehypothesis that life in the cold increases instantaneous mortality without anaccumulating role for energy expenditure. We therefore included a thirdgroup of 60 mice exposed to 10°C early in life (age 2-15 months) and to22°C afterwards (cold-warm, CW). Cold exposure increased overall dailyenergy expenditure, assessed by Doubly Labeled Water (DLW) by 46%. Nodifferences in life span were shown between WW and CC mice (median lifespan was circa 830 days in both groups). Also exposure to cold only early inlife did not affect life span compared to mice housed under warm tempera-tures throughout life (median life span was 751 days). In the cold, mice hadlower body mass and fat content compared to mice at 22°C. No differenceswere found in UCP levels in BAT, WAT or muscle. Lifetime energy potential(calculated over the 90 percentile life span) was 65717 kJ in CC mice,47941kJ in WW and 62550 kJ in CW, when expressed over the whole bodymetabolism. When expressed per gram body mass, lean dry weight or pergram metabolically highly active organ tissue (heart, liver, kidney, brain)these differences became more extreme. The study leads to a refutation ofthe rate of living theory.

Chapter8

INTRODUCTION

The “Rate of Living” theory, proposed by Pearl in 1928, states that an increased rateof energy metabolism increases the rate of ageing and shortens life (Pearl, 1928).The theory was derived from a study by Rubner in 1908 who showed that the life-time energy potential (mass-specific energy metabolism times maximum life span)was size independent in animals over a wide range of body masses (Rubner, 1908).The energy per gram body mass that an animal spends in its life time was similarfor a guinea pig and horse. In Rubner’s study humans were an exception. Humansspent more than predicted for their body mass, but even when including humansthe variation in life-time energy potential was still much smaller than the observedvariation in body mass. Rubner himself speculated that food intake rates per grammass would eventually explain longevity. Thus, the original argument for the rate ofliving theory was based on interspecific allometric comparison. In comparingspecies we look at the product of evolution. Natural selection has acted on bodymass, on energy metabolism and on life span in all species. It has been pointed outthat in interspecific allometry whole-body rates of energy turnover tend to increasewith a mass exponent of about 0.7 (and thus mass-specific exponent -0.3), whilelife span (and other time measures) increases with a mass exponent of about 0.3(Kozlowski and Weiner, 1996; Daan and Tinbergen, 1997). Their product (i.e., Life-time Energy Potential, LEP) is then proportional to mass to the power 1, and thusmass-specific LEP scales to the body mass to the power 0. Effects of energy expen-diture on life span are thus mass-independent. This interspecific proportionalityclearly does not prove that one causes the other.

The rate of living theory needs to be tested within species. Although there arestudies supporting the theory within species (mice; (Johnson et al., 1963),Drosophila; (Loeb and Northrop, 1917), Kestrels; (Daan et al., 1996), honey bees;(Wolf and Schmid-Hempel, 1989)), several studies in rodents have yielded eitherno relationship or even a positive relationship between energy turnover and lifespan (Holloszy et al., 1985; McCarter et al., 1985; Holloszy and Smith, 1986;Navarro et al., 2004; Speakman et al., 2004). Speakman (2002; 2005a) noted a num-ber of problems associated with such tests in the literature. Firstly, maximum lifespan is often used, but is not a valid measure of ageing, because it is measured in asingle individual of the population and depends strongly on the sample size used.Using the maximum survival of a group of animals (top 5-10%) would be a morereliable measure. Secondly, a single measurement of basal metabolic rate is usuallyused to calculate the total energy spent in a lifetime. Basal metabolic rate repre-sents the lowest metabolic rate of an animal in rest at thermo-neutral temperaturesand only comprises a small part (40%) of the total energy budget, depending onenvironmental factors. A better estimate of lifetime energy expenditure would betotal daily energy expenditure (DEE) under the normal housing conditions meas-ured at different ages throughout life. A third problem arises when energy expendi-ture is expressed per gram body mass. Not all components of body mass contribute

Chapter 8128

equally to metabolic rate. Greenberg (1999; 2000) has pointed out that in an idealtest of the rate of living idea one should therefore express energy turnover rates notwith respect to the whole body but with respect to the most relevant tissue, i.e., themetabolically most active organs (heart, liver, kidney, brain). A fourth concern isthat experimental manipulation of energy turnover rate always involves the manip-ulation of something else (temperature, workload, nutrition etc.). This may make ithard to conclusively distinguish between proper attribution of the modified lifespan to energy turnover per se rather than to the conditions imposed. The solutionto this problem may be found in letting the effects accumulate early in life, andstudy the natural mortality under standard conditions late in life. Since the rate ofliving theory implies a cumulative effect of energy turnover, we should expect theeffects on precipitated death also if the manipulation is restricted to the early partof life, leaving conditions late in life unchanged.

In the present study, we investigated the effects of elevated energy turnoverrates on life span, and we have heeded the warnings from these considerations. Wemanipulated energy metabolism by exposing animals to cold (10°C) throughout lifeor only early in life, and studied the relationship between energy metabolism andlife span. A large subgroup of animals (60 in each of the two experimental groupsand in the warm control group) was left undisturbed throughout life to generatereliable survival curves. In a smaller subgroup of the population body compositionand metabolic rate (resting metabolic rate and daily energy expenditure) weremeasured at four different ages to get good estimates of the life-time energy poten-tial (LEP). The mechanism linking metabolic rate and ageing may lie in theinevitable production of free radicals during oxygen consumption, the so-called freeradical theory of ageing, postulated half a century ago by Harman (Harman, 1956;Beckman and Ames, 1998). Free radicals (or reactive oxygen species, ROS) cancause damage to macromolecules, that could eventually result in cell death. Theamount of radicals that are produced during oxidative phosphorylation highlydepends on the speed of the process affected, i.e., by the supply of substrates, andthe amount of uncoupling that occurs. Uncoupling proteins (UCP) uncouple oxida-tive phosphorylation from ATP production and energy is subsequently lost as heat(Brand, 2000). Specifically in animals exposed to cold that need to produce extraheat to maintain body temperature, uncoupling might be beneficial because itwould enable the animal to spend more energy with minimal production of ROS.We therefore included in the analysis an assessment of UCP expression in severaltissues of mice at different ages.


Animals & housingMale C57BL/6JOlaHsd mice, 4 weeks of age, were obtained from Harlan Nether-lands BV, Horst. Animals were housed in groups of three at 22°C until the age of 2

Ageing in the cold 129

months when they were housed individually and at different ambient temperatures.At this time all mice were divided randomly over three experimental conditions.The first (control) group of mice was housed at 22°C throughout their lives (warm;WW), the second group, was housed at 10°C from the age of 2 months onwardsuntil their spontaneous death (cold; CC) and the third group was housed at 10°Cfrom 2 till 15 months of age and at 22°C from age 15 months onwards (cold-warm;CW). Animals were housed in Macrolon® type II cages (UNO Roestvaststaal,Zevenaar, NL) with Hemparade® (HempFlax B.V., Oude Pekela, Netherlands) asbedding material and EnviroDry® (BMI, Helmond, Netherlands) as nesting materi-al. Animals had ad libitum food (Standard rodent chow, RMH-B, Hopefarms BV,Woerden, NL) and water and were on a 12:12 light-dark cycle throughout theirlives. Body mass of all animals was measured once a month.

Each experimental group consisted of 100 mice which was randomly split intotwo subgroups of 60 and 40 mice. The 60 were left undisturbed throughout theirlives (except for cage cleaning and monthly measurements of body mass) and thetime of spontaneous death was noted to construct survival curves (life span sub-group). Of the other 40 animals we used 8 mice at each of four ages (3, 11, 19 and27 months) to measure food intake and metabolic rate and to collect tissues anddetermine body composition (test subgroup, see below). At the later ages whenmortality had occurred, sample sizes were smaller than 8 in some of the groups(see samples sizes given below tables 8.2 and 8.3).

Test subgroupFood intake and metabolic rate were measured in each age sample of the test sub-group at the ages 3, 11, 19 and 27 months. Food intake (g d-1) was measured over aperiod of 3 days. To express food intake in metabolizable energy intake we correctedfor changes that occur in wet food mass due to evaporation of water or uptake ofwater from the air. A tray with food was put in the room of which the weightchange was measured over the same three day period. In addition, a sample wastaken from the food that was dried to constant weight over 4 hour in an oven at103°C (ISO 6496-1983(E)), to enable comparison between food intakes at differentages.

Daily energy expenditure (kJ d-1) was measured for each mouse with the doublylabeled water technique. Before each trial, the mouse was weighed on a balance tothe nearest 0.1 g. Thereafter it was injected with about 0.1 g doubly labeled water(2H and 18O concentrations of the mixture 37.6% and 58.7%, respectively) allow-ing an equilibration period of 1 hour. The dose was quantified by weighing thesyringe before and after administration to the nearest 0.0001 g. After puncturingthe end of the tail, an “initial” blood sample was collected and stored in 3 glass cap-illary tubes each filled with about 15 µl blood. These capillaries were immediatelyflame-sealed with a propane torch. Thereafter the mouse was put back in its homecage. After 48 hours the animal was weighed again and a “final” blood sample wascollected as described before. Per sampling period, we collected blood samples of 4

Chapter 8130

mice which had not been injected with DLW, to assess the natural abundances of2H and 18O in the body water pools of the animals.

The determinations of the 2H/1H and 18O/16O isotope ratios of the blood sam-ples were performed at the Centre for Isotope Research employing the methodsdescribed in detail by Visser and Schekkerman (1999) using a SIRA 10 isotope ratiomass spectrometer. In brief, each capillary was microdistilled in a vacuum line. The18O/16O isotope ratios were measured in CO2 gas, which was allowed to equili-brate with the water sample for 48 h at 25ºC. The 2H/1H ratios were assessed fromH2 gas, which was produced after passing the water sample over a hot uraniumoven. With each batch of samples, we analysed a sample of the diluted dose, and atleast three internal laboratory water standards with different enrichments. Thesestandards were also stored in flame-sealed capillaries and were calibrated againstIAEA standards. All isotope analyses were run in triplicate. The rate of CO2 produc-tion (rCO2, moles d-1) for each animal was calculated with Speakman's (1997)equation:

rCO2 = N/2.078 * (ko - kd) - 0.0062 * N *kd

where N represents the size of the body water pool (moles), ko (1 d-1) and kd (1 d-

1) represent the fractional turnover rates of 18O and 2H, respectively, which werecalculated using the age-specific background concentrations, and the individual-specific initial and final 18O and 2H concentrations. The value for the amount ofbody water for each animal was obtained from the carcass analyses. Finally, the rateof CO2 production was converted to energy expenditure assuming a molar volumeof 22.4 l mol-1 and an energetic equivalent per l CO2 based on RQ measurements inour respirometry setup (on average 22 kJ l-1 CO2, (Gessaman and Nagy, 1988)).

Three days after the DLW measurements, the same mice were moved to our 8-channel open flow respirometry system and CO2 production (V

. CO2, l h-1) and O2

(V.

O2, l h-1) consumption were measured in concurrence with ambient temperatureand activity (passive infra-red, PIR) (described earlier by Oklejewizc et al. (Okleje-wicz et al., 1997)). Oxygen and carbon dioxide concentration of dried inlet and out-let air (drier: molecular sieve 3 Å, Merck) from each cage was measured with aparamagnetic oxygen analyzer (Servomex Xentra 4100) and carbon dioxide by aninfrared gas analyzer (Servomex 1440). The system recorded the differentials inoxygen and carbon dioxide between dried reference air and dried air from the meta-bolic cages. Flow rate of inlet air was measured with a mass-flow controller (Type5850 Brooks). Data were collected every 10 minutes and automatically stored on acomputer. All mice were measured for 24h at an ambient temperature of 22°C. Micethat were housed in the cold were then also measured for 24h at 10°C. This enabledus to determine whether changes had occurred in metabolic rate between thegroups (when measured at a similar temperature) and to determine the restingmetabolic rate that the mice experienced under the experimental conditions.Metabolic rate (MR, kJ h-1) was calculated using the following equation: MR=16.18 x V

. O2 + 5.02 x V

. CO2 (Romijn and Lokhorst, 1961; Gessaman and Nagy,


1988). Using this versatile equation we could accurately estimate the heat produc-tion of mice under various nutritional states. Resting metabolic rate (RMR, kJ d-1)was defined as the lowest value of heat production calculated as the running meanover half an hour.

After the respirometry measurements, animals were weighed and sacrificedusing CO2 gas followed by decapitation. Trunk blood was collected in tubes withanticoagulant (EDTA), centrifuged at 4°C for 15 min at 2600 g, plasma collectedand stored at -80°C for later hormone (corticosterone, leptin and adiponectin)analyses (analysed elsewhere; see Box 4.1). Heart, liver, kidneys, intestines, stom-ach, lung, brain, testes, hind limb muscles, brown adipose tissue, white adipose tis-sue, and skin were dissected out and each weighed to 0,0001 g. Subsamples ofheart, liver, kidney, hind limb muscle, brown adipose tissue (BAT) and white adi-pose tissue (WAT) were immediately frozen at –80°C. The gut fill of stomach andintestines was removed and the samples were weighed again. Tissues were storedat –20°C until the water and fat content was determined. Water content was deter-mined by drying for 4 hours to constant weight in an oven at 103°C (ISO protocol6496-1983(E). Fat was extracted using a soxhlet and petroleumether and sampleswere dried again for 2 hours at 103°C. Dry lean masses of the organs of which subsamples had been taken, were calculated using the left over pieces with the assump-tion that sub samples taken contained the same proportions of fat and water. In theestimation of the dry lean mass of the remainder of the carcass we assumed thatthe leg muscle taken out consisted of protein only and that BAT and WAT consistedof fat only.

mRNA isolation and UCP expressionmRNA extraction, hybridisation and detection was performed as described byTrayhurn et al. (Trayhurn et al., 1994). In short, mRNA was extracted from BAT,WAT and hind limb muscle and subjected to agarose gel electrophoresis. The RNAwas transferred to a charged nylon membrane by Northern blotting and fixed withUV light. Prehybridisation was performed at 42°C in prehybridisation buffer (fordetails see (Trayhurn et al., 1994)). Hybridisation was at 42°C overnight in thebuffer together with an oligonucleotide (25–50 ng ml-1). Membranes were thenwashed, incubated with blocking buffer containing a poly-clonal antibody againstDIG, washed again and subsequently incubated with the chemiluminescence sub-strate CDP-star (Roche, 25 mM stock). Membranes were exposed to film for 2–5min at 37°C immediately after incubation with the chemiluminescence substrate.Probes, synthesised with DIG ligand, were: 5’ CGG ACT TTG GCG GTG TCC AGCGGG AAG GTG AT for UCP-1 in BAT, 5’ GTG GCA AGG GAG GTC ATC TGT CATGAG GTT GG for UCP-2 in WAT, and 5’CCC TGA CTC CTT CCT CCC TGG CGATGG TTC TG for UCP-3 in muscle. Blots were reprobed for 18S RNA (5’ CGC CTGCTG CCT TCC TTG GAT GTG GTA GCC G) to adjust for variations in RNA load-ing. Quantification of UCP mRNA levels was determined by creating density his-tograms in ImageJ and calculating the area below the curve. These values were

Chapter 8132

expressed as ratio’s relative to 18S data per gel. To enable comparisons betweengels we blotted a reference sample on each gel and normalized data to that.

Data analysisGeneral linear models were applied in SPSS for Windows (version 14.0). We testedfor effects of group, age and interactions between group and age in cold and warmmice at the all ages measured using a two-way AN(C)OVA with group, age andgroupxage as fixed factors. Subsequently differences between the three experimen-tal groups at 19 and 27 months of age were analysed using one-way AN(C)OVAmodels with group as fixed factor. Survival curves were analyzed using theLifeTables option in the survival analysis in SPSS for Window was used to create lifetables. Significance level was set at p≤ 0.05 and all tests were two-tailed.

RESULTS

SurvivalThe survival curves of all groups are shown in Figure 8.1A, and a summary of themain descriptors of age at death is shown in Table 8.1. We calculated finite mortali-ty rates (FMR) over intervals of 100 days using the following formula: FMR = 1-Ne/Nb (see (Krebs, 1994)), where Ne is the number of animals left over at the endof the interval out of Nb, the number at the start of the interval. The values areshown in Figure 8.1B.

Both mean and median age at death were virtually identical for mice exposed to10°C and 22°C throughout their life (CC and WW in Table 8.1). In mice that wereexposed to cold only early in life, median age at death was 82 days less than themedian in cold and warm mice (751 vs. circa 833 days; Table 8.1). This appears tobe due to increased mortality in the CW group between 600 and 800 days of age(i.e., 150-250 days after the switch in ambient temperature, see Figure 8.1B).Maximal survival was similar in all groups. We tested statistically for differencesbetween groups using the Wilcoxon (Gehan) test in life tables of the survival analy-sis of SPSS for windows (version 14.0). The Wilcoxon (Gehan) test compares sur-vival distributions between groups. Overall and pair-wise comparisons showed nosignificant differences between any of the groups.

Development of body massFigure 8.2 shows the development of body mass for the three groups. Initially CCmice had a similar growth rate as WW mice. After approximately 300 days CC micestopped growing, whereas WW mice kept increasing in body mass up till about 500days of age. When the temperature was switched from 10 to 22°C, CW mice imme-diately increased their body mass and reached a plateau at a level intermediatebetween cold and warm mice. For statistical analyses average body mass duringthree intervals was calculated (0–250 days, 251–500 days, 501–750 days) and we


tested for differences between groups at different ages using one-way ANOVA. Atall ages the warm animals had significantly higher body mass compared to the CWand cold mice. In addition, in the oldest age group (501-750 days) the CW micehad significantly higher body mass compared to the cold mice (p<0.05).

Chapter 8134

B

finite

mor

talit

y ra

te

0.0

0.6

0.2

0.8

0.4

0 200 400age (d)

1.0

600 800 1000

% a

live

0

60

20

40

100 A

80

1200

Figure 8.1. Effect of cold-exposure on mice survival (a) and finite mortality rates (b; see text forformula). White circles represent mice housed at 22°C (WW), black circles represent micehoused at 10°C (CC), and grey circles are mice housed at 10°C early in life (up to 15 months) andat 22°C later on (CW).

Table 8.1. Survival data.

Group n Mean (d) sem Median (d) sem 90% (d) Max (d)

WW 57 801 25 832 21 1002 1071CC 57 798 21 834 19 939 1053CW 58 768 27 751 19 1035 1121

Mean age at death (±sem), median life span (50%), 90 percentile and maximum age at death in mice exposed tovarious ambient temperatures is shown in days.

Body compositionAt four ages a subgroup of mice from each of the three treatment groups was sacri-ficed and body composition determined (see Table 8.2). It turned out that the signif-icant reduction in body mass of mice exposed to cold compared to warm mice wasdue to reduced fat mass. Fat free mass and dry lean mass were even significantlyincreased in cold mice compared to warm mice (see Table 8.3 for statistical analysis).

Several changes in organ mass occurred in mice exposed to cold relative to WWmice. In tests of significance between cold and warm mice we always included bodymass as a covariate. Thereby significance only refers to changes in the proportion oforgans relative to body mass. Cold exposed mice had significantly increased relativeheart and kidney weights compared to warm mice, while relative skin mass was sig-nificantly decreased in cold mice. Relative liver, stomach, intestines and lung mass-es did not differ significantly between cold and warm mice.

In the models applied we also evaluated effects of age on relative organ masses.All organ masses, except for the brain showed a significant increase with age. Forheart and kidney mass a significant interaction between group and age was alsofound, indicating that heart and kidney mass increased more rapidly with age incold mice than in warm mice (see table 8.3).

To see whether body composition of mice that were exposed to cold early in lifeand housed at normal conditions later was more similar to cold or warm mice one-way ANOVA with a factor group was applied on data on body composition from 19and 27 month old animals. In most cases CW mice showed an intermediate pheno-type to warm and cold mice. Significant effects of group were found for relative drylean, fat, heart, kidney and skin mass (p<0.01). In these cases organ masses in theCW mice were similar to those of warm mice and differed from those in cold mice.


body

mas

s (g

)

0

20

30

40

0 200 400age (d)

10

600

50

800 1000

Figure 8.2. Development of body mass in mice exposed to warm (white circles) or cold (blackcircles) ambient temperature throughout life, and in mice exposed to cold only early in life and atwarm temperatures later on (grey circles).

Chapter 8136

Tabl

e 8.

2.Bo

dy c

ompo

siti

on o

f mic

e ho

used

at d

iffer

ent a

mbi

ent t

empe

ratu

res

at v

ario

us a

ges.

Age

(m

onth

s)3

1119

27

Gro

upW

WC

CW

WC

CW

WC

CC

WW

WC

CC

Wn

816

816

77

66

67

Body

mas

s (g

)27

.4±

0.4

28.1

±0.

337

.4±

1.9

32.5

±0.

535

.8±

0.5

32.5

±0.

837

.3±

2.3

34.7

±3.

229

.7±

0.9

30.2

±1.

3Fa

t fre

e m

ass

(g)

22.6

±0.

323

.6±

0.2

24.2

±0.

524

.6±

0.3

25.5

±0.

627

.0±

0.6

26.7

±0.

327

.1±

0.9

26.5

±1.

125

.8±

0.6

Dry

lean

mas

s (g

)6.

1±0.

16.

4±0.

17.

0±0.

16.

8±0.

17.

3±0.

17.

2±0.

17.

5±0.

36.

9±0.

36.

6±0.

26.

4±0.

2Fa

t mas

s (g

)4.

8±0.

54.

5±0.

213

.2±

2.0

8.0±

0.6

10.3

±0.

95.

5±0.

610

.5±

2.1

7.6±

2.8

3.2±

0.6

4.4±

1.2

Hea

rt (

g)0.

16±

0.01

0.18

±0.

010.

18±

0.01

0.22

±0.

010.

19±

0.01

0.28

±0.

030.

21±

0.01

0.24

±0.

010.

28±

0.01

0.23

±0.

01Li

ver (

g)1.

46±

0.20

1.41

±0.

031.

77±

0.11

1.63

±0.

031.

73±

0.08

1.74

±0.

071.

78±

0.13

1.84

±0.

081.

74±

0.05

1.62

±0.

13K

idne

y (g

)0.

42±

0.01

0.48

±0.

010.

47±

0.02

0.55

±0.

010.

52±

0.01

0.59

±0.

010.

57±

0.01

0.55

±0.

030.

66±

0.03

0.58

±0.

01Br

ain

(g)

0.47

±0.

000.

48±

0.01

0.48

±0.

010.

46±

0.01

0.48

±0.

010.

46±

0.01

0.49

±0.

010.

48±

0.01

0.48

±0.

000.

48±

0.01

Stom

ach

(g)

0.16

±0.

010.

16±

0.01

0.19

±0.

010.

18±

0.01

0.18

±0.

010.

19±

0.01

0.20

±0.

010.

24±

0.02

0.23

±0.

020.

20±

0.01

Inte

stin

es (

g)1.

51±

0.06

1.62

±0.

051.

89±

0.16

1.77

±0.

041.

84±

0.09

1.87

±0.

111.

99±

0.10

2.16

±0.

072.

39±

0.34

2.16

±0.

07Lu

ng (

g)0.

18±

0.01

0.20

±0.

010.

21±

0.01

0.20

±0.

010.

21±

0.02

0.23

±0.

010.

23±

0.02

0.25

±0.

020.

25±

0.01

0.22

±0.

01Sk

in (

g)3.

7±0.

23.

7±0.

15.

5±0.

54.

1±0.

15.

2±0.

23.

9±0.

15.

2±0.

44.

9±0.

93.

1±0.

23.

6±0.

4Re

st (

g)17

.0±

0.3

17.0

±0.

124

.4±

1.6

20.7

±0.

422

.9±

0.3

20.1

±0.

624

.3±

1.7

21.1

±2.

118

.0±

0.5

18.4

±1.

1

Mea

n (S

EM)

wei

ghts

of v

ario

us c

ompo

nent

s of

the

body

are

sho

wn

for m

ice

hous

ed a

t 22°

C (

WW

), a

t 10°

C (

CC

) an

d m

ice

hous

ed a

t 10°

C u

ntil

the

age

of 1

5 m

onth

s an

dat

22°

C th

erea

fter (

CW

). M

easu

rem

ents

wer

e do

ne a

t fou

r age

s: 3

, 11,

19

and

27 m

onth

s. S

ampl

e si

ze (

n) fo

r eac

h gr

oup

is s

how

n.

Food intake and energy expenditureFigure 8.3 shows food intake, resting metabolic rate and daily energy expenditurefor the different experimental groups and Table 8.4 gives a summary of the statisti-cal analyses done comparing warm and cold mice. Food intake was significantlyincreased in mice exposed to cold compared to warm mice and increased with age.At 3 months of age no differences in food intake were shown, but food intake wasstrongly increased in cold mice at 11, 19 and 26 months of age compared to warmmice.

Resting metabolic rate (RMR) of cold exposed mice was measured at two ambi-ent temperatures; at the experimental temperature (10°C, RMREXP) and at the tem-perature the warm group was housed in (22°C, RMR22°C), to enable comparison ofRMR at a similar temperature. In the warm group resting metabolic rate was onlymeasured at the control temperature (22°C). Comparing measurements at 22°C(RMR22°C) in both groups, we found no significant differences in RMR between CCand WW mice (Table 8.4, data not shown). Age did affect RMR22°C and was slightlyincreased at 11 months of age compared to the other ages. Under experimental con-ditions, RMREXP (10°C for CC mice and 22°C for WW mice) was increased byapproximately 60% in CC mice compared to WW mice (see Figure 8.3). Age affect-


Table 8.3. Statistics on body composition: testing for differences between cold and warm mice.

Group Age Group x Age Covariate

Trait d.f. F p d.f. F p F p p

Body mass (g) 1,66 15.4 <0.001 3,66 21.1 <0.001 3.4 0.024 noneFat free mass (g) 1,65 7.2 0.009 3,65 16.9 <0.001 1.0 - BM 0.006Dry lean mass (g) 1,65 12.1 0.001 3,65 8.7 <0.001 0.2 - BM <0.001Fat mass (g) 1,65 7.0 0.010 3,65 17.6 <0.001 1.0 - BM <0.001

Heart (g) 1,65 46.9 <0.001 3,65 21.2 <0.001 3.7 0.016 BM -Liver (g) 1,65 0.2 - 3,65 2.2 - 0.5 - BM 0.001Kidney (g) 1,65 104.9 <0.001 3,65 36.9 <0.001 4.6 0.005 BM <0.001Brain (g) 1,65 1.6 - 3,65 0.9 - 1.5 - BM -Stomach (g) 1,65 0.3 - 3,65 20.8 <0.001 0.6 - BM -Intestine (g) 1,65 0.4 - 3,65 10.0 <0.001 0.8 - BM -Lung (g) 1,65 0.8 - 3,65 9.6 <0.001 1.2 - BM -Skin (g) 1,65 12.2 0.001 3,66 11.1 <0.001 1.3 - BM <0.001Rest (g) 1,65 1.8 - 3,65 6.3 <0.001 1.0 - BM <0.001

Two-way ANCOVA was applied to data on body composition to test for differences between cold (CC) and warm(WW) mice and look for effects of age. Group, age and group x age were added to the models as fixed factors andbody mass (BM) as a covariate (except when testing for differences in body mass). F and p-values are shown for allfixed factors and p-values for the covariate are shown. Absolute values were used in the analysis. Total sample sizewas 74, with 8, 8, 7 and 6 mice at 3, 11, 19 and 27 months respectively in warm mice, 16, 16, 7 and 6 in cold mice.Significant p-values (p≤ 0.05) are shown in bold. d.f.=degrees of freedom. See text for analysis of differencesbetween the three groups (WW, CC and CW) at 18 and 26 months of age (One-way ANOVA).

ed RMREXP in both groups, but RMR decreased slightly faster with age in the coldmice (as shown by a significant interaction effect; see Table 8.4). Daily energyexpenditure (DEE) was measured in the home cage of the animals (CC mice at10°C and WW mice at 22°C) and was sharply increased in CC mice compared toWW mice, and decreased significantly with age in both groups.

With one-way ANOVA we tested for differences between all three groups at ages19 and 27. There was a significant effect of group on food intake, DEE and RMREXP

(See Figure 8.3, p<0.001). Animals in the CW group differed significantly from CCmice and had similar levels of energy expenditure as mice in the WW group.

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Uncoupling proteins (UCP)mRNA expression of UCP1, UCP2 and UCP3 was measured in BAT, WAT and hind-leg muscle respectively in 3 and 27 months old mice (see Figure 8.4). In all threetissues and at both ages, UCP expression was on average higher CC mice comparedto WW and CW mice, but not significantly so (Figure 8.4).

Life-time energy potentialTraditionally, the life-time energy potential (LEP, kJ) has been estimated on thebasis of measurements of resting metabolic rate and maximum life-span. Life is,however, not passed solely in the resting state, and the life-time energy potentialshould equal DEE times life span. RMR might be used as an estimator of DEE, butonly if DEE and RMR have a fixed ratio. In most animals that is obviously not thecase. In the mice in this study, RMR was on average 92% of DEE in WW, and 86 %in CC mice. These differences, although small, call for the use of DEE to calculateLEP as a more accurate estimate of the life-time energy potential. Maximum lifespan represents only a single event in the colony and is therefore subject to largevariance and highly dependent of the sample size that is used. Using the 90% mor-tality yields a more reliable measure of life span (see also (Speakman et al., 2002)). Taking these considerations into account we estimated LEP based on measurementson DEE (with DLW) and the age at which 90% of the animals had died. To incorpo-rate changes that occur in DEE with age, we calculated the average DEE per groupbased on measurements at 4 ages throughout life (3, 11, 19 and 27 months, seeTable 8.5). On the basis of the rate of living theory we expect CC mice to have theshortest life span, followed by CW mice and WW mice. We focus here on the com-


Table 8.4. Two-way ANCOVA on food intake and metabolic rate: testing for differences betweencold and warm mice.

Group Age Group x Age Covariate

Trait d.f. F p d.f. F p F p p

Food intake (g d-1) 1,64 16.9 <0.001 3,64 8.0 <0.001 4.0 0.011 BM -RMR22°C (kJ d-1) 1,65 0.03 - 3,65 11.9 <0.001 1.5 - BM 0.002RMREXP (kJ d-1) 1,65 132 <0.001 3,65 11.3 <0.001 3.8 0.015 BM 0.027DEE (kJ d-1) 1,66 237 <0.001 3,66 14.6 <0.001 1.9 - BM -

Two-way ANCOVA was applied to data on metabolic rates to test for differences between cold (CC) and warm(WW) mice and look for effects of age. Group, age and group x age were added to the models as fixed factors andbody mass (BM) as a covariate. F and p-values are shown for all fixed factors and p-values for the covariate areshown. Absolute values were used in the analysis. Total sample size was 75, with 8, 8, 7 and 7 mice in the warmgroup and 16, 16, 7 and 6 in cold group at 3, 11, 19 and 27 months respectively. One mice (WW, age 27) died duringthe respirometry measurements and total samples size is 74 here. For food intake sample size is 73 because data for2 mice was missing. RMR22°C compares RMR measured at 22°C in both the WW and CC group. RMREXP comparesvalues measured at experimental conditions; 22°C for WW mice and 10°C for CC mice. Significant p-values(p≤ 0.05) are shown in bold. d.f.=degrees of freedom. See text for analysis of differences between the three groups(WW, CC and CW) at 18 and 26 months of age (One-way ANOVA).

parison between CC and WW mice. CC mice had a slightly shorter life span thanWW mice. LEP on a whole-animal basis was 47941 in WW and 65717 kJ in CCmice respectively. LEP on a whole-animal basis was thus considerably higher in CCthan WW mice.

Body mass is known to affect metabolic rate and to be able to compare LEPbetween animals, LEP should be expressed per gram body mass. LEP per gram bodymass (LEPBM) was 1416 and 2138 kJ g-1 in WW and CC mice respectively. Whencorrected for body mass CC mice thus still spent about 50% more energy in theirlife-time.

Total body mass contains water and fat that are not metabolically active, and drylean mass may be a better representative of the metabolically active tissue in thebody. We calculated LEP per gram dry lean mass; LEPDL was 7029 and 9748 kJ g-1

respectively. Dry lean mass still includes matter such as skeleton and skin that ismetabolically rather inactive. Greenberg (1999) and Lynn (1992) have thereforeproposed that we should even go one step further and express LEP relative to the

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mass of the most metabolically active organs: the heart, liver, kidney and brain. Wecalculated the sum of the dry lean weight of the heart, liver, kidney and brain(Organ mass, OM; see Table 8.5), and calculated the LEP per gram of organ mass(LEPOW): 78467 and 101119 kJ g-1 in WW and CC, respectively. Table 8.5 providesa summary of these results.

The problem with these comparisons is obviously that we have only a singlemeasure for life span in each group. We can thus not readily test for differencesbetween groups in average individual LEP. We do however have individual valuesfor total and mass-specific (per dry lean and wet mass) DEE in each group. We cantest the group averages against each other, both before and after multiplying allindividual data with the group’s life span. The basic data as well as the results oftesting are supplied in Table 8.5. We applied two-way ANOVA with a factor group,age and the interaction group x age on whole-body and mass-specific DEE. After-wards we estimated LEP for each individual, by multiplying the mass-specific valueof DEE with the maximum life span (90%) of its group and ran the same statisticaltest. CC mice had significantly (ANOVA, F1,67=192.8, p<0.001) increased DEEand LEP and this increase in energy expenditure remained apparent when valueswere expressed relative to body mass, dry lean mass or organ mass.


Table 8.5. Life-time energy potential.

WW CC CV (%) Sign. CW

Body mass (g) 33.8 30.7 32.0Total dry lean mass (g) 6.8 6.7 6.8Organ mass (dry lean, g) 0.61 0.65 0.63DEE (kJ d-1) 47.8 70.0 0.001 60.4DEEBM (kJ g-1 d-1) 1.4 2.3 0.001 1.9DEEDL (kJ g-1 d-1) 7.0 10.4 0.001 8.9DEEOM (kJ g-1 d-1) 78 108 0.001 97Max. Life span (90%, days) 1002 939 1035LEP (kJ) 47941 65717 22 0.001 62550LEPBM (kJ g-1) 1416 2138 29 0.001 1953LEPDL (kJ g-1) 7029 9748 23 0.001 9224LEPOM (kJ g-1) 78467 101119 21 0.001 99928

Life-time energy potential (LEP; kJ Live-1) is the product of energy expenditure and life span and was calculatedusing average daily energy expenditure (DEE, kJ d-1) measured at 4 ages (2, 10, 18 and 26 months) throughout lifein the test group, and maximum life span (90 percentile) measured in the life span animals. In addition, LEP (kJ g-1

Live-1) was corrected for various measures of body composition measured at the same ages: BM; body mass, DL; drylean mass, OM; organ mass (sum of dry lean heart, liver, kidney and brain mass). CV represents the coefficient ofvariation calculated over the two groups (s.d. divided by mean x100%). Sign. shows the p-values for the two-wayANOVA performed to look at differences between the CC and WW mice (see text for detailed description). For com-pleteness values for CW mice are also shown in the last column.

DISCUSSION

Despite a 50% increase in daily energy expenditure throughout life, no difference insurvival was found between cold and warm mice. These results cast doubt on therate of living theory that states that increased mass-specific metabolic rate reduceslife span (Rubner, 1908; Pearl, 1928). However, a more precise formulation of thetheory would have to take changes in body mass and composition into account. InTable 8.5 we have calculated the life-time energy potential (LEP) for all groups,based on measurements of daily energy expenditure and the 90 percentile life span.Rubner (1908) found that LEPBW (calculated using measures of food intake andmaximum LSP, 100%) was size invariant for a variety of species. In our inter-specif-ic comparison, LEPBW clearly increased due to life in the cold without shorteninglife. Expressing metabolic rate relative to dry lean mass, or metabolic (organ) massdid not change this conclusion.

Previous studies investigating the effects of cold-exposure on life span in ratshave found conflicting results. Kibler et al. (Kibler and Johnson, 1961; Johnson etal., 1963; Kibler et al., 1963) have shown that rats kept at 9°C continuously hadshorter life span than control rats (28°C), and these studies have been cited as pro-viding experimental support for the rate of living theory. Holloszy and Smith (1986)argued that the continuous cold exposure in Johnson study was a chronic stressorand thereby will have deleterious effects on health and longevity (Paré, 1965),mediated by chronic elevation of stress hormones (e.g., corticosteroids), regardlessof whether or not there was an increase in energy expenditure. To minimize thenonspecific effects of chronic stress and maximize the effects of increased energyexpenditure they therefore immersed rats (to the upper border of their scapulae) inwater kept at 23°C for 4 h d-1 for 5 days per week (Holloszy and Smith, 1986). Inour study we applied a similar protocol to that of Kibler and found no differences incorticosterone level (unpublished results, see Chapter 10) or life span betweencold-exposed or control mice. These results indicate that cold-exposure was not achronic stressor; at least it did not result in increased levels of corticostrone, andthe effects of cold exposure on life span were similar to those found in the cold-immersed rats of Holloszy. They found cold-immersed rats had increased foodintake and metabolic rate compared to controls housed at 20°C, but did not differ inlife span. These findings join a growing body of evidence suggesting that (in mam-mals) life-time energy expenditure per se does not underlie senescence (Holloszyand Smith, 1986; Holloszy and Smith, 1987; Speakman et al., 2003b; Navarro et al.,2004; Speakman et al., 2004) and refute the rate of living theory.

Even thought there is no direct relationship between metabolic rate and lifespan, metabolic rate is known to play a important role in ageing via the productionof reactive oxygen species (ROS; (Harman, 1956; Beckman and Ames, 1998)). ROSare produced in mitochondria during oxidative phosphorylation and can cause dam-age to lipids, DNA and proteins, which may eventually result in cell death. If ROSare increased in the cold, how did the mice in the cold protect themselves against

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the increased oxidative stress they faced due to their high metabolic rates? Severaldefense systems can reduce the amount of ROS or reduce the damage they cause:e.g., uncoupling oxidative phosphorylation from ATP production, enhancing antiox-idant enzyme activity, and increasing protein turnover to remove damaged proteinsfrom the circulations. Uncoupling oxidative phosphorylation from ATP productionby enabling protons to leave the intracellular space via uncoupling proteins reducesthe production of ROS (Brand, 2000). This process might in principle enable miceto combine high metabolic rates with long life spans, as has indeed been shown inmice (Speakman et al., 2004). Specifically in cold-exposed animals it would be bene-ficial to uncouple oxidative phosphorylation, because when uncoupling occurs ener-gy is dissipated as heat to become available for thermoregulatory purposes (Brand,2000). We measured UCP activity in BAT, WAT and muscle at two ages and found aslight, but not significant increase in UCP expression in cold mice. We can thus notrule out the possibility that differences in UCP expression have been present atintermediate ages, but we have no proof to support the idea that animals in the coldincreased uncoupling. Antioxidant enzymes such as superoxide dismutase, catalaseand glutathion peroxidase can scavenge ROS before they cause damage to macro-molecules. We measured activity of several antioxidant enzymes in heart and liverof these mice at various ages (Chapter 9). No differences in SOD or GPx activity inliver and heart were found between warm and cold mice, except for SOD activity inliver of 19 months old animals, which was lower in cold-exposed mice. These find-ings do not support the idea that animals in the cold up-regulate their antioxidantenzyme activity to reduce ROS. With a similar antioxidant capacity the protectionper kJ was actually lower in the cold mice. We also found no evidence for anincrease in protein turnover in these animals (Chapter 9). At present we thus donot know how the mice in the cold dealt with the increased oxidative stress toenhance survival. Some studies (Holloszy and Smith, 1986) have shown a reductionin the occurrence of tumors in mice exposed to cold, which might extend their lifespan. Cold-exposure may thus have positive effects on other aspects that influencesurvival and thereby counteract the negative effects of ROS on survival. If that istrue, the metabolic rate itself is apparently not a good predictor of life span.

In cold environments homeothermic animals face high metabolic demands tomaintain body temperature (+50% in mice exposed to 10°C relative to mice at22°C). Based on simple physical principles we might expect an adaptive increase inbody mass in cold environments. In contrast to this expectation, the mice exposedto cold (10°C) had reduced growth and adult body mass relative to control mice(22°C). Body mass was strongly correlated with resting metabolic rate and reducingbody mass in that way helped to conserve energy (see (Deerenberg et al., 1998;Speakman, 2005a) and Chapter 4). Fat mass and skin mass were also reduced incold-exposed mice, which may have compromised insulation and led to further heatloss. Barnett et al (1965) showed decreased body mass, fat mass and skin mass inmice bred at –3°C, but this did not result in reduced insulation, due to thicker fur.We did not assess insulation but we did observe larger nests and less activity in the


cold, which may have reduced heat loss (see also (Barnett, 1965; Rowland, 1984)).In wild rodents a similar response to cold environments is known (Klaus et al.,1988), but an increase in body (and fat) mass in winter was shown in field voles(Krol et al., 2005).

The higher metabolic rate and food intake at low temperatures imposed an extraburden on some organs, while others were unaffected. Heart and kidney mass wereincreased relative to body mass in the cold, but no changes in liver, stomach orintestine mass occurred despite the strong increase in food intake in cold mice.Mice bred in the cold are also smaller, have less fat, lower skin mass and increasedheart and kidney mass compared to mice bred at high temperature (25). The differ-ences in body composition we found between cold and warm mice were maintainedwith age.

In conclusion, mice exposed to cold ambient temperatures responded with anumber of physiological adaptations; a decrease in body mass, steep increase infood intake and metabolic rate and changes in body composition (reduction in fatmass). Daily energy expenditure was increased by 46% (mass-specific; 64%) incold-exposed mice, but this did not result in an altered life span compared to micehoused at normal ambient temperatures. Also, mice that were housed in the coldonly early in life and that had an intermediate energy expenditure had a similar lifespan to the other groups. Lifetime Energy Potential (LEP) was calculated relative tobody mass, dry lean mass and metabolic organ mass (heart, liver, kidney and liver)and in all ways expressed LEP was about 40% greater in mice housed under coldconditions relative to mice housed under warm conditions. This is in disagreementwith predictions from the rate of living theory. This study thus joins a growing bodyof evidence suggesting that (within species) energy expenditure per se is not a goodpredictor of the ageing processes that ultimately determine life span.

AcknowledgementsWe thank Saskia Helder for taking excellent care of the animals, and Gerard Overkamp andJacqueline Duncan for technical assistance and help with experimental procedures. BertheVerstappen performed the isotope analyses. We thank John Speakman for enabling us to dothe UCP measurements. We also thank Peter Meerlo, Kristin Schubert and Jan-AlbertManenschijn for their practical help at various stages of the project. L.M. V. was recipient ofa Marie Curie Training Site Award to the Rowett institute, Aberdeen. S.D. is supported byEUCLOCK (EC 6th framework).

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Protein synthesis and antioxidant capacity inageing mice: effects of life-long cold exposure

Lobke M. Vaanholt, John R. Speakman, Gerald Lobley , G. Henk Visser

AbstractSubstantial evidence supports a key role for reactive oxygen species (ROS)in causing cumulative damage to cellular macromolecules, thereby con-tributing to senescence. Antioxidants can scavenge ROS while proteinturnover removes and replaces oxidized proteins. How these defence sys-tems vary with age and with metabolic demand is not well known. In thepresent study 2H5-phenylalanine was injected into young (3 months) andold (27 months) mice chronically exposed to cold to explore effects of coldexposure on age-related changes in liver and muscle protein synthesis. Inaddition, effects of cold exposure (10∞C) on antioxidant enzyme activitieswere investigated in two metabolically active tissues in mice at various ages(3-27 months). Cold exposure did not affect fractional synthesis rates (FSR)in liver or muscle. FSR rates did decrease with age in both tissues, and inliver this occurred more rapidly in cold-exposed animals than in controls.Antioxidant enzyme activity (SOD and GPx) was also affected by age. SODactivity peaked in 11 month old mice followed by a decline, while GPx activi-ty slowly declined with age. SOD activity in heart was unaffected by cold. Inliver, SOD activity was decreased in cold-exposed animals, but GPx activitywas not. No relationship between energy expenditure and enzyme activitywas found. Cold exposure increased metabolic rate by approximately 40%with no concurrent increase in antioxidant enzyme activity. Cold-exposedanimals did not up-regulate either protein synthesis or antioxidant enzymeactivities to provide protection against extra production of ROS.

Chapter9

INTRODUCTION

Reactive oxygen species (ROS) are produced as by-products of aerobic metabolismin mitochondria. They can cause damage to macromolecules (lipids, DNA and pro-teins) (Beckman and Ames, 1998; Davies et al., 1982; Mecocci et al., 1999; Tyler,1975), and thereby contribute to senescence and several degenerative diseases asso-ciated with ageing (e.g. cardiovascular disorders, Parkinson disease) (McEwen et al.,2005; Melov et al., 1999; Wallace, 2005). An elaborate defence system consisting ofendogenous antioxidant enzymes, such as catalase (CAT), superoxide dismutase(SOD), glutathione peroxidase (GPx), and numerous non-enzymatic antioxidant(vitamins, flavenoids), exist that scavenge ROS to prevent deleterious effects(Beckman and Ames, 1998). Antioxidant enzymes cannot scavenge all the ROS pro-duced; a small part escapes conversion and can damage proteins. This will impacton essential functions within the cell, such as maintenance of the structural archi-tecture and enzyme activity. Indeed, the accumulation of oxidized proteins with agehas been reported for many experimental ageing models (Stadtman, 2004). Suchaccumulation could be due either to increased generation of reactive oxygen speciesor to reduced elimination of oxidized proteins (Shringarpure and Davies, 2002;Stadtman, 2004).

Protein turnover, the composite of protein degradation (removal of inactive ordamaged proteins) and protein synthesis (replacement with new, undamaged pro-teins) plays a potentially vital role in the ageing process (Rattan, 1996; Ryazanovand Nefsky, 2002; Sohal, 2002). Maintaining high rates of protein synthesis in oldage could diminish the accumulation of damaged proteins and increase cell survival.Indeed, protein turnover is increased in calorically-restricted animals (Lambert andMerry, 2000; Lewis et al., 1985), a nutritional condition that increases life span inseveral species, including mice (Weindruch et al., 1986), and rats (McCay et al.,1935), for recent reviews see (Masoro, 2005; Merry, 2005). Other factors can alsoinfluence protein synthesis and protein degradation, including cold exposure (rats:(McAllister et al., 2000; Samuels et al., 1996); calves: (Scott et al., 1993), chickens:(Yunianto et al., 1997)) and age (mice: (Blazejowski and Webster, 1983); rats:(Lewis et al., 1985) and humans: (Short et al., 2004; Young et al., 1975); for reviewssee: (Dorrens and Rennie, 2003; Rattan, 1996; Van Remmen et al., 1995; Ward,2000)).

Protein turnover accounts for 20-35% of the resting metabolic rate at ther-moneutrality (Lobley, 1990; Newsholme, 1987). It has been hypothesised that pro-tein turnover would increase in cold-exposed animals to cope with the increaseddemands on heat production (McAllister et al., 2000). Indeed, in hearts of ratsexposed to cold for 21d an increase in protein synthesis rates was shown(McAllister et al., 2000), although contradictory results have also been reported(Lindsay et al., 1988; Scott et al., 1993; Yunianto et al., 1997). Cold-exposureincreases metabolic rate and ROS production and this may counteract any beneficialeffect of increased protein breakdown. Alternatively, animals may adapt to the extra

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ROS production by increased activity of antioxidant enzymes and, in concert withelevated protein breakdown, protect cells from problems arising from the presenceof oxidized proteins. Such protection may vary with age, however, due to alteredresponsiveness in protein turnover and antioxidant systems.

The current study addresses some of these questions. First, the impact of cold-exposure on age-related changes in protein synthesis and the antioxidant enzymesystem was monitored in C57BL mice exposed to 10°C and 22°C throughout life.This included comparisons between liver and muscle (protein synthesis) and liverand heart (anti-oxidant activity). Second, the relationship between metabolism andantioxidant enzyme activity between individual animals was examined.


Animals & housingMale C57bl6J mice were obtained from Harlan Nederland B.V. at the age of fourweeks and individually housed in standard cages (15x30x15 cm, Macrolon type II,UNO Roestvaststaal BV, Zevernaar, NL) with standard bedding (Hemparade®,HempFlax, Oude Pekela, NL; and Envirodry®, BMI, Helmond, NL). Food (Standardrodent chow RMB-H (2181), HopeFarms, Woerden, NL) and water were providedad libitum. Animals were divided over two rooms and housed at an ambient temper-ature of 22°C (Control group; WARM) or 10°C (Cold group; COLD). Animals weretested and sacrificed at four ages: 3, 11, 19, and 27 months.

Tissue collectionAt each sacrifice age, five to eight mice per group were lightly anaesthesized withCO2 and then killed by decapitation. Samples of liver and heart were quicklyremoved and immediately frozen in liquid nitrogen and stored at –80°C for antioxi-dant measurements.

Protein synthesis was assessed only in 3 and 27 month old animals. The latterwere the same animals used for the antioxidant measurements, but for the 3 monthold group different animals were used (n=8 per group). Food intake (g d-1) andbody mass was measured for two consecutive days prior to the sampling of tissuesfor protein synthesis measurements. Protein synthesis was measured using thelarge-dose method as described by Garlick et al. (1980). Mice were given an intra-peritoneal injection of 150 mM 2H5-phenylalanine (1,5 ml per 100 g animal). After15 minutes the mice were euthanized using CO2, followed by decapitation. Trunkblood was collected in pre-chilled tubes with heparin as anti-coagulant. Blood sam-ples were centrifuged at 2600 g at 4°C for 15 min, and the plasma was collected andstored at –80°C until analysis. Liver and hind-leg muscle were rapidly removed,weighed to 4 decimal places, rinsed in ice-cold saline, frozen in liquid nitrogen, andstored at –80°C until analysis. Exact times (nearest second) of injection and freez-ing of tissues were recorded.

Oxidative stress in cold-exposed mice 149

Protein synthesisFree and protein-bound enrichments of phenylalanine in liver and muscle tissueswere quantified as described by Wester et al. (2004) (Wester et al., 2004).Approximately 300 mg of frozen tissue was homogenised on ice in 3 ml 7% (w/v)sulphosalicylic acid (SSA). Free phenylalanine was separated from protein-boundphenylalanine by centrifugation at 1000 g at 4°C for 15 minutes and the super-natant retained. The pellet was then washed three times with 3 ml 7% sulphosali-cylic acid to remove free phenylalanine. The initial supernatant fraction (free pool)was passed through a 0.4 ml column of Dowex AG 50W-X8 (100-200 mesh) andthe resin rinsed with 2x3.5 ml water before the phenylalanine was eluted with 2 ml2M NH4OH and 1 ml water. The eluate was freeze-dried and stored at –20°C forlater analysis. Half of the washed pellet (protein-bound pool) was transferred to a 8ml screw-topped Pyrex hydrolysis tube and solubilised in 1 ml 0,5 ml 0,3 M NaOHfor 30 minutes. A few phenol crystals were added and the sample was hydrolysedby adding 7 ml 4M HCl and heating on a dri-block at 110°C for 18 hours.Hydrolysates were dried under vacuum, resuspended in 1 ml 0,5 M sodium citrate(pH 6.2), and stored at -80°C until later analysis.

For the plasma samples, 150 ml was treated with 150 ml 15% SSA, centrifugedat 1000 g at 4°C for 10 minutes and 150 ml of the supernatant was passed througha 0.2 ml column of Dowex AG 50W-X8. Elution conditions and subsequent treat-ments were similar to the tissue free pool samples.

STable isotope enrichments of the tissue and plasma free pools were measuredby gas chromatography mass spectrometry (GC/MS) after conversion to the terti-ary-butyldimethylsilyl (TBDMS) derivatives (Calder and Smith, 1988). In thehydrolysed samples (protein-bound pool, low enrichment) phenylalanine was con-verted to phenylethylamine by enzymatic decarboxylation prior to forming theTBDMS derivative. This was separated by capillary column gas chromatography andenrichments obtained from electron impact ionization selective ion monitoring (EI-SIM) mass spectrometry (see Calder et al., 1992 and Slater et al., 1995).

The fractional synthesis rate (FSR, % d-1) was calculated using the followingequation: FSR = 100*(BP/FP)*1440/t, where BP is the bound pool of phenylala-nine in mole percent excess (MPE), FP is the MPE of the free pool of phenylalaninemeasured in either plasma or tissue, and t is the time (min) between injection ofphenylalanine and freezing of the tissue in minutes. The ratio between FP meas-ured in plasma and liver or muscle was calculated to determine whether the inject-ed phenylalanine had equally mixed with both free pools. The ratio was 1.04±0.07and 0.99±0.08 (mean±sd) in muscle and liver respectively. Neither ratio was sig-nificantly different from unity. From these data it was concluded that the injectedphenylalanine effectively equilibrated between tissue and plasma and remained sountil time of death. In consequence, the FSR reported represent the FSR calculatedbased on plasma free phenylalanine as representative of the precursor pool.

Chapter 9150

Antioxidant enzyme activities & protein contentPrior to enzyme activity determinations, tissue samples were homogenized by soni-cation in 20 volumes of ice cold 50 mM phosphate buffer. Following centrifugation(25 min at 3000g), the supernatant fraction was collected, divided over severaltubes and stored at –80°C for enzyme activity and protein measurements.

Total superoxide dismutase (SOD) activity was determined at 25°C by the inhi-bition of the auto-oxidation of pyrogallol by SOD in the supernatant, following themethod of Marklund and Marklund (Marklund and Marklund, 1974). The reactionwas followed spectrophotometrically at 420 nm in the following reaction mixture:50 mM Tris-DTPA buffer, 15 µl supernatant and 15 µl pyrogallol in a total volume of800 µl. Each triplicate measurement was preceded by a blank, containing only pyro-gallol in Tris-DTPA buffer. One unit of SOD was defined as the amount of enzymecausing 50% inhibition of pyrogallol auto-oxidation.

Glutathione peroxidase (GPx) activity was determined at 25°C via the oxidationof NADPH in the presence of reduced glutathione (GSH) and H2O2 (combining theassays of Paglia and Valentine (1967), and Lawrence and Burk (1976)). The follow-ing reaction mixture was used: 4.28 mM sodium azide (to block catalase activity),1.07 mM EDTA, 4.286 mM GSH, 0.214 mM NADPH, 1 U ml-1 GR in ice cold 50mM phosphate buffer. 25 µl H2O2 and 25 µl sample were added to the reaction mix-ture. Reactions were followed spectrophotometrically at 340 nm in a total volumeof 700 µl. To correct for spontaneous oxidation reactions independent of GPx,blanks without H2O2 were measured and subtracted from the assay values. Oneunit of GPx was defined as the amount of enzyme that oxidized 1 µmol of NADPHper minute in the presence of reduced glutathione. Protein content of the super-natant fraction was determined using a Bradford assay (Quick start Bradford pro-tein essay kit 2; Biorad Laboratories B.V., Veenendaal, NL).

Indirect calorimetryPrior to killing, resting metabolic rates (RMR) and daily energy expenditure (DEE)were measured for each animal in which antioxidant enzyme activity was measured,using an eight-channel indirect calorimetry system, as described by Oklejewicz et al.(1997). The mice were put in airtight chambers where oxygen consumption (V

. O2,

l h-1) and carbon dioxide production (V.

CO2, l h-1) was measured simultaneouslywith ambient temperature and activity (passive infrared detectors). Oxygen and car-bon dioxide concentrations of dried inlet and outlet air (drier: molecular siever 3 Å,Merck) from each chamber were measured with a paramagnetic oxygen analyzer(Servomex Xentra 4100) and carbon dioxide by an infrared gas analyzer (Servomex1440). The system recorded the differentials in oxygen and carbon dioxide betweendried reference air and dried air from the metabolic cages. The flow rate of inlet airwas measured with a mass-flow controller (Type 5850 Brooks). Computerised datawere collected every 10 minutes. All mice were measured for 24h at an ambienttemperature of 22°C, mice from the cold group were measured for an additional24h at 10°C. Oxygen consumption was calculated according the equation 2 of Hill


(Hill, 1972) to correct for volume changes with respiratory quotient below 1 andexpressed in standard temperature and pressure. Metabolic rate (MR, kJ h-1) wasestimated using the following equation: MR= 16.18 x V

. O2 + 5.02 x V

. CO2

(Romijn and Lokhorst, 1961). Resting metabolic rate (RMR, kJ d-1) was defined asthe lowest value of metabolic rate calculated from cumulative means over 30 min-utes. Daily energy expenditure (DEE, kJ d-1) was calculated as the total metabolicrate over the 24-h measurement period.

Statistical analysisResults are reported as means ± SEM. To test for effects of treatment and/or ageANOVA models in the MIXED procedure in SAS for Windows (version 9.1) wereapplied. Group, age, and group x age were added as fixed factors. Factors that mayhave influenced the outcomes were examined by including these as covariates inthe models (i.e., food intake, body mass). Adjusted means were calculated by usingthe least squares means command in SAS MIXED. Data on antioxidant enzymeswere log10-transformed as necessary to improve normality. Significance was set atp≤ 0.05.

RESULTS

Body mass & Food intakeTable 9.1 shows the characteristics of the mice housed under cold (10°C) and warm(22°C) conditions. Prior to the cold treatment there were no differences in bodymass between groups (One way ANOVA: F1,29=1.71, p=0.279). Mice housedunder cold conditions had lower body mass at 3 and 27 months of age and in bothgroups body mass increased with age. Food intake differed between groups andwith age; it was increased in COLD and aged animals.

Protein synthesisLiver and muscle FSR decreased with age, i.e. between 3 and 27 months, but therewas no main effect of COLD treatment (Table 9.1). In liver the age-related decreasewas approximately 35% in COLD and 22% in WARM mice. These magnitudes dif-fered as shown by a significant interaction effect between group and age. In muscle,the decreases in FSR with age averaged 26% and 39% in COLD and WARM mice,respectively. These responses were similar as shown by the lack of an interactionbetween group and age. Food intake can affect protein synthesis rates and wasadded to the models as a covariate to explore this relationship. In both liver andmuscle food intake was not a significant covariate and adding food intake into themodels as covariate did not substantially alter the effects of cold exposure or age.

Chapter 9152

Antioxidant enzyme activity & MetabolismAntioxidant enzyme activities in liver and heart of cold exposed and control miceare shown in Figures 9.1 and 9.2, respectively. The statistical analyses are shown inTable 9.2. Age affected SOD activity in both heart and liver. In both tissues, SODactivity increased between 3 and 11 months and decreased thereafter (Figure 9.1,top graph and Figure 9.2). The highest activity of hepatic GPx was observed at 3months of age followed by a decline (Figure 9.1, bottom graph).

Cold exposure did not affect SOD activity in heart or GPx activity in liver, butdid affect SOD activity in liver. This effect was mainly caused by a decrease inhepatic SOD activity in COLD mice compared to WARM mice at 19 months of age(Tukey test: p<0.001). In consequence, the COLD mice showed a faster decrease inSOD activity between 11 and 19 months of age (shown by a significant interactionbetween group and age, Table 9.2). For WARM animals the major decrease in activi-ty occurred between 19 and 27 months.

Overall, resting metabolic rate (RMR, kJ d-1) was (mean±sd) 44.2±.5.0 and60.8± 8.8 and daily energy expenditure (DEE, kJ d-1) was 57.1± 5.7 and 80.0± 5.7in WARM and COLD mice respectively. RMR and DEE were highly increased at allages in COLD mice (Two-way ANOVA, effect of group: F1,50=165, p<0.001 forRMR and F1,50=372, p<0.001 for DEE). RMR and DEE were added into the mod-els as covariate to test whether they affected antioxidant enzyme activity. NeitherRMR nor DEE predicted changes in enzyme activity within these models nor didthey alter the effects of age or group. The first is also apparent in Figure 9.3 thatshows the relationships between DEE and SOD activity in liver at all ages measured(none of which were significant; linear regression). Notice that variations in bothmetabolic rate and SOD activity were small in these inbred mice.


Table 9.1. Effects of cold exposure on body mass, food intake, and fractional synthesis rates inliver and muscle.

3 months 27 months p-values

Variable name WARM COLD WARM COLD df Group Age GxA

n 8 8 5 6Age (d) 98 98 833±1 836±1Body mass (g) 28.9±1.2 26.6±1.2 37.3±1.5 30.7±1.4 1,23 0.003 <0.001 0.122Food intake (g d-1) 3.0±0.4 5.1±0.4 5.4±0.5 6.7±0.4 1,23 <0.001 <0.001 0.303

FSR liver (% d-1) 63.6±2.0 68.0±2.0 49.8±2.5 44.5±2.3 1,23 0.841 <0.001 0.039FSR muscle (% d-1) 4.3±0.3 4.0±0.3 2.6±0.4 2.9±0.3 1,23 0.967 <0.001 0.320

Results for ANOVA are given in addition to least square (adjusted) means±SE for all groups. Bold values representsignificant results (p<0.05). One mouse from the control group at 27 months was removed, because results indicat-ed that the phenylalanine injection was not performed properly. n= sample size per group, df= degrees of freedom,GxA= GroupxAge interaction.

Chapter 9154

GPx

act

ivity

(µm

ol N

AD

PH m

g-1 p

rote

in)

0.0

0.2

0.4

0.6

3age (month)

warmcold-exposed

11 19 27

SO

D a

ctiv

ity(U

mg-1

pro

tein

)

0

200

50

100

0.8

250

150

Figure 9.1. SOD (top graph) and GPx (bottom graph) activity in liver of mice exposed to cold(10°C) throughout their lives and of control mice (22°C) at different ages. One unit of SOD wasdefined as the amount of enzyme that causes 50% inhibition of pyrogallol auto-oxidation. Oneunit of GPx is defined as the amount of enzyme that oxidizes 1 µmol of NADPH per minute inthe presence of reduced glutathione.

0

20

40

60

3age (month)

warmcold-exposed

11 19 27

SO

D a

ctiv

ity (U

mg-1

pro

tein

) 100

80

Figure 9.2. SOD enzyme activity in heart of cold-exposed (10°C) and control (22°C) mice at dif-ferent ages. One unit of SOD was defined as the amount of enzyme that causes 50% inhibition ofpyrogallol auto-oxidation. Values given are mean±sem.

DISCUSSION

The primary purpose of this study was to examine the effects of cold exposure onage-related changes in protein synthesis and antioxidant enzymes in mice. In addi-tion, the relationship between metabolic rate and antioxidant enzymes wasexplored.

In accordance with previous reports on rodents (mice: (Blazejowski andWebster, 1983; Vaanholt et al., 2006), rats: (Lewis et al., 1985)),decreases in frac-tional protein synthesis rates with age in all experimental groups and both tissueswere found. On average, FSR decreased approximately 30% between 3 and 27months in both liver and muscle. Studies in humans, however, have shown conflict-


1000

200

300

100

SO

D a

ctiv

ity li

ver

(U m

g-1 p

rote

in)

40 50 80daily energy expenditure (kJ d-1)

60 70 90

3 month11 month19 month27 month

warm cold

Figure 9.3. Relationship between daily energy expenditure and SOD activity in liver of warm(white symbols) and cold-exposed mice (black symbols) at various ages.

Table 9.2. ANOVA on effects of group and age on antioxidant enzyme activities

Group Age Group x Age

Trait N d.f. F p d.f. F p F p

SOD Liver 55 1,47 15.1 <0.001 3,47 50.7 <0.001 6.4 0.001GPX Liver 53 1,45 0.2 0.47 3,45 18.9 <0.001 1.7 0.19SOD Heart 57 1,49 0.5 0.68 3,49 7.2 <0.001 1.7 0.19

ANOVA models were performed in the MIXED procedure of SAS for Windows (version 9.1). Group, age and groupx age were added as fixed factors. Data were log10-transformed as necessary to improve normality. N represents thetotal sample size and d.f. the degrees of freedom. Sample sizes were: at 3 months, n=8 and 7 respectively in warmand cold; 11 months, n=8; 19 months, n=7; 26 months, n=6. Liver samples of 2 mice were missing and for 2 othermice sample volume was too small to measure both SOD and GPx reducing the sample sizes for these measure-ments. Significant effects are in bold.

ing effects of age on muscle protein synthesis, with either decreases (Short et al.,2004; Yarasheski et al., 1993; Young et al., 1975) or no differences (Sheffield-Mooreet al., 2005; Volpi et al., 2001). Such discrepancies between studies may have manycauses, including muscle type used, sex, diet and activity level of subjects (Dorrensand Rennie, 2003). The technique used may affect the response, as the large (flood)dose procedure in fasted humans may stimulate protein synthesis (Cuthbertson etal., 2005; Smith et al., 1998). In fed animals, however, such responses are notobserved (Rocha et al., 1993) and this technique is routinely used in non-fastedrodent studies.

Long-term cold exposure (at 10°C) did not change protein synthesis rates inmuscle or liver at 3 and 27 months of age. This is in agreement with several studiesthat have shown no change in muscle protein synthesis rates after long-term cold-exposure (rats (McAllister et al., 2000; Samuels et al., 1996), calves (Scott et al.,1993), pigs (Lindsay et al., 1988)), but contradicts observations in chickens wheremuscle protein synthesis rates increased (Yunianto et al., 1997). The effects of coldexposure on protein synthesis are complex and probably depend on various factors,including the tissue measured, the intensity and the duration of the cold exposure,and the species studied. Furthermore, Scott et al (1993) have shown that (muscle)protein synthesis rates are influenced by food availability (Scott et al., 1993). Incold-exposed calves that were food restricted, protein synthesis rates in muscle andskin were reduced but these were unaffected when calves were not food restricted.In the current study with mice fed ad libitum there was no change in muscle pro-tein synthesis in response to cold exposure. Probably the 30% increase in foodintake in the cold-exposed mice was sufficient to maintain levels of protein synthe-sis in muscle. Age (or period of development) may also be an important factor indetermining the effect of cold exposure on fractional synthesis rates. Lewis et al.(1985) studied effects of caloric restriction on whole-body protein turnover in ratsat different ages and found that the most pronounced response in FSR occurred at12 months of age (increased by 45% compared to 6% at 24 months in caloricallyrestricted mice vs. controls) (Lewis et al., 1985). In the present study protein syn-thesis was only measured on 3 and 27 month old animals and at neither age wasthere an effect of cold exposure. Further studies would be necessary to determine ifthe mice were more sensitive at intermediate ages.

It has been hypothesised that increased rates of tissue protein turnover maycontribute to heat production in cold-exposed animals (McAllister et al., 2000) andthis could have positive effects on survival. This is not supported by either previousreports (McAllister et al., 2000; Scott et al., 1993) or the current study. In contrast,protein synthesis rates in liver of the current cold-exposed animals decreased morewith age than control mice kept at 22°C, and in muscle, the age-related change inprotein synthesis rate did not differ between cold-exposed and control animals.

Age had a strong effect on antioxidant enzyme activity of SOD and GPx inmurine heart and liver. An increase in SOD activity with a peak at 11 months of ageand a subsequent decline was observed in both tissues. GPx activity in liver showed

Chapter 9156

a steady decline in enzyme activity from the age of 3 months onwards. Conflictingdata exist on the effects of age on antioxidant enzyme activities (Gunduz et al.,2004; Rao et al., 1990; Sohal et al., 1990; Tsay et al., 2000; Vaanholt et al., 2006), butcomparison with other studies is complicated due to the use of different species,the choice of organs studied, and measurements at different ages (or only 2 ages).In studies where antioxidant enzyme activities were measured in mice heart andliver (see Chapter 7, Fig. 7.1 and 7.2) or rat brain (Tsay et al., 2000) at multiple agesthroughout life a similar pattern with age (with a peak at 10-12 months) wasfound as we show here for SOD activity. These data highlight the importance ofmeasuring antioxidant enzyme activities at various ages when exploring develop-mental responses. We believe this will resolve many of the current discrepanciesthat appear to exist between different studies.

Long-term cold exposure increased metabolic rate by approximately 50% butdid not affect antioxidant enzyme activity to early maturity (up to 11 months) inheart or liver. Data on SOD activity at 2 months of age are in agreement with thosefrom voles of a similar age that had been bred and raised under cold conditions(~8°C) (Selman et al., 2000). In that study, GPx and CAT activity were elevated inthe heart of the cold-exposed voles. Other studies provide contradictory evidenceon the effects of cold-exposure (Davidovic et al., 1999; Kaushik and Kaur, 2003;Siems et al., 1999; Spasic et al., 1993) but again this may be a feature of the experi-mental conditions, including the tissue measured and the duration and/or intensityof the cold exposure. Age-dependent effects of cold-exposure on antioxidantenzyme activities have not been previously reported. Unexpectedly, the SOD activi-ty in liver of cold-exposed mice (10°C) was, markedly decreased at 19 months ofage compared with controls housed at 22°C. This may reflect either a higher sus-ceptibility to oxidative stress or a lower production of free radicals at this age.

Despite a 50% increase in daily energy expenditure in cold-exposed animalsthroughout life, no major compensatory changes were observed in the antioxidantsystem in heart and liver tissue in these mice. Rather, SOD activity in the liver waseven decreased at 18 months of age. CAT activity was not measured, and this mayshow compensatory changes occurred, as is observed in other studies (Kaushik andKaur, 2003; Selman et al., 2000). It has also been suggested that basal levels of SODare sufficient to reduce the superoxide anion to hydrogen peroxide during moderateoxidative stress (Ji, 1999), and, if CAT was up-regulated, the available SOD andGPx might have been sufficient to cope with increases in radical production.Opposite to what we showed here (Figure 9.3), in exercising mice metabolic ratedid significantly predict antioxidant enzyme activity (Vaanholt et al., 2006). The lowbetween-individual variation for both variables in this study may explain this dis-crepancy.

Increased protein turnover rates have been hypothesized to be an important fac-tor contributing to the extension of life span in response to food restriction(Tavernarakis and Driscoll, 2002). The current study showed that protein synthesisand antioxidant enzyme activity decreased more steeply with age in the liver of


cold-exposed animals. This may cause cold-exposed animals to be more susceptibleto ageing, because decreased levels of antioxidants would diminish the protectionagainst ROS while lowered protein turnover will increase the half-life of proteins,enabling damaged proteins to accumulate in the cell and cause potential malfunc-tion. However, cold exposure had no effect on median life span in rats (Holloszyand Smith, 1986). This would suggest that other compensatory changes (e.g.uncoupling) to reduce oxidative stress in response to high metabolic rates occur incold-exposed animals. Uncoupling of mitochondrial respiration would be beneficial,particularly in cold-exposed animals, because energy is then dissipated as heat andthis would markedly reduce oxidative stress (Brand, 2000; Erlanson-Albertsson,2003; Speakman et al., 2004).

In summary, long-term cold exposure did not result in compensatory changes inantioxidant enzyme activities in the heart and liver of mice or in protein synthesisrates in liver and muscle. Age strongly affected antioxidant enzyme activities andthese showed either a peak at 11 months (SOD) or a gradual decline with age(GPx). Fractional protein synthesis rates also showed a decline with age in bothliver and muscle. Numerous studies have shown that oxidative damage increaseswith age, and a decrease in antioxidant enzyme activity and/or protein turnovercould explain this effect. Metabolic rate did not predict SOD and GPx levels in theanimals in this study. Cold-exposed animals may have compensated for the highmetabolic rates required to maintain constant body temperature by increasing theexpression of uncoupling protein, thereby dissociating oxidative respiration fromATP production and reducing the generation of free radicals. Further study isrequired to determine whether this is the case.

AcknowledgementsWe thank Suzan Anderson and David Bremner for performing the analysis of fractional syn-thesis rates and Annemieke Meijer for performing the antioxidant enzyme activity measure-ments. Serge Daan is thanked for commenting on earlier versions of the manuscript. L.M.Vaanholt was a recipient of an EU Marie Curie Training Site award to the Rowett ResearchInstitute (Mass school), Aberdeen, Scotland.

Chapter 9158

159

General Perspective

Chapter10

Lobke M. Vaanholt

The role of energy expenditure in ageing: “The rate of living theory”

Almost a century ago Rubner (1908) calculated the amount of food, expressed incalories, eaten over the life span by several domestic species of mammal (guineapig, cat, dog, horse and cow). He was struck by the fact that these amounts wereroughly proportional to the typical body mass of the species, so that the ratio, i.e.,the caloric intake per lifetime per gram body tissue was little different betweenguinea pig and cow. These data led to the rate of living theory that was formulatedin 1928 by Pearl based on Rubners work and on his own observations (Pearl, 1928).The theory postulates that the rate of senescence that ultimately leads to sponta-neous death is positively associated with the rate of energy turnover of body tissue.In the remainder of the century an extensive debate followed and numurous studieswere published inspired by this theory Interspecific comparative studies in birdsand mammals have now well established that metabolic rate is indeed inverselyrelated to life span and that the Lifetime Energy Potential (LEP) is fairly constantbetween species within the order of birds and within mammals (Ku et al., 1993;Speakman, 2005a).

There are also experimental approaches. One line of evidence comes from exper-iments in exotherms. In fruitflies Drosophila melanogaster (Loeb and Northrop,1917), houseflies Musca domestica (Ragland and Sohal, 1975) and the NematodeCaenorhabditis elegans (Van Voorhies and Ward, 1999) increasing ambient tempera-ture (and thereby metabolic rate) results in reduced longevity. Increased activityresults in decreased life span (Ragland and Sohal, 1975) and genetic factors relatedto longevity have been shown to decrease metabolic rate (Van Voorhies and Ward,1999). Studies in endotherms (rodents) manipulating energy expenditure by volun-tary exercise (Goodrick, 1980; Holloszy and Smith, 1987; Navarro et al., 2004), coldexposure (Johnson et al., 1963; Holloszy and Smith, 1986), caloric restriction(McCay et al., 1935; Yu et al., 1985) or evaluating interindividual variation in meta-bolic rate (Speakman et al., 2004) do not yield a unoform picture. The interpreta-tion of these results is complicated because in most studies no attempt was madeto accurately measure metabolic rate and/or body composition throughout the lifespan of the animals. Exercise studies have for instance been used to challenge therole that energy expenditure may play in the ageing process. Moderate exercise gen-erally increases energy expenditure during the exercise bout, but does not decreaseaverage life span: if anything, it increases the life span (in rats; (Holloszy andSmith, 1987) and mice; (Navarro et al., 2004)). It may, however, well be that ani-mals compensate for the extra expenditure in exercise by reduced energy metabo-lism during subsequent rest (Deerenberg et al., 1998). The necessary measurementsto determine whether (mass-specific) daily energy expenditure (DEE) is indeedincreased in exercising animals, have usually not been made.

Chapter 10162

Testing the rate of living theory: manipulations of energy expenditureTo investigate the relationship between energy expenditure and ageing withinspecies, we manipulated energy expenditure in two strains of mice (Hsd:ICR andC57BL6J). In the former strain we manipulated energy expenditure by exploitingthe spontaneous increase in exercise resulting from selective breeding, and in thelatter we decreased ambient temperature (from 22°C to 10°C). In the exercise study(Chapter 6) we used mice selectively-bred for high voluntary wheel-running activity(S+) and compared them with their random-bred controls (C+). We also studieddifferences between activity-selected mice housed with (S+) or without (S-) a run-ning wheel. As expected, S+ mice had the highest daily energy expenditurethroughout life (increased by average 8 kJ d-1), and C+ and S- mice had similar, lowlevels of DEE (average 59.0 and 61.2 kJ d-1, respectively). According to the rate ofliving theory one would expect C+ and S- to have similar life spans, which shouldexceed that of S+ mice. We did find differences in life span, but unexpectedly S+mice and S- mice both had life spans approximately 100 days shorter than C+ mice.To accurately compare experimental groups, we calculated life-time energy potential(LEP). Mass-specific LEP, no matter whether expressed per animal, per gram bodymass, dry lean mass of the carcass or metabolic organ mass, was always higher inthe S+ mice compared to the other groups.

In the temperature experiment (Chapter 8) we also reared three experimentalgroups. One group was housed at 22°C (warm, WW) throughout life, another at10°C (cold, CC) and a third group was housed at 10°C until 15 months of age andat 22°C thereafter (coldwarm, CW). The cold mice spent approximatly 50% moreenergy than warm mice. The CW mice had an increased daily energy expenditureearly in life, but not later in life compared to warm mice. The rate of living theoryimplies a cumulative effect of energy turnover on life span. We should thus expectthe effects of the manipulation restricted to early life and leaving conditions late inlife unchanged, to have an impact on life span as well. Strikingly, cold mice had asimilar life span to warm mice throughout life, and CW mice had a ~80 days short-er median life span. LEP expressed either per gram body mass, dry lean mass ororgan mass was highly increased in CC mice compared to WW and CW mice in thecold experiment.

In conclusion, these two experiments manipulating energy expenditure neithershowed the expected inverse relationship between metabolic rate and life spanbased on the rate of living theory, nor did they show a constant LEP between thegroups. We emphasize that the conclusion is based on relatively large sample sizesfor longevity assessments (n=60), on full-day measurements of energy metabolismin the home cages, and on careful analysis based on age-specific assays of bodymass and composition,

Caloric restriction experiments and the Lifetime Energy PotentialCaloric restriction (CR) is the only manipulation that increases both median andmaximum life span in rodents, as first shown in rats by McCay et al. (1935).

General Perspective 163

Because CR decreases the rate of ageing, it constitutes an excellent approach to bet-ter understand the mechanisms underlying the ageing process. The fact that CRslows the rate of ageing suggests that a reduction in some aspects of energy metab-olism should be related to the rate or ageing. This idea is supported by the suppres-sion of growth and many findings of reductions in levels of biochemical parametersin CR animals, like serum glucose, insulin, growth hormone and glucocorticoids(Masoro, 2005). In addition, oxidative damage to proteins, lipids, and DNA isreduced in CR animals compared to Ad libitum fed controls of the same age(Gredilla et al., 2001; Lopez-Torres et al., 2002; Barja, 2002a). Several investigationshave reported that these decreases in oxidative damage are related to a lowering ofmitochondrial free radical generation rate in various tissues of the CR animals(Sohal et al., 1994; Gredilla et al., 2001; Lopez-Torres et al., 2002). Thus, similar towhat has been described for long-lived animals in comparative studies (Perez-Campo et al., 1998; Barja, 2002b), a decrease in mitochondrial free radical genera-tion has been suggested to be one of the main determinants of the extended lifespan observed in CR animals (Barja, 2004a).

The question whether these effects of CR can be attributed to a reduction inenergy expenditure in calorically restricted animals has been the subject of consib-erable debate. Masoro, McCarter and co-workers showed that caloric intake pergram body mass is actually increased in mice subjected to CR (Masoro et al., 1982),although the total energy turnover rate decreases. There is no significant effect on24h metabolic rate expressed per gram lean mass (McCarter et al., 1985; McCarterand McGee, 1989). Right after the start of caloric restriction, a slight decrease in24-h metabolic rate (corrected for lean mass) was observed, but this difference diss-apeared after approximately 10 weeks (McCarter and McGee, 1989). Interpretationof the results is complicated because metabolic rate has to be corrected for bodysize in some way. Greenberg and Boozer (2000) have shown that the mass of themost metabolically active organs (heart, liver, kidney, brain) better explained differ-ences in metabolic rate (see also (Daan et al., 1990). Studies measuring organ-spe-cific metabolic rates have shown that even though the internal organs compriseonly ~5% of the total body mass, they are responsible for approximately 50% of theresting energy expenditure in both humans and rats (for summary of the results seeRamsey (Ramsey et al., 2000), Table 4). Changes in organ mass that are too small toresult in a significant change in total or lean body mass can thus exert large effectson the energy expenditure of the animal, and expressing energy expenditure pergram “organ mass” would thus allow a more accurate comparison between experi-mental groups. A problem that remains when interpreting data normalized for leanorgan masses is that is assumes that all components (heart, liver, kidney and brain)have similar rates of oxygen consumption. Although this assumption is probablyincorrect, there is no way of further partitioning the total metabolic rate into organ-specific contributions.

Chapter 10164

Calculations based on organ metabolic ratesGreenberg estimated energy expenditure based on organ metabolic rates, and foundthat metabolic rate per unit of organ mass was directely related to the rate of ageingin studies on CR animals, cold-exposed animals or exercising animals (Greenberg,1999). In his calculations Greenberg used data from McCarter’s study on lifelongmetabolic rate in Fischer 344 rats that were fed ad libitum or CR (60%). He foundthat the ratio of whole-body BMR to total body-part BMR was decreased in CR ani-mals compared to ad libitum fed controls. He thus concluded that the rate of livingtheory was valid as long as one took organ tissue metabolism into account. Weused data from the same study by McCarter (metabolic measurements, (McCarterand Palmer, 1992) and Yu (life span measurements, (Yu et al., 1982)) to calculatethe LEP of the animals (see Table 10.1 for a summary of the data). LEPBM was muchgreater (+48%) in CR animals than ad lib fed animals, with 478 and 324 kJ g-1

respectively. This difference became slightly less when we calculated LEP based onorgan mass, but was still much greater (+34%) in CR animals (LEPOM = 11240 vs.8378 kJ g-1, respectively). These findings show that although there was a negativerelationship between overall energy expenditure and life span, life span is not quan-titatively predictable from the hypothesis of a constant LEP as predicted by the rateof living theory, even when expressed per gram mass of the four metabolicallyexpensive organs. This conclusion is the same as for our studies on the effects ofhigh activity (chapter 6) and low temperature (chapter 8). Within species, the rateof living theory is thus not supported by the data in its strict formulation of con-stancy of lifetime mass-specific energy turnover.

Interspecific comparisons We may now also have an other look at where the rate of living theory came from:the comparison of species. As mentioned in chapter 1 interspecific comparisonsyield an inverse relationship between energy expenditure and longevity. However,body mass and lack of phylogenetic control are confounding factors in such analysis(Speakman, 2005b). Energy expenditure rate per gram scales to body mass with anexponent of approximately –0.3, and life span scales to body mass with the expo-nent of approximately +0.3 like durations of most biological periods (Daan &Aschoff 1982). The product of the two, being the mass-specific Lifetime EnergyPotential, thereby has an exponent of 0, i.e. is mass independent.

This dependence is based on correlation. It does not prove that there is a causalrelationship between energy turnover and lifespan. If there is a causal relationship,it does not prove that it should also cause the constancy of LEP. The associationmay come about only due to the causal association of both to body mass. Forinstance, the relationship between body mass and energy expenditure may bedefined by physical constraints in relation to body size, i.e., larger animals musthave lower metabolic rates per gram because of their surface area (where heat canbe dissipated) is relatively smaller. Ecological constraints (e.g., predation risk) inrelation to size may determine the association between body mass and life span.


Analysis of residuals (also correcting for phylogeny, see (Speakman, 2005b)) stillyield a negative association between energy expenditure and life span. This showsthat it is not a simple artefact emerging from their relationship to body mass(Speakman et al., 2002; Speakman, 2005a). Nonetheless, a large part of the varia-tion around the regression line remains unexplained. For instance, birds expendtypically in the order of 1.47 times as much energy as a mammal of similar size inBMR and 1.59 in DEE (Daan et al., 1991), but have greater longevity (Holmes etal., 2001; Brunet-Rossinni and Austad, 2004). Marsupials spend less energy thanwould be expected for a certain mass and have shorter life spans than other mam-mals (Austad and Fischer, 1991). These discrepancies cannot be explained by therate of living theory, and may be related to physiological or ecological differencesthat have occurred during evolution between larger taxa.

The role of free radicals & defense against them in ageing: “The freeradical theory of ageing”

Reactive Oxygen SpeciesWe have shown that within species life span is not solely dependent on the rate ofenergy expenditure throughout life and that Lifetime Energy Potential is not neces-sarily a constant, even if expressed in the proper way, as the total turnover per gramof the high-energy turnover organs (Chapter 6 and 8). This does not imply thatmetabolic rate is not involved in ageing, and that increased energy turnover doesnot speed up the ageing process. The free radical theory of Denham Harman(Harman, 1956) proposed that ageing was caused by the accumulation of oxidativedamage by oxygen free radicals (reactive oxygen species, ROS) produced duringaerobic metabolism.

Free radicals influence cellular function because they can cause damage tomacromolecules: DNA, lipids and proteins. With age this damage accumulates andeventually results in death (Harman, 1956). Numerous studies have investigatedthe involvement of free radicals in the ageing process, and is clear that they play animportant role (Beckman and Ames, 1998; Sohal et al., 2002; Barja, 2002b).Theoretically a higher metabolism would result in a higher production of reactiveoxygen species (ROS) and thus in more rapid ageing and a shorter life span. Therelationship between metabolic rate and the production of ROS is not linear. Theamount of ROS that is produced is thought to depend on the state of respiration inmitochondria (state 3 or 4), and also on the activity of uncoupling proteins (UCP)(Brand, 2000). When non-limiting amounts of ADP are available, mitochondria arein state 3 respiration. When ADP is absent there can be no ATP production andproton transduction mechanisms become backed up: state 4 respiration. Duringstate 3 respiration there is an abundant flow of protons across the inner membrane,while during state 4 respiration no protons flow through complex IV, and free radi-cal production is expected to be higher (Brand, 2000). During state 4 oxygen con-

Chapter 10166

sumption is reduced (leak) and in state 3 the demand for energy and O2 consump-tion is the highest. UCP’s uncouple oxidative phosphorylation from ATP generationand generate heat instead. When uncoupling occurs, the respiratory chain is speed-ed up, and less ROS are produced per unit O2 consumed. That this can have a posi-tive effect on life span has been shown in a study by Speakman and Selman (2004)where mice with more uncoupling (and higher metabolic rate) had longer lifespans.

Once ROS are produced there are several protection mechanisms that reducethe damage they can cause. First, antioxidant enzymes can scavenge ROS. The mainendogenous antioxidant is superoxide dismutase (SOD) which catalyzes the dismu-tation of superoxide (O2·) into oxygen (O2) and hydrogen peroxide (H2O2).Hydrogen peroxide is still a ROS and can fall apart in hydroxyl radicals (OH·).Catalase (CAT) and glutatione peroxidase (GPx) can prevent this by catalyzing thedecomposition of hydrogen peroxide to the harmless water (H2O) and oxygen.Antioxidant enzymes thus reduce the amount of ROS and the damage they cancause. Studies on mice with the genes for such enzymes knocked out have support-ed the important role of these enzymes. Mice lacking manganese superoxide dismu-tase die within 10 days (Li et al., 1995). When damage does occur, DNA repair andprotein turnover can counteract the damage to macromolecules before it causespermanent loss of function. These defense mechanisms (uncoupling, antioxidantenzymes and repair) influence the relationship between metabolism and ageing(see Figure 1.1).

The fact that exercising mice generally spend more energy, but do not show areduced (or even an increased) life span (Holloszy and Smith, 1987; Navarro et al.,2004), could be explained by changes in the systems described above. For instance,exercise moves mitochondrial respiration toward state 3, which may reduce theROS produced. Also, voluntary exercise increases the production of antioxidantenzymes and cellular protection against cellular damage (Powers et al., 1999;Kakarla et al., 2005).

Changes in defense systems and life spanTo see whether changes in these mechanisms could explain the differences weobserved in the life span in our experimental groups we measured UCP mRNAexpression in muscle, brown adipose tissue and white adipose tissue, SOD and GPxactivity in heart and liver, SOD, CAT and GPx mRNA expression in liver and mus-cle, and protein turnover in liver and muscle (Chapter 7 and 9, for summary seeTable 10.2). No significant differences in UCP expression were found between thegroups in any of the tissues (data for exercise experiment not shown, for coldexperiment see Figure 9.5, for summary see Table 10.1). In the cold experiment,UCP expression was slightly increased in all tissues and at both ages measured,indicative of an increase in uncoupling. Several other studies have reported increas-es in UCP mRNA expression in response to cold exposure (Carmona et al., 1998;Simonyan et al., 2001; von Praun et al., 2001), but others did not (Boss et al., 1998;


von Praun et al., 2001). It remains unclear whether increases in mRNA UCP levelsdo result in an increase in mitochondrial uncoupling. Differences in uncoupling,antioxidant defense and/or protein synthesis could not explain the difference weobserved in life span. No major differences were found between the groups in ourexercise or cold experiment for either variable.

The role of endocrine factors in ageing

The difference in life span we observed between control and activity-selected micemay be due to factors that have unintentionally been influenced during the selec-tion procedure. This could potentially include any physiological or behavioural traitthat has previously been shown to differ between the lines, such as for instance cor-ticosterone levels (Girard and Garland, Jr., 2002; Malisch et al., 2006). In a study onrats, Cavigelli (2003) showed that neophobic rats (inactive in new environment)had increased basal levels of corticosterone throughout life, and that they had a60% higher chance to die relative to neophilic animals (active in new environment)at all ages. The median life span was 100 days shorter for the neophobic rats. Wemeasured basal corticosterone levels in our mice at various ages, but found no dif-ferences in basal corticosterone between the groups in the exercise (Chapter 3,Figure 3.2) experiment. A previous study did show an increase in basal corticos-terone at 2 months of age, mainly in female activity-selected mice (Malisch et al.,2006). Based on our results we may presume that these differences become smallerwith age (when differences in wheel-running activity also become smaller, seeChapter 4 and 6). Also, when measured in an elevated-plus maze, no differences inanxiety between control and activity-selected mice were observed (personal obser-

Chapter 10168

Table 10.1. Life-time energy potential in calorically restricted (CR) animals.

Ad lib CR Ref

DEE (kJ d-1) 183 105 McCarter (1992)90% survival (d) 800 1240 Yu (1985), from graphBody mass (g) 451 272 McCarter (1992)Lean mass (g) 355 232 McCarter (1992)Organ mass (g) 17.4 11.6 Yu (1985), Greenberg (1999)DEEBM (kJ g-1 d-1) 0.40 0.39DEEOM (kJ g-1 d-1) 10.5 9.1LEPBM (kJ g-1) 324 478LEPOM(kJ g-1) 8378 11240

McCarter (1992) measured body mass, lean mass and 24h metabolic rate in ad libitum fed and calorically restrictedFisher rats at 6, 12, 18 and 24 months of age. The graph shows the average value for these measurements. Data on90% survival were obtained from the survival curve in Yu et al. (1985). Estimates of organ masses were taken fromGreenberg (1999), based on measurements by Yu.

vation, data not shown). Differences in corticosterone or response to a novel situa-tion thus do not appear to have had a role in differences in life span between thecontrol and selected groups.

In Chapters 4 and 5, we have shown that mice selected for high wheel-runningactivity show some interesting adaptations to their active phenotype. The mostintriguing was the observation that plasma adiponectin levels are significantlyincreased in S mice compared to the levels found in their random-bred controls(Chapter 4, Figure 4.2). This increase was found in S mice irrespective of the avail-ability of running wheels and occurred in all of the separate selection lines. Thissuggests that the increase in adiponectin is a trait genetically co-segregated withselection for increased wheel-running activity, instead of being mediated viaincreased physical activity per se. There are several reasons to believe that increasedcirculating adiponectin levels might contribute to the different phenotypes seen in Smice compared to C mice. For example, Fruebis et al (Fruebis et al., 2001) foundthat chronic administration of gAcrp30 (i.e., adiponectin) caused weight loss inmice despite the fact that food consumption was unaffected. This is a phenotypewhich appears homologous to the one found in activity-selected animals (Swallowet al., 1999; Swallow et al., 2001). Fruebis et al. (2001) attributed the effect ofadiponectin on body mass to increased fat oxidation, specifically in liver and mus-cle, and this was confirmed in subsequent studies (Berg et al., 2002; Yamauchi et al.,2002; Bruce et al., 2005). In addition, we observed a decrease in the respiratoryquotient (RQ) of S mice compared to C mice measured over a period of 24 hours innon-fasted animals (see Chapter 5), which indeed indicates higher levels of fat oxi-dation in selected mice. We speculate that an increased capacity to downgradelipids in muscular tissue contributes the increased physical activity displayed byactivity-selected mice. The effects of adiponectin on fat oxidation are believed toarise through stimulation of AMP-activated protein kinase (AMPK)(Berg et al.,2001; Yamauchi et al., 2002). Zhang et al. (2006) have shown increased levels ofphosphorylated AMPK in the aorta of male activity-selected mice compared withcontrols, which is consistent with the observed elevated levels of adiponectin in ourstudy (Chapter 4). Another interesting finding was that the distribution of the fatover the body differ between control and selected male mice, even though the totalamount of fat did not. Activity-selected mice stored fat more viscerally and one canimagine that this shift in fat distribution is beneficial for mice that run intensively.

The important role of adiponectin in insulin sensitization (Yamauchi et al., 2001;Baratta et al., 2004; Schondorf et al., 2005) makes the selected mice an interestingmodel to study factors related to the metabolic syndrome. In chapter 5 we investi-gated whether selected mice were less prone to develop diet-induced obesity. Wefound the most striking differences between control and selected females. Selectedfemales on the fat diet did not develop obesity. Control mice reduced their foodintake, because their food efficiency was higher on the fat diet, but selected femalesshowed opposite results. Selected males did show an increase in fat mass, similar tothat of control males on the fat diet. In the plasma levels of several metabolic hor-


mones, we also observed significantly different responses between control andselected males. On the fat diet, both selected males and females had increasedadiponectin levels.

Because of their abnormal response to the fat diet, which does not lead to devel-op obesity, these mice are an interesting model to further study the metabolic syn-drome. Future studies should establish whether increased adiponectin levels pro-tect the selected mice against developing insulin resistance on a high-fat diet.

We also measured hormone levels in the cold-exposed mice (see Box 4.1). Cold-exposure is perhaps experienced by the mice as a chronic stressor. Chronic stress isknown to have adverse effects on health and longevity (Paré, 1965). We measuredbasal corticosterone levels in the mice at various ages and found no differencesbetween the groups, indicating that the HPA-axis was not upregulated in responseto the cold exposure. Plasma leptin and adiponectin were significantly decreased incold-exposed mice relative to warm mice throughout life (see Box 4.1). When cor-rected for fat mass the decrease in leptin, but not adiponectin remained significant.

No studies investigating the direct effect of adiponectin or leptin on ageing havebeen undertaken. Adiponectin is known to improve factors associated with themetabolic syndrome, which is quickly becoming one of the most important factorscompromising human health. Circulating adiponectin levels are reduced in obesehumans compared with lean individuals (Arita et al., 1999) and this is associatedwith increases in cardiovascular risk factors such as insulin resistance and athero-genic lipid profiles. Adiponectin protects against vascular diseases by inhibitinglocal proinflammatory signals, preventing preatherogenic plaque formation, and byimpeding arterial wall thickening (Schondorf et al., 2005). Also in nonobese,healthy adults hypoadiponectinemia results in increased cardiovascular risk factors(Im et al., 2006). Opposite to adiponectin, leptin levels in obese humans areincreased in compared with lean individuals (Park et al., 2004). Leptin may play animportant role in the pathogenesis of hypertension related to obesity and metabolicsyndrome. Furthermore, the lipotoxic effect of leptin resistance may cause insulinresistance and _ cell dysfunction, increasing the risk of type 2 diabetes and leptinhas also been shown to possess proliferative, pro-inflammatory, pro-thrombotic,and pro-oxidative actions (Correia and Rahmouni, 2006). Low levels of leptin andhigh levels of adiponectin thus seem to protect against developing several patholo-gies.

Differences we observed in adiponectin and/or leptin levels between the groups(see Table 10.2), may thus have had a role in protecting or making animals morevulnerable to develop associated pathologies. If so, these effects can not explain thedifferences we observed in life span between the groups, since selected mice (withhigh adiponectin levels, and low leptin) actually lived shorter than control mice. Inthe cold-exposed mice the picture is also complex since they had low adiponectinand low leptin levels.

Chapter 10170

The role of fat in ageing

One property common to exercising, cold-exposed and calorically-restricted mice isthat they all have a significantly reduced fat content compared to their respectivecontrols. Ad libitum fed animals in captivity usually develop obesity because of theirsedentary life style, which may have adverse affects on their health (e.g., developingmetabolic syndrome and associated diseases, see Chapter 4 and 10.3) and reducetheir life span. A negative relationship between body mass and life span (Miller etal., 2002) is an indicator of these effects.

Fat is a major source of nitric oxide (NO) stimulated by leptin, and as fat storesincrease, leptin and NO release increase in parallel. NO is highly toxic and cancause damage to macromolecules. NO may be responsible for increased coronaryheart disease as obesity progresses (McCann et al., 2005). It has further been shownthat fat cells increase carcinogenesis in mice (Lu et al., 2006). In this study, volun-tary wheel running activity stimulated UVB-induced apoptosis in the epidermis andin tumours of mice. This effect was related to the reduced fat content in exercising


Table 10.2. Overview of our results.

S+ vs. S- Table/Fig Cold vs. Warm Table/Fig

Energy expenditure + T6.4, F6.4 + T8.5, F8.4Life span = T6.1, F6.3 = T8.1, F8.1LEPBM + T6.4 + T8.5LEPOM = T6.4 + T8.5Body compostion:

Body mass - T6.2 - T8.2Dry lean mass + T6.2 + T8.2Fat content - T6.2 - T8.2Organ mass = T6.2 + T8.2

Defense systems:Antioxidant enzyme activity = F7.1-F7.3 =- F9.1, F9.2Protein synthesis = T7.1 = T9.1Uncoupling proteins = Not shown = F8.5

Hormones:Corticosterone = F4.2 = Box 4.1Leptin = F4.2 - Box 4.1Adiponectin = F4.2 - Box 4.1

Overview of the results from the ageing study in exercising and cold-exposed mice. We compare mice that have beenselected for high wheel-running activity that were housed with a running wheel (S+) with their sedentary controls(S-). We do not include te comparison between C+ and S+ mice, because in this comparison, differences betweenthe animals that have occurred during the selection process are a confounding factor. In addition, we compared cold-exposed mice to warm mice. + indicates an increase in S+/Cold mice compared to S-/warm mice, - indicates adecrease in S+/Cold mice compared to S-/warm mice, and = indicates no differences between the groups. TheTable/Fig. columns show in which tables and figures of the thesis the results are shown.

animals; removal of the parametrial fat pads (partial lipectomy) 2 weeks beforeUVB irradiation also enhanced UVB-induced apoptosis. The enhancement of apop-tosis eventually resulted in a 30% lower incidence of tumours in the lean exercisingmice. Fat cells may thus secrete substances that inhibit apoptosis in cells with DNAdamage, and possibly also in tumours, thereby increasing the incidence of cancer(Lu et al., 2006). It is known in humans that the incidence of certain cancersincreases in obese subjects (Bray and Bellanger, 2006).

The reduced fat content in cold-exposed, exercising and calorically restrictedanimals compared to their controls, may thus counteract the effects of their energyexpenditure, and thereby result in a longer life span than expected based on predic-tions from the rate of living theory. Indeed all three factors have been shown toreduce tumour growth and the incidence of cancer: cold-exposure (Holloszy andSmith, 1986), exercise (Kritchevsky, 1990; Lu et al., 2006) and caloric restriction(Kritchevsky, 2001). Whether there is a direct link between fat content and tumourincidence under these conditions needs to be examined.

In summary

Between species there is a general pattern showing an inverse relationship betweenenergy expenditure and longevity. We show that within a species (mice), manipu-lating energy expenditure by increased activity or decreasing ambient temperature,did not affect life span in the direction predicted, and the quantitative expectationof constancy of the Lifetime Energy Potential was not upheld. These results mayrefute the strict rate of living theory. Yet they are consistent with a causal influenceof energetics on ageing, and they highlight the importance of understanding howdifferent systems work together in ageing animals. ROS that are produced duringnormal aerobic metabolism are known to have an important role in ageing, becausethey can cause damage to DNA, lipids and proteins. There are different ways to pre-vent ROS damage to cellular macromolecules, i.e., by reducing ROS production (byreducing metabolic rate or increasing uncoupling), by reducing the amount that cancause damage (antioxidant enzymes), and by repairing damage that does occur(DNA repair, protein turnover). We showed large differences in daily energy expen-diture between exercising (+14%) and cold-exposed mice (+50%), but we did notfind strong evidence that differences in uncoupling, antioxidant enzyme activity orprotein synthesis occurred. The observed differences in life span between thegroups could thus not be explained by differences in these processes. Also, differ-ences in hormone levels could not explain differences in longevity between thegroups. Other factors involved in ageing, may have enabled the mice to expendlarge amounts of energy without a change in life span. One of these factors may bethe reduced fat content in exercising and cold-exposed mice, since it seems to pro-tect against developing tumours.

Chapter 10172

Ageing from an evolutionary perspective

In their natural environment, animals do not survive long enough to reach a verylong life span due to extrinsic mortality (i.e., predation, starvation, disease). Forthis reason it is suboptimal to invest indefinitely in maintaince of the body and pro-tection against ageing. There is a trade-off in the investment of resources betweenreproduction and survival. This is described by the disposable soma hypothesis(Kirkwood, 2002). The theory states that ageing results from the twin principlesthat (1) the force of natural selection declines with age, and (2) longevity requiresinvestments in somatic maintenance and repair that must compete with invest-ments in growth, reproduction and activities that enhance fitness. Animals withlow extrinsic rates of mortality are thus expected to invest more in maintenancethan animals with high extrinsic mortality rates (e.g., predation). During evolution,animals have developed various systems that can protect them against damage thatoccurs due to aerobic metabolism. The disposable soma hypothesis should predictthat species will adjust their investment in these systems to the forces of naturalselection, and this may lead to differences in life span.

Birds on average expend energy at a rate on average 1.5 times faster than amammal of similar mass (Daan et al., 1991), but have yet greater longevity. Has thespecific way of living of birds, possibly associated with reduced risks resulted inincreased investment in maintenance? There is evidence that birds have evolvedmitochondria that produce less ROS per ml oxygen consumed, are better protectedagainst ROS and have lower oxidative damage (Barja et al., 1994; Herrero and Barja,1998; Herrero and Barja, 1999). Comparing long- and short-lived mammalianspecies, the long-lived species also generally have lower levels of ROS productionand higher protection against ROS (Barja, 1998; Perez-Campo et al., 1998; Barja,2002b). During evolution differences in ROS production at a certain metabolic rateand in the capacity to defend against oxidative stress have thus emerged. Also with-in species, manipulations that increase life span like caloric restriction enhancemaintenance processes (e.g., protein turnover, antioxidant enzymes, DNA repair). Aging could thus be defined as the failure of maintenance and repair. Differentmaintenance mechanisms exist and most of them have been shown to decreasewith age. They depend on many genes and a considerable investment of metabolicresources is necessary to keep up their activity. Individual theories of ageing revolvearound the failure of given maintenance systems, and highlight different aspects ofa complex process rather than being mutually exclusive explanations. Ageing doesnot result in a given cause of death; the system that fails first is largely a matter ofchance. Ageing should be viewed as a multi-factorial process. No manipulation willaffect only one factor of this process at the same time and this causes complicationswhen testing a single theory such as the rate of living theory.

Most manipulations of energy expenditure usually lead to changes in body mass(which by itself affects life span) and in body composition. The expression of ener-gy expenditure then requires a correction for body size. The best way to do this is


still under debate (Ramsey et al., 2000; Speakman, 2005a). Changes occur innumerous physiological parameters (e.g., hormone levels, fat content, antioxidants,protein turnover) that may affect life span. This makes the attribution of differencesin life span to a single overall process such as energy expenditure nearly futile. Ourstudies reject the rate of living theory in its simplest quantitative form that statesthat the lifetime energy turnover per gram (whether body, lean or organ mass)remains constant when the instantaneous rate of turnover changes. They do notprove the absence of a negative effect of energy turnover on life span. In fact toomany studies have demonstrated such an effect in one form or an other.

Taking different consequences of energy turnover into consideration implies thatthe relationship between energy turnover and lifespan can not be a simple unidirec-tional process. In Figure 10.1 we conclude with a more realistic, if schematicalproposition. Energy turnover increases deleterious effects (e.g. ROS) as proposed inthe rate of living theory that will cause a decline in expected lifespan. Simulta-neously, a decrease in energy turnover rate may cause other negative effects mediat-ed by conditional problems, as excess fat content in rodents in captivity. Togetherthese two processes will generate an intermediate optimum as far as the maximiza-tion of life span is concerned (This needs not be the same energy expenditure thatmaximizes individual fitness, since fitness, i.e. the expected rate of gene propaga-tion to the next generation, includes the additional component of reproductive out-put). On this basis of opposing processes we should not even expect to find evi-dence for the rate of living theory across the whole range of energy metabolic rates.

Chapter 10174

surv

ival

e.g. fat

energy expenditure

e.g. ROS

Figure 10.1. The relationship between energy expenditure and survival. The graph shows thepresence of two separate processes that occur with changing energy expenditure and can affectlife span. An increase in energy expenditure can have deleterious effects on survival, e.g. via theincreased production of ROS, shown as a negative relationship. On the other hand, a decrease inenergy expenditure may also have deleterious effects on survival, for instance because it causesexcess body fat, which is a health risk and will negatively affect life span (positive relationship).These two opposing processes then result in an optimal survival at a certain energy expenditure(striped line). See text for further explanation.

175

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ACT = Costs of Activity (DEE-RMR) (kJ d-1)AN(C)OVA = Analysis of (Co-)VarianceBM = Body mass (g)BP = Bound pool of phenylalanine (mpe)C+ = Control mice with wheelsCAT = CatalaseCC = Mice housed at 10°CCOT= Costs of Transport (kJ km-1) CTRL = Control miceCW = Mice housed at 10°C and 22°C DEE = Daily Energy Expenditure (kJ d-1)DL = Dry lean mass (g)DLW = Doubly labeled WaterERS = Event Recording SystemFE = Food efficiency (g kJ-1)FFM = Fat free mass (g)FP = Free pool of phenylalanine (mpe)FSR = Fractional synthesis rate (% d-1)GC/MS = Gas Chromatography / Mass

spectrometryGEI = Gross Energy Intake (kJ d-1)GLM = General Linear ModelGPx = Glutathione Peroxidasekd = Fractional turnover rate of 2H (d-1)ko = Fractional turnover rate of 18O (d-1)LEP = Lifetime energy potential (kJ Live-1)

MEI = Metabolisable Energy Intake (kJ d-1)MPE = Mole percent excessMR = Metabolic rate (kJ d-1)n = Sample sizeN = Size of the body water pool (moles)OM = Organ mass (g)p = PropabilityPIR = Passive infraredrCO2 = Rate of carbondioxide productionRMR = Resting Metabolic Rate (kJ d-1)ROS = Reactive oxygen speciesRQ = Respiratory QuotientRWA = Wheel-running activity (km d-1)S- = Selected mice without wheelsS+ = Selected mice with wheelsSD = Standard deviationSEL= Selected miceSEM = standard error of the meanSOD = Superoxide DismutasSSA = Sulphosalisylic acidt = Time (s)V = Running speed (km h-1)V̇ CO2 = Production of carbondioxide (l h-1)V̇ O2 = Oxygen consumption (l h-1)W = Waste (kJ d-1)WW = Mice housed at 22°C

List of abbreviations 193

List of abbreviations

194

Samenvatting – Dutch summary

Wordt de levensduur bepaald door de energieomzetting?

De “Rate of Living” theorieWij weten allemaal dat een auto of een elektrisch apparaat dat vaker en intensievergebruikt wordt eerder kapot gaat. Hetzelfde zou van toepassing kunnen zijn op delevensduur van mensen en dieren. De opvatting dat de levensduur samenhangt metde hoeveelheid verbruikte energie, is bekend als de “rate of living” (=snelheid vanleven) theorie. Aan het begin van de vorige eeuw ontdekte Max Rubner dat de hoe-veelheid voedsel die verschillende dieren eten (per gram lichaamsgewicht), lagerwas in bij soorten met een langere levensduur. Hij berekende de energie inname pergram lichaamsgewicht gedurende het gehele leven – wat nu de life-time energy poten-tial (LEP) genoemd wordt – voor 5 verschillende diersoorten (cavia, hond, kat, koeen paard) en vond dat deze ongeveer gelijk was in alle soorten. Per gram lichaams-gewicht verbruikten een cavia en een paard dus een gelijke hoeveelheid energie inhun leven. Als dat binnen een soort ook op zou gaan, zou dit betekenen dat eendier dat meer energie verbruikt dan een soortgenoot een korter leven zal hebben.Een verklaring voor het verband tussen energie verbruik en levensduur wordt gege-ven in de “free radical” (=vrije radikalen) theorie van Harman (1956). Deze theoriestelt dat veroudering en dood veroorzaakt worden door de gevolgen van toxischebij-produkten die vrijkomen tijdens de energie omzetting (metabolisme). Energieomzetting vindt plaats in onderdelen in de cel, de mitochondria, waar de ingeadem-de zuurstof en bepaalde substraten omgezet worden in kooldioxide en water, waar-bij ATP gevormd wordt. ATP is als het ware een klein energie bommetje dat alle cel-len in het lichaam van energie voorziet. Bij de omzetting van zuurstof in het mito-chondrion worden zeer kortstondig de vrije radikalen gevormd. Als deze radikalenniet onschadelijk gemaakt worden, kunnen ze schade veroorzaken aan DNA, eiwit-ten en vetten. Tijdens de veroudering zou deze schade zich opstapelen, wat uitein-delijk tot het falen van de cel en celdood kan leiden. Er bestaan enkele mechanis-men die bescherming bieden tegen de effekten van deze vrije radikalen, waaronderantioxidant enzymen, eiwit synthese en DNA reparatie. Antioxidant enzymen rea-geren met de vrije radikalen waardoor zij onschadelijk worden. De vrije radikalendie niet worden weggevangen kunnen nog steeds schade veroorzaken, maar deschade kan deels gerepareerd worden door het vervangen en nieuw produceren vaneiwitten (eiwit turnover) of via DNA reparatie.

De rate of living theorie veronderstelt in zijn scherpste formulering dat delevenslange energieomzet per gram lichaamsgewicht min of meer een constante is.Een verhoging van de energieomzet zou dan leiden tot een voorspelbare reductievan de levensduur. Een argument hiervoor is het reeds een halve eeuw bekende feitdat ratten en muizen die op rantsoen gezet zijn veel langer leven dan hun soortge-noten die zich onbeperkt aan voedsel tegoed kunnen doen. Dat heeft tot uitgeberei-de discussies geleid. Tegenstanders van de rate of living theorie wijzen er op dat dedieren op rantsoen veel lichter zijn, en dat hun energieverbruik per gram lichaams-gewicht niet gereduceerd is. De theorie zou dus in het geheel geen langere levens-

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duur voorspellen. Daar is weer tegen in gebracht dat de stofwisseling zeer uiteen-loopt tussen verschillende delen van het lichaam, en dat lichaamsgewicht duseigenlijk niet de relevante maat is. Men zou het gewicht moeten weten van de orga-nen in het lichaam die de hoogste stofwisseling hebben, met name hersenen, hart,nieren, lever, en niet het gewicht van huid en skelet meerekenen, weefsels die nau-welijks meedoen aan de stofwisseling.

Voor een complete toetsing van de theorie zou het daarom noodzakelijk zijnzowel de energieomzet als de lichaamssamenstelling te kennen van dieren waarbijhet energieverbruik verhoogd of verlaagd wordt en de levensduur bepaald. In ditproefschrift wordt precies dit gedaan, in twee omvangrijke experimenten. In het enewordt getracht de energieomzet van muizen te verhogen langs genetische weg, inhet andere door de temperatuur te veranderen. Deze experimenten hebben elk circa3 jaar geduurd, tot de laatste muis spontaan gestorven was. Ze vinden hun neerslagin Deel II (Energieverbruik en Veroudering) van het proefschrift. Daarnaast wordtin deel I Activiteit & Energieverbruik een aantal experimenten beschreven waarinde relatie tussen energieverbruik en activiteit onderzocht wordt.

Gebruik is dus gemaakt van muizenlijnen die zijn geselekteerd op een hogeloopwielactiviteit. Wanneer deze geselekteerde muizen op een leeftijd van 6-8weken de beschikking krijgen over een loopwiel in hun kooi, dan lopen zijgemiddeld 10 km per dag, dat is 2,7 keer zo ver als controle (= niet-geselekteerde)muizen op deze leeftijd. Ze zijn kleiner en hebben minder vet dan controle muizen.Wel hebben ze een hogere voedselopname. Naast de relatie tussen energieverbruiken activiteit is gezocht naar verdere aanpasssingen die zijn ontstaan in deze op acti-viteit geselekteerde muizen. Heeft de selektie op een hoge loopwielactiviteit ookgeleid tot een verhoging van de efficiëntie van het lopen en verbruiken geselekteer-de muizen dus minder energie per afgelegde kilometer (Hoofdstuk 2)? Zijn zij betervoorbereid op een voedselsituatie waarin zij grotere afstanden moeten afleggen vooreenzelfde hoeveelheid voer (Hoofdstuk 3)? Zijn er verschillen in metabole hormo-nale factoren tussen de geselekteerde en controle muizen (Hoofdstuk 4)? En zo ja,zijn zij hierdoor ook beter beschermd tegen het ontwikkelen van het metabole syn-droom (Hoofdstuk 5)?

In beide hoofdexperimenten hebben wij het energieverbruik beïnvloed (door deactiviteit te verhogen of de omgevingstemperatuur te verlagen) en de effecten hier-van op de levensduur, voedselopname, loopwiel activiteit, energieverbruik enlichaamssamenstelling gemeten (Hoofdstuk 6 en 8). In beide experimenten isbovendien onderzocht of muizen met een hoog energieverbruik zich beter bescher-men tegen vrije radikalen door een verhoogde antioxidant enzym activiteit en/ofeiwit synthese snelheid (Hoofdstuk 7 en 9)?

Dutch summary 197

Activiteit en Energieverbruik

Hoofdstuk 2 beschrijft welke veranderingen optraden in loopwielgedrag en energie-verbruik bij muizen gehuisvest bij verschillende omgevingstemperaturen. Daarnaastis onderzocht of muizen geselekteerd op een hoge loopwielactiviteit een anderereactie laten zien op de omgevingstemperatuur. Het zou gunstig kunnen zijn om bijlage temperaturen veel te rennen als de warmte die hierbij geproduceerd wordtgebruikt kan worden voor thermoregulatie. Controle en geslekteerde muizen wer-den blootgesteld aan drie temperaturen (10, 20 en 30°C) en hun loopwielactiviteiten energieverbruik werd gemeten. Gemiddeld hadden de geselekteerde muizendoor hun hogere activiteit ook een hoger energieverbruik dan de controle muizen.Bij de laagste temperatuur liepen beide groepen minder dan bij de twee hogere tem-peraturen. De relatie tussen loopsnelheid en energieverbruik was gelijk bij alle tem-peraturen. Dit bewijst dat de muizen geen energie besparen door de overtolligewarmte tijdens het rennen in de kou te gebruiken voor warmteregulatie. Er warengeen verschillen in de kosten van het lopen (in kilojoule per kilometer) tussen gese-lekteerde en controle dieren. De geselekteerde muizen rennen dus niet effciienterdan de andere.

Zijn de selectielijnen wel beter aangepast aan een situatie waarin zij grotereafstanden moeten lopen om aan voedsel te komen? Dit is onderzocht in Hoofdstuk3. Loopwielen werden aangesloten op voedselverdelers die een voerbrokje van 45mg loslieten wanneer het dier een bepaald aantal rondjes had gelopen in het wiel.Dat aantal rondjes per ‘pellet’ werd langzaam opgevoerd en veranderingen inlichaamsgewicht, voedselopname, stress hormoon en energie budget werden geme-ten. Het lichaamsgewicht nam af en het corticosteron niveau nam toe (wat duidt opstress) zowel bij controle als bij geselekteerde muizen. Beide typen waren in staathetzelfde maximale loopniveau te bereiken. Op dit niveau lieten ze ook dezelfdeveranderingen ten opzichte van het basisniveau zien in lichaamsgewicht, hormoonniveau’s en energiebudget. Op het hoge werkniveau was er een positieve relatie tus-sen de afgelegde afstand en het energieverbruik, maar er waren geen significanteverschillen meetbaar tussen controle en geselekteerde dieren. Dieren die veel ren-den, hadden in rust een lager metabolisme, maar gemiddeld over de dag een hogerenergieverbruik.

Metabole hormonen spelen een belangrijke rol in het handhaven van de energie-regeling in het lichaam. In Hoofdstuk 4 is onderzocht of er verschillen zijn ontstaanin de niveaus van de hormonen leptine en adiponectine tussen controle en geselek-teerde muizen en of de aanwezigheid van een loopwiel deze niveau’s beinvloedt. Bijde geselekteerde muizen (met of zonder loopwiel) vonden wij verhoogde niveau’svan adiponectine. De leptine in het bloed (gecorrigeerd voor vet massa van de die-ren) verschilde niet tussen de groepen. De hogere adiponectine in het bloed van opaktiviteit geselecteerde muizen zou betrokken kunnen zijn bij het bepalen van hetlagere gewicht bij een verhoogde voedselopname. Het is bekend dat adiponectineinjecties bij muizen leiden tot gewichtsafname bij een gelijke voedselopname. Ook

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speelt adiponectine een belangrijke rol bij de insuline resistentie. Insuline resisten-tie ontstaat met name in mensen die leiden aan obesitas en veroorzaakt een aantalproblemen voor de gezondheid.

De geselekteerde muizen met hun hoge adiponectine niveau zijn dus wellichtbeter beschermd tegen het ontwikkelen van insuline resistentie en andere sympto-men van het metabole syndroom. Om dit te testen hebben wij in Hoofdstuk 5 gese-lekteerde vrouwen en mannen blootgesteld aan een vet dieet en het effect hiervanop voedselopname, lichaamsgewicht en metabole hormonen onderzocht. Op het vetdieet lieten de vrouwtjes in de geselekteerde lijn geen gewichtstoename zien, ter-wijl de mannen en vrouwen van de andere groepen wel zwaarder werden. Ook wasde voedselopname in de geselekteerde vrouwen verhoogd op het vette dieet, terwijlde voedselopname lager was in de andere groepen. Dit wijst erop dat de voedseleffi-ciëntie verlaagd was bij de vrouwtjes. Deze waren dus beter beschermd op het vetdieet. Ze waren ook de enige groep die moeite had met het wegwerken van glucoseop dit dieet. Juist doordat deze dieren anders reageren op het vet dieet zijn zij eengoed model om het metabole syndroom te bestuderen.

Metabolisme en Veroudering

Voor het toetsen van de rate of living theorie heb ik dus gebruik gemaakt van de opaktiviteit geselecteerde muizen. Deze selectie is door Garland al 31 generaties vol-gehouden. Muizen uit deze lijn leggen inmiddels een ruim twee keer grotereafstand afleggen per dag in een loopwiel dan controle muizen. In het tweede experi-ment heb ik het energieverbruik verhoogd door muizen bloot te stellen aan kou(10°C) en te vergelijken met een controle groep die werd gehuisvest bij 22°C. Indeze verouderingsexperimenten bestond elke experimentele groep uit 100 dieren.Zestig hiervan werden regelmatig gewogen en voor elk van hen werd de dag geno-teerd dat zij dood gingen. Bij de overige 40 muizen werden op verschillende leeftij-den (2-3, 10-11, 18-19 en 26-27 maanden) voedselopname, stofwisseling (metbehulp van de dubbel gemerkt water methode) en lichaamssamenstelling bepaald –op elke leeftijd bij ~8 muizen.. Ook werden monsters van de lever, hart, spier envetweefsel ingevroren voor analyses van antioxidant enzymen, ontkoppelings eiwit-ten en eiwit synthese snelheid.

In het eerste experiment (Hoofstukken 6 en 7) hebben wij gebruik gemaakt vandrie experimentele groepen: 1. Controle muizen met loopwiel (C+), 2. Geselek-teer-de muizen met loopwiel (S+), en 3. Geselekteerde muizen zonder loopwiel (S-).Ook in het tweede experiment (Hoofstukken 8 en 9) hebben wij metingen verrichtaan drie groepen muizen: 1. Dieren gehuisvest bij 22°C (warm, WW), 2. Dierengehuisvest bij 10°C (koud, CC) en 3. Dieren die het eerste deel van hun leven (tot 15maanden) gehuisvest waren bij 10°C en vervolgens bij 22°C (koud-warm, CW). Dezelaatste groep test de voorspelling van de rate of living theorie dat een hoog energie-verbruik vroeg in het leven ook een effect heeft op de uiteindelijke levensduur.

Dutch summary 199

In beide experimenten was het energieverbruik sterk verhoogd in de tweedegroep (S+ en CC) in vergelijking met de andere groepen in het experiment(Hoofdstukken 6 en 8). Bij de actieve muizen was het energieverbruik verhoogdmet 14% en in de muizen in de kou met 46%. Toch leidden deze verschillen niet totverschillen in levensduur. De geselekteerde muizen gehuisvest met loopwiel haddeneen gemiddelde levensduur (704 dagen) die niet verschilde van die van de muizenzonder loopwiel (711 dagen). Ook de dieren in de kou hadden een gemiddeldelevensduur (798 dagen) nagenoeg gelijk aan die in de warmte (801 dagen) en dedieren die eerst in de kou en later in de warmte waren gehuisvest (768 dagen). Erbestond dus geen directe relatie tussen de verbruikte energie en de levensduur. Detotale energie verbruikt over het leven (LEP) was geen constante voor de soort, ookniet als we die LEP uitdrukken per gram orgaangewicht.

Wij vonden geen grote verschillen in de activiteit van antioxidant enzymen ofsterke veranderingen in eiwitsynthese (Hoofdstukken 7 en 9), die misschien zou-den verklaren dat de snelheid van veroudering niet toeneemt. Bij de actieve dierenwas de eiwitsynthese iets verhoogd in de spieren op jonge leeftijd (als zij veel ren-nen). Op latere leeftijd, als dit proces belangrijker wordt om de accumulatie vanbeschadigde eiwitten te voorkomen, vonden wij geen verschillen tussen actieve enniet actieve dieren. In de lever was er op beide leeftijden geen verschil (Hoofdstuk7). Ook de antioxidant enzym activiteiten waren niet verschillend in hart, lever ofspier in de geselekteerde dieren versus de controles. Wanneer dit werd uitgedruktin de bescherming per verbruikte kilojoule (energieverbruik) bleek de beschermingtegen vrije radikalen ook gelijk in beide groepen. In de koude dieren was de antioxi-dant enzym activiteit verlaagd in 19 maanden oude dieren vergeleken met de warmedieren op deze leeftijd. Door hun veel hogere energieverbruik was de resulterendebescherming door antioxidanten verlaagd (Hoofdstuk 9). Ondanks deze schijnbaarlagere bescherming in koude dieren, werd de levensduur niet beïnvloed.

In beide experimenten was de hoeveelheid lichaamsvet verlaagd in de groepenwaarin het energieverbruik verhoogd werd (de S+ en CC muizen). Zowel de actievegeselekteerde dieren, en de dieren gehuisvest in de kou hadden minder vet en leef-den langer dan wij verwachtten gebaseerd op de “rate of living” theorie. Vet kan hetontstaan van tumoren bevorderen. Bij muizen met hoge activiteit of die in de kouzitten is aangetoond dat zij minder tumoren ontwikkelen. Mogelijk wordt dus eennegatief effect van energieverbruik op levensduur te niet gedaan door het lage vet-gehalte van deze dieren.

Conclusies

Muizen die zijn geselekteerd op een hoge loopwielactiviteit waren niet beter aange-past aan een situatie waarin zij waren gehuisvest in de kou of wanneer zij moestenwerken voor hun voedsel en lieten geen andere veranderingen in energieverbruik enlichaamssamenstelling zien dan controles. Wel bleken de vrouwtjes beter bestand

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tegen de negatieve effecten van een vet dieet zoals het ontwikkelen van obesitas. In geen van beide grote experimenten leidde verhoogd energieverbruik tot een kor-tere levensduur. Dit kon niet verklaard worden door compenserende veranderingenin ontkoppelingseiwit, antioxidant enzym activiteit of eiwit synthese snelheid.Wellicht spelen veranderingen in metabole hormonen als adiponectine in actievegeselekteerde muizen een rol,. Tevens zou het lage vetgehalte van actieve en koudemuizen beschermend kunnen werken en de negatieve effecten van hun hogere ener-gieverbruik te niet kunnen doen.

Veroudering is een complex proces met vele oorzaken. Terwijl energie zeer goedeen rol hierbij kan spelen zijn er vele andere aspecten van verhoogd energieverbuikdie een simpele voorspelling als in de rate of living theorie doorkruisen. De LifeTime Energy Potential is daardoor geen constante voor de soort, hoe we hem ookuitdrukken, per muis, per gram muis, of per gram van de energetisch duren orga-nen.

Dutch summary 201

202

Addresses of co-authors 203

Serge Daan: Department of Behavioural Biology, University of Groningen, P.O. Box 14, 9750 AAHaren, The Netherlands, [email protected]

Gertjan Van Dijk: Department of Endoneurocrinology, University of Groningen, P.O. Box 14,9750 AA Haren, The Netherlands, [email protected]

Mark Doornbos: Department of Endoneurocrinology, University of Groningen, P.O. Box 14,9750 AA Haren, The Netherlands, [email protected]

Theodore Garland Jr: Department of Biology, University of California, Riverside, 109 UniversityLab Building, Riverside, CA 92521, USA, [email protected]

Izabella Jonas: Department of Endoneurocrinology, University of Groningen, P.O. Box 14, 9750AA Haren, The Netherlands, [email protected]

Berber De Jong: Department of Behavioural Biology, University of Groningen, P.O. Box 14, 9750AA Haren, The Netherlands, [email protected]

Gerald E. Lobley: Metabolic Health Group, Rowett Research Institute, Greenburn Road,Bucksburn, Aberdeen, AB21 9SB, Scotland (UK), [email protected]

Peter Meerlo: Department of Molecular Neurobiology, University of Groningen, P.O. Box 14,9750 AA Haren, The Netherlands, [email protected]

Kristin Schubert: Department of Behavioural Biology, University of Groningen, P.O. Box 14,9750 AA Haren, The Netherlands, [email protected]

John R. Speakman: Aberdeen Centre for Energy Regulation and Obesity, University ofAberdeen, School of Biological Sciences, Tillydrone Ave, Aberdeen, AB24 2TZ, Scotland(UK), [email protected]

G. Henk Visser: Department of Behavioural Biology, University of Groningen, P.O. Box 14, 9750AA Haren, The Netherlands and Centre for Isotope Research, Nijenborg 4, 9747 AG,Groningen, The Netherlands, [email protected]

Addresses of co-authors

204

Dankwoord – Acknowledgements

In de afgelopen jaren heb ik de wondere wereld van de wetenschap heel wat beterleren kennen. Hoewel deze wereld prachtig is en op verschillende momenteneuphorie opwekt, heeft het ook zijn duistere kanten. Op al deze momenten is desteun en luisterend oor van collega’s en vrienden onmisbaar en in dit dankwoordwil ik iedereen bedanken die op wat voor manier dan ook heeft bijgedragen aanmijn boekje.

Een aantal mensen wil ik graag persoonlijk bedanken. Als eerste wil ik Henkbedanken voor alle praktische en theoretische input. Ik zal nooit de (brakke) zater-dagen zondagochtenden vergeten waarop we samen met wat muizen in de kelderstonden om bloed monsters te nemen voor dubbel gelabeld water metingen. Je per-soonlijke betrokkenheid bij alles wat ik deed, heb ik erg gewaardeerd. Je hebt meerg laten schrikken toen je vertelde dat je kanker had en ik heb veel bewonderingvoor de manier waarop je hiermee om bent gegaan. Het is ongelovelijk hoe snel jeelke keer weer op het lab verscheen alsof er niks gebeurd was en hoe positief en volvertouwen je houding was. Ben erg blij dat het er nu op lijkt dat je de strijd voor-goed hebt gewonnen.

Als tweede wil ik Serge bedanken. Met name in de laatste fase van mijn proef-schrift en toen Henk ziek werd, heb je de begeleiding van hem overgenomen. Ophet moment dat ik het overzicht een beetje kwijt was, heb je me geholpen een goedplan te maken en ondanks de strakke deadlines heeft dit goed uitgepakt. Bedanktvoor alle vruchtbare discussies en het verbeteren van mijn manuscripten.

I also would like to thank John Speakman and Gerald Lobley for making it possi-ble for me to come over to the Rowett institute in Aberdeen to do measurementson the samples I had collected in my ageing experiments. John, thank you for thehospitality you showed me every time I visited Aberdeen. I really enjoyed sharingyour office with you all those times, and even though not everybody agrees I stillbelieve your idea to save journal space by organising references differently deservesattention. Gerald, thanks for your statistical insights and the enlightning talksabout science and my future.

Ted Garland thank you for sending us the ‘runner’ mice I worked with and forinviting me over in Riverside to teach me the tricks of SAS. Also thanks for all theuseful discussions about my work.

Verder wil ik mijn paranimfen Kristin en Daan bedanken. Kristin, vanaf onzeeerste ontmoeting hebben we veel inhoudelijke discussies gehad over experimentenen andere zaken, en bovenal erg veel lol. Ongeveer een week nadat je bij me in huiskwam wonen, brak ik mijn enkel en ik wil je ook erg bedanken voor de goede zorgdie je me toen hebt gegeven. Kristin rocks! Daan, toen ik bij gedragsbiologie begonkende ik je al via de duikvereniging, maar onze vriendschap is daarna alleen maarsterker geworden. Ik hoop dat we onze gezellige avondjes in de kroeg vol weten-schap en levensbeschouwingen voort kunnen zetten in de pub! Daan rules!

Natuurlijk wil ik ook mijn overige (aio-)collega’s bedanken voor alle leukeavondjes uit en nog veel meer: Peter (de rasoptimist), Kamiel, Ate, Roelof (stoereman), Arjen, Barbara, Nicolaus, Wendt, Bernd (lekker kippetje), Marian, Vivian,

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Margriet, Ralf, Martijn, Egbert, Sandra, Sarah, Izabella, Kristina, Ton, Cor, Simon,Mareike, Martha en Domien. De dierverzorgers, Monique, Roelie, Sjoerd en metname Saskia bedankt voor de goede zorgen voor mijn dieren. Gerard, bedankt vooralle hulp; zonder jou zijn we allemaal de weg kwijt. Jackie, David, Fiona, Suzan andmy other colleagues at the Rowett for making my time there a pleasure. En ookmijn fantastische master studenten; Alinde, Mark, Berber, Jan-Albert enAnnemieke.

Dan wil ik Peter M. bedanken. De stage die ik bij jou deed in Chicago heeft voormij de doorslag gegeven om aio te worden. Nou bedankt daarvoor he! Ook bedanktvoor alle hulp bij het verzamelen van bloed en breintjes en voor de sarcastischenoot zo af en toe en voor alle kopjes koffie, oh nee, thee. Gertjan v.D., bedankt voorde leuke samenwerking. Dick Visser, mijn boekje ziet er echt prachtig uit, bedanktdaarvoor!

Ook al hebben zij niet direct bijgedragen aan mijn boekje, zij zorgden voor dejuiste afleiding die nodig was om alles tot een goed einde te brengen en dus wil ikal mijn vrienden bedanken voor alles: oa. Gertjan (hij deugt) voor de heerlijk ont-spannen stapavondjes; Suuz, Mayo en Mareike voor alle leuke avontuurtjes; Judithen Johan, voor de gezelligheid en alle keren dat jullie op mijn poezen hebben gepasten mij naar het vliegveld hebben gereden als ik weer eens naar Aberdeen ging; Daanen Ilse voor alle gezelligheid (Ilse ook voor het aanhoren van alle science talk);Henk en Manon; Hiske; Anke; Claes; Dorris; Irene; Iris; Rudolf en al mijn anderevriendjes van biologie, de duikvereniging en onderwaterhockey. Then I’d like tothank Dave for opening up his house to me all those times I visited Aberdeen, andPaula for showing me around the lab and taking me to the underwaterhockey club.

Verder wil ik mijn ouders bedanken voor het onvoorwaardelijke vertrouwen inmij. Het is fijn dat jullie proberen me te helpen voorkomen dat ik in dezelfde val-kuilen val als jullie en ook al is dat niet altijd succesvol, jullie steun maakt veelgoed. Dan mijn lieve broers Remco en Tijs en hun lieftallige schone dames Patriciaen Mathanje. Ik hoop dat jullie de komende jaren net zo van Schotland gaan houdenals ik. En natuurlijk Marijn, mijn lieve neefje, die op ieders gezicht een glimlachtovert (ook al huilt ie zelf).

One of the best things science brought me may be meeting you in a pool inAberdeen. Jon thanks for your unconditional love.

“I cannot control the truth of death, whatever my desperation. I can only make certain thatthose moments of my life I have remaining are as rich as they can be”.

Drizzt Do’Urden in The Icewind Dale Trilogy – The Halflings Gem – Book 1:Halfway to Everywhere by R.A. Salvatore

CARPE DIEM!!

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Documents

pure.rug.nlThe research reported in this thesis was carried out at the Behavioural Biology Group at the University of Groningen, The Netherlands and at the Rowett Institute in Aberdeen,