AMINO ACID REQUIREMENTS FOR LACTATING DAIRY COWS: RECONCILING PREDICTIVE MODELS AND BIOLOGY

H. Lapierre1, G. E. Lobley2, D. R.Ouellet1, L. Doepel3 and D. Pacheco4

1Agriculture and Agri-Food Canada, STN Lennoxville, Sherbrooke, QC, Canada, J1M 1Z3; 2Rowett Research Institute, Aberdeen, UK, AB21 9SB; 3Dept. of Agricultural, Food

and Nutritional Science, University of Alberta, Edmonton, AB, Canada, T6G 2P5; 4AgResearch, Palmerston North, New Zealand.

INTRODUCTION With the increased concerns of the consumer regarding the impact of animal

production on the environment and the need to be competitive in a global market, more attention has been directed in recent years to the efficiency of dairy production, especially regarding nitrogen (N). Nitrogen is the element that distinguishes proteins from fat and carbohydrates. Animals do not have a real protein storage capacity and, in a non-gestating, non-growing lactating cow not repleting her body reserves in response to the demands of early lactation, all the apparently digested N not secreted in milk is excreted in urine. Excretion of urinary N is creating concern as a potential source of pollution of water and air, the latter as N2O, a green house gas, or forming small particulate aerosols having a negative effect on air quality (smog).

Balancing an adequate ration requires a supply of nutrients that matches

requirements exactly. This challenges dairy nutritionists to first define the targeted output and then to determine both the supply and the requirement of the lactating dairy cow to reach this target. Hence, an optimal ration will vary depending on the first objective, the targeted output. Is this to maximize milk production? Is it to maximize the efficiency? Is it to yield the highest profit? If so, which parameters influence the profit equation? So far, in North America there has been no cost associated with the amount of N excreted, but in certain European countries there is a tax based on the amount of N excreted on the farm. Whatever the objective, an adequate prediction of milk protein secretion from a known protein supply is critical to balance a ration that will effectively meet the selected target.

It is now acknowledged that improving the formulation of rations depends not only on

determining supply and requirements based on metabolizable protein (MP) but also on individual amino acids (AA), as adopted for a long time in non-ruminants. In fact, most of the North American predictive models (e.g. NRC, 2001; CNCPS, Fox et al., 2004) have invested time and effort to develop complex rumen sub-models to obtain an adequate prediction of the flow of proteins that are digested and available to the cow, as MP and the associated digestible AA flows. Despite the fact that fine-tuning of these models might still be needed to improve predictions, they are yielding predicted values of duodenal flows of proteins or digested AA that fit well with measured values (Bateman et al., 2001; Pacheco et al., 2006 and personal communication). But, once

the digestible flows of proteins or AA are estimated, the predictions of milk protein secretion from calculated inputs rely on a single efficiency of conversion, independent of supply. This simplification of the complex biological event of lactation was necessary as a starting point, because “the knowledge of metabolism of nutrients is not as advanced as the prediction of ruminal fermentation, because of the almost infinite metabolic routes connecting various tissue and metabolic compartments, the multiple interactions, and the sophisticated metabolic regulations that determine the partitioning of absorbed nutrients” (Fox and Tedeschi, 2003). It is acknowledged, however, that such a linear relationship between supply and output is biologically unrealistic. Indeed, MP allowable milk is usually an overestimation of real production achieved at high protein intakes and an underestimation at low protein intakes with both NRC (2001) and CNCPS (Fox et al., 2004) models (Van Amburg et al., 2005; Lapierre et al., 2006- Figure 1). Improving these predictions forms the basis of the current review.

Figure 1. Milk protein yield observed in the study of Raggio et al. (2004) compared with

predictions of NRC (2001) or CNCPS (Fox et al., 2004) at low, medium or high supply of metabolizable protein (MP)

This presentation does not pretend to answer all the questions on the “infinite metabolic routes of AA”. Nonetheless, substantial work conducted in recent years on AA metabolism enables us to step back and re-examine the basic concepts used initially to develop predictive models. Reconciling actual knowledge of AA metabolism with the predictive models should provide improvements in estimation of utilization of protein and AA (i.e. requirements) and allow better prediction of milk protein secretion. The concepts presented on AA metabolism will be based on two independent, but complementary, approaches. The first approach consists of in vivo measurements performed to follow the utilization of individual AA from absorption to milk output. This was mainly achieved by measurement of net fluxes across the main tissues using AA in the dairy cow, namely the gut, the liver and the mammary gland (see reviews: Lobley and Lapierre, 2003; Hanigan, 2004; Lapierre et al., 2005a and 2006; Reynolds, 2006). Net fluxes determine the difference between the “exit” and the “entrance” of a tissue, i.e.

0

400

800

Lo-MP Med-MP Hi-MP

g/d

MeasuredPredicted-NRCPredicted-CNCPS

the amount of AA released or extracted across this organ, on a net basis. Additionally, the total utilization of single AA at the whole body level or across a tissue, including its oxidation and its incorporation into milk or endogenous secretions, has also been monitored using AA labelled with stable isotopes (Bequette et al., 1996;Lapierre et al., 2002, 2003 and 2005b; Ouellet et al., 2002 and 2005; Reynolds, 2006). As these studies are expensive in time and cost and therefore, limited, we have complemented the in vivo measurements of AA utilization across tissues with a meta-analysis including published studies where proteins or AA have been infused in dairy cows. We have related supply of AA to their secretion in milk protein (Doepel et al., 2004), and this meta-analysis has been extended for the purposes of this presentation.

In this presentation, we will limit our considerations to essential AA (EAA). They are

called “essential” because they cannot be synthesized by the dairy cow and, therefore, must be supplied from the combination of rumen undegradable protein (RUP) and microbial protein. This class includes histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine. This does not deny the importance of the non-EAA. The polypeptide chains constituting proteins contain both EAA and non-EAA, but the non-EAA can be synthesized by the dairy cow. However, there is a sub-group within the non-EAA for which synthesis is not sufficient to support high levels of production and these are called semi-essential. Arginine is a typical AA of this group as de novo synthesis can represent up to 40% of total supply to the animal (Doepel et al., 2004). Histidine could theoretically also be synthesized by mammals, but the synthetic pathway has very limited capacity and therefore endogenous synthesis is usually ignored. It has been suggested that glutamine may also be classified as semi-essential under certain circumstances, especially around calving (Meijer et al., 1995; Doepel et al., 2006). We will therefore limit our discussion to the fate of the EAA due to the scarcity of quantitative data on non-EAA, particularly with regards to measurements of de novo synthesis required to have an accurate estimate of the total supply to the cow.

DEFINITION OF REQUIREMENTS

To simplify the discussion, we will discuss requirements considering cows as non-

gestating and non-growing, therefore having only maintenance and lactation needs to be predicted by the models and met by the supply of AA. For additional simplicity, this first section will also assume a standard efficiency of utilization of MP of 67% to support the different metabolic functions, whereas the second part will demonstrate that, in practice, MP and AA are used with variable efficiency as protein supply is altered. Predictive Models

Lactation requirement per se is quite straightforwardly defined by the protein or AA

secretion into milk protein. In contrast, the definition of maintenance requirement in lactating dairy cows is anything but simple. Indeed, it must almost be considered as a virtual concept developed from models of growing animals. A direct measurement of maintenance cannot be made in the lactating dairy cow as she will begin to draw on her body proteins before reaching a “maintenance” level. i.e. she will run in negative N balance to support milk production. In addition, estimation of a protein requirement for

maintenance in the ruminant has always been a challenge due to the inability to feed a ruminant a protein-free diet without creating a negative impact on the rumen microflora, a problem exacerbated by the recycling of N into the rumen and associated microbial protein synthesis. As witness to this difficulty, the definition of maintenance itself has changed over the years. For example, in the most recent NRC model (2001) the maintenance protein requirement includes endogenous urinary losses, scurf, endogenous MP (as the contribution of endogenous protein to the duodenal flow is considered as part of supply) and metabolic fecal protein (MFP). In NRC (1989), however, MFP was considered separately from the other components of maintenance. The CNCPS model includes needs for endogenous urinary losses, scurf, and MFP, but does not include a requirement for endogenous MP (as these are not included in the supply).

Endogenous urinary losses are based on estimates derived from Swanson (1977) and represent the equivalent N that would be lost if consuming a diet adequate in energy but no protein. These losses include creatinine, urea, purine derivatives, nucleic acids, hippuric acid and small quantities of some AA (NRC, 1985). Urinary losses in cattle fed very low-N diets, but adequate energy were used to determine endogenous urinary losses, relative to BW0.50. The assumed AA composition used for these requirements is based on that of whole body tissues, with the rationale that the urea urinary loss arises from AA catabolism of body tissue proteins. Maintenance requirements for integumental protein includes loss and growth of hair, scurf and scales rubbed from the skin surface, along with some N-compounds in skin secretions (Swanson, 1977) and requirements are estimated relative to BW0.60. The AA composition of scurf has been proposed to match that of keratin (O’Connor et al., 1993), but whole-body tissue composition is used currently (Fox and Tedeschi, 2003).

It is not as easy to have a clear description of what MFP represents. For example,

the definition of MFP has changed with time, from “a residue of body secretions and tissue incident to movement of food through the gastrointestinal tract” (Swanson, 1977) to the definition of Swanson (1982) adopted in the last NRC (2001) “bacteria and bacterial debris synthesized in the caecum and large intestine, keratinized cells, and a host of other compounds”. Determination of requirement for MFP used by NRC and CNCPS is also based on studies of Swanson (1977, 1982) using two approaches. The first involved studies feeding diets with different CP contents: the intercept of the regression of intake of digestible CP against intake of CP was taken as MFP. The second method measured fecal N output when animals were fed low CP diets and subtracted an estimation of undigested feed N. Best correlations were obtained relative to indigestible dry matter, but due to uncertainties related to indigestible dry matter, the most recent NRC (2001) has chosen to use DMI as the basis to determine MFP (30 g MP/kg DMI). In addition, the NRC estimated that rumen microbial protein (included in the calculation of MFP) should be excluded and, therefore, 50% of the undigested microbial protein is subtracted from this estimation (the other half is assumed to be digested in the hindgut). CNCPS currently calculates MFP as 9% of indigestible dry matter (Fox et al., 2004), as previously estimated by NRC (1989), and therefore presents higher estimations of MFP than NRC.

Knowledge of Amino Acid Metabolism Over the last decade, a number of studies have focused their attention on the net

fluxes of AA across tissues in dairy cows. Overall, measurements of net fluxes support the grouping of EAA into 2 groups based on certain specific pathways (Blouin et al., 2002; Berthiaume et al., 2002 and 2006; Lapierre et al., 2005a; Hanigan, 2005; Reynolds, 2006). Group 1 AA (histidine, methionine, phenylalanine+tyrosine and tryptophan) are mainly, if not exclusively, catabolized by the liver, and their post-liver supply is approximately equal to mammary uptake and their secretion in milk protein. In contrast, Group 2 AA (isoleucine, leucine, valine and lysine) exhibit little, if any, hepatic extraction and have a post-liver supply greater than mammary uptake, itself greater than AA secretion in milk protein. Threonine lies somewhere between these two definitions. This grouping is in agreement with the previous observation by Mepham (1982) based solely on mammary metabolism.

Gut utilization. A major net loss of AA across the gut is the endogenous proteins

secreted into the lumen across the whole gut and not re-absorbed. Endogenous proteins comprise mainly mucoproteins, saliva, sloughed epithelial cells and enzyme secretions (Tamminga et al., 1995). The endogenous losses in the feces have been quantified recently using long term (8 days) infusion of labelled leucine (Ouellet et al., 2002, 2005 and 2007). More studies are needed to determine which factors might influence these secretions but, as a first step, a relationship between endogenous fecal loss and DMI has been used to estimate fecal endogenous losses, yielding an average value of 10.5 g MP/kg DMI. In fact, the endogenous losses in the feces underestimate the real losses as a fraction of endogenous secretions still present at the ileum are digested in the large intestine, from which AA absorption is limited. Preliminary data obtained with a limited number of cows with ileal canulae suggest that estimated fecal endogenous losses can be increased by 17% to represent ileal endogenous losses, therefore estimating endogenous loss at 12.7 g of protein/kg DMI. Applying the “standard” efficiency of 0.67, the relationship becomes 19 g MP supply/kg DMI.

The loss of AA in the feces from endogenous origin is not “seen” in net portal flux:

the amount needed to support these losses has been withdrawn from lumen or arterial supply and is therefore already discounted in this net flux measurement. Another loss across the gut, i.e. disappearance on a net basis between intestinal digestibility and portal net absorption, is oxidation. This cannot be measured directly by net portal flux but requires concomitant infusion of labelled AA into the blood to determine oxidation from arterial supply and into the lumen to determine catabolism from luminal supply. Only certain EAA are probably oxidized by the digestive tract. For example, oxidation of leucine across the portal-drained viscera (PDV) has been quantified in dairy cows (Lapierre et al., 2002), whereas there was no oxidation of phenylalanine in cows (Reynolds et al., 2000) or of phenylalanine and lysine in sheep (Lobley et al. 2003). Although Lobley et al. (2003) reported methionine oxidation across the PDV in sheep, comparison of estimated digestive flows of EAA and net portal measurement would suggest little if any oxidation of methionine in productive dairy cows (Pacheco et al., 2006). With the limited data available so far, we will assume no oxidation of lysine and methionine across the PDV of a lactating dairy cow.

Liver extraction. The liver is the major site of ureagenesis, ensuring that excess N

is transformed (to urea) to allow excretion in urine. Nonetheless, a mechanism has to exist to recover part of the urea synthesized to allow sufficient available N for milk secretion because ureagenesis, in our example, is greater than N digested (Table 1)! Indeed, there is substantial recycling of the urea produced back to the gut: in the rumen, this source of N can be used for microbial protein synthesis (Lapierre and Lobley, 2001; Ouellet et al., 2007). Although the liver is the major site of ureagenesis, not all the EAA are removed directly by this organ. As described above, there is very little hepatic extraction of Group 2 AA in lactating dairy cows (see reviews: Hanigan, 2005; Lapierre et al., 2005a, Reynolds, 2006). For AA of Group 2, N losses will occur in non-hepatic tissues with the extra N returned to the liver by N shuttles, such as alanine or glutamine, or directly transferred to carbon skeletons for the synthesis of non-EAA. Table 1. Nitrogen balance (g N/d) and urea kinetics in cows fed diets supplying low,

medium or high metabolizable protein level

Low Med Hig

h Intake 475 550 624 Feces 222 226 246 Digested 253 324 378 Urine 79 122 165 Milk 131 141 151 Hepatic ureagenesis 296 404 423 Urea returned to the portal-drained viscera 136 112 58

From Raggio et al. (2004). For Group 1 AA, liver removal is substantial with, on average, hepatic removal

relative to net portal absorption varying from 36% for methionine to 48% for phenylalanine (Lapierre et al., 2005a). We will discuss below how these values change in response to dietary supply and lactation output. Furthermore, the liver removes these AA for various purposes, including the synthesis of proteins exported to the plasma and gluconeogenesis plus the important role of avoiding plasma hyperaminoacidemia by extraction of excess AA. In addition, some AA have specific roles. For example, methionine is required for the synthesis of very low density lipoproteins (VLDL), needed to transport lipid fractions from the liver. Methionine is also catabolized to provide one-carbon units, with key functions in cell proliferation including synthesis of nucleotides and regulation of gene expression (Wajed et al., 2001), synthesis of phosphatidylcholine, secreted in milk, and synthesis of polyamines, vital for proliferative cells with short half-lives (Attaix and Meslin 1991). Adequate determination of these needs, either under basal “maintenance” conditions and therefore obligate or related to milk production, are not yet defined.

All EAA, except leucine and lysine, provide substrate C for hepatic glucose synthesis. If all the carbons of the AA extracted by the liver were used for the synthesis of glucose, maximal contribution of AA to gluconeogenesis would be approximately 15% (Reynolds, 1992). This limited impact on glucose supply may be important under conditions where energy supply is limited. Nonetheless, use of EAA for gluconeogenesis might then impose a penalty on AA availability to the mammary gland for milk protein synthesis. More studies are needed to determine which biological function has precedence over the other and to decipher the complex links between energy and protein supply and milk component production.

Post-liver and mammary utilization. For the Group 1 AA, for which post-liver

supply is approximately equal to mammary uptake, it is important to keep in mind that no net utilization does not imply that these AA are not used for protein synthesis in other tissues, but on a net basis, the amount used to make proteins is equivalent to the amount returned from protein breakdown. On average, a dairy cow synthesizes between 3 and 5 kg of protein per day (Bequette et al., 1996; Lapierre et al., 2002), but from those synthesized proteins, only export proteins, i.e. milk, scurf and metabolic fecal proteins demand a net input. The AA needed to support scurf losses would be included in the difference between post-liver supply and mammary uptake. This quantity however is minimal and is beyond the sensitivity of current net flux measurements. For Group 1 AA, mammary uptake is approximately equivalent to output in milk protein with therefore the uptake:output ratio equal to one. This ratio does not really identify the global efficiency of a lactating cow to use these AA to make milk, as suggested within CNCPS: instead it reflects the localization of enzymes responsible for AA catabolism.

Group 2 AA post-liver supply is greater than mammary uptake, which means that

there is a net utilization by peripheral tissues. Indeed, primary enzymes for catabolism of leucine, and the other branch-chain AA (BCAA), the BCAA-transaminase and –dehydrogenase, are widely distributed across tissues, including liver, muscle, fat, and mammary gland as well as the intestine (Goodwin et al., 1987; DeSantiago et al., 1998). Mammary uptake of Group 2 is also usually in excess of milk protein output. It has been shown, using lysine labelled with 15N, that the excess N taken up was transferred into the mammary gland for the synthesis of non-EAA (Lapierre et al., 2003). Although the role of AA taken up by the mammary gland has been mainly considered in terms of protein synthesis, it has been shown recently that casein infusion, which increased milk yield, also increased the uptake of excess Group 2 AA, even to a greater extent than the uptake of the non-EAA, which have a mammary uptake insufficient to cover milk output (Raggio et al., 2006). From this, we can hypothesize that the mammary gland extracts these EAA to supply not only N and C for the synthesis of non-EAA but also as an internal energy source. Indeed, leucine oxidation across the mammary gland increased with casein infusion (Raggio et al., 2006), in line with increased milk output and mammary gland metabolism. Similarly, recent work from Bequette et al. (2006) has shown, using MG explants in vitro, that the carbons of EAA could be used for the synthesis of galactose, a component of lactose.

So where does this knowledge fit with how we should adapt current prediction

schemes? Obviously, elements of this metabolic information cannot be easily

incorporated into the current schemes, although it will be important for future models based on detailed mechanistic understanding of the dairy cow. Nonetheless, there are important practical and theoretical consequences. For example, for the prediction of available AA supply net portal flux provides an important tool, especially for those AA that are not oxidized by the gut tissues. The findings that Group 1 and Group 2 uptakes across the mammary gland do not reflect ‘real’ efficiency of utilization but instead reflect the anabolic route means that model concepts need to be adjusted. The flexibility of the mammary gland to adjust to excesses or deficiencies of individual AA has implications for ‘imbalanced’ diets and how their impacts might be predicted.

Reconciliation Leads to Recommendations

Linking together estimation of the various requirements for AA from predictive

models with measurements of AA metabolism across tissues is a first step to integrate the new knowledge developed on AA metabolism into these models. To simplify the discussion, we will first compare the estimations of requirement with results obtained in Raggio et al. (2004) for the cows fed the Medium level of MP (MedMP), covering 103% (CNCPS) to 107% (NRC) of the MP requirement. We will also focus our comparison on MP (Table 2) and on two AA, one from each group mentioned above: methionine (group 1) and lysine (group 2; Table 3).

First, the estimated requirements for maintenance determined with NRC (2001) and CNCPS (Fox et al., 2004) for the three treatments of Raggio et al. (2004) are very similar as they are driven solely by dry matter intake and body weight, neither of which was significantly different among treatments during the study. Although the NRC (2001) model is not intended to determine the AA requirement for definite functions, for comparison purposes the lysine and methionine requirements have been estimated using the appropriate AA composition of the MP requirement (from Table 4).

The inclusion of urinary endogenous and scurf requirements fits well the definition of

“basal” maintenance but, with their contribution to total maintenance requirement limited to less than 15%, the impact is quite small. Part of urinary endogenous losses occurs through hepatic removal but, as discussed previously, direct measurement of these losses at maintenance cannot be made. Furthermore, such losses would only account for AA removed by the liver. So, in the absence of a better proposal, the current estimation will be used in our calculations. The AA needed to support scurf losses are included in the difference between post-liver supply and mammary uptake. This quantity however is minimal (Table 3) and is beyond the current sensitivity of net flux measurements.

Table 2. Supply and requirements of metabolizable protein (MP; g/d) as determined by NRC (2001) or CNCPS (Fox et al., 2004) models and associated efficiency of maintenance or lactation compared with measured milk protein production and urinary N losses in cows fed diets supplying low, medium or high MP levels

NRC CNCPS Low Med High Low Med High MP supply 1922 2264 2517 1938 2292 2638 Maintenance requirement 856 859 876 917 917 937 - urinary endogenous 105 105 105 105 105 105 - scurf 15 15 15 15 15 15 - MFP 570 570 583 797 797 817 - from endogenous MP 167 170 173 Supply less maintenance 1066 1405 1641 1021 1375 1701 Efficiency of lactation 0.67 0.67 0.67 0.65 0.65 0.65 Predicted milk protein yielda 714 941 1008 664 894 1041 Efficiency of maintenance 0.67 0.67 0.67 0.65 0.65 0.65 Inefficiency maintenanceb 298 298 302 389 389 396 Inefficiency lactationc 236 311 333 232 313 364 Total inefficiencyd 533 608 635 621 702 761 Estimated urinary N loss from AA catabolism 85 97 102 99 112 122 Measured milk protein yield 802 853 902 802 853 902 Measured efficiency of lactation 0.75 0.61 0.55 0.79 0.62 0.53 Measured total urinary N losses 79 122 165 79 122 165

From Raggio et al. (2004)

a Predicted milk protein yield was restricted at high MP supply by energy supply. bCalculated as MFP x (1-efficiency) + scurf x (1-efficiency) + urinary endogenous. cCalculated as predicted milk protein yield x (1-efficiency). dAssumed to be lost in urine.

Table 3. Variation in lysine and methionine net flux (g/d) across tissues and prediction of requirement from NRC (2001) and CNCPS (Fox et al., 2004) models and associated efficiency of lactation in cows fed low, medium and high metabolizable protein levels

Lysine Methionine Low Med High Low Med High Measurements Portal absorption 102 133 141 36 48 51 Liver removal -0.4 3 -17 -11 -15 -24 Post-liver supply 102 135 125 25 33 28 Mammary uptake -87 -93 -106 -24 -25 -28 Milk 70 75 80 24 25 27 NRC Total maintenance rqt 50 50 50 15.7 15.7 15.7 - urinary endogenous 8 8 8 2.5 2.5 2.5 - scurf 1 1 1 0.4 0.4 0.4 - MFPa 41 41 42 14 14 14 True SIDb 130 161 169 45 58 60

Efficiency maintenancec 0.67 0.67 0.67 0.67 0.67 0.67

Inefficiency maintenancec 22 22 22 7 7 7

Efficiency of lactationc 0.67 0.67 0.67 0.67 0.67 0.67

Inefficiency lactationc 23 25 26 8 8 9

Total inefficiencyc 45 46 48 15 16 16 Measured inefficiency 60 86 89 21 33 34

Measured efficiency of lactation 0.87 0.68 0.67 0.83 0.61 0.61

CNCPS Total maintenance rqt 45 45 45 14 14 14 - urinary endogenous 5.1 5.1 5.1 1.6 1.6 1.6 - scurf 0.7 0.7 0.7 0.2 0.2 0.2 - MFPa 39.3 39.3 39.3 12.2 12.2 12.2 True SIDb 128 160 167 44 57 59

Efficiency maintenancec 0.85 0.85 0.85 0.85 0.85 0.85

Inefficiency maintenancec 14 14 14 5 5 5

Efficiency lactationc 0.82 0.82 0.82 1.00 1.00 1.00

Inefficiency lactationc 13 14 14 0 0 0

Total inefficiencyc 26 27 28 5 5 5 Measured inefficiency 59 85 88 20 31 33

Measured efficiency of lactation 0.84 0.66 0.65 0.79 0.59 0.59

From Raggio et al. (2004) a Metabolic fecal protein. b SID: small intestinal disappearance: estimated as net portal absorption + 0.67 × MFP. c Used or calculated from the models.

Table 4. Amino acid composition of the different fractions of maintenance requirement (g AA / 100 g AA)

AA Whole empty

bodya Abomasal Isolatesb

Intestinal endogenousc

Proposed for MFPd Keratine

Arg 7.3 4.9 4.9 4.9 4.4 His 2.7 3.6 2.0 2.8 1.2 Ile 3.1 4.6 3.6 4.1 5.8 Leu 7.4 4.8 5.5 5.1 11.6 Lys 7 7.3 4.4 5.9 3.7 Met 2.2 1.5 1.3 1.4 1.2 Phe 3.9 4.6 3.8 4.2 4.3 Thr 4.3 6.5 6.3 6.4 8.4 Trp 0.8 1.9 1.6 1.8 1.6 Val 4.4 6.1 5.0 5.6 7

From Doepel et al. (2004). aAverage of values from Ainslie et al. (1993), Rohr and Lebzien (1991) and Williams (1978). bØrskov et al. (1986), except Trp (Stein et al., 1999). cJansman et al. (2002), in pigs. dMFP: metabolic fecal protein: average of abomasal isolates and intestinal endogenous. eBlock and Bolling (1951).

In contrast, MFP contributes to the majority of “maintenance” requirement. If we

consider what should be included in MFP, the first definition of MFP proposed by Swanson (1977) is certainly closer to metabolic demand because bacteria synthesized from feed or urea do not impose a demand on AA available to the animal, even when synthesized within the intestine. This definition corresponds exactly to endogenous fecal losses. The advantage of adopting the concept that MFP represents the endogenous fecal losses is that if we want to attribute an AA composition to these losses then reasonable estimates can be obtained from sources such as ruminal or abomasal isolates (Ørskov et al., 1986) from cattle fed exclusively via pre-duodenal infusions or from intestinal endogenous secretions in pigs (see Doepel et al., 2004 or Jansman et al., 2002). With the limitation that a similar digestibility within the small intestine has been ascribed to all sources of proteins, then model advanced by Ouellet et al. (2002) estimated that between 20 to 30% of the fecal endogenous losses originate from the small intestine, with the remainder arising from undigested endogenous secretions appearing at the duodenum. However, because the AA composition in endogenous secretion flowing at the duodenum is only derived from one study (Ørskov et al., 1986), and because of peculiar values for the ruminal isolate, probably the most appropriate current estimate is to average the AA composition values of abomasal isolate from Ørskov et al. (1986) with those of intestinal endogenous protein in pigs from Jansman et al. (2002). These latter values are the average from 16 studies where protein-free diets have been fed (Table 4) and are correspondingly more robust, although important differences may exist between ruminants and non-ruminants. If we compare the AA composition of whole empty body, used by CNCPS to estimate MFP losses with those used in our proposition, this yields very different values for requirement, especially for those AA with a high concentration in endogenous secretions, such as threonine or

those with a low AA content, such as methionine (Table 4). It can be seen that that estimations obtained with the new proposal are lower than those predicted by NRC and CNCPS (Table 5). Table 5. Estimation of average maintenance requirement for metabolizable protein

(MP), lysine and methionine according to NRC (2001)a, CNCPS and “new” (g/d) in cows from Raggio et al. (2004)

MP Lysine Methionine

urinary scurf MFP Endo MP TOTAL urinary scurf MFP TOTAL urinary scurf MFP TOTAL

NRC 105 15 574 170 694 7.6 1.1 41.3 50.0 2.5 0.4 13.8 16.7 CNCPS 105 15 807 927 5.1 0.7 39.3 45.1 1.6 0.2 12.2 14.0 New 105 15 457 577 7.4 0.5 26.8 34.7 2.3 0.2 6.3 8.8

aLysine and methionine requirement from NRC estimated with MP requirement and appropriate AA composition

NRC (2001) also includes in the maintenance requirement a component for

endogenous MP delivered at the duodenum. As we have seen in the previous paper (Ouellet et al., 2007), there is more knowledge emerging on this issue and endogenous proteins certainly contribute significantly to duodenal protein flow. In addition, other endogenous proteins are also secreted into the small intestine. Together, these secretions represent up to 0.70 kg of protein per day (Ouellet et al., 2002), almost as much as milk protein output! However, the real loss is only the amount that is not re-absorbed and secreted in the feces. Requirement for the synthesis of these proteins does not need to be taken separately from the synthesis of all other proteins not exported from the animal. Indeed, this synthesis represents only a fraction of total protein synthesis in a cow, which averages between 3 and 5 kg/d (Bequette et al., 1996; Lapierre et al., 2002).

The choice of the uptake to output ratio of AA across the mammary gland as the

efficiency of lactation is an over-simplification of the complexity of AA metabolism. This will be discussed in more detail, when the use of a variable efficiency of lactation in relation with supply is considered.

So, based on biological principles, it is recommended that: • MFP:

o represents the endogenous protein losses, o is calculated based on the estimation of ileal endogenous losses obtained

by Ouellet et al. (2002, 2005 & 2007) of 19 g MP/kg DMI, o has an AA composition corresponding to a composite value of abomasal

isolates in cattle and ileal endogenous secretions in pigs. • There is no need to include requirement for endogenous flows at the

duodenal level, this protein synthesis can be considered similar to protein synthesis in other tissues.

• The mammary uptake to output ratio should not represent the efficiency of lactation.

Clearly, these suggestions still have limitations and refinement is needed to better assess factors that would affect their magnitude as well as a more accurate quantification of AA composition. However, their description relies on biology and measurements made in dairy cows under practical conditions and should introduce improvements in the current models.

DEFINITION OF EFFICIENCY

Predictive Models

In NRC (2001) and CNCPS (Fox et al., 2004), once the “raw” requirements for net protein have been determined for maintenance or lactation, they are “transformed” into requirement of MP supply, using a single transfer coefficient (67% and 65%, respectively). NRC has a further recommendation for two AA based on their proportion of AA in MP (lysine at 7.2% and methionine at 2.4% of MP), hence also indicating a fixed efficiency for utilization of these AA. CNCPS defines the requirement of individual EAA based on a factorial approach, where the total requirement of each AA is the sum of requirements for maintenance, gestation, growth and lactation. To each function is attached a fixed efficiency, different for each individual AA but independent of supply (Table 6). The efficiencies of utilization of AA for maintenance are largely derived from one article (Evans and Patterson, 1985), whereas the efficiency of lactation is based on the uptake to output ratio of each AA across the mammary gland (Fox et al., 2004). Although it is acknowledged that “AA absorbed in excess will be used less efficiently, and those absorbed at levels below requirements will be used with a higher efficiency” (Ruiz et al., 2002; Fox and Tedeschi, 2003), there is no attempt to propose variations in these efficiencies. Table 6. Coefficients of efficiency for individual AA for maintenance or lactation used by

CNCPS (Fox et al., 2004) AA Arg His Ile Leu Lys Met Phe Thr Val Maintenance 0.85 0.85 0.66 0.66 0.85 0.85 0.85 0.85 0.76 Lactation 0.35 0.96 0.66 0.66 0.82 1.00 0.98 0.78 0.62

If we continue our comparison with the study of Raggio et al. (2004), examination of Table 2 clearly indicates that the estimation of maintenance requirements, from either NRC (2001) or CNCPS (2003), is unaltered by protein supply. This is not a surprise as they are estimated based on body weight and DMI in both cases. The predicted protein yield is the difference between the supply and the maintenance requirement divided by a fixed efficiency of utilization of the MP supply. Two results are striking when comparing predictions with real measurements: 1) at LowMP supply there is a clear underestimation of the production achieved whereas with the HighMP supply there is a overestimation of the protein yield (limitation from energy has been considered in the predicted yield in this case) and 2) as a result, the decrease in milk protein yield from HighMP to LowMP was less than half of that predicted. If we calculate the estimated

incremental loss of N in urine due to AA catabolism, using the fixed efficiencies, this should increase by 17 to 23 g of N /d, whereas the extra urinary N excretion increased by a measured 86 g/d, of which 64 g/d was related to increased AA catabolism, with the remainder attributed to increased ammonia and purine derivative absorption (Lapierre, unpublished). Determination of the “real” efficiency of lactation, based on milk protein output relative to MP supply available for milk clearly decreased with increased supply in this example, and would necessitate changes from 0.75 to 0.55 and from 0.79 to 0.53, for NRC and CNCPS respectively (Table 2). Knowledge on AA Metabolism

Extending the comparison with the net flux measurements made in the Raggio et al. (2004) study and focusing now on changes in AA metabolism occurring with alterations in protein supply, a similar conclusion on the variation of efficiency of utilization of single AA is also obtained (Table 3). The true small intestinal digestible flow was estimated from net portal flux plus MFP times the efficiency (0.67), as MFP is not accounted in the net portal absorption. With a maintenance cost based on NRC or CNCPS prediction removed, measured efficiency of lactation can be calculated as milk output divided by (supply minus maintenance requirement). Clearly, as was the case for total MP, the efficiency of utilization of each individual AA decreases as supply increases.

If we examine lysine first, of the 40 g/d increment in lysine supply (measured as the difference in true small intestinal disappearance) of the High vs. LowMP 10 g/d were incorporated into milk protein whereas 30 g/d were removed for non-productive purposes. The increased removal of lysine at the high MP supply was distributed between the liver and the peripheral tissues, including the mammary gland. This 30 g/d ‘loss’ (i.e. not used to support milk protein output) compares with predicted values by the NRC (2001) and CNCPS models of only 2 to 3 g. Overall, this decreased the “measured” efficiency of lactation from 0.87 to 0.67 (NRC) or from 0.83 to 0.61 (CNCPS) from LowMP to HighMP supply (Table 3). It is interesting to note that the mammary uptake to output ratio decreased with increasing supply for lysine, as it did for other AA of Group 2 (Raggio et al., 2004 and 2006). In the case of severe limitation of lysine supply, the ratio was as low as 1.05 (Lapierre et al., 2005b).

Similarly, of the incremental supply of 15 g/d of methionine only 3 g/d appeared as extra milk protein and, thus, the inefficient use of methionine increased by 12 g/d. For this AA, all of the increased removal occurred across the liver. The predicted increment of inefficiency would have been only 1 g/d by NRC and zero(!) for CNCPS. Therefore, as supply of MP or AA increases, tissues remove AA at a higher rate than the incorporation into milk protein. This increased catabolism probably occurs in many tissues for Group 2 AA and mainly in the liver for Group1 AA. The factors regulating uptake and removal of AA across tissues are not well defined. Many reviews have discussed the role of the liver as a responder or a controller of AA availability (Lobley and Lapierre, 2003; Ortigues-Marty et al., 2003; Hanigan, 2005; Reynolds, 2006). Although there is no clear answer to that question, it seems that the liver would be a responder of AA availability as the measurement that correlated the best with hepatic removal was the total influx to the liver: total influx is driven by arterial and portal

concentrations, the former being the result of supply and tissue utilization. Therefore, the mammary gland has “seen” an AA molecule several times before it is removed by the liver and so ‘catabolism’ only occurs if ‘anabolism’ is not increased. Nonetheless, clearly an increased supply results in an increased hepatic removal greater than milk protein output. Similarly, for Group 2 AA not extracted by the liver, increased oxidation of leucine has been observed across the PDV (Lapierre et al., 2002) or the mammary gland (Raggio et al., 2006) with increased supply. Whatever the site of AA catabolism, the message is clear that utilization does vary as a function of supply in our reference study.

Reconciliation Leads to Recommendations

Efficiencies calculated in Tables 2 and 3 are derived from a single study and although they clearly demonstrate the concept of the variable efficiency of utilization both a MP and AA level, the calculated efficiencies cannot really be extended to a wider range of studies. Therefore, to complement these observations, a meta-analysis was conducted with studies where AA or proteins were infused post-ruminally: these studies were selected in order to rely on a predictive model to assess only the AA supply of the control treatment, any additional supply of AA was known exactly as it was infused post-ruminally (see Doepel et al., 2004 for details of studies included therein). From that database, total milk protein yield, or yield of AA in milk protein, was defined as a function of MP, or individual AA supply, respectively, using a segmented-linear and a logistic model to obtain estimates of the efficiency of conversion of AA into milk protein. Both models were similar in their reliability, but the logistic model allowed for an estimation of the variable efficiency of conversion of AA as the supply is altered and, therefore, corresponded better to in vivo measurements (Figure 2; Hanigan et al., 1998). Estimations of the optimal supply of individual AA, as defined in Doepel et al (2004), are presented in Table 7. While the outcomes strictly apply only to this dataset, based on 59 studies and where average milk protein yield was 794 g/d, these optimal supplies have been summed and then each AA has been expressed relative to the summation to yield recommendations for individual AA relative to MP provision. Calculations of variable efficiency of lactation in relation to these optimal supplies have been updated from Doepel et al. (2004), using the AA composition of MFP proposed in this publication (Table 8). In continuation with Doepel et al. (2004), MP maintenance requirement have been estimated using the NRC model (2001).

One can argue, that by definition, the efficiency of maintenance requirement, representing what is happening when the cow is at maintenance, should not change, as adopted by current models and used by Doepel et al (2004). However, if we carefully examine the requirements included in maintenance, we observe that MFP, estimated based on the high DMI of a producing dairy cow, represents a large proportion of maintenance requirements. With the knowledge on AA metabolism, does it fit biological

Figure 2. Relationship between metabolizable protein supply and milk protein yield, expressed with the logistic modela

a Logistic model detailed in Doepel et al. (2004).

Table 7. Optimal absolute and relative amounts of digestible amino acid (AA) supply estimated from a logistic curve model fitted to the database of Doepel et al. (2004).

AA g/d % EAA %MP

Arg 91 9.6 4.6

His 48 5.1 2.4

Ile 105 11.1 5.3

Leu 175 18.5 8.9

Lys 142 15.0 7.2

Met 50 5.3 2.5

Phe 108 11.4 5.5

Thr 98 10.4 5.0

Val 129 13.6 6.5

Table 8. Efficiencies of utilization of amino acids from the database of Doepel et al. (2004)

% of optimal Efficiency for lactationa Combined efficiencyb Combined efficiencyc

supply 50% 75% 100% 125% 50% 75% 100% 125% 50% 75% 100% 125%Arg 0.73 0.57 0.50 0.44 0.72 0.67 0.62 0.55 0.68 0.63 0.58 0.52 His 1.17 0.92 0.79 0.71 1.02 0.90 0.80 0.72 0.96 0.85 0.76 0.68 Ile 0.88 0.73 0.65 0.59 0.80 0.75 0.70 0.63 0.77 0.72 0.67 0.60 Leu 0.80 0.68 0.60 0.54 0.76 0.69 0.63 0.57 0.74 0.67 0.61 0.55 Lys 0.94 0.78 0.68 0.61 0.84 0.78 0.72 0.65 0.81 0.75 0.69 0.62 Met 0.90 0.75 0.66 0.59 0.88 0.77 0.69 0.61 0.85 0.74 0.66 0.59 Phe 0.75 0.61 0.53 0.48 0.73 0.66 0.60 0.54 0.70 0.63 0.57 0.51 Thr 0.87 0.70 0.61 0.56 0.73 0.73 0.70 0.64 0.68 0.68 0.66 0.60 Val 0.89 0.72 0.63 0.57 0.83 0.76 0.69 0.62 0.79 0.72 0.66 0.59 MP 0.80 0.67 0.60 0.53 0.75 0.71 0.66 0.59 0.72 0.67 0.62 0.56

a Estimated as AA in milk protein on supply, after discounting the maintenance requirements, based on NRC model .

b Estimated as AA in milk protein plus the net requirement for maintenance (NRC, 2001) / divided by net supply of AA.

c Estimated as AA in milk protein plus the net requirement for maintenance (proposition in this paper) / divided by net supply of AA.

events to have a different efficiency for “maintenance” and for lactation? When considering the distribution of the enzymes responsible for the catabolism of AA and consequent localization of removal, with all (or almost all) of the Group 1 AA, including methionine, removed by the liver, why should we attribute different efficiencies with different functions? We have therefore recalculated a “combined” efficiency, where the net needs for maintenance and milk output were summed and the efficiency then calculated as the ratio of these net needs on total supply (Table 8). Just by mathematical reasoning, this efficiency is lower than the efficiency of lactation alone, when the efficiency of lactation was greater than 0.67, and greater for efficiency of lactation lower than 0.67. With the actual lack of knowledge on the real requirements for maintenance and, due to the large contribution of MFP to maintenance requirement, this combined efficiency may give a better practical assessment of AA requirement, especially for those AA in which AA composition is different between milk and maintenance requirements. In addition, the efficiencies have been estimated using the new proposed estimation for MFP, i.e. a net requirement of 12.7 g protein/kg DMI, yielding slightly lower efficiencies, due to the lower estimate of maintenance costs.

So, based on biological principles, it is recommended that: • Variable efficiencies of transfer of MP and AA supply need to be used, • A combined efficiency for both maintenance and lactation is proposed, as

we have such a poor appraisal of the real maintenance requirements. • The mammary uptake to output ratio is altered with protein supply for Group

2 AA and again, should not represent the efficiency of lactation.

CONCLUSION

The suggested recommendations offer stopgap, or intermediate, solutions to improve the current models. They each represent approximations and do not allow for interactions (such as between use of different AA-N across the mammary gland, or how changes in amount or type of energy alter outputs and efficiencies). Furthermore, as all the factors are based on empirical observations and equation fits, they do not permit accurate prediction between herds with cows with different genetic potential. Nonetheless, better appreciation of ‘true’ losses via digestive tract metabolism and the inclusion of a variable coefficient will yield immediate practical benefits to both feed producers and dairy farmers. Adoption of such changes should not deflect us from developing more mechanistic models, capable of responding to improved genetic selection, animal husbandry and feed processing technology and that can predict both within and between animal responses to both subtle and gross changes in nutrient inputs.

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