STABLE ISOTOPE TRACER ANALYSIS OF EXHAUSTIVE EXERCISE-INDUCED METABOLISM USING ULTRA-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY-

HIGH RESOLUTION MASS SPECTROMETRY

By

MICHELLE ELIZABETH REID

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2018

© 2018 Michelle Elizabeth Reid

To my parents

4

ACKNOWLEDGMENTS

An individual does not complete the doctoral process without the support and

encouragement of a community. I want to thank those who assisted me throughout this

journey, acknowledge those who motivated me, and thank God for guidance over my

life.

This process was riveting because of my academic advisor’s approval to explore

the many aspects of academia. I am indebted to you, Dr. Richard A. Yost, for the

knowledge and training I received under your direction. I appreciate your model as a

mentor; you are the “dad” and as one of your numerous “children,” I hope to follow your

path.

During my five-year tenure, I picked up mentors that assisted me with my

research and future scientific goals. I am thankful for my relationships with Timothy J.

Garrett, Ph.D., Irwin J. Kurland, M.D., and R. Elaine Turner, Ph.D. Dr. Garrett has been

a great resource for scientific discussions. Dr. Kurland has helped me significantly with

isotopic enrichment analysis and biological interpretations. As my collaborator, Dr.

Kurland prepared all of my mice samples at Albert Einstein Institute. Dr. Turner has

been a phenomenal role model and reading partner!

I would be remiss if I didn’t think my lab mates, past and present, for their

engaging scientific discussions. Candice Ulmer, Ph.D., Yu-Hsuan Tsai, Ph.D., Elizabeth

Dhummakupt, Ph.D., and Rainey Garland, Ph.D. helped me during my adjustment

phase into graduate school and with initial research projects. I want to especially thank

Dr. Garland for supporting my application to become a National Science Foundation-

Graduate Research Fellow (NSF-GRF). I am grateful for Atiye Ahmedi, Ph.D., Michael

Wei, Russell Lewis, Jiajun Lei and Emily Gill, Ph.D. for their feedback.

5

My friends have motivated me throughout this entire process and I express my

deepest sense of gratitude to them! The friends I made while at UF made Gainesville

feel more like home every passing year. I could not ask for better “sister friends” than

my adolescent best friends and Spelman sisters. I love you all.

I would like to thank my immediate family for consistent encouragement. My

extended family has cheered me on since the age of four and as they continue to cheer,

I value the words so much more. I thank my Aunt Portia, Aunt Kay, cousin Baayan and

cousin Nettie for conscious dialogue, moments of grounding, and reminding me that my

ancestors fought to overcome every system put before them.

Finally, Stephanie and James Reid, I am forever grateful for your parenting. I

cannot thank you enough for how you have saved my life and provided comfort in

moments of mental hardship. I am appreciative of your unconditional love and

dedication to my success as well as your endless provision of support to see me

accomplish my dreams. Thank you Mom and Dad for your prayers!

6

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 8

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 11

ABSTRACT ................................................................................................................... 15

CHAPTERS

1 INTRODUCTION .................................................................................................... 17

Exercise Physiology ................................................................................................ 17 Metabolomics .......................................................................................................... 18

Targeted Metabolomics Approach .................................................................... 19

Untargeted Metabolomics Approach ................................................................ 20 Liquid Chromatography........................................................................................... 21

Column Chemistry ............................................................................................ 22 Reverse Phase Chromatography ..................................................................... 23

Electrospray Ionization............................................................................................ 24

Mass Spectrometry ................................................................................................. 26

Quadrupole Mass Filter .................................................................................... 26 Orbitrap Mass Analyzer .................................................................................... 27

Data Processing ..................................................................................................... 29

Compound Identification ................................................................................... 30 Data Interpretation ............................................................................................ 31

2 ANALYSIS OF IN VIVO MODEL FOR PLASMA .................................................... 40

Limits in Exercise Physiology .................................................................................. 40 Cellular Oxidation During Exercise ................................................................... 40 Tricarboxylic Acid Cycle ................................................................................... 42

Isotopic Labeling Experimental Design ................................................................... 45

Chemicals ......................................................................................................... 49 Sample Preparation .......................................................................................... 50

Experimental Chromatography Parameters ............................................................ 52

Analytical UHPLC RP Columns ........................................................................ 52 Chromatographic System ................................................................................. 54

High Resolution Mass Spectrometry Instrumentation ............................................. 55 Data Analysis .......................................................................................................... 56 Results and Discussion........................................................................................... 57

7

3 TISSUE PRE-DATA ACQUISITION NORMALIZATION FOR MASS SPECTROMETRY .................................................................................................. 74

Tissue Preparation Background .............................................................................. 74 Experimental Design ............................................................................................... 75

Chemicals and Reagents ................................................................................. 75 Animal Preparation ........................................................................................... 76

Pre-Data Acquisition Normalization ........................................................................ 76

Liquid-Liquid Extraction........................................................................................... 78 Data Acquisition ...................................................................................................... 79 Data Processing ..................................................................................................... 82 Results and Discussion........................................................................................... 83

4 IN VIVO EXERCISE ENGAGED TISSUE ANALYSIS ............................................ 98

Physiologic Support System ................................................................................... 98 Experimental Methods ............................................................................................ 98

Sample Preparation .......................................................................................... 99 Data Acquisition ............................................................................................... 99

Data Processing ............................................................................................. 100 Metabolic Data and Interpretation ......................................................................... 100

5 CONCLUSIONS AND FUTURE WORK ............................................................... 106

REFERENCES ............................................................................................................ 108

BIOGRAPHICAL SKETCH .......................................................................................... 115

8

LIST OF TABLES

Table page 2-1 Partial list of stable isotopes ............................................................................... 69

2-2 Raw data parameters used for isotopic enrichment. ........................................... 70

2-3 MZmine data processing parameters. ................................................................ 71

2-4 Abbreviated in-house metabolite library ............................................................. 72

2-5 Raw signal of labeled and unlabeled samples .................................................... 73

3-1 Quadricep tissues total protein concentration. .................................................... 87

3-2 Liver tissues total protein concentration ............................................................. 88

3-3 Kidney tissue total protein concentration ............................................................ 89

3-4 Gastrocnemius tissue total protein concentration ............................................... 90

3-5 Fat tissue total protein concentration .................................................................. 91

3-6 Adjusted total protein concentrations for metabolomics and lipidomics analysis .............................................................................................................. 92

3-7 Cer (d18:1/18:0) S/N ratio .................................................................................. 93

3-8 LPE (18:0) S/N ratio ........................................................................................... 94

3-9 PE (18:0_22:6) S/N ratio .................................................................................... 95

3-10 Internal standard S/N ratio. ................................................................................. 96

3-11 Endogenous samples S/N ratio. ......................................................................... 97

9

LIST OF FIGURES

Figure page 1-1 Metabolic responses to exercise ........................................................................ 32

1-2 KEGG map of the cellular metabolome .............................................................. 33

1-3 Schematic of chromatographic elution ................................................................ 34

1-4 Schematic of electrospray ionization .................................................................. 35

1-5 Schematic of a quadrupole mass spectrometer.................................................. 36

1-6 Schematic of the Mathieu stability diagram ........................................................ 37

1-7 Schematic of an ion beam oscillating in an Orbitrap ........................................... 38

1-8 Data processing workflow ................................................................................... 39

2-1 Glycolysis pathway ............................................................................................. 60

2-2 Tricarboxylic acid cycle ....................................................................................... 61

2-3 Isotopic distribution of [U-13C]-glucose into glutamate of mammalian cells without anaplerotic pyruvate carboxylation ......................................................... 62

2-4 Isotopic labeling of glutamate of mammalian cells with [U-13C]-glucose incorporated through anaplerotic pyruvate carboxylation ................................... 63

2-5 Isotope tracer dilution experimental design ........................................................ 64

2-6 Schematic of Q Exactive Hybrid quadrupole-Orbitrap ........................................ 65

2-7 [U-13C]-glucose tracer incorporated into the TCA cycle in BALB/c mice plasma ................................................................................................................ 66

2-8 [U-13C]-glucose tracer incorporated into carnitine metabolism in BALB/c mice plasma. ...................................................................................................... 67

2-9 [U-13C]-glucose tracer incorporated into serine and glutamine in plasma from BALB/c mice ....................................................................................................... 68

3-1 Schematic for LipidMatch workflow .................................................................... 85

3-2 Total ion chromatogram of pooled quality control gastrocnemius samples ........ 86

3-3 Extracted ion chromatogram of ceramide (d18:1/18:0) in pooled quality control samples. ................................................................................................. 93

10

3-4 Extracted ion chromatogram of lysophosphatidylethanolamine (18:0) in of pooled quality control samples. .......................................................................... 94

3- 5 Extracted ion chromatogram (EIC) of phosphatidylethanolamine (18:0_22:6) in pooled quality control samples. ....................................................................... 95

4-1 Experimental design for exercised mice .......................................................... 102

4-2 Heart tissue [U-13C]-glucose enrichment data .................................................. 103

4-3 Liver tissue [U-13C]-glucose enrichment data ................................................... 104

4-4 Gastronemius tissue [U-13C]-glucose enrichment data ..................................... 105

11

LIST OF ABBREVIATIONS

acetyl-CoA Acetyl coenzyme A

ADP Adenosine diphosphate

AIF All ion fragmentation

AM Accurate mass

APE Atom percent excess

arb Arbitrary units

ATP Adenosine triphosphate

ATPase Adenosine triphosphatase

BEH Bridged ethylsiloxane/silica hybrid

BFF Blank feature filtering

C-trap Curved quadrupole linear ion trap

Cer Ceramide

CID Collision-induced dissociation

CL Cardiolipin

CP Creatine phosphate

CRM Charge residual model

D or 2H Deuterium

DC Direct current

ddMS2-topN Data-dependent top # MS/MS

DG Diacylglyceride

EIC Extracted ion chromatogram

ESI Electrospray ionization

FAD Flavin adenine dinucleotide

GC Gas chromatography

12

GTP Guanosine triphosphate

GTT Glucose tolerance test

H-ESI/HESI Heated-ESI

HCD High-energy collisional dissociation

HET Heterozygous

HR High resolution

i.d. Inner diameter

i.p. Intraperitoneal

IEM Ion evaporation mechanism

IRMS Isotope ratio mass spectrometer

KEGG Kyoto Encyclopedia of Genes and Genomes

LC Liquid chromatography

LIT Linear ion trap

LMQ LipidMatch Quant

LPC Lysophosphatidylcholine

LPE Lysophosphatidylethanolamine

m/z Mass-to-charge ratio

MPE Mole percent excess

MS Mass spectrometry

MS/MS Tandem mass spectrometry

NAD+ Nicotinamide adeninedinucleotide

NCE Normalized collision energy

NL Normalized intensity levels

NMR Nuclear magnetic resonance

PFP Pentafluorophenyl

13

ann Parts per million

QC Quality control

QE Q Exactive

QMF Quadrupole mass filter

rbk Raw isotopic ratio of heavy to light ions of the background

PC Pyruvate carboxylation or Phosphatidylcholine

PE Phosphatidylethanolamine

PG Phosphatidylglycerol

PS Phosphatidylserine

RCP American Red Cross blood plasma

Redox Oxidation-reduction

RF Radio frequency

Rh30 Rhabdomyosarcoma cells

ROS Reactive oxygen species

RP Reverse phase

rr Reference gas

rsa Raw isotopic ratio of heavy to light ions of the sample

RT Retention time

S-lens Stacked-ring ion guide

SIL Stable isotope labeling

SM Sphingomyelin

S/N Signal-to-noise ratio

TCA Tricarboxylic acid

TIC Total ion chromatogram

TG Triacylglyceride

14

TTR Tracer/tracee ratio

U-13C Uniformly Labeled 13-Carbon

UHPLC Ultra-high performance liquid chromatography

WT Wild-type

15

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

STABLE ISOTOPE TRACER ANALYSIS OF EXHAUSTIVE EXERCISE-INDUCED

METABOLISM USING ULTRA-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY-HIGH RESOLUTION MASS SPECTROMETRY

By

Michelle Elizabeth Reid

August 2018

Chair: Richard A. Yost Major: Chemistry

Exercise has numerous health benefits, including molecular remodeling for

preventative health as well as impact on nearly every biological system in the human

body. Exhaustive exercise infers substrates are involved in extensive recycling to small

molecules and complex lipids. Metabolomics and lipidomics are employed to investigate

the phenomena that occur in the body due to exercise. Metabolomics is an ever-

growing field that studies the small molecules in the metabolome. Stable isotope

labeling is an additional tool used to understand the consumption of substrates and

reuse/depletion of tracers for metabolome coverage. Chapter 1 covers the majority of

the background pertaining to this study, including exercise physiology, instrumentation,

and data processing.

The changes in the concentration of metabolites in plasma and muscle as a

result of exercise, both in humans and mice, are correlated with whole body

engagement. During exercise, glucose uptake is enhanced in tissues due to glucose

oxidation and glycolysis; however, limited metabolic interactions between tissues are

known. Mice were exercised at various conditions to monitor their response to a bolus

16

(2 mg/g body weight) intraperitoneal injection of uniformly labeled 13C [U-13C]-glucose

prior to exercise as a stable isotope tracer. Chapter 2 offers an informative background

on stable isotope tracer analysis as well as discussion of plasma enrichment patterns.

The tissues samples for analysis were normalized to total protein concentration for full

coverage of the lipidome; this work is detailed in Chapter 3. Lipids were extracted using

the traditional Folch method, and a separate aliquot was extracted in

acetonitrile:methanol:acetone (8:1:1, v:v:v) for metabolic analysis by liquid

chromatography/mass spectrometry. Data were collected on a Dionex liquid

chromatography system integrated with a Thermo Q-Exactive Hybrid Quadrupole-

Orbitrap mass spectrometer. This body of work interprets metabolic networks in

exercise-engaged biological specimen (plasma, gastrocnemius, heart and liver tissue)

of C57BL/6J and BALB/c mice.

In the present study, the metabolic response of gastrocnemius, heart and liver

tissues were investigated in an exercised mouse model to determine whether

metabolites changed in response to exercise, and if their response is correlated with the

bout of exercise, as covered in Chapter 4.

17

CHAPTER 1 INTRODUCTION

Exercise Physiology

During exercise, the entire body is impacted by the movement and energy

required to perform. The human body abides by the law of conservation of energy, and

it requires food for the utilization of energy to perform complex functions.1 Exercise

causes a brief metabolic shift throughout the entire body from its internal self-regulating

biological stability at rest, and can be measured by the following four parameters:

duration, frequency, intensity (or strenuousness), and circumstance (or purpose). The

energy required to complete a physical activity expressed as energy expenditure, and

one way of measuring expended energy is oxygen consumption (mL of O2 per kg of

body weight over a course of time, or oxygen taken into the body and used in the

tissues). Energy expenditure happens by three processes in humans and animals,

including resting metabolic rate, dietary induced thermogenesis, and muscular activity

(or exercise). The three major forms of exercise are aerobic, anaerobic and flexibility,

where endurance and high-intensity are classified under aerobic exercise. Individual

bouts of aerobic exercise disrupt the metabolism and as a result of training, these

various forms of exercise can permanently shift the metabolism. High-intensity exercise

at shorter time frames improves maximal oxygen uptake (VO2 max), as opposed to

moderate exercise over longer time periods.1–5

Through exercise and exercise training, molecular and cellular changes occur.

Exercise is an environmental exposure (or exposome) that structures the human

genotype and phenotype. From an evolutionary perspective, as our culture has

industrialized and become far more sedentary in motion, the average size and strength

18

of skeletal muscles in the human body has decreased. Epigenetic adaptations related to

exercise have grown exponentially, with some notable examples, including histone

methylation and increased oxygen capacity of myotubes, but further research is

necessary to uncover biochemical mechanisms.6–8 Furthermore, phenotypic changes of

higher insulin resistance and greater homeostatic disruption of cellular metabolism

during extraneous exercise are noted in literature.6 Yet, present-day phenotypic

responses of altered protein content and enzyme activity are noted of exercise and

exercise training.3,9 During exhaustive exercise, ATP turnover within skeletal muscle

can increase up to 100-fold, oxygen consumption can increase 30-fold and TCA flux

can increase up to 100-fold from rest, as highlighted in Figure 1-1.10–12 Additionally,

exercise has many health benefits, including the decrease of intrahepatic lipids in non-

alcoholic fatty liver disease, reduced risk of depression, hindered muscular aging,

decreased risk of cardiovascular disease, increased insulin sensitivity in type 2 diabetes

patients, and improved physical function in breast cancer patients.13–17

Metabolomics

Metabolomics is a comprehensive tool for investigating small molecules, as they

are perturbed by chemical reactions within a biological system becoming products,

substrates, or metabolites, through identification and/or quantification.18,19 This

experimental tool represents the activity of the metabolic network and reflects the

underlying status of the system studied. The goal of the field is to understand the result

of a perturbation to a biological system, such as cancer, disease, exposure and diet, as

well as to uncover the breadth of the metabolome.20–22 Metabolites primarily function is

metabolism and energy storage utilization. Furthermore, cell signaling is a secondary

role of metabolites within the cell.

19

This field also includes lipidomics, a subclass of metabolomics that covers the

lipidome and lipid-related species.23 These small molecules (less than 1,500 Da) are

characterized and quantified to better understand biological responses to internal and

external stimuli of biospecimens in a diverse group of fields.24 The scope of this work

focuses on the human metabolome coverage depicted in Figure 1-2 by the Kyoto

Encyclopedia of Genes and Genomes (KEGG) mapped pathways.

Metabolites chemically range from polar to non-polar compounds and can be

highly volatile. These characteristics are correlated to their function, thus requiring

innovative tools to study their behavior in biological specimens. Due to the broad

chemical diversity of metabolites, several approaches have been taken to characterize

and quantify these species. Mass spectrometry (MS) and nuclear magnetic resonance

(NMR) are two leading technologies predominately used for uncovering the

metabolome. Each platform has advantages compared to the other. For example, NMR

has high reproducibility, excellent quantitation and it is a nondestructive technique.

However, NMR requires a larger quantity of sample than MS and NMR possesses a

limited concentration range. Alternatively, MS is provides elemental information for a

wide range of metabolites at varying concentrations (orders of magnitude) with better

sensitivity and selective than NMR. Due to numerous ionization methods, MS can also

be used for selective analysis across various polarities. For this study, mass

spectrometry was employed for those reasons.

Targeted Metabolomics Approach

Targeted metabolomics is usually driven by a biological question that targets

specific pathways or distinct set of metabolites within the studied organism. This

approach of studying metabolites is considered the original process of inquiry. Although

20

the term was recently coined, what early scientists were investigating can be considered

targeted metabolomics, specifically the early works of investigating glucose in urine.19,25

During targeted investigations, known ions of specific metabolites within a

pathway are monitored for comparative levels for an in-depth understanding or pathway

analysis. The selectivity of targeted metabolomics allows for extraction of information

pertaining to a precise metabolite in a complex mixture that could be overlooked or at

low concentration levels when analyzed using an untargeted method. Classically,

tandem mass spectrometry (MS/MS) is used to identify select ions and fragment them

to identify the compound based on elemental composition. Additionally, NMR can detect

the absolute concentration of molecules within the sample without the use of standards

and can also provide greater structural information than MS/MS.18,19,26

Untargeted Metabolomics Approach

Untargeted (or nontargeted) metabolomics is thought to be a discovery method,

and deemed a global or profiling representation of the metabolome to measure as many

metabolites as possible for a phenotypic signature. Therefore, this approach is ideal for

biomarker discovery, diagnostic and comparative (i.e. disease v.s. normal) research. In

contrast to a targeted metabolomics experiment, which measures ions from known

metabolites, an untargeted metabolomics experiment registers all ions within a certain

mass range, including ions belonging to structurally novel metabolites. A major

challenge of untargeted metabolomics is the ability to cover a wide range of molecules

(i.e. hydrophobic and hydrophilic) in a single extraction and analytical run.27,28 Thus, an

extraction for metabolites and lipids is utilized to explore both chemical regions.

Because of its ability to detect metabolites without separation, ionization or

derivatization, NMR is an exceptional technique for global profiling. However, if in the

21

discovery phase there are low abundant metabolites, MS is preferential. Another

characteristic of untargeted metabolomics is excess samples; typically high throughput

screening is conducted to measure numerous extracts and thousands of unknown

peaks.18

Liquid Chromatography

Based on the affinity of an analyte to the mobile or stationary phase, liquid

chromatography (LC) allows for separation based on chemical properties to be

achieved as well as isolation of interferents from complex matrices along an analytical

column. Mikhail Tswett is considered the father of chromatography and arguably liquid

chromatography because of his use of an adsorbent and nonpolar liquid to isolate

chlorophylls and carotenoids.29 The popularity of LC is due to its simple sample

preparation, column versatility and broad applicability.

Prior to analysis, the analytes of interest are prepared in a solvent compatible to

the mobile phase. When injected into the system, the sample experiences a continuous

flow of mobile phase through the stationary phase resulting in differential migration. The

analytes with polarity favorable towards the mobile phase move quickly through the

column while those with a strong affinity to the stationary phase elute at a slower rate.

Ultra high performance liquid chromatography (UHPLC) is attributed to sub-2 μm

particles and high pressure in the range of 6,000 to 15,000 PSI.30 The decrease in

particle size from 5 μm to 2 μm allows for increased separation efficiency and

sensitivity, as well as, increased speed of analysis. Furthermore, the smaller particle

size aids metabolite identification by structural separate due to chemical modifications.

22

Column Chemistry

As mentioned before, the polarity of analyte strongly impacts its affinity towards

the stationary or mobile phase the resultant being a shorter or longer retention time (tR)

as the analyte moves through the column. Equation 1-1 describes retention time

𝑡𝑅 = 𝑡𝑆 + 𝑡𝑀 (1-1)

where tS represents the time an analyte is retained in the stationary phase and tM is the

total volume obtained by the column (void volume) multiplied by the flow rate (Figure 1-

3). The tR can also be described by Equation 1-2 as the time needed for the apex of the

peak to reach the end of the column (L) inversely proportional to the average velocity

(𝒱) of solute crossing at that plane. The flow rate is determined by the LC pump and

equivalent to the volume of the mobile phase over a period of time.

The thermodynamic and kinetic properties that occur within the column impact

the column’s separation efficiency and reproducibility. Additionally, the number of

theoretical plates (N) is one of the factors that determines the chromatography

efficiency, which can be defined by Equation 1-3

𝑁 = 16 (

𝑡𝑅𝑊𝑏)2

(1-3)

where Wb is the peak width at the baseline. This term is an imaginary number of plates

that a column can be divided into and the plate height (H) is another important

theoretical factor. As plate height decreases and plate numbers increase, the column

efficiency increases. These two factors are related by their contribution to column length

(L) as detailed in Equation 1-4.31

𝑡𝑅 =

𝐿

𝒱

(1-2)

23

𝐻 =

𝐿

𝑁

(1-4)

The movement of analytes through a packed column can be described by the

application of the peak height theory in the high-velocity form of the van Deemter

equation (Equation 1-5).

𝐻 = 𝐴 +

𝐵

𝑢+ 𝐶𝑢

(1-5)

In the van Deemter equation, A represents the Eddy diffusion, B is longitudinal diffusion,

C is the mass transfer, and u is the linear velocity. All terms impact band broadening of

the chromatographic peak. The Eddy diffusion term is directly proportional to the particle

size and inversely related to the solute diffusion in the mobile phase, which evaluates

the column density and uniformity. The B/u is the diffusion of through numerous

obstacles (or particles) and their engagement with the edge or center of the column.

Finally, Cu pertains to the resistance to mass transfer of the analyte from the stationary

phase to mobile phase and vice versa. This term is directly proportional to the square of

the particle diameter, and therefore smaller particle size vastly reduces the C term in the

van Deemter equation (Equation 1-5).32 Smaller particle size (less than 5 μm) columns

can be operated at high linear velocity (or flow rate) without impacting the efficiency

enabling increased separation speed.33

Therefore, the UHPLC produces improved separation through narrower peaks,

faster analysis times, higher peak capacity and decreased solvent use compared to LC,

which requires longer run times.

Reverse Phase Chromatography

The mechanism of retention impacts the elution of a molecule. In reverse phase

(RP) liquid chromatography, the mobile phase is more polar than the stationary phase

24

(non-polar or hydrophobic) and because the mobile phase is typically an aqueous

solution this technique is ideal for metabolite separation. During RP separation, more

polar analytes elute first (which can be problematic if in the void volume) and the non-

polar molecules come off the column towards the end of the chromatographic run due to

their affinity. The quick equilibration of the stationary phase to the mobile phase

facilitates the use of gradient elution instead of the sole practice of isocratic ratios.

Additionally, pH adjustments can be made to the mobile phase and modifications can

be made to the stationary phase in order to tailor this technique towards an ideal

separation mechanism.31,33

Electrospray Ionization

In the late 1960s, Malcolm Dole sought ways to successfully ionize intact, gas-

phase synthetic polymers. He did so with the invention of electrospray ionization (ESI).34

However, John Fenn’s research group is recognized for revolutionizing the technique

and interfacing ESI with LC and MS.35 Sir Geoffrey Taylor and John Zeleny detailed the

underlying electrostatic hydrodynamic instability principles of electrospray, as early as

1917.36,37 The soft atmospheric pressure ionization technique generates multiply-

charged ions that allow for the detection of macromolecules, such as proteins, and

accurate mass measurements of small molecules, alluding to the wide m/z value range

(greater than 100,000) of ESI.38–40

During ESI, a solution containing analytes moves through a capillary and is

exposed to an electric field, creating a fine mist sprays and droplets form as differential

pressure aids the movement of ions into the MS inlet. This simplistic description of ESI

is depicted in Figure 1-4. The electric field promotes ionization, and the spraying

mechanism is achieved by an electric potential between the tip of the electrospray

25

needle and a counter-current electrode on the MS inlet, which can be adjusted based

on the charge of preformed ions in solution or gas phase ion-ion reactions. Heated

auxillary gas or capillary, termed heated-electrospray ionization (H-ESI), can assists in

the transfer through the electric field by eliminating freezing of the small droplets due to

endothermic loss. At the tip of the electrospray needle, high voltage and sheath gas

assist in the development of highly charged droplets while auxiliary gas aids in

desolvating the droplets thus, forming a Taylor cone at the spraying orifice.

The continued mechanism of ion formation is debated amongst the field. Two

proposed models for the droplet minimizing to become ions include the charge residual

model (CRM) and ion evaporation model (IEM). As the solvent evaporates, both models

propose that increasingly smaller droplets reach the onset of coulombic repulsion (or

explosion), then, the Rayleigh stability limit is reached and electrostatic repulsion

overcomes surface tension, generating a plume of gas phase ions that enter the ion

transfer tube.35,38,39,41

Complete solvent evaporation is theorized in the CRM, where sequential loss of

solvent by evaporation causes an analyte to have remaining charges after droplet

fission. This theory is expected to occur with larger polymeric molecules, such as

proteins. The IEM mechanism releases ions from the surface of droplets. The ions are

directly freed or desorbed from the small droplet and the separation due to identical

electric charge at the surface between very small droplets suppresses the effect of the

cohesive force.35,38,40,41 These models generate a wide range of ions and cooperate with

the use of any solvent.

26

Mass Spectrometry

Quadrupole Mass Filter

The quadrupole mass filter (QMF) has seen few modifications to the design since

its introduction in 1953 by Wolfgang Paul and Helmut Steinwegen, which further

investigated Nicholas Christophilos’ 1951 ion focusing research.42 Until this innovation,

mass spectrometers were unable to adjust magnetic or electric fields while analyzing

ions. These sector instruments resolved ions based on their momentum instead of m/z

value.43 Once commercialized the quadrupole mass spectrometer was popularized due

to its combination with GC, providing a shorter flight path and faster scanning periods.

The design of a quadrupole is shown in Figure 1-5, with four round rod electrodes that

are parallel to one another in a symmetrically square arrangement about the z-axis.

The QMF allows selected m/z values to pass through the rods along the z-axis.

This is possible because of the combination of direct current (DC) and radio frequency

(RF) quadrupolar electric fields. In time, the RF amplitude and DC potential are set to

constant values only permitting ions of a specific m/z value. These ions are mass

filtered through the QMF by avoiding impact with the surface of rods. By scanning the

RF/DC amplitude along the operating line (or mass scan line, with a constant RF/DC

ratio) from low to high mass, or visa versa, stable ions will traverse through the

quadrupole producing filtered mass analysis. The separation of ions according to their

m/z value occurs based on the stable trajectories in the quadrupolar electric fields.43

The ions avoid collision surfaces based on their stability according to the Mathieu

equations (Equations 1-6 and 1-7) deduced from Wolfgang Paul’s work representing the

motion along the x- or y-axis.40,44

27

𝑈 = 𝑎𝑢

𝑚𝜔2𝑟02

8𝑒

(1-6)

𝑉 = 𝑞𝑢

𝑚𝜔2𝑟02

4𝑒

(1-7)

Equations 1-6 and 1-7 detail the motion of an ion dependent upon the mass and

electronic charge ratio (m/e) when the DC potential (U) or RF amplitude (V) are applied.

The stable trajectories in the QMF of the DC potential (U) and RF amplitude (V)

correspond to au and qu, respectively. The additional measurements included in the

equations are the applied frequency (ω), distance from the z-axis to an electrode

surface (r0).45 In Figure 1-6, the Mathieu diagram for the most commonly used stable

trajectory region is shown with the RF amplitude (V) and the positive DC potential (U)

depicted. Theoretically, the negative U should function in the same manner.44

As the U/V ratio is increased the operating line will approach the tip of the stable

region, producing higher resolution but poor transmission of ions. However, those

properties have been addressed with further development of the mass filters.46

Additionally, the ions of lower mass are unstable in the x direction while the higher mass

ions are unstable in the y direction because their m/z values is inversely proportional to

the q and a, respectively. The QMFs increased resolving power, fast scan time, and

tolerance of relatively high pressure makes it an ideal instrument for isolating ions.

Orbitrap Mass Analyzer

The Orbitrap mass analyzer was developed in 2000 by Alexander Makarov,

which was designed based on the Knight-style Kingdon trap.47–49 The original trapping

device theorized the removal of the electric field from Albert Hull’s theory of motion. In

Hull’s theory, ions moved between coaxial cylinders with electric and magnetic fields,

28

and the anode cylinder was larger than the cathode.50 The Kingdon trap confined

positive ions at 10-7 Torr neutralizing the emission from the central filament in previous

designs. This Kingdon trap design removed limiting factors such as leakage of ions and

effects of gas molecules colliding with ions. This trap went on to be used for detecting

molecular beams, the “Orbitron” ion pump, and increased trapping times (a few

seconds) of molecular ions that were singly charged. 51

The Kingdon trapping device has been widely used in spectroscopy. Makarov’s

research brought this technology into mass analysis as a means to trap in an

electrostatic field orbiting with a potential difference as well as optimized ion cluster

introduction into the trap. The design consists of an inner axial spindle-shaped electrode

and outer barrel, coaxial electrode depicted in Figure 1-7. The trapped ions do not

experience a dynamic or magnetic electric field. Instead, two outer cylindrical electrodes

(with an applied DC voltage) generate a constant electric radial nonlinear (logarithmic)

field. This effect electrically isolates the inner electrode. The ions are introduced at 90°

to the z-axis through a narrow channel and orbit the inner electrode due to the strong

radial electric field. As the voltages are applied upon injection, the tangential speed

accelerated by the reflector creates an opposing centrifugal force. The amplitude of the

circular orbiting ion packet (r) is equivalent to the kinetic energy (eV) of the ions upon

entrance and inversely proportional to the applied electrodynamic forces (eE) explicitly

detailed in Equation 1-8.

𝑟 =

2 𝑒𝑉

𝑒𝐸

(1-8)

As the ions oscillate, the axial inhomogeneous electric field moves the cluster towards

the dielectric end-spacers (or widest region between the electrodes) resulting in

29

harmonic mass-dependent oscillations around the central electrode.52 Equation 1-9

describes the motion of the ion packet in the quadrologrithmic potential due to the ion

trap and cylindrical electrode fields,

U(r, z) =

𝑘

2(𝑧2 −

𝑟2

2) +

𝑘

2(𝑅𝑚)

2ln (𝑟

𝑅𝑚) + 𝐶

(1-9)

where r and z are cylindrical coordinates, k is the field curvature, Rm is the characteristic

radius, and C is a constant. Due to oscillation mass dependence, the frequency of the

ions oscillating along the axis (ω) can derive the mass-to-charge (m/q) ratio according

to the relationship shown in Equation 1-10.

𝜔 = √(

𝑞

𝑚)𝑘

(1-10)

The axial oscillations, which are independent of energy and spatial spread of

ions, induce an image current on the two split outer electrodes that are used as receiver

plates. The image current is then amplified and detected as a time-domain signal. The

frequencies are converted to a mass spectrum using fast Fourier transform. The mass

range is 4,000 with a resolving power of 100,000 to 240,000 that aids separation of

similar mass ions.53 The orbitrap’s mass accuracy is as low as 2 ppm (parts per million)

to assist in properly assigning a mass. The acquisition speed ranges from 1-5 Hz with a

5,000 linear dynamic range.54 The major advantages of using an orbitrap for mass

analysis include fast scan speeds, high mass accuracy, high resolving power, and

increase dynamic range and sensitivity.

Data Processing

Data processing is the major bottleneck of UHPLC/HRMS untargeted

metabolomics studies. The samples can be directly infused requiring no preparation.

30

However, sample preparation can take several hours. The data acquisition phase can

range from seconds to days where as, data post-acquisition processing can take

minutes to months depending on the type of experiment. Luckily, there are software

platforms (proprietary and open source) that speed up the procedure of untargeted

metabolomics. This step conveys the metabolomics and lipidomics extracted samples to

the biological question at hand.

Several necessary steps are needed to resolve complex chromatography and

mass spectra data. The complete general workflow is shown in Figure 1-8. The left

three boxes indicate major open source software platforms that are used for data

conversion, filtering and reduction. Mass spectra data files can range from 200 to 300

MB, and require high processing computers for data handling. However, for online

software platforms the data are entirely too large to process. msConvert (ProteoWizard)

was used to convert files from proprietary .raw files to open source .mzXML (full scan)

and .ms2 (MS/MS) files.55 To reduce file size data are centroided from profile collection,

and mass peaks are picked based on the center of the mass peak. The output .mzXML

files are input files for MZmine to detect peaks, alignment peaks (retention and mass),

smooth, and gap fill.56 Data are output into an Excel file (.csv) for manual formatting by

removing ion adducts and complexes, and blank chemical noise using Blank Feature

Filtering (BFF).57

Compound Identification

Identification is noticeably challenging yet necessary for untargeted

metabolomics. The identification of features produced by mass spectrometry is rigorous

due to the similarity in compound structure and generic fragmentation patterns.

Untargeted metabolomics identification has to distinguish between metabolites of

31

different nominal masses. Additionally, the same nominal mass but different molecular

formula, and monoisotopic mass must be differentiated. Also, untargeted metabolomics

has to be address the distinction between the same nominal and monoisotopic masses,

but different chemical structures. This single step in the metabolomics process is

notably the major bottleneck amongst mainly scientists based on a 2009 survey

(http://fiehnlab.ucdavis.edu/staff/kind/Metabolomics-Survey-2009/) and very few

changes have advanced this struggle.58–61

For metabolites, an in-house library, established by several graduate students,

technicians, and scientists within the Garrett research group, containing over 1,000

metabolites (in positive and negative mode) was used to identify features once entered

into MZmine. The in-house library is based on accurate m/z values and retention times

(RT) of authentic standards curated specifically to our instrumentation. The exact

retention time and experimental monoisotopic mass enables compound identification

with the addition of tandem mass spectral data. The data acquired for lipids utilized an

in silico library with fragmentation data called LipidMatch for identification detailed in

Chapter 2. LipidMatch was developed by Jeremy Koelmel to identify lipid compounds

based on exact mass and fragmentation patterns.62

Data Interpretation

Finally, acquired data were interpreted with the help of literature and my

collaborators to best determine the impact of exercise on the metabolic physiology. The

scientific findings throughout this document consulted literature studies on exercised

humans as well as mouse models that utilized similar experimental designs. Specific

biochemical pathways were targeted for a deeper understanding of the work and

interpretation of the data. These metabolic pathways are significant in cellular

32

energetics as well as exercise physiology. For metabolomics and lipidomics samples,

an untargeted approach was appropriate for a biological interpretation beyond metabolic

pathways within this document to observe perturbations and distributions of isotope

dilution due to exhaustive exercise.

Figure 1-1. This image highlights various systems and organs that engaged during exercise. Notably, the graphic displays the on whole body engagement during exercise indicating central nervous system responses, adipose tissue and skeletal muscle metabolism. (Modified with permission from Hawley, J. A.)5

33

Figure 1-2. KEGG map of the cellular metabolome branched into six major compartments, lipids, nucleotides, amino acids, vitamins, xenobiotic biodegradation and central carbohydrate metabolism. (Modified from KEGG Atlas)63

34

Figure 1-3. An ideal, simple chromatographic depiction of retention where tM is the total volume obtained by the column (void volume), tR is the retention time lacking the void volume, and Wb is the peak width at the baseline. (Interpreted from Hoffmann, E. et. al.)44

35

Figure 1-4. Simplified electrospray ionization of a solution exposed to an electric field moving through a capillary generating a fine mist spray. The droplets form as differential pressure aids separation and ions disperse (exaggerated for clarity) from the Taylor cone into the MS inlet. (Interpretated from Hoffmann, E. et. al.)44

36

Figure 1-5. Schematic of a quadrupole mass spectrometer with the RF and DC voltages supplied to two opposite pairs of rods enabling the transmission of stable ions (Adapted from Miller, P. et. al.)43

37

Figure 1-6. The Mathieu stability diagram shows the RF (V) and DC (U) potentials of this region that allows ions along the operating line that are within the stable oscillation region (m+1) to move through the mass filter. When scanning from high to low potentials at a constant RF/DC ratio larger ions along the operating line are permitted because the fall within the stable oscillation region. When the DC amplitude is set to zero, all ions transverse through the QMF. (Adapted from Miller, P. and Brubaker, W.)42,43

38

Figure 1-7. The red arrows indicate the oscillation trajectory of an ion packet that enters

the orbitrap through a narrow channel and applied voltage to the deflector lens excites ions as they enter the trap. The quadrologrithmic field that aids harmonic oscillations is described in Equation 1-9. (Adapted from Makarov, A.)64

39

Figure 1-8. The successive steps of my data processing workflow are shown. msConvert minimizes the file size and converts the data to an open source file format that is an input for MZmine. Peak picking, chromatogram and mass alignment are the major benefits of using MZmine and the output Excel file is filtered by blank feature filtering (BFF).

40

CHAPTER 2 ANALYSIS OF IN VIVO MODEL FOR PLASMA

Limits in Exercise Physiology

Exercise has a positive impact on human physiology and metabolism. These

characteristics can be quantified in earlier mentioned metabolic responses. However,

the mechanism and pathways that are explicitly altered by exercise are still elusive.65 In

this chapter the phenotype of plasma is explored as a means to understand the

increase circulation of oxidative compounds. This work will start by considering cellular

metabolism in literature and progress to organ tissue LC/MS analysis.

In eukaryotic cells, catabolism of glucose is central to energy metabolism.

Glucose catabolism to two pyruvic acid molecules (Figure 2-1) occurs in both anaerobic

and aerobic exercise, but the degradation to carbon dioxide and water is characteristic

of aerobic energy. The major biochemical reactions that harvest aerobic energy are

phosphorylation and oxidation.

Cellular Oxidation During Exercise

In the late 1970s, Dillard and Brady discovered that throughout muscular

exercise an increase in oxidative biomarkers occur in human and animals, leading to

the evaluation of reactive oxygen species (ROS) in exercise-engaged organs.66 This

research led to the conclusion that exhaustive exercise increases production of

oxidative stress biomarkers and led to the recent focus on oxidation-reduction (redox)

balance. While oxidative stress will not be the focus of this work, the mechanisms

behind oxidative stress are discussed to provide context for the biochemical reactions

that occur during exercise.

41

Determining the source of energy is important for effective physical activities.

During the biochemical reactions, exergonic energy is released from carbohydrates and

incorporated into the body through the free energy carrier, adenosine triphosphate

(ATP). The energy-receiver and energy-donor model within the cell is utilized to form

and conserve ATP, and extract phosphate bond energy for cellular energetic activities.

Energy is stored in ATP in the form of high-energy phosphate bonds within the

molecules. The breaking of those phosphate bonds (through hydrolysis) leads to the

formation of adenosine diphosphate (ADP) liberating approximately 7.3 kcal per mole of

energy, which is directly transferred to other energy-requiring reactions. Though ATP is

a great source of energy, its storage within the body is limited to roughly 85 g at any

instance.2 Therefore, creatine phosphate (CP or phosphocreatine), an additional

phosphate-rich compound is used for energy conversion. Creatine phosphate is broken

down by the enzyme creatine kinase to form creatine and phosphate, releasing energy

from the high-energy phosphate bond for the reformation of ATP.2,67

𝐴𝑇𝑃

𝐴𝑇𝑃𝑎𝑠𝑒↔ 𝐴𝐷𝑃 + 𝑃 −

7.3 𝑘𝑐𝑎𝑙

𝑚𝑜𝑙𝑒

(2-1)

𝐶𝑃 𝑐𝑟𝑒𝑎𝑡𝑖𝑛𝑒 𝑘𝑖𝑛𝑎𝑠𝑒↔ 𝐶𝑟𝑒𝑎𝑡𝑖𝑛𝑒 + 𝑃 (2-2)

Storage levels for CP is considerably higher than ATP and they do not require the

constant consumption of oxygen, thus making utilization instantaneous.2,68,69

Another essential energy biochemical process within the cell is oxidation-

reduction reactions. During oxidation reactions electrons are donated and during

reduction reactions electrons are accepted. Carrier molecules in the mitochondria

remove the electrons in the oxidation step and pass them to an oxygen in the reduction

step. These carrier molecules are the coenzymes nicotinamide adenine dinucleotide

42

(NAD+) and flavin adenine dinucleotide (FAD) (both are electron acceptors). The NAD2,

or FAD, is reduced to NADH, or FADH2 by accepting electrons from hydrogen atoms.

The NADH and FADH2 formed, carry electrons from the hydrogen atom with high-

energy transfer potential, which is conserved in the formation of ATP.67,70

This process of oxidative phosphorylation is synthesis of ATP through the

transfer of electrons from NADH is depicted in Equation 2-3. FADH2 undergoes a similar

mechanism

𝑁𝐴𝐷𝐻 + 𝐻+ + 3 𝐴𝐷𝑃 + 3 𝑃 +

1

2𝑂2 → 𝑁𝐴𝐷

+ + 𝐻2𝑂 + 3 𝐴𝑇𝑃 (2-3)

Oxygen plays a critical role that can easily be overlooked, as the final electron acceptor

to form water. Oxidative phosphorylation is an aerobic metabolic process, and to refer

back to VO2 max, determines the capacity for ATP production as well as the oxygen

consumption. This cascading process accounts for over 90% of ATP synthesis and

approximately 40% of chemical energy is harnessed for exercise while the remaining is

lost as body heat.

Tricarboxylic Acid Cycle

For exhaustive exercise, anaerobic metabolism provides the final steps for

energy transfer once rapid energy is released through glycolysis and lactate

accumulates in skeletal muscle. Energy from glucose and stored glycogen is provided

for the rapid resynthesis of ATP required during strenuous exercise. As exercise

continues for more than 2-3 minutes, high-energy phosphates are resynthesized rapidly

this energy to phosphorylate comes from glucose and stored glycogen. If the energy

demands outperform cellular capacity for the aerobic resynthesis of ATP or exceed

oxygen supply, lactic acid is formed.2

43

Blood lactate concentrations rise exponentially for aerobic metabolism as

exercise prolongs and intensities increase. Two theories are believed to promote lactic

acid build up. One mechanism, during rapid glycolysis, an imbalance of hydrogen

release and oxidation would occur causing pyruvate to accept excess hydrogens. The

second mechanism involves fast-twitch fibers utilization of lactate dehydrogenase to

favorably form lactate. Once lactate is reconverted to pyruvate (as sufficient oxygen is

taken up during steady state aerobic metabolism) and the hydrogen atoms are

relinquished by the NAD+ co-factor, additional energy can be obtained by irreversibly

converting pyruvate to acetyl coenzyme acetic acid (acetyl-CoA), detailed in Equation 2-

4.

𝑃𝑦𝑟𝑢𝑣𝑖𝑐 𝑎𝑐𝑖𝑑 + 𝑁𝐴𝐷+ + 𝐶𝑜𝐴 → 𝐴𝑐𝑒𝑡𝑦𝑙 − 𝐶𝑜𝐴 + 𝐶𝑂2 + 𝑁𝐴𝐷𝐻 + 𝐻+ (2-4)

Acetyl-CoA subsequently enters the tricarboxylic acid (TCA) cycle (or Krebs cycle). The

TCA cycle breaks down the acetyl-CoA substrate to 4 carbon dioxide molecules and 16

reducing equivalents for oxidative phosphorylation regeneration of ATP.2,67,70

As previously mentioned, glucose is utilized as the central substrate for energy

metabolism during extreme exercise. Once glucose is catabolized through glycolysis

(Figure 2-1), the products are 2 molecules of pyruvic acid that are decarboxylated and

shuttled into the TCA cycle (Figure 2-2). The key role of the TCA cycle is to generate

reducing agents, NAD+ and FAD, for the electron transport chain, which yields a total of

24 ATP molecules. The 8 reactions of the TCA cycle begin with acetyl Co-A and

oxaloacetate reaction catalyzed by citrate synthase to form citryl CoA (an enzyme-

bound intermediate), which is hydrolyzed to produce citrate and HS-CoA. Aconitate

hydratase (or aconitase) catalyzes the isomerization reaction of citrate to 2R,3S-

44

isocitrate (or D-isocitrate, a secondary alcohol) for the succeeding redox reaction. The

next step initiates the consecutive four oxidation-reductions. Oxidative decarboxylation

of D-isocitrate and NAD+ molecules are catalyzed by isocitrate dehydrogenase, the

production and subsequent uptake of a proton results in carbon dioxide and α-

ketoglutarate (or 2-oxoglutarate). The enzyme for the next step, α-ketoglutarate

dehydrogenase complex, is comprised of three components (α-ketoglutarate

dehydrogenase, dihydrolipoamide succinyl transferase, and dihydrolipoamide

dehydrogenase) proceeds with decarboxylation of α-ketoglutarate, reduction of NAD+

and transference of CoA from HS-CoA to form succinyl CoA.71 The byproducts of this

multistep enzymatic complex are NADH and CO2.2,67,70,72

In the final four steps of the TCA cycle, the conversions of reactants to products

are near-equilibrium allowing amino acids and additional organic acids to interact with

the cycle due to transport reactions like the malate-aspartate shuttle, for example. The

following reaction is catalyzed by succinyl CoA synthetase (or succinate thiokinase) and

this highly energetic phosphorylation reaction produces ATP (or guanosine

triphosphate, GTP) and hydrolysis of a CoA thioester. Succinyl CoA experiences a

nucleophilic inorganic phosphate attack that displaces coenzyme A and releases

another CoA thioester forming succinyl phosphate intermediate. The succinyl phosphate

intermediate interacts with a histidine residue of the enzyme that transfers the

phosphoryl group and liberates succinate. The stable phosphoenzyme intermediate that

is formed transfers the phosphoryl group to ADP (or GDP) forming a nucleoside

triphosphate byproduct. Succinate is oxidized as it reacts with succinate dehydrogenase

and loses two hydrogen atoms resulting in a fumarate. The two protons and two

45

electrons are accepted by quinone, instead of NAD+, and reduced to QH2. Fumarate to

malate reaction is catalyzed by fumarate hydratase (or fumarase) through the addition

of water, which is a stereospecific trans addition to the double bond of fumarate. Finally,

malate is oxidized by NAD+-dependent malate dehydrogenase to regenerate

oxaloacetate for the formation of NADH and release of H+.2,67,70,72

The investigation of the central carbon metabolism focuses primarily on the TCA

cycle and substrate utilization by the cycle in perturbed systems. Fan et. al. explored the

phenotype of rhabdomyosarcoma (Rh30) cells and neighboring myocytes within the

skeletal muscle using stable isotope labeling (SIL) to understand the fate of glucose

during the transformation of primary cells to rabdomyosarcoma cells. Figure 2-3 and

Figure 2-4 depict the two pathways, anaplerotic pyruvate carboxylation (PC) and

glycolysis, that [U-13C]-glucose can be incorporated into 2-oxoglutarate. Moreover, NMR

enables the identification of the positional atom that is labeled with a heavy atom and

MS assists in isolating each isotopically labeled intermediate. Ultimately, the group

concluded that PC was actived in the rabdomyosarcoma cells instead of the primary

myocytes promoting increased biosynthetic precursors and oxidative phosphorylation

through the malate-aspartate shuttle for rapid cell proliferation.73 The SIL technique is

further discussed in the next section and will be used in determining the impact of

exhaustive exercise throughout exercise-engaged tissues.

Isotopic Labeling Experimental Design

In 1935, Rudolf Schoenheimer and David Rittenberg observed deuterium (D or

2H) labeled linseed oil (a stable hydrogen containing substance) in mice fat deposits by

measuring the refractive index using a Zeiss interferometer, and the submerged float

method (for higher accuracy).74,75 Their work was conducted nearly 20 years before

46

radioactive isotopes were used for in vivo studies due to the introduction of scintillation

counting. Radioactive isotopes were predominately utilized to determine metabolic

enzymatic rates. Stable isotope tracer analysis is a field that rapidly evolved in the

1970s once isotopically labeled material was developed in larger quantities making it

less expensive. This boom was also assisted by the wide availability of a newly

developed of commercial instrument, the quadrupole mass spectrometer specifically

coupled to a GC. Additionally, radioactivity is a health hazard in clinical studies and

radioisotopes hinder the ability to assign the isotope to a positional atom within a

compound promoting the rise in stable isotopes began.76,77

Isotope labeling experiments incorporate a tracer molecule identical in chemical

structure, and function as the intended tracee (compound of interest). The notable

difference between the tracer and tracee is the ability to separately detect the additional

atoms of the tracer. Although isotopes differ in nuclear neutrons, that does not impact

the chemical properties, and function within the cell. That is ultimately determined by the

electronic configuration. The most common stable isotopes are listed in Table 2-1 with

the top four (hydrogen, carbon, nitrogen and oxygen) being ubiquitous amongst literary

work in the field.77

Within metabolic research there are three mechanisms for tracer analysis,

tracers are (i) bound to a molecule that is subsequently injected into the body and the

molecule is tracked, (ii) used to measure the rate of a reaction through incorporation

into another molecule, or (iii) used to measure the rate of a substrate appearing within

collected plasma, also known as tracer dilution.77 It is critical that chemical and physical

properties of a tracer are indistinguishable from the unlabeled compound when

47

metabolized by an organism. However, an analytical instrument is capable of identifying

the unique tracer from the natural occurring tracee. An outlier of this statement is

deuterated water. Although the chemical and physical properties of deuterated water

are similar to that of water, replacement in excess of 20% of total water notably

changed the growth of microorganisms.78 Furthermore, isotope effects can be

concerning due to certain enzymatic effects noted within in vitro studies. The

experimental design is pivotal within this field.79,80 Stable isotope tracers containing 2H,

13C, and 15N minimize that effect because of low mass displacement.

The measurement of isotope tracers is quite simple in theory. The tracee is

inversely proportional to the tracer. Radioisotopes are quantified by count of the

radioactive tracee divided by unlabeled tracee (or specific activity) using a scintillation

counter. However, stable isotopes are calculated based on enrichment of the naturally

occurring isotope. The different ways of expressing enrichment are the following:

tracer/tracee ratio (TTR, similar to specific activity for radioisotopes), atom percent

excess (APE), mole percent excess (MPE), and delta (δ).

When measuring TTR, the raw natural abundance stable isotope of the

isotopomer (a molecule with an isotopic tracer incorporated) M+n is divided by M+0 (no

tracer enrichment) for the background abundance (rbk). The raw measurements of the

dosed tracer M+n is divided by M+0 (rsa), the difference of rbk and rsa times the skew

correction factor measures the complete TTR fully depicted in Equation 2-5,

𝑇𝑇𝑅 = (𝑟𝑠𝑎 − 𝑟𝑏𝑘) ∗ (1

− 𝐴)𝑛

(2-5)

48

where A is equal to the measured isotope’s natural abundance and n is the number or

stable isotopes incorporated into the molecule of interest.

The prevalent means of calculating enrichment using an isotope ratio mass

spectrometer (IRMS) is by delta (δ) using a reference gas (rr) follows the below equation

(Equation 2-6)

𝛿 (𝑝𝑎𝑟𝑡 𝑝𝑒𝑟 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑) =𝑟𝑠𝑎 − 𝑟𝑟𝑟𝑟

∗ 1000 (2-6)

This equation is primarily applied to geological studies observing a limited isotope

analysis yet the precise isotope ratio of tracer enrichment is obtained on an outdated

magnetic sector mass analyzer.

The APE can be calculated using the following two equations (Equations 2-7 and

Equations 2-8):

𝐴𝑃𝐸 (%) =𝑟𝑠𝑎 − 𝑟𝑟

(𝑟𝑠𝑎 − 𝑟𝑟) + 1∗ 100 (2-7)

𝐴𝑃𝐸(%) =

𝑇𝑇𝑅

𝑇𝑇𝑅 + 1∗ 100

(2-8)

The latter is currently found in literature for the percent of enrichment within a sample

analyzed on various instrument platforms.

Finally, the MPE equation (Equation 2-9) takes into account the mass isotopomer

abundance within the molecule or percent of molecules containing a labeled atom.

𝑀𝑃𝐸(%) = 𝐴𝑃𝐸 ∗ 𝐹 (2-9)

MPE takes into account the tracer number of labeled atoms (i.e. 1,2-13C2-pyruvate

contains two labeled carbons) and the total number of that atom in the compound of

interest (i.e. citrate is comprised of six carbons, 12C6). Those two figures make up the

49

fraction of carbons that have the chance to be enriched, F, where the tracer atom count

is divided by the total number of atoms in the molecule containing the tracer.77,81–83

Recently, software platforms such as MAVEN (now El-MAVEN), X13CMS,

NATCALC, geoRge, mzMatch-ISO, and IsoMS are available for LC/MS isotope tracer

analysis.84–89 These software packages enable new approaches and novel techniques

for the analysis of isotope tracer data, including dual- and triple-isotope tracer

analysis.90,91 This body of work will incorporate the tracer dilution mechanism using a

[U-13C]-glucose tracer and manually calculated enrichment in Excel is by TTR. Table 2-

2 lists the details collected to determine isotopic enrichment by TTR.

The focus of this chapter will be to investigate the dilution of [U-13C]-glucose into

the blood plasma of the mouse model used for this study with the intention of

understanding whole body exhaustive exercise of different genotypes and matched

resting time point to account for basal metabolism. [U-13C]-glucose is the ideal tracer for

central carbon metabolism and energetics because the carbons are preserved through

glycolysis, however, during recycling and continual exercise to exhaustion, the 13C-

label is incorporated by other molecules, released through carbon dioxide and

perfusion, and diluted by substrate that is already present within the mouse.

Chemicals

The [U-13C]-glucose (99% 13C) was purchased from Cambridge Isotope

Laboratories (Tewksbury, MA) and distributed by Albert Einstein Institute. The following

internal standards were purchased from C/D/N Isotopes (Pointe-Clare, Quebec):

salicylic acid-d6, caffeine-d3 (1-methyl-d3), N-BOC-L-tert-leucine, and N-BOC-L-

aspartic acid. The tert-BOC injections standards (BOC-L-tyrosine, BOC-L-trypotophan,

BOC-D-phenylalanine) were purchased from Acros Organics (Thermo Fisher

50

Scientifics, New Jersey). The internal standards were dissolved in water:acetonitrile

(90:10, v:v), as well as BOC-L-tyrosine and BOC-D-phenylalanine. The BOC-L-

tryptophan standard readily dissolved into water:methanol (50:50, v:v). The final

concentration for each internal standard was 4 μg/mL to monitor extraction efficiency

and 10 μg/mL of the injection standards to observe injection accuracy. Acetone (HLPC

grade) was purchased from Fisher Scientific. Water, methanol, acetonitrile and water

with 0.1% formic acid were Fisher Optima LC/MS grade solvents used in the extraction

and for mobile phase.

Sample Preparation

BALB/c lipin-1fld/+ (heterozygous, HET) mice were handled at the Albert Einstein

Institute by collaborators. Lipin-1 catalyzes the conversion of phosphatidic acid to

diacylglycerol (DG). Lipin-1fld/fld (fld, fatty liver dystrophy) mice are lipodystrophic, and

over produce triglycerides (TG) due to hepatic lipin-2 and lipin-3 overcompensation.

Prior to experimental exercise and sacrifice, mice were exercise trained for two

weeks and their averaged rate of exercising was noted. The day of the experiment,

mice were fasted in the morning for 5 hours and 30 minutes. Mice then received a

glucose tolerance test (GTT) 10 minutes before rest or exercise. The intraperitoneal

(i.p.) GTT measures the clearance of an injected glucose load from the body, and used

to detect disturbances in glucose metabolism that can be linked to human conditions

such as diabetes or metabolic syndrome. For stable isotope tracer analysis with

uniformly labeled 13C-glucose (U-13C6 glucose) or unlabeled glucose, mice were injected

i.p. with 2 mg of glucose per g of body weight of [U13C]-glucose or unlabeled glucose 10

minutes prior to exercise or at time zero for rest samples (Figure 2-5). Rest was time

matched to total exercise time to determine if metabolic responses were a result of

51

exercise. T0 represents time zero, E50 represents exercised for 50 minutes, R60

represents rested for 60 minutes, E50R35 represents exercised for 50 minutes

subsequently resting for 35 minutes, and R95 represents rest for a total of 95 minutes.

After rest or exercise routine, samples were immediately sacrificed, lyophilized, and

stored at -80 °C.

The plasma samples were thawed on ice, vortexed and then 20 μL of each

sample was transferred to a 600 μL Eppendorf tube. Isotopically labeled internal

standards (listed in the previous section) mix was briefly sonicated and 4 μL was added

to each sample to monitor extraction efficiency. Next, 160 μL of

acetonitrile:methanol:acetone (8:1:1, v:v:v) mixture was added to precipitate the protein.

The mixture were vortexed and chilled for 30 mins in a 4 °C refrigerator to aid the

precipitation of protein from solution. Subsequently, samples were centrifuged at 20,000

x g for 10 minutes at 8 °C to pellet the protein and easily remove the supernatant.92

After the extraction, 150 μL of the supernatant was transferred to a new 1 mL

Eppendorf tube and dried under nitrogen gas (Organomation Associates, Berlin, MA,

USA). The dried samples were reconstituted with a 20 μL mixture of the injection

standards in water with 0.1% formic acid, the appropriate starting conditions of mobile

phase, and vortexed. The reconstituted samples were transferred to a glass vial with

fused glass insert for data acquisition. All 37 plasma samples were prepared in the

similar fashion. A quality control (QC) sample of American Red Cross (RCP) blood

plasma were prepared, neat QC sample comprised of injection standards and amino

acids prepared in water with 0.1% formic acid, extraction blank of solvents used during

the extraction, and blank of water with 0.1% formic acid were analyzed with

52

experimental plasma samples to monitor the stability and validity of instrumental

acquisition.

Experimental Chromatography Parameters

The UHPLC RP columns utilize an adsorbent material as the stationary phase

that impacts the separation of complex biological samples. For metabolite liquid

chromatography separation, two distinct columns were used for the chemical

interactions with specific biological molecules. Column identification is an important

aspect of UHPLC/MS because slight deviations in retention times or chromatographic

resolution due to temperature fluctuations and column degradation are nonlinear and

complicate metabolite identification, especially if batches of samples are analyzed at

separate time points.27 The two columns used were modified for improved efficiency

and retention to a non-polar (C18) stationary phase, which is historically used for small

polar molecule separation.93–95

Analytical UHPLC RP Columns

The column used for metabolite separation was an ACE Excel Ultra-Inert C18-

PFP column. This column has hydrophobic, π-π, dipole-dipole and hydrogen bonding

interactions, resulting in shape selectivity retention mechanisms that combine

pentafluorophenyl (PFP) and alkyl-bonded silica phase properties.96 The column was

designed with a proprietary C18 endcapped ligand bound to the PFP moiety. The

fluorinated stationary phase is significantly more polar than its non-fluorinated analogue

due to the the partial negative charges on the fluorine atoms surrounding the phenyl

ring, which increases the dipole-dipole and hydrogen bonding interactions.97,98 These

enhanced interactions result in a better retention of polar molecules. The column was

100 X 2.1 mm inner diameter (i.d.) in length with a particle size of 2.0 μm and could

53

operate at pressures up to 1000 bar under RP mobile phase conditions. The pore size

that supports small molecules is 100 Å. This particle has a 300 m2/g surface area and

dynamic pH range maximized at 1.5 to 10. However, a pH of 2-8 is recommended. The

maximum temperature recommended is 100 °C, but, it is noted that greater than 60 °C

runs will decrease the column lifetime.

Lipidomic separation was achieved using a Waters Acquity BEH C18 column.

Similar to the ACE Excel column, the Waters Acquity column employs hydrophobic, π-

π, and hydrogen bonding interactions. It utilizes shape selectivity retention mechanisms

for chromatographic separation. The trifunctional ligand bond to C18 of a bridged

ethylsiloxane/silica hybrid (BEH) particle is proprietary. The chemistry of this BEH

column enabling wide mobile phase selectivity with a pH ranging from 1 to 12.99 The

BEH fully porous particle chemistry promotes good peak shape for basic compounds

due to the electron deficient particle functioning as a Lewis acid.93 The larger pore size

assists in separating larger analytes such as the lipid species.100,101 The column was 50

X 2.1 mm i.d. in length with a particle size of 1.7 μm, which improves sensitivity and

separation 1.4 times (sa compared to particle sizes of 3.5 μm). The recommended

column operating pressure is less than to 684 bar under RP mobile phase conditions.

The pore size that supports small molecules is 130 Å and this particle has a 185 m2/g

surface area.102 The maximum temperature is 90 °C and as low as 20 °C, but

temperatures less than 70 °C preserve the columns lifetime.103 The C18 BEH column

maintains retention and efficient separation over a course of 2000 injections making this

column ideal for high-throughput lipidomics studies.104

54

Chromatographic System

The Thermo Scientific-Dionex Ultimate 3000 RSLC system was utilized for

UHPLC. The maximum pressure is 15,000 PSI (or 1034 bar) with a flow rate range of

0.1-8 mL/min. The injector type was variable flow-through with a 15 sec injection time

and 293 μL gradient delay volume.54 The column heater and sample manager were

kept at 25 ºC (metabolomics) and 50 ºC (lipidomics), and 9 ºC, respectively.

The column used for metabolites was an ACE Excel C18-PFP (100 X 2.1 mm,

2.0 μm) using nanoViper (75 μm X 350mm) solvent lines with separation via gradient

elution with mobile phase A as 0.1% formic acid in water and mobile phase B as

acetonitrile. The gradient was as follows: 0–3 min, 100% A isocratic; 3–13 min, 0–80%

B linear; 13–16 min, 80% B isocratic; 16–16.5 min, 80–0% B linear; followed by 3 min of

re-equilibration of the column before the next run. The flow rate was 350 μL/min, and

the injection volume was 4 μL and 2 μL for positive and negative mode, respectively.

The lipidomics column and chromatographic detail is discussed in Chapter 3.

The analytical sequence for LC/MS analysis started with three blanks (0.1%

formic acid in water for metabolites and isopropanol for lipids), an extraction blank that

was handled with solvent throughout the extraction for background solvent observation,

one RCP QC, and a neat QC containing just isotopically labeled internal and injection

standards, followed by 10 randomized plasma. Both QCs were used for relative

quantification calculated in Xcalibur Quantitative Analysis (Thermo Scientific) to monitor

extraction efficiency, moreover, mass accuracy and retention time drift were checked in

the Excel output. The randomization of samples prior to data acquisition decreased

variance associated with sample preparation and injection sequence. The sequence

continued with one blank, one extraction blank, one RC QC, and one neat QC and 10

55

randomized samples to monitor instrumental drift. The order repeated until the samples

were completed.

High Resolution Mass Spectrometry Instrumentation

Accurate mass (AM) high-resolution mass spectrometers (HRMS) have become

ubiquitous among researcher interested in “omics” approaches as well as isotope tracer

and flux studies. High-resolution mass spectrometers are defined as a mass analyzer

with greater than 10,000 resolving power, which is defined in Equation 2-10

𝑟 = 𝑚

∆𝑚50% (2-10)

where Δm50% is the full width at half maximum of the mass spectral peak and m is the

observed mass. The resolution (in data) is the difference between two mass spectral

peaks. Fortunately, AM measurements allow mass analysis to adequate number of

significant features to undeniably determine the elemental composition of an analyte of

interest.105

The Thermo Scientific Q Exactive (QE), a hybrid quadrupole Orbitrap mass

spectrometer, was utilized for all experiments in this document. The QE is designed with

multiple ion optics, as shown in Figure 2-6. The instrumental design includes a stacked-

ring ion guide (S-lens), bent flatapole, QMF, curved quadrupole linear ion trap (C-trap),

high-energy collisional dissociation (HCD) cell, and Orbitrap mass analyzer. The

capabilities of this instrument make it ideal for isotopic labeling experiments. The QE

has a resolution of 140,000 at m/z 200, mass range of m/z 50 to 6,000, mass accuracy

less than 3 ppm, and fast scan times up to 12 MS/MS events per second.

Ions are introduced to the ion transfer tube by a HESI II probe after

chromatographic separation in the Dionex UltiMate 3000 UHPLC system. The high-

56

density ions are focused by the S-lens RF potential into a tight beam and traverse into

the bent flatapole. The RF potential and spacing of the stacked-ring electrodes provides

increased transmission and sensitivity. The bent flatapole is a quadrupole that has 2

mm gaps spacing amongst the rods allowing solvent droplet and neutral species are

eliminated from the ion beam. From the bent flatapole, the ions enter the segmented

quadrupole for mass filtering and selection, which has the capability to separate ions

down to an isolation width of m/z 0.4 at m/z 400.106,107 Ions are focused in time and

space upon arrival in the C-trap interfaced with an HCD cell and then injected off-axis

into the orbitrap for full scan analysis. Ions oscillate around the central electrode of the

orbitrap and are separated by frequency, producing exceptional mass accuracy of less

than 2 ppm error.108,109

For the breadth of this work three different resolutions were used for accurate

mass assignment and adequate mass separation of metabolites. The full scan

metabolomics data were collected in positive and negative mode with a mass resolution

of 70,000 at m/z 200 with a m/z range of 70-1000. The experimental parameters for

MS2 scans in this work include isolation range with a correlating m/z window include m/z

50-400 (0.4 amu), m/z 400-700 (0.7 amu), m/z 700-1000 (1 amu) and m/z 1000-2000 (2

amu). The mass resolution for data-dependent top-5 MS2 (ddMS2-top5) mode full scans

for metabolites was 35,000 at m/z 200, while the ddMS2-top5 resolution was 17,500 at

m/z 200 with an isolation window of 2.0 amu and normalized collision energy (NCE) of

40 eV for metabolites.

Data Analysis

After data acquisition, if reanalysis was necessary the samples were stored in the

-80 °C. The data were processed from .raw to .mzXML using the following MSConvert

57

parameters: peak picking (MS level, 1-1), msLevel (level, 1-1), threshold peak filter

(absolute count, 0.0001, most intense), and output format (.mzXML). The MZmine

parameters are detailed here in Table 2-3. The MZmine platform allows for user input of

externally developed libraries. The curated in-house metabolite library (abbreviated in

Table 2-4) was used for first level compound identification. The library was customized

for our QEs in the lab, notably the retention times for standards and compound

identified within samples based on the chromatrographic separation techniques used in

this document. The libraries major compounds consist of the monoisotopic m/z value,

experimental retention time, sample in which the compound was found or standard

used, and KEGG metabolite identifier. For all metabolites within this document, the in-

house library and MZmine were used to identify compounds for targeted isotopic

enrichment analysis. Then, a target set of metabolites was used to manually determine

isotope enrichment patterns. The isotopomer enrichment was obtained from the .raw

files and noted in tabular form like Table 2-2. The table indicates the monoisotopic m/z

value, observed m/z value, retention apex, retention time range, scan range, and signal

response of the isotopomer. The ppm error of the observed m/z value was less than 5

ppm or the value was not used for enrichment calculations. After the raw signal was

obtained, the enrichment ratios were calculated using the TTR and the values in Table

2-5.

Results and Discussion

TCA cycle intermediates could serve as potential biomarkers for exercise

exhaustion and may assess both glucose tolerance/disposal, as well as indicate how

glucose metabolites may be incorporated into lipids. TCA cycle intermediates were

targeted for enrichment interpretation in plasma to understand central carbon

58

metabolism that occurs due to exhaustive exercise and with the hope of finding a

biomarker in plasma. Dynamic flux interchanges between tissues and circulating blood

during exhaustive exercise can be interpreted by 13C-label. In Figure 2-7, [U-13C]-

glucose tracer was incorporated into plasma of BALB/c mice and enrichment was

observed within the TCA cycle making this a successful tracer dilution study.

The TCA cycle produced interesting enrichment patterns. Citrate M3 and M4

enrichment were nearly equivalent at 10.2% and 10.6% in E30. Moreover, M4

enrichment prompts further investigation into glutamate and the malate-aspartate

shuttle to determine if PC is activated, or acetyl-CoA catabolism from pyrimidines

impacting M4 enrichment. Citrate results indicated continual recycling of labeling post-

exercise, suggesting increased mitochondrial activity (TCA cycle). Succinate E30 had a

unique isotopic enrichment pattern for M2, M3 and M4. The isotopic distribution in

succinate gradually increases across the three isotopomers. The succinate enrichment

patterns of R60 and E50R35 resemble one another signifying that recycling of the 13C-

label in circulating succinate within plasma slows down once exercise is complete.

Malate does not have a significant isotope distribution pattern.

Acylcarnitine shuttle fatty acids into the mitochondria for fuel utilization and even

chain acylcarnitine are a measurement of lipid β-oxidation.110 Figure 2-8 depicts

acylcarnitine metabolism and isotope enrichment in BALB/c mice plasma. C2 carnitine

showed similar enrichment patterns to C4 carnitine. The C4 carnitine was a promising

biomarker for with increasing enrichment after exercise and rest exposure. This isotopic

pattern could indicate continual lipid recycling post-exercise. C3 (proprionylcarnitine)

59

carnitine M2 enrichment was at zero and M3 data for Rest and Ex+R were not detected

for the unlabeled isotopomer. This is most likely due to sensitivity.

Serine and glutamine enrichment in Figure 2-9 suggests circulating serine was

significant during exercise and could indicate extensive lipid production in other organs.

While serine showed a significant decrease of labeling after rest had begun, this may

suggest tapering of glycolysis (serine potentially formed from 3-phosphoglycerate in

glycolysis). Additional metabolites needed to be measured to determine where the label

is coming from and if the label is incorporated into ceramide, or other shingosines.

These data are compared to heart, liver and skeletal muscle in Chapter 5 to investigate

glucose utilization through the whole body.

60

Figure 2-1. Glycolysis pathway depicting the catabolism of glucose to yield two pyruvate molecules.

61

Figure 2-2. The tricarboxylic acid (TCA) cycle consumes a single pyruvate molecule that is broken down into acetyl CoA (Equations 2-4) and consumed by oxaloacetate to initial the cycle.

62

Figure 2-3. Isotopic distribution of [U-13C]-glucose into glutamate of mammalian cells without anaplerotic PC indicated by the labeling pattern of C4 and C5 positional carbons. The green labeled carbons were (not labeled by PC) and red labeled carbons were labeled through glycolysis; succinyl CoA synthetase is abbreviated as SCS (Modified with permission from Fan et. al.)73

63

Figure 2-4. Isotopic labeling of glutamate of mammalian cells with [U-13C]-glucose incorporated through anaplerotic PC indicated by the labeling pattern of C2 and C3 positional carbons. All of the carbons that are red were labeled through PC; again succinyl CoA synthetase is abbreviated as SCS (Modified with permission from Fan et. al.)73

64

Figure 2-5. The isotope tracer dilution experimental design for mice exposed to five different treatments: rest, exercise, exercise then rest, and immediately sacrificed. Rest and exercise/exercise plus rest are time matched because of the GTT and i.p. injection of [U-13C]-glucose.

65

Figure 2-6. Schematic of Q Exactive Hybrid Quadrupole-Orbitrap displaying the major regions of the instrument (Adapted with permission from Michalski et. al.)106

66

Figure 2-7. [U-13C]-glucose tracer incorporated into plasma through the TCA cycle in BALB/c mice. Citrate M3 and M4 enrichment E30 are elevated above the time matched rest enrichment points. Succinate E30 has a unique isotopic enrichment pattern that of M2, M3 and M4 increasing. However, the enrichment patterns of R60 and E50R35 resemble one another. T0 represents time zero, E50 is exercise 50 minutes, R60 stands for rest 60 minutes, E50R35 indicates exercise 50 minutes plus 35 minutes of rest, and R95 is rest 95 minutes.

67

Figure 2-8. [U-13C]-glucose tracer incorporated into carnitine metabolism in BALB/c mice plasma. The C4 carnitine is promising with increasing enrichment after exercise and rest exposure. Ex is exercise 50 minutes, Ex+R indicates exercise 50 minutes plus 35 minutes of rest.

0

2

4

6

8

Rest Ex Ex+R

Pe

rce

nt

Enri

chm

en

tC2 Carnitine

M2/M0

0

5

10

15

Rest Ex Ex+R

Pe

rce

nt

Enri

chm

en

t

C3 Carnitine

M3/M0

0

5

10

15

20

25

30

Rest Ex Ex+R

Pe

rce

nt

Enri

chm

en

t

C4 Carnitine

M2/M0

M3/M0

68

Figure 2-9. [U-13C]-glucose tracer incorporated into serine and glutamine in plasma from BALB/c mice. Circulating serine is significant during exercise and could indicate extensive lipid production in other organs. Ex is exercise 50 minutes, Ex+R indicates exercise 50 minutes plus 35 minutes of rest.

69

Table 2-1. A partial list of nonradioactive isotopes used in tracer analysis, the first four (H, C, N, and O) being the most commonly used for biological advancements.77

Element Stable Isotope % Natural Abundance

Hydrogen (H) 1 99.99

2 0.02

Carbon (C) 12 98.89

13 1.11

Nitrogen (N) 14 99.63

15 0.37

Oxygen (O) 16 99.76

17 0.04

18 0.20

Sulfur (S) 32 95.00

33 0.76

34 4.22

Iron (Fe) 54 5.82

56 91.66

57 2.19

58 0.33

Zinc (Zn) 64 48.89

66 27.81

67 4.11

68 18.57

70 0.62

70

Table 2-2. Data points used to determine isotopic enrichment. monoisotopic m/z RT Compound Name &

Isotopomer RT Range Scan # m/z observed Signal Response

133.0142 1.07 Malate M0 1.04-1.13 158-173 133.0142 47713228

134.017555 1.07 Malate M1 1.04-1.13 158-173 134.0175 2197642

135.02091 1.07 Malate M2 1.04-1.13 158-173 135.0206 31221.9

136.024265 - Malate M3 - - - -

137.02762 - Malate M4 - - - -

191.0197 1.92 Citrate M0 1.87-2.04 282-309 191.0196 6292406

192.023055 1.92 Citrate M1 1.87-2.04 282-309 192.023 420442.1

193.02641 - Citrate M2 - - - -

194.029765 - Citrate M3 - - - -

195.03312 - Citrate M4 - - - -

196.036475 - Citrate M5 - - - -

197.03983 - Citrate M6 - - - -

191.0197 1.13 Isocitrate M0 1.08-1.23 165-188 191.0197 317235.7

192.023055 - Isocitrate M1

192.0231 20065.7

193.02641 - Isocitrate M2

-

194.029765 - Isocitrate M3

-

195.03312 - Isocitrate M4 -

117.0193 2.31 Succinate M0 2.25-2.38 339-357 117.0192 540898.8

118.022655 2.31 Succinate M1 2.25-2.38 339-357 118.0226 22989.3

119.02601 - Succinate M2 - - - -

120.029365 - Succinate M3 - - - -

121.03272 - Succinate M4 - - - -

115.0037 2.05 Fumarate M0 1.99-2.13 301-322 115.0036 814888.8

116.007055 2.05 Fumarate M1 1.99-2.13 301-322 116.007 32221.3

117.01041 - Fumarate M2 - - - -

118.013765 - Fumarate M3 - - - -

119.01712 - Fumarate M4 - - - -

71

Table 2-3. A list of significant parameters used for MZmine data processing.

Process Parameters

Mass detection MS level: 1

Mass detector: Centroid

Noise level: 1 E5

Chromatogram builder Minimum time span: 0.1 min

Minimum peak height: 5.0 E5

m/z tolerance: 0.005 m/z or 15 ppm

Smoothing Filter width: 5

Chromatogram deconvolution Algorithm: Local minimum search

Gap-filling Intensity tolerance: 25%

m/z tolerance: 0.005 m/z or 15 ppm

RT tolerance: 0.3 min

Duplicate peak filter m/z tolerance: 0.005 m/z or 15 ppm

RT tolerance: 0.1 min

72

Table 2-4. An abbreviated list of the formatting in our in-house library used for metabolite identification in MZmine.

Garrett Lab ID M/Z Retention Name Formula

Molecular Weight KEGG

428p 75.0917 0.62 1,3-DIAMINOPROPANE C3H10N2 74.1249 C00986

750p 76.0393 0.736 GLYCINE C2H5NO2 75.0666 C00037

2723790p 77.0168 0.908 THIOUREA CH4N2S 76.12086 C14415

… … … … … … …

73

Table 2-5. Raw signal of labeled and unlabeled samples used to determine isotopic enrichment

Name Labeled Unlabeled

Malate M0 43326772 54597556

Malate M1 6738383.5 2509045.5

Malate M2 2376462.8 36789.3

Malate M3 400485.7 -

Malate M4 41959.9 -

Citrate M0 3050427.3 5424083.5

Citrate M1 548498.8 367944.5

Citrate M2 404484.9 -

Citrate M3 67658.1 -

Citrate M4 12596.1 -

Citrate M5 1669.9 -

Citrate M6 - -

Isocitrate M0 252661.9 319017.4

Isocitrate M1 45419.3 19892.4

Isocitrate M2 30607.8

Isocitrate M3 4905.3

Isocitrate M4

Succinate M0 290729.5 407957.5

Succinate M1 33011.2 17698

Succinate M2 11952.2 -

Succinate M3 905.7 -

Succinate M4 - -

Fumarate M0 858997.7 1051996.6

Fumarate M1 127322.1 44018.7

Fumarate M2 45701.4 -

Fumarate M3 - -

Fumarate M4 - -

74

CHAPTER 3 TISSUE PRE-DATA ACQUISITION NORMALIZATION FOR MASS SPECTROMETRY

Tissue Preparation Background

Sample preparation is a critical step to obtain accurate mass spectrometry

measurements of a biological system. The analysis of biological small molecules, or

metabolites, naturally presents challenges due to metabolites’ chemical reactivity,

concentration span, diverse polarity and ionization efficiency.53 For lipidomics tissue

analysis, sample preparation is of particular importance due to the vast concentration

range. Thus, prior to data acquisition the normalization of tissue should be consciously

evaluated before diluted or perturbed by an extraction method to ensure analytical

measurements reflect the metabolic phenotype of the study.

Cell count, total DNA, RNA and protein content are several common techniques

used for pre-data acquisition normalization.111 The appropriate method required for

analysis is determined by the metabolic phenotype and perturbations to the

metabolome. For example, exercise training can modify mRNA production in skeletal

muscle. When exercised skeletal muscle is normalized with non-exercised skeletal

muscle, the total RNA concentration would be inadequately normalized and result in

excess dilution of the exercise trained sample set.65 For this project the total protein

content was used for pre-data normalization. However, that presented a new challenge.

Normalization of metabolites and lipids can present a challenge in various tissues

due to excessive solvent dilution.53 Intracellular protein concentration is approximately 7

mM and metabolite concentration is approximately 300 mM; these figures were helpful

for final metabolite concentration levels, but lipids do not have a similar point of

reference. Early literature denotes the total fat content of numerous tissues related to

75

protein concentration. Yet, the type of lipids that comprise the total fat concentration is

rarely noted making it difficult to optimize the concentration for low abundant lipids. The

following measurements are a guide for normal tissue total protein concentrations. Note

that disease states can cause a fluctuation in intercellular protein concentration. In that

case, total protein concentration should not be used for pre-data acquisition

normalization.

Experimental Design

Chemicals and Reagents

The [U-13C]-glucose (99% 13C) used by the Albert Einstein Institute group was

purchased from Cambridge Isotope Laboratories (Tewksbury, MA). An extensive

number of exogenous lipid standards were purchased from Avanti Polar Lipids

(Alabaster, AL) to cover the lipidomics chromatographic range. The internal standards

are as follows: lysophosphatidylcholine (LPC 17:0), phosphatidylcholine (PC 17:0/17:0),

phosphatidylglycerol (PG 14:0/14:0), phosphatidylethanolamine (PE 15:0/15:0),

phosphatidylserine (PS 14:0/14:0), monoacylphosphatidylinositol (8:0), triacylglyceride

(TG 15:0/15:0/15:0), lysosphingomylin (d17:1), sphingomyelin (SM d18:1/17:0),

ceramide (Cer d18:1/17:0), diacylglyceride (DG 14:0/14:0), cardiolipin (CL

15:0/15:0/15:0/16:1), sphingosine (SP d17:1), and oxidized phosphatidylcholine

(PAzePC). The internal standards were diluted to 50 ppm and the injection standards

were diluted to 100 ppm to achieve these concentrations the standards were dissolved

in either methanol, chloroform or chloroform:methanol (2:1, v:v).

Lysophosphatidylcholine (LPC 19:0), phosphatidylcholine (PC 19:0/19:0),

phosphatidylglycerol (PG 17:0/17:0), phosphatidylethanolamine (PE 17:0/17:0),

phosphatidylserine (PS 17:0/17:0), and triacylglyceride (TG 17:0/17:0/17:0) were used

76

for injection standards. Acetone (HLPC grade) was purchased from Fisher Scientific.

Water, isopropanol, acetonitrile and 1% formic acid were Fisher Optima LC/MS grade

solvents for mobile phase. Ammonium formate was purchased from Sigma-Aldrich (St.

Louis, MO).

Animal Preparation

Irwin Kurland at Albert Einstein Institute was critical in assisting with animal

sampling and performed all experiments on female C57BL/c and male BALB/c mice.

Lipin-1+/+ (wild-type, WT) and lipin-1fld/+ (heterozygous, HET) BALB/c, and lipin-1fld/+

(HET) C57BL/c mice were included in this experimental design. Prior to exercise and

sacrifice, mice were fasted in the morning for 5 hours and 30 minutes. For stable

isotope tracer analysis with [U-13C]- glucose or unlabeled glucose, mice were injected

i.p. with 2 mg of glucose per g of body weight. The mice then received a glucose

tolerance test (GTT) 10 minutes before rest or exercise. The i.p. GTT measures the

clearance of an injected glucose load into the body, and the metric is used to detect

disturbances in glucose metabolism that can be linked to human conditions such as

diabetes or metabolic syndrome. After rest or exercise, mice were immediately

sacrificed. Fat, gastrocnemius, heart, kidney, liver and quadriceps tissues were

collected, lyophilized, and stored at -80 °C.

Pre-Data Acquisition Normalization

To create a stock concentration, tissue samples were weighed on dry ice to

obtain an accurate weight for dilution with 5 mM ammonium acetate in water. The stock

solution was created at 50 mg/250 μL with buffered water for all tissue except fat, which

was 20 mg/750 μL, to preserve the total protein concentration of the tissue. The stock

solution of each tissue was used as a reference aliquot, after multidirectional

77

homogenization with three 3 mm borosilicate glass beads by mechanistic and

reproducible vortexing (Disruptor Genie, Scientific Bohemia, NY) at 1800 rpm. The

solutions were vortexed three times for 1 minute and chilled on ice for 30 minutes to

prevent thermal degradation.

Once the homogenization was complete, protein concentration was accurately

measured for each sample set using a Qubit 3.0 fluorometer (Thermo Fisher Scientific).

The Qubit protein assay comes with three standards (0, 200, 400 ng/μL) used to

generate a calibration curve (modified Hill plot) based on a curve-fitting algorithm. The

standards are prepared using 10 μL mixed with 190 μL of fluorescent buffered solution

(Protein Reagent:Protein Buffer, 1:199, v:v). Prior to measurement, an aliquot of 1 μL

per sample was combined with 199 μL of fluorescent buffered solution, vortexed briefly

and incubated for 15 minutes. The equation 3-1

𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 = 𝑄𝐹 𝑣𝑎𝑙𝑢𝑒 ×

200

𝑥

(3-1)

is used to calculate each samples concentration in μg/mL. The QF value represents an

arbitrary value provided by the Qubit fluorometer and x is the volume (μL) added to the

assay tube. A new calibration was conducted for each tissue set. Tables 3-1 to 3-5 note

genotype, unique sample identifier (tissue_genotype[exercise/rest/initial time point and

time frame]_labeled/unlabeled), the dried weight (mg) of the tissue and measured total

protein concentration prior to normalization for each tissue type. Samples were then

normalized to a new protein concentration (Table 3-6) based on the anticipated amount

of lipid concentration within each tissue type. Two aliquots were taken (one for lipids

and another for metabolites) to dilute, and normalization each tissue to a respective final

total protein concentration. Typically, the lipid concentration is higher than the

78

metabolite concentration. However, fat has a much smaller protein concentration

because the tissue is predominantly lipid triacylglycerols.

Liquid-Liquid Extraction

The lipidomics liquid-liquid extraction used in this body of text was optimized

based on extraction efficiency, lipid coverage, and throughput for cells, plasma, skeletal

tissue.112 In 1957, Jordi Folch originally conducted this extraction since then several

additional solvents, and different solvent ratios have modified it. His research isolated

the lipid content in plasma, brain, liver and muscle tissue.113

Samples were thawed on ice and 50 μL of each sample was transferred to a

glass culture tube to prevent plastic leaking due to the harsh solvents used in this

extraction. Next, 10 μL of a 10-fold diluted isotopically labeled standards mixture,

detailed in the Chemicals and Reagents section, was added to each sample and

vortexed. Samples were diluted by 600 μL of ice-cold mixture of chloroform:methanol

(2:1, v:v) to decrease the abundance of naturally occurring lipids. The samples were

incubated in a 4 °C refrigerator for 20 minutes and vortexed. This extraction is biphasic

due to the 100 μL addition of water and chilled for 10 minutes to further induce

separation. Thereafter, samples are centrifuged at 3260 x g for 10 minutes at 4 °C to aid

polar and non-polar fractionation. The bottom, organic layer was withdrawn using an

ultra fine point micropipette tip and transferred to a new culture tube, while the top,

aqueous layer was re-extracted in 200 μL of chloroform:methanol (2:1, v:v). The

solution was vortexed and centrifuged under the same prior conditions. Then, the

organic layers were combined.

Post-extraction, the collective organic layer was dried under nitrogen gas

(Organomation Associates, Berlin, MA, USA) at 17 °C. The dried samples were

79

reconstituted 100 μL of isopropanol and 10 μL of a 10-fold diluted mixture of the

injection standards, and vortexed. For LC/MS analysis, 20 μL of sample was transferred

to a glass LC vial with a fused glass insert for data acquisition. All tissue samples were

similarly extracted and prepared for analysis. A pooled quality control (QC) sample of

each tissue type (per tissue type, e.g. all gastrocnemius samples had 5 μL aliquots

pooled into one LC vial post-extraction), QC of RCP was prepared to compare across

all tissue types, neat QC of internal and injection standards, extraction blank and

isopropanol blank were analyzed with experimental plasma samples to monitor the

instrumental acquisition post-data acquisition.

Data Acquisition

The Waters Acquity BEH C18 (50 X 2.1 mm, 1.7 μm) with a Waters VanGuard

BEH C18 1.7 μm guard column was employed for lipid separation using standard PEEK

(100 μm X 350mm) tubing and a gradient elution of mobile phase C as acetonitrile:water

(60:40, v:v) and mobile phase D as isopropanol:acetonitrile:water (90:8:2, v:v:v). Both

mobile phases had 0.1% formic acid and 10 mM ammonium formate. The gradient was

as follows: 0–1 min, 80% C isocratic; 1–3 min, 20–30% D linear; 3–4 min, 30-45% D

linear; 4–6 min, 45–60% D linear; 6–8 min, 60–65% D linear; 8–10 min, 65% D

isocratic; 10–15 min, 65–90% D linear; 15–17 min, 90–98% D linear; 17–18 min, 98% D

isocratic; 18–19 min, 98–20% D linear; followed by 4 min of re-equilibration of the

column before the next run. The flow rate was 500 μL/min, and the injection volume was

5 μL and 3 μL for positive and negative mode, respectively.

The chromatographic sequence, handled by an autosampler, started with three

blanks (0.1% formic acid in water for metabolites and isopropanol for lipids), an

extraction blank (comprised of solvent that was extracted with the samples for

80

background solvent subtraction), one pooled QC, one RCP QC, and a neat QC

(containing just isotopically labeled internal and injection standards), followed by 10

randomized plasma samples. The randomization of samples prior to data acquisition

decreased variance associated with sample preparation. The sequence continued with

one blank, one extraction blank, one RCP QC, and one neat QC and 10 randomized

samples.

Prior to data collection, the QE was calibrated using Pierce™ LTQ Velos ESI

Positive Ion Calibration Solution (Thermo Fisher Scientific, San Jose CA, USA) enabling

a 0.09-0.3 ppm m/z value standard deviation. The solution is composed of 0.0005% of

n-butylamine, 2 µg/mL of caffeine (both for low mass calibration), 1 µg/mL of MRFA (for

mid-mass), and 0.001% of Ultramark 1621 (for high mass). For negative ion mode a

mixture of 500 μL of the Pierce ESI Negative Ion Calibration Solution (Thermo Fisher

Scientific, San Jose CA, USA) comprised of 2.9 µg/mL of sodium dodecyl sulfate (for

low mass), 5.4 µg/mL of sodium taurocholate (for mid-mass) and 0.001% of Ultramark

1621 (for high mass); and 5 μL of the API-TOF Reference Mass Solution (Agilent

Technologies, Santa Clara CA, USA) 5 mM purine (for low mass) delivered a 0.1-0.22

ppm m/z value standard deviation with lock mass.

The heated electrospray ionization (HESI) parameters were as follows: auxiliary

gas flow 10 arb, capillary temperature 320 °C for negative ionization and 325°C for

positive ionization, probe temperature 350 °C, sheath gas flow 45 arb (arbitrary units),

spray voltage 3.5 kV, sweep gas flow 1 arb. In-source collision-induced dissociation

(CID) was 2 eV.

81

For confident lipid identification (defining functional groups and structural

composition) data must be obtained by MS2 and fragmentation patterns should be

matched back to the parent ion. Lipid data were collected in MS2 scan mode with a scan

range of m/z 200-2200. Data acquisition in MS/MS mode used data-dependent (ddMS2-

topN) and data-independent (all ion fragmentation or AIF) modes. The mass resolution

for data-dependent top-10 MS2 (ddMS2-top10) mode full scans was 35,000 at m/z 200

and ddMS2-top10 resolution for MS2 scans was 17,500 at m/z 200 with an isolation

window of m/z 2.0 and NCE of 25 ± 5 eV. Lipids were scanned at 35,000 at m/z 200 for

ddMS2-top10 with an isolation window of m/z 1.0. An additional MS2 scanning mode

(AIF) was used for lipids to take advantage of mass resolution for full scans at 70,000 at

m/z 200 and scanning m/z range of 200-2200. The MS/MS mode resolution for AIF was

70,000 at m/z 200 with NCE at 25 ± 5 eV and scanned across a m/z range of 146.7-

1800.

MS2 scanning begins in the QMF where an ion packet selected “in space” is

filtered to the set m/z range (ddMS2) and transferred to the C-trap, a curved RF only

quadrupole linear ion trap (LIT). AIF occurs in the Q Exactive by allowing all ions to

through the QMF without mass selection for fragmentation of all ions. An ion beam is

accelerated into the HCD cell (an octopole collision cell) due to lower potential energy of

the RF rods and the axial field compared to the C-trap. As ions rapidly fill the HCD,

high-energy collisions occur with high pressure N2 gas. If the potential of the RF rods

are varied multiple precursor ions can enter the HCD cell for fragmentation. To transfer

the ion packet back to the C-trap for MS2 analysis by the orbitrap, the voltage of the

82

HCD is increased and ejected ions enter the C-trap. Subsequent, injection occurs off-

axis into the orbitrap.114

Data Processing

The parameters for the msConvert software of the uploaded .raw files for lipid

identification by LipidMatch were as follows: peak picking (MS level, 2-2) and output

format (.ms2). Thermo ddMS2-topN and AIF MS2 .raw files were converted to .ms2 files

and were formatted. The .ms2 files were input files to LipidMatch Flow software, a GUI

interface version of LipidMatch. The input files for LipidMatch Flow must indicate what

form of MS/MS mode was used “ddMS2” or “AIF” has to be in the file name.

Furthermore, files must indicate which ionization mode (positive or negative) was used

with a “p” or “pos” in the file name and blanks should be identified in the same fashion

for blank feature filtering (BFF). LipidMatch software has an in silico library written in R-

script that uses MS/MS fragmentation patterns based on exact monoisotopic mass as

well as neutral loss fragments. AIF, data-independent mode, acquires all ions within the

previous full scan m/z range and indiscriminately fragments them. Quite the opposite of

ddMS2, which is a data-dependent acquisition mode, that takes the top designated

number (top-N) of ions in a scan window for fragmentation. A limitation of AIF is the

inability to easily pair the fragment ions with a parent ion for identification. Both

acquisition modes were used for lipid identification. The workflow for LipidMatch is

detailed in Figure 3-1.62 Once files are added to the GUI, two windows request to add

two subfolders within the final output folder. The subfolders contain msConvert .ms2

files and MZmine .csv data. Files within those subfolders are used by LipidMatch R-

script to determine (i) lipid class by matching the exact mass of possible lipid species to

the library, (ii) fatty acid tail composition by matching exact mass of MS2 scans within

83

the previously selected RT window, (iii) fragmentation data are then ranked based on

RT at maximum, maximum intensity, averaged m/z, total number of MS2 scans and (iv)

an identifier of 1-4 is designated (1 being the highest rank) with the identified lipid class

and fatty acid tail. LipidMatch outputs five total .csv files, including LipidMatch

parameters, feature table with Lipid identifies, list of all identities assorted by rank,

detailed fragment information and fragment data for confirmed lipid identities.

Results and Discussion

This research is intended to be a point of reference for other analytical scientists

working with precious tissue samples. Prior to starting this study, literature was pieced

together to determine an approximation of total protein concentration to lipid abundance

in tissue. Total protein data in Tables 3-1 through 3-5 were acquired with the help of

Atiye Ahmed and Lily Silsby. Although the samples were re-weighed on dry ice,

handling had to be done quickly because of atmospheric heat exposure that could thaw

samples and begin enzymatic active within the flash frozen tissues.

The total ion chromatogram (TIC) of pooled QCs from total protein

concentrations 600 µg/mL and 1500 µg/mL in Figure 3-2 depict the importance of pre-

data acquisition normalization. However, the background noise of the sample with total

protein concentration of 600 μg/mL is increased to roughly 5% of the TIC, presenting a

challenge when attempting to identify or determine isotope enrichment patters of low

abundance lipid species. If 600 µg/mL total protein concentration were used for

analysis, low abundance lipid classes such as LPE and PE would be challenging to

identify and quantity due to the elevated background noise.

Specific endogenous lipids were used for Table 3-9 to determine S/N ratios for

pooled QC samples with 600 µg/mL and 1500 µg/mL total protein concentration. The

84

lipids used were identified by LipidMatch and relatively quantified by LipidMatch Quant

(LMQ), which calculates the relative quantitation based on the internal or injection

standard (concentration and volume) used within the same lipid class. All the lipids in

Tables 3-10 and 3-11 were identified and quantified to levels that permitted analysis of

the signal-to-noise (S/N) ratio. Although the standards LPC (17:0), PC (17:0/17:0), PC

(19:0/19:0), and TG (17:0/17:0/17:0) were well above any S/N ratio cutoff, the more

interesting cell signaling and membrane fusion lipids like LPE (18:0) and PE (18:0_22:6)

could be misidentified or too dilute to obtain MS2 fragmentation data (see data in

Figures 3-3, 3-4, and 3-5). Cer (d18:1/18:0) has poor chromatographic peak shape in

the 600 µg/mL total protein concentration EIC (bottom graph). The results are similar for

LPE (18:0) and PE (18:0_22:6).

In a study using tissue sample of diseased verses healthy control, an abnormal

low abundance lipid species could be a biomarker for the disease progression. This is

why the final table of total protein concentrations (Table 3-2) is an ideal reference for

tissue LC/MS analysis of metabolites and lipids.

85

Figure 3-1. Schematic for LipidMatch workflow from data file input to identification of lipid class and confirmed lipid identifies per class. (Modified with permission from Koelmel et. al.)62

Sample

Sample

Output

86

Figure 3-2. Total ion chromatogram (TIC) of pooled QC gastrocnemius samples. The samples have relatively similar normalized intensity levels. However, the background noise of the lower chromatogram is elevated making it potentially challenging to monitor low abundant lipid species.

87

Table 3-1. Quadricep tissue weighed and total protein concentration determined. Tube Name

Genotype Sample ID Weight (mg) Original Concent. (ug/mL)

Q1 C57BL/6 Q_C57[T0] 36.9 4140

Q2 C57BL/6 Q_C57[E30_L] 27.3 3720

Q3 C57BL/6 Q_C57[E30_UL] 26.4 3440

Q4 C57BL/6 Q_C57[E30R_L] 31.6 3140

Q5 C57BL/6 Q_C57[E30R_UL] 16.3 3820

Q363 HET Q_HET[E55_L] 20.0 3840

Q367 HET Q_HET[E55_UL] 20.7 3460

Q364 WT Q_WT[E55_L] 16.6 2800

Q365 WT Q_WT[E55_UL] 20.6 3500

Q406 HET Q_HET[E55_L] 28.1 2760

Q417 HET Q_HET[E55_L] 26.6 4880

Q418 KO Q_KO[E10_L] 7.1 5200

Q378 WT Q_WT[E55R_L] 16.1 2780

Q379 HET Q_HET[E55R_L] 18.6 3240

Q380 HET Q_HET[E55_L] 35.6 4460

Q381 HET Q_HET[E10_L] 26.6 4880

Q382 WT Q_WT[E55_L] 18.1 2560

88

Table 3-2. Liver tissue weighed and total protein concentration determined. Tube No

Genotype Sample ID Weight (mg) Original Concent. (ug/mL)

L1 C57BL/6 L_C57[T0] 52.3 4300

L2 C57BL/6 L_C57[E30_L] 53.0 4360

L3 C57BL/6 L_C57[E30_UL] 50.0 4260

L4 C57BL/6 L_C57[E30R_L] 64.7 4280

L5 C57BL/6 L_C57[E30R_UL] 46.0 4280

L363 HET L_HET[E55_L] 71.1 4300

L367 HET L_HET[E55_UL] 49.7 4280

L364 WT L_WT[E55_L] 48.7 4280

L365 WT L_WT[E55_UL] 59.0 4280

L406 HET L_HET[E55_L] 81.2 4300

L417 HET L_HET[E55_L] 56.9 4300

L418 KO L_KO[E10_L] 59.4 4280

L378 WT L_WT[E55R_L] 70.2 4300

L379 HET L_HET[E55R_L] 49.6 4300

L380 HET L_HET[E55_L] 85.8 4300

L381 HET L_HET[E10_L] 49.4 4300

L382 WT L_WT[E55_L] 63.8 4300

89

Table 3-3. Kidney tissue weighed and total protein concentration determined. Tube No

Genotype Sample ID Weight (mg) Original Concent. (ug/mL)

K1 C57BL/6 K_C57[T0] 15.3 4540

K2 C57BL/6 K_C57[E30_L] 32.1 4540

K3 C57BL/6 K_C57[E30_UL] 35.5 4540

K4 C57BL/6 K_C57[E30R_L] 18.2 4560

K5 C57BL/6 K_C57[E30R_UL] 34.8 4540

K363 HET K_HET[E55_L] 22.5 4560

K367 HET K_HET[E55_UL] 26.4 4540

K364 WT K_WT[E55_L] 22.2 4560

K365 WT K_WT[E55_UL] 16.9 4520

K406 HET K_HET[E55_L] 21.0 4540

K417 HET K_HET[E55_L] 30.8 4500

K418 KO K_KO[E10_L] 17.9 4520

K378 WT K_WT[E55R_L] 22.5 4520

K379 HET K_HET[E55R_L] 25.7 4520

K380 HET K_HET[E55_L] 16.0 4540

K381 HET K_HET[E10_L] 30.7 4540

K382 WT K_WT[E55_L] 31.9 4540

90

Table 3-4. Gastrocnemius tissue weighed and total protein concentration determined. Tube Name

Genotype Sample ID Weight (mg) Original Concent. (ug/mL)

G1 C57BL/6 G_C57[T0] 24.1 4940

G2 C57BL/6 G_C57[E30_L] 23.8 4440

G3 C57BL/6 G_C57[E30_UL] 22.8 3720

G4 C57BL/6 G_C57[E30R_L] 27.6 4920

G5 C57BL/6 G_C57[E30R_UL] 37.0 4020

G363 HET G_HET[E55_L] 21.2 4740

G367 HET G_HET[E55_UL] 27.4 3500

G364 WT G_WT[E55_L] 30.1 3800

G365 WT G_WT[E55_UL] 17.8 3779

G406 HET G_HET[E55_L] 24.6 3579

G417 HET G_HET[E55_L] 19.8 4160

G418 KO G_KO[E10_L] 16.3 4079

G378 WT G_WT[E55R_L] 23.5 3100

G379 HET G_HET[E55R_L] 14.9 3979

G380 HET G_HET[E55_L] 17.9 3979

G381 HET G_HET[E10_L] 27.2 2580

G382 WT G_WT[E55_L] 17.0 4480

91

Table 3-5. Fat (adipose) tissue weighed and total protein concentration determined. Tube Name

Genotype Sample ID Weight (mg) Original Concent. (ug/mL)

F1 C57BL/6 F_C57[T0] 14.0 478

F2 C57BL/6 F_C57[E30_L] 14.5 158

F3 C57BL/6 F_C57[E30_UL] 13.5 760

F4 C57BL/6 F_C57[E30R_L] 20.5 177

F5 C57BL/6 F_C57[E30R_UL] 16.8 718

F363 HET F_HET[E55_L] 15.6 176

F367 HET F_HET[E55_UL] 16.4 366

F364 WT F_WT[E55_L] 24.6 886

F365 WT F_WT[E55_UL] 18.7 443

F406 HET F_HET[E55_L] 27.6 802

F417 HET F_HET[E55_L] 20.1 782

F378 WT F_WT[E55R_L] 12.8 168

F379 HET F_HET[E55R_L] 21.7 154

F380 HET F_HET[E55_L] 12.4 185

F381 HET F_HET[E10_L] 29.9 411

F382 WT F_WT[E55_L] 28.3 492

92

Table 3-6. The final adjusted total protein concentrations of each tissue sample for metabolomics and lipidomics analysis

Metabolomics Lipidomics

Fat: 12-29 mg

154 µg/mL 50 µg/mL

Heart: 16-30 mg

500 µg/mL 1000 µg/mL

Liver: 46-85 mg

750 µg/mL 1500 µg/mL

Kidney: 15-31 mg

750 µg/mL 1500 µg/mL

Gastrocnemius: 20-35 mg

750 µg/mL 1500 µg/mL

Quadriceps: 7-31 mg

750 µg/mL 1500 µg/mL

93

Table 3-7. Measurements taken into account to determine the S/N ratio of Cer (d18:1/18:0)

Total Protein Concentration (μg/mL)

Compound Adduct Mass-to-charge (m/z)

Averaged Scans (min)

1500

Cer (d18:1/18:0) [M+H]+ 566.5509

10 scans (8.95-9.13)

600 10 scans (8.86-8.98)

Figure 3-3. Extracted ion chromatogram (EIC) of ceramide (d18:1/18:0) for total protein concentration at 600 μg/mL (bottom) and 1500 μg/mL (top) of pooled QC samples.

94

Table 3-8. Measurements taken into account to determine the S/N ratio of LPE (18:0)

Total Protein Concentration (μg/mL)

Compound Adduct Mass-to-charge (m/z)

Avg Scans (min)

1500

LPE (18:0) [M+H]+ 482.3239

10 scans (2.44-2.62)

600 10 scans (2.30-2.42)

Figure 3-4. Extracted ion chromatogram (EIC) of lysophosphatidylethanolamine (18:0) for total protein concentration at 600 μg/mL (bottom) and 1500 μg/mL (top) of pooled QC samples.

95

Table 3-9. Measurements taken into account to determine the S/N ratio of PE (18:0_22:6)

Total Protein Concentration (μg/mL)

Compound Adduct Mass-to-charge (m/z)

Avg Scans (min)

1500

PE (18:0_22:6) [M+Na]+ 814.5355

9 scans (8.28-8.40)

600 9 scans (8.20-8.30)

Figure 3- 5. Extracted ion chromatogram (EIC) of phosphatidylethanolamine (18:0_22:6) for total protein concentration at 600 μg/mL (bottom) and 1500 μg/mL (top) of pooled QC samples.

96

Table 3-10. Internal standard S/N ratio.

Total Protein Concentration (μg/mL)

Lipid Species Adduct Mass-to-charge value (m/z)

Average Scans (min)

Signal Response

Noise Intensity

Signal-to-Noise Ratio (S/N)

1500 LPC (17:0) [M+H]+ 510.3558 12 scans (1.68-1.90)

2569990.5 246.84 10411.5

600 LPC (17:0) [M+H]+ 510.3558 12 scans (1.61-1.75)

5952885.5 851.33 6992.4

1500 PG (14:0/14:0) [M+H]+ 667.4549 7 scans (6.35-6.46)

373953.4 436.15 857.3

600 PG (14:0/14:0) [M+H]+ 667.4549 7 scans (6.37-6.45)

350774.6 2516.38 139.3

1500 PE (15:0/15:0) [M+Na]+ 686.4731 9 scans (7.76-7.90)

76591.7 618.12 123.9

600 PE (15:0/15:0) [M+Na]+ 686.4731 8 scans (7.70-7.79)

120195.3 2594.85 46.3

1500 PC (17:0/17:0) [M+H]+ 762.6008 10 scans (8.91-9.09)

1870746.9 387.37 4829.3

600 PC (17:0/17:0) [M+H]+ 762.6008 10 scans (8.82-8.94)

5066887.5 1943.33 2607.3

1500 PC (19:0/19:0) [M+H]+ 818.6634 10 scans (10.53-10.71)

2127577.3 352.43 6036.8

600 PC (19:0/19:0) [M+H]+ 818.6634 10 scans (10.37-10.49)

4763659.0 1605.38 2967.3

1500 TG (17:0/17:0/17:0)

[M+NH4]+ 866.8173 10 scans (15.67-15.81)

8676334.0 578.95 14986.3

600 TG (17:0/17:0/17:0)

[M+NH4]+ 866.8173 10 scans (15.60-15.71)

60327360.0 3130.4 19271.4

97

Table 3-11. Endogenous samples S/N ratio.

Total Protein Concentration (μg/mL)

Lipid Species Adduct Mass-to-charge value (m/z)

Average Scans (min)

Signal Response

Noise Intensity

Signal-to-Noise Ratio (S/N)

1500 LPE (18:0) [M+H]+ 482.3239 10 scans (2.44-2.62)

136795.1 232.01 589.6

600 LPE (18:0) [M+H]+ 482.3239 10 scans (2.30-2.42)

53356.0 836.26 63.8

1500 PE (18:0_22:6) [M+Na]+ 814.5355 9 scans (8.28-8.40)

150694.8 1224.29 123.0

600 PE (18:0_22:6) [M+Na]+ 814.5355 9 scans (8.20-8.30)

95205.5 5278.43 18.0

1500 Cer (d18:1/18:0) [M+H]+ 566.5509 10 scans (8.95-9.13)

199632.8 298.35 669.1

600 Cer (d18:1/18:0) [M+H]+ 566.5509 10 scans (8.86-8.98)

279007.3 1338.31 208.4

98

CHAPTER 4 IN VIVO EXERCISE ENGAGED TISSUE ANALYSIS

Physiologic Support System

Exercise engages the entire body; from pulmonary ventilation exchanging

oxygen with ambient air that is diffused through the skin, to transport and rapid delivery

of oxygen through the cardiovascular system, to the muscular system that utilizes

substrates stored in the digestive system, and removal of toxins via sweat perfusion

through the skin.2 Exercise disrupts cellular homeostasis, and specific cell types

respond to the metabolic challenge. At the cellular level, energy turnover is increased

during exhaustive exercise in skeletal muscle and heart tissue. Furthermore, glucose

carbons are recycled by lactate and alanine (Cori and Cahill cycle) to the liver quickly

during exercise and generate new glucose molecules. Liver and kidney cells contain

phosphatase promoting the split of phosphate from glucose 6-phosphate (G6P) creating

another form of available glucose.2 The immune and nervous systems are impacted

positively by exercise, alleviating symptoms of disorders like Alzheimer’s disease and

chronic inflammatory disease.115–117 This chapter details the measurement of recycling

labeled glucose carbons throughout skeletal muscle, heart, and liver tissues,

highlighting the TCA cycle intermediates’ isotope enrichment results.

Experimental Methods

Glucose incorporation into tissues is enhanced through exercise regimes.

However, the metabolic interactions between tissues are limited in literature. The

analysis of an exhaustive exercise state infers metabolites are involved in extensive

recycling to both small molecules and complex lipids. This experiment interprets

metabolic networks affected by exhaustive exercise through the analysis of 13C label

99

incorporation into gastrocnemius, heart, and liver tissue of C57BL/6J and BALB/c mice

exercised at various conditions. Potential biomarkers may assess both glucose

tolerance and disposal, as well as indicate how glucose is incorporated into lipids during

exhaustive exercise. After rest or exercise, samples were immediately sacrificed

gastrocnemius, heart, and liver tissues were collected, lyophilized, and stored at -80 °C.

Sample Preparation

Ten minutes prior to exercise, mice were injected with 2 mg/g of [U-13C]-glucose

or unlabeled glucose (for background subtraction). This bolus injection rapidly increases

the TTR for glucose, thus a 10 minute delay time is required before taking the GTT. The

exercise groups (Ex30 and E55) were trained for two weeks before the day of the

experiments. Mice ran on treadmills until exhausted. The exercise plus rest (E30R35)

group ran to exhaustion then rested for 35 minutes; one group received [U-13C]-glucose,

the other unlabeled glucose. Likewise, the exercised (Ex30) group exercised for 30

minutes; one group received [U-13C]-glucose, the other unlabeled glucose. Additionally,

the exercise 30 minutes and 35 minutes of rest (E30R35), and exercise 55 minutes

(E55) groups have a matched unlabeled counterpart. All mice were sacrificed

immediately upon completion of treatment, and the tissues were rapidly frozen in liquid

nitrogen, then stored in a – 80 °C freezer until extraction. Metabolites were extracted in

acetonitrile:methanol:acetone (8:1:1, v:v:v) detailed in Chapter 2.

Data Acquisition

Data were collected on a Dionex liquid chromatography system integrated with a

Thermo Q-Exactive™ Hybrid Quadrupole-Orbitrap mass spectrometer. The column

used for metabolites was an ACE Excel C18-PFP (100 X 2.1 mm, 2.0 μm) using

nanoViper (75 μm X 350mm) solvent lines with separation via gradient elution (detailed

100

in Chapter 2) of mobile phase A as water with 0.1% formic acid and mobile phase B as

acetonitrile. The Waters Acquity BEH C18 (50 X 2.1 mm, 1.7 μm) with a Waters

VanGuard BEH C18 1.7 μm guard column was used for lipid separation and standard

PEEK (100 μm X 350mm) tubing connecting to the HESI Probe, a gradient elution

(detailed in Chapter 3) of mobile phase C as acetonitrile:water (60:40, v:v) and mobile

phase D as isopropanol:acetonitrile:water (90:8:2, v:v:v), both mobile phases had 0.1%

formic acid and 10 mM ammonium formate.

Data Processing

Data were processed using the aforementioned techniques in Chapter 2. The

same msConvert and MZmine parameters were used for all tissue types, as well as the

in-house metabolite library for identification. It is common to optimize MZmine

parameters for different types of samples but this was a targeted analysis and did not

require modified parameters. MS2 fragmentation data were also used to confirm the ion

adduct of the metabolites within this document. For the tissue samples, extra

documentation was done to keep track the isotope enrichment pattern, including the

retention time windows, chromatographic scan number, and m/z ion adduct. In case

another graduate student revisits the data, accurate records were noted similar to Table

2-2 and Table 2-5.

Metabolic Data and Interpretation

Again, TCA cycle intermediates were targeted because of their role in exhaustive

exercise and to further validate our method of analysis. TCA cycle intermediates in the

heart do not depict a significant difference between exercise and exercised plus rest

mice in Figure 4-2. Aspartate M1 in C57 E30R35 mice heart extract seems to continue

101

recycling with the addition of rest. Branched-chain amino acids had similar enrichment

patterns as valine. Valine’s enrichment pattern was negligible.

Liver citrate for C57 E30R35 had increased enrichment after exercise while

resting and recuperating. Nevertheless, that doesn’t transfer to the latter half of the

cycle in Figure 4-3. The citrate and succinate enrichment patterns do not remain in the

cycle for fumarate. Yet, fumarate M3 enrichment increases from C57 E30 to C57

E30R35. 13C M3 incorporation in furmarate was most likely from pyruvate carboxylation

and the malate M3 ion was not present in mass spectra data to further confirm the

hypothesis. Leucine and isoleucine enrichment patterns resemble valine. The branched-

chain amino acids within the liver did not indicate unique recycling of 13C label.

Gastrocnemius tissue data shows the incorporation of 13C-label from [U-13C]-

glucose tracer into TCA cycle intermediates through citrate enrichment of HET and WT

E55 in Figure 4-4. BALB/c HET and WT were increased for citrate compared to C57

counterpart. When rounding out the cycle succinate levels of C57 have approached

enrichment of HET and WT. This interesting enrichment pattern could indicate that the

muscle tissue may process glucose differently from C57 genotype.

Malate and succinate display distinct patterns in the liver and heart, respectively,

that aids the theory of a potential biomarker in plasma, though none of the tissue data

resembles plasma enrichment patterns. Glucose and pyruvate enrichment levels are

critical for confirming exhaustive exercise’s impact on the metabolic phenotype.

Adipose, quadriceps, and kidney tissue data need to be processed for further

interpretation of the organ-organ crosstalk and the role of exercise in unexplored

biological systems within the human body. Once additional tissue data are processed

102

known pathways and cycles will be investigated first moving towards lipid enrichment

analysis.

Figure 4-1. Experimental design for exercised mice in this study.

103

Figure 4-2. In the heart tissue, TCA cycle intermediates and branched-chain amino acids were targeted for enrichment pattern by [U-13C]-glucose tracer. Malate M2 for HET E55 has an elevated enrichment level. Valine and other branched-chain amino acids did not indicate unique recycling of glucose.

104

Figure 4-3. Liver tissue TCA cycle intermediates have an interesting enrichment patterns. Citrate C57 E30R35 is enrichment increased after exercise while resting. Fumarate M3 incorporatation is most likely from pyruvate carboxylation. Valine and other branched-chain amino acids did not indicate unique recycling of glucose.

105

Figure 4-4. Gastronemius tissue data shows the incorporation of 13C-label from [U-13C]-glucose tracer into TCA cycle

intermediates. Citrate enrichment of HET and WT E55 indicate that the muscle tissue may process glucose differently from C57 genotype. Succinate does not indicate unique enrichment.

106

CHAPTER 5 CONCLUSIONS AND FUTURE WORK

Unfortunately, the field of exercise physiology is heavily focused on tissues that

have an obvious metabolic role in exercise response. Literature researchers seem to

disregard other systems within the body, such as the urinary system that may play a

part in exercise endurance. The TCA cycle was the pathway of choice in this study due

to the previously noted energetics regulated by central carbon metabolism.

In Chapter 2, plasma TCA cycle intermediate citrate M4 in E30 should be

explored further to understand the role of pyruvate carboxylation during exhaustive

exercise. Additionally, citrate showed continual recycling of labeling post-exercise,

suggesting increased mitochondrial activity. C4 carnitine is promising with increasing

enrichment after exercise and rest exposure and could indicate continual lipid recycling

post-exercise supported by serine enrichment patterns. Further work will investigate

lipid utilization of the 13C label and resolve the hypothesis that extensive lipid production

in other organs (i.e. adipose tissue) is causing elevated serine recycling in plasma

during exercise that tapers off glycolysis (serine potentially formed from 3-

phosphoglycerate in glycolysis) post-exercise with the addition of rest. As stated in

chapter 2, additional metabolites should be measured to determine if the label is coming

from glutamate or 3-phosphoglycerate and if the label is incorporated into ceramide or

other shingosines.

Ultimately, none of the tissue enrichment data resembled plasma enrichment

patterns; however, malate and succinate display distinct patterns in the liver and heart,

respectively, and that information can assist in considering other metabolites or lipids

that may serve as better representative within tissue of circulating plasma. Additionally,

107

changes in branched-chain amino acids were not inconsequential in response to

exhaustive exercise routines within the measured tissues. Glucose and pyruvate

enrichment levels are critical for confirming exhaustive exercise’s impact in all of the

discussed metabolic responses. The other tissue samples need to be processed for

further interpretation of the organ-organ crosstalk and the role of exercise in unexplored

biological systems within the human body.

The measurements in Chapter 3 are intended to be a reference for other

analytical scientists working with tissue samples. The simple total ion chromatogram

(TIC) comparison of two pooled QCs at different total protein concentration (600 µg/mL

and 1500 µg/mL) in Figure 3-2 immediately portrays the mishap of improper pre-data

acquisition normalization. Internal lipid standards were used as housekeeping

metabolites to determine S/N ratios for pooled QC samples with varying total protein

concentration. LPE (18:0) and PE (18:0_22:6), which are naturally low abundance and

are challenging to ionize, were too low to measure reliably at S/N of 63.8 and 18.0.

Further margins (higher and lower) of gastrocnemius tissue will be investigated to

provide the optimum concentrations for total protein. Fat, liver, kidney, heart and

quadriceps tissue should be re-analyzed for upper and lower limits, as well.

108

REFERENCES

(1) Montoye, H. J.; Kemper, H. C. G.; Saris, W. H. M.; Washburn, R. A. Measuring Physical Activity and Energy Expenditure; Fowler, M. E., Basic, M., Eds.; Human Kinetics: Champaign, IL, 1996.

(2) McArdle, W. D.; Katch, F. I.; Katch, V. L. Essentials of Exercise Physiology; Harris, J. M., Stead, L., Luken, R., Eds.; Lea & Febiger: Malvern, PA, 1994.

(3) Booth, F. W.; Thomason, D. B. Physiol. Rev. 1991, 71 (May), 541–574.

(4) Teixeira, P. J.; Carraça, E. V; Markland, D.; Silva, M. N.; Ryan, R. M. Int. J. Behav. Nutr. Phys. Act. 2012, 9 (78), 1–30.

(5) Hawley, J. A.; Hargreaves, M.; Joyner, M. J.; Zierath, J. R. Cell 2014, 159 (4), 738–749.

(6) Booth, F. W.; Chakravarthy, M. V; Spangenburg, E. E. J. Physiol. 2002, 543 (2), 399–411.

(7) Denham, J.; Marques, F. Z.; O’Brien, B. J.; Charchar, F. J. Sport. Med. 2014, 44 (2), 189–209.

(8) Heden, T. D.; Ryan, T. E.; Ferrara, P. J.; Hickner, R. C.; Brophy, P. M.; Neufer, P. D.; Mcclung, J. M.; Funai, K. Med. Sci. Sports Exerc. 2017, 49 (12), 2151–2157.

(9) Egan, B.; Zierath, J. R. Cell Metab. 2012, 17 (2), 162–184.

(10) Andersen, P.; Saltin, B. J. Physiol. 1985, 366, 233–249.

(11) Gibala, M. J.; MacLean, D. A.; Graham, T. E.; Saltin, B. Am. J. Physiol. 1998, 275 (2), 235–242.

(12) Gaitanos, G. C.; Williams, C.; Boobis, L. H.; Brooks, S. J. Appl. Physiol. 1993, 75 (2), 712–719.

(13) Hallsworth, K.; Fattakhova, G.; Hollingsworth, K. G.; Thoma, C.; Moore, S.; Taylor, R.; Day, C. P.; Trenell, M. I. Gut 2011, 60, 1278–1283.

(14) Teychenne, M.; Ball, K.; Salmon, J. Prev. Med. 2008, 46, 397–411.

(15) Li, L.; Kang, C.; Zhang, Y. Free Radic. Biol. Med. 2016, 98, 113–122.

(16) McNeely, M. L.; Campbell, K. L.; Rowe, B. H.; Klassen, T. P.; Mackey, J. R.; Courneya, K. S. CMAJ 2006, 175 (1), 34–41.

(17) Gordon, B. A.; Benson, A. C.; Bird, S. R.; Fraser, S. F. Diabetes Res. Clin. Pract. 2009, 83, 157–175.

109

(18) Liu, X.; Locasale, J. W. Trends Biochem. Sci. 2017, 42 (4), 274–284.

(19) Patti, G. J.; Yanes, O.; Siuzdak, G. Nat. Publ. Gr. 2012, 13 (4), 263–269.

(20) Viant, M. R.; Kurland, I. J.; Jones, M. R.; Dunn, W. B. Curr. Opin. Chem. Biol. 2017, 36, 64–69.

(21) Zamboni, N.; Saghatelian, A.; Patti, G. J. Mol. Cell 2015, 58 (4), 699–706.

(22) Evans, A. M.; Dehaven, C. D.; Barrett, T.; Mitchell, M.; Milgram, E. Anal. Chem. 2009, 81 (16), 6656–6667.

(23) Watson, A. D. J. Lipid Res. 2006, 47, 2101–2111.

(24) Tyurin, V. A.; Tyurina, Y. Y.; Kochanek, P. M.; Hamilton, R.; DeKosky, S. T.; Greenberger, J. S.; Bayir, H.; Kagan, V. E. In Methods in Enzymology; Khosravi-Far, R., Zakeri, Z., Lockshin, R. A., Piacentini, M., Eds.; Elsevier Inc.: Pittsburgh, 2008; Vol. 442, pp 375–393.

(25) Davison, J. M.; Cheyne, G. A. Med. Hist. 1974, 18 (2), 194–197.

(26) Roberts, L. D.; Souza, A. L.; Gerszten, R. E.; Clish, C. B. Curr. Protoc. Mol. Biol. 2012, 30 (2), 1–24.

(27) Patti, G. J.; Tautenhahn, R.; Siuzdak, G. Nat. Protoc. 2012, 7 (3), 508–516.

(28) Patti, G. J. J. Sep. Sci. 2011, 34, 3460–3469.

(29) Zechmeister, L.; Cholnoky, L. Principles and Practice Of Chromatography, 2nd ed.; Bacharach, A. L., Robinson, F. A., Eds.; Chapman & Hall Ltd.: London, 1943.

(30) Plumb, R.; Castro-perez, J.; Granger, J.; Beattie, I.; Joncour, K.; Wright, A. Rapid Commun. Mass Spectrom. 2004, 18, 2331–2337.

(31) Poole, C. F. The Essence of Chromatography, 1st ed.; Elsevier Science B.V., 2003.

(32) MacNair, J. E.; Lewis, K. C.; Jorgenson, J. W. Anal. Chem. 1997, 69 (6), 983–989.

(33) Wilson, I. D.; Nicholson, J. K.; Castro-perez, J.; Granger, J. H.; Johnson, K. A.; Smith, B. W.; Plumb, R. S. J. Proteome Res. 2005, 4, 591–598.

(34) Dole, M.; Mack, L. L.; Hines, R. L.; Mobley, R. C.; Ferguson, L. D.; Alice, M. B. J. Chem. Phys. 1968, 49 (5), 2240–2249.

(35) Gross, J. H. Mass Spectrometry, 2nd ed.; Springer: New York, 2011.

(36) Zeleny, J. Phys. Rev. 1917, 10 (1), 1–6.

110

(37) Taylor, G. Proc. R. Soc. A 1964, 280, 383–397.

(38) Smith, R. D.; Loo, J. A.; Edmonds, C. G.; Barinaga, C. J.; Udseth, H. R. Am. Chem. Soc. 1990, 62 (9), 882–899.

(39) King, R.; Bonfiglio, R.; Fernandez-Metzler, C.; Miller-Stein, C.; Olah, T. Am. Soc. Mass Spectrom. 2000, 11, 942–950.

(40) Watson, J. T.; Sparkman, O. D. Introduction to Mass Spectrometry: Instrumentation, Applications, and Strategies for Data Interpretation; 2007; Vol. 104.

(41) Awad, H.; Khamis, M. M.; El-Aneed, A. Appl. Spectrosc. Rev. 2014, 50 (2), 158–175.

(42) Brubaker, W. M.; Tuul, J. Rev. Sci. Instrum. 1964, 35 (8), 1007–1010.

(43) Miller, P. E.; Denton, M. B. J. Chem. Educ. 1986, 63 (7), 617–623.

(44) Hoffman, E.; Stroobant, V. Mass Spectrometry: Principles and Applications; 2007.

(45) Konenkov, N. V.; Sudakov, M.; Douglas, D. J. Am. Soc. Mass Spectrom. 2002, 13, 597–613.

(46) Scheltema, R. A.; Hauschild, J.; Lange, O.; Hornburg, D.; Denisov, E.; Damoc, E.; Kuehn, A.; Makarov, A.; Mann, M. Am. Soc. Biochem. Mol. Biol. 2014, 13, 3698–3708.

(47) Makarov, A. Anal. Chem. 2000, 72 (6), 1156–1162.

(48) Kingdon, K. H. Phys. Rev. 1923, 21, 408–418.

(49) Knight, R. D. Appl. Phys. Lett. 1981, 38 (4), 221–223.

(50) Hull, Albert, W. Phys. Rev. 1921, 18 (1), 31–57.

(51) Lewis, R. R. J. Appl. Phys. 1982, 53 (6), 3975–3980.

(52) Zubarev, R. A.; Makarov, A. Anal. Chem. 2013, 85 (11), 5288–5296.

(53) Lu, W.; Su, X.; Klein, M. S.; Lewis, I. A.; Fiehn, O.; Rabinowitz, J. D. Annu. Rev. Biochem. 2017, 86, 277–304.

(54) Fountain, K. J.; Iraneta, P. C. UHPLC in Life Sciences; Guillarme, D., Veuthey, J.-L., Eds.; Royal Society of Chemistry: Cambridge, UK, 2012.

(55) Holman, J. D.; Tabb, D. L.; Mallick, P. Curr. Protoc. Bioinforma. 2014, 46 (13), 1–9.

111

(56) Pluskal, T.; Castillo, S.; Villar-Briones, A.; Orešič, M. BMC Bioinformatics 2010, 11, 395–406.

(57) Patterson, R. E.; Kirpich, A. S.; Koelmel, J. P.; Kalavalapalli, S.; Morse, A. M.; Cusi, K.; Sunny, N. E.; McIntyre, L. M.; Garrett, T. J.; Yost, R. A. Metabolomics 2017, 13 (11), 1–11.

(58) Dunn, W. B.; Erban, A.; Weber, R. J. M.; Creek, D. J.; Brown, M.; Breitling, R.; Hankemeier, T.; Goodacre, R.; Neumann, S.; Kopka, J.; Viant, M. R. Metabolomics 2013, 9, 44–66.

(59) Zampieri, M.; Sekar, K.; Zamboni, N.; Sauer, U. Curr. Opin. Chem. Biol. 2017, 36, 15–23.

(60) Schrimpe-Rutledge, A. C.; Codreanu, S. G.; Sherrod, S. D.; McLean, J. A. J. Am. Soc. Mass Spectrom. 2016, 27 (12), 1897–1905.

(61) Creek, D. J.; Dunn, W. B.; Fiehn, O.; Griffin, J. L.; Hall, R. D.; Lei, Z.; Mistrik, R.; Neumann, S.; Schymanski, E. L.; Sumner, L. W.; Trengove, R.; Wolfender, J. L. Metabolomics 2014, 10 (3), 350–353.

(62) Koelmel, J. P.; Kroeger, N. M.; Ulmer, C. Z.; Bowden, J. A.; Patterson, R. E.; Cochran, J. A.; Beecher, C. W. W.; Garrett, T. J.; Yost, R. A. BMC Bioinformatics 2017, 18 (1), 1–11.

(63) Okuda, S.; Yamada, T.; Hamajima, M.; Itoh, M.; Katayama, T.; Bork, P.; Goto, S.; Kanehisa, M. Nucleic Acids Res. 2008, 36 (Web Server issue), 423–426.

(64) Makarov, A. Anal. Chem. 2000, 72 (6), 1156–1162.

(65) Gabriel, B. M.; Zierath, J. R. Cell Metab. 2017, 25 (5), 1000–1011.

(66) Powers, S. K.; Radak, Z.; Ji, L. L. J. Physiol. 2016, 594 (18), 5081–5092.

(67) Freeman, S. Biological Science, 4th ed.; Wilbur, B., Ed.; Benjamin Cummings: San Francisco, CA, 2011.

(68) Bessman, S. P.; Carpenter, C. L. Annu. Rev. Biochem. 1985, 54 (1), 831–862.

(69) Holloszy, J. O.; Booth, F. W. Annu Rev Physiol 1976, 38, 273–291.

(70) Moran, L. A.; Horton, H. R.; Scrimgeour, K. G.; Perry, M. D. The Principles of Biochemistry, 5th ed.; Jaworski, A., Ed.; Pearson: Glenview, IL, 2012.

(71) McCartney, R. G.; Rice, J. E.; Sanderson, S. J.; Bunik, V.; Lindsay, H.; Lindsay, J. G. J. Biol. Chem. 1998, 273 (37), 24158–24164.

(72) Nelson, D. L.; Cox, M. M. Lehninger: Principles of Biochemistry, 5th ed.; Ahr, K., Rossignol, R., Eds.; W. H. Freeman and Company: New York, NY, 2008.

112

(73) Fan, T. W. M.; Kucia, M.; Jankowski, K.; Higashi, R. M.; Ratajczak, J.; Ratajczak, M. Z.; Lane, A. N. Mol. Cancer 2008, 7 (79), 1–20.

(74) Schoenheimer, R.; Rittenberg, D. J. Biol. Chem. 1935, 111 (1), 175–181.

(75) Rittenberg, D.; Schoenheimer, R. J. Biol. Chem. 1935, 111 (1), 169–174.

(76) Matwiyoff, N. A.; Ott, D. G. Science (80-. ). 1973, 181 (4105), 1125–1133.

(77) Wolfe, R. R.; Chinkes, D. L. Isotope Tracers in Metabolic Research, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, New Jersey, 2005.

(78) Caprioli, R. M. In Biochemical Applications of Mass Spectrometry; Waller, G. R., Ed.; Wiley-Interscience: New York, 1970; pp 735–771.

(79) Bell, P. M.; Firth, R. G.; Rizza, R. A. J. Clin. Invest. 1986, 78 (6), 1479–1486.

(80) Mahler, H. R.; Douglas, J. J. Am. Chem. Soc. 1957, 79 (5), 1159–1166.

(81) Cobelli, C.; Toffolo, G.; Foster, D. M. Am. J. Physiol. Metab. 1992, 262 (6), E968–E975.

(82) Buescher, J. M.; Antoniewicz, M. R.; Boros, L. G.; Burgess, S. C.; Brunengraber, H.; Clish, C. B.; DeBerardinis, R. J.; Feron, O.; Frezza, C.; Ghesquiere, B.; Gottlieb, E.; Hiller, K.; Jones, R. G.; Kamphorst, J. J.; Kibbey, R. G.; Kimmelman, A. C.; Locasale, J. W.; Lunt, S. Y.; Maddocks, O. D. K.; Malloy, C.; Metallo, C. M.; Munger, J.; Noh, K.; Rabinowitz, J. D.; Ralser, M.; Sauer, U.; Stephanopoulos, G.; St-Pierre, J.; Tennant, D. A.; Wittmann, C.; Vander Heiden, M. G.; Vazquez, A.; Vousden, K.; Young, J. D.; Zamboni, N.; Fendt, S. Curr. Opin. Biotechnol. 2015, 34, 189–201.

(83) Fernandez, C. A.; Rosiers, C. Des; Previs, S. F.; David, F.; Brunengraber, H. J. Mass Spectrom. 1996, 31, 255–262.

(84) Clasquin, M. F.; Melamud, E.; Rabinowitz, J. D. Curr. Protoc. Bioinforma. 2012, 14 (11), 1–23.

(85) Huang, X.; Chen, Y.-J.; Cho, K.; Nikolskiy, I.; Crawford, P.; Patti, G. J. Anal. Chem. 2014, 86, 1632–1632.

(86) Capellades, J.; Navarro, M.; Samino, S.; Garcia-Ramirez, M.; Hernandez, C.; Simo, R.; Vinaixa, M.; Yanes, O. Anal. Chem. 2016, 88 (1), 621–628.

(87) Chokkathukalam, A.; Jankevics, A.; Creek, D. J.; Achcar, F.; Barrett, M. P.; Breitling, R. Bioinformatics 2013, 29 (2), 281–283.

(88) Bueschl, C.; Kluger, B.; Lemmens, M.; Adam, G.; Wiesenberger, G.; Maschietto, V.; Marocco, A.; Strauss, J.; Bödi, S.; Thallinger, G. G.; Krska, R.; Schuhmacher,

113

R. Metabolomics 2014, 10 (4), 754–769.

(89) Zhou, R.; Tseng, C. L.; Huan, T.; Li, L. Anal. Chem. 2014, 86 (10), 4675–4679.

(90) Trotzmuller, M.; Triebl, A.; Ajsic, A.; Hartler, J.; Kofeler, H.; Regittnig, W. Anal. Chem. 2017, 89, 12252–12260.

(91) Ahn, W. S.; Antoniewicz, M. R. Metab. Eng. 2013, 15, 34–47.

(92) Liu, H.; Garrett, T. J.; Su, Z.; Khoo, C.; Gu, L. J. Nutr. Biochem. 2017, 45, 67–76.

(93) Swartz, M. E. J. Liq. Chromatogr. Relat. Technol. 2005, 28 (7–8), 1253–1263.

(94) Cheng, Y.; Walter, T. H.; Lu, Z.; Iraneta, P.; Alden, B. A.; Gendreau, C.; Neue, U. D.; Grassi, J. M.; Carmody, J. L.; O’Gara, J. E.; Fisk, R. P. LCGC 2000, 18 (11), 1162–1172.

(95) Horvath, C.; Melander, W. J. Chromatogr. Sci. 1977, 15, 393–404.

(96) ACE. ACE Ultra Inert Base Deactivated HPLC Columns; 2015.

(97) West, C.; Lemasson, E.; Khater, S.; Lesellier, E. J. Chromatogr. A 2015, 1412, 126–138.

(98) Santasania, C. T.; Bell, D. S. LCGC Eur. 2016, 29 (2), 86–92.

(99) New, L.; Chan, E. C. Y. 2018, 46 (July), 209–214.

(100) Waters’ Appl. Noteb. 2014.

(101) Grumbach, E. S.; Wheat, T. E.; Kele, M.; Mazzeo, J. R. Sep. Sci. Redefined 2005, 40–44.

(102) Waters. ACQUITY UPLC TM BEH Column; 2006.

(103) Grumbach, E. S.; Wheat, T. E.; Mazzeo, J. R. Combining Elevated Temperature and UPLC TM to Achieve Ultra-High Efficiency Separations; 2006.

(104) Grumbach, E. S.; Diehl, D. M.; Neue, U. D. J. Sep. Sci. 2008, 31 (9), 1511–1518.

(105) Marshall, A. G.; Hendrickson, C. L. Annu. Rev. Anal. Chem. 2008, 1, 579–599.

(106) Michalski, A.; Damoc, E.; Hauschild, J.-P.; Lange, O.; Wieghaus, A.; Makarov, A.; Nagaraj, N.; Cox, J.; Mann, M.; Horning, S. Mol. Cell. Proteomics 2011, 10 (9), M111.011015.

(107) Thermo Scientific Q Exactive; 2012.

(108) Olsen, J. V; de Godoy, L. M. F.; Li, G.; Macek, B.; Mortensen, P.; Pesch, R.;

114

Makarov, A.; Lange, O.; Horning, S.; Mann, M. Mol. Cell. Proteomics 2005, 4 (12), 2010–2021.

(109) Hu, Q.; Noll, R. J.; Li, H.; Makarov, A.; Hardman, M.; Cooks, R. G. J. Mass Spectrom. 2005, 40 (4), 430–443.

(110) Sas, K. M.; Kayampilly, P.; Byun, J.; Nair, V.; Hinder, L. M.; Hur, J.; Zhang, H.; Lin, C.; Qi, N. R.; Michailidis, G.; Groop, P.-H.; Nelson, R. G.; Darshi, M.; Sharma, K.; Schelling, J. R.; Sedor, J. R.; Pop-Busui, R.; Weinberg, J. M.; Soleimanpour, S. A.; Abcouwer, S. F.; Gardner, T. W.; Burant, C. F.; Feldman, E. L.; Kretzler, M.; Brosius, F. C.; Pennathur, S. J. Clin. Investig. Insight 2016, 1 (15), 1–19.

(111) Silva, L. P.; Lorenzi, P. L.; Purwaha, P.; Yong, V.; Hawke, D. H.; Weinstein, J. N. Anal. Chem. 2013, 85 (20), 9536–9542.

(112) Ulmer, C. Z.; Patterson, R. E.; Koelmel, J. P.; Garrett, T. J.; Yost, R. A. In Lipidomics: Methods and Protocols; Bhattacharya, S. K., Ed.; Springer: Clifton, 2017; pp 91–106.

(113) Folch, J.; Lees, M.; Stanley, G. H. S. J Biol Chem. 1957, pp 497–509.

(114) Michalski, A.; Damoc, E.; Hauschild, J.; Lange, O.; Wieghaus, A.; Makarov, A.; Nagaraj, N.; Cox, J.; Mann, M.; Horning, S. Am. Soc. Biochem. Mol. Biol. 2011, 10 (9), 1–11.

(115) Walsh, N. P.; Gleeson, M.; Shephard, R. J.; Gleeson, M.; Woods, J. A.; Bishop, N. C.; Fleshner, M.; Green, C.; Pedersen, B. K.; Hoffman-Goetz, L.; Rogers, C. J.; Northoff, H.; Abbasi, A.; Simon, P. Exerc Immunol Rev 2011, 17, 6–63.

(116) Hillman, C. H.; Erickson, K. I.; Kramer, A. F. Nat. Rev. Neurosci. 2008, 9, 58–65.

(117) Mattson, M. P. Cell Metab. 2012, 16, 706–722.

115

BIOGRAPHICAL SKETCH

A native Texan, Michelle Reid moved to Atlanta, Georgia, for an undergraduate

education at Spelman College, where she received her Bachelor of Science in

Biochemistry. Michelle participated in an internship at the Organic Trace Lab at Pacific

Northwest National Laboratory (PNNL), where she worked on licorice root analysis

under the supervision of Dr. James Campbell. The research project tested various

licorice root extracts at different gradient levels by gas chromatography/ triple

quadrupole mass spectrometry and liquid chromatography/ accurate-mass time-of-flight

mass spectrometry for additional bioactivity compounds. The project in Dr. Kimberly M.

Jackson’s lab at Spelman prompted her to continue studies in the field of mass

spectrometry.

After graduating from Spelman, Michelle began working at Analytical Food

Laboratories to gain post-baccalaureate industry experience. In an effort to advance her

career, she then pursued graduate education. Thus, she joined Dr. Richard A. Yost’s

research group at the University of Florida. Her doctoral research focus involved the use

of mass spectrometry to better understand translational research pertaining to exercise,

specifically the analysis of fatigued exercise extensive recycling of [U-13C]-glucose in a

whole body mouse model. This experiment interpreted metabolic networks from

exercise by liquid chromatography/mass spectrometry analysis of tissue-tissue

interactions of C57BL/6J and BALB/c mice exercised at various conditions in response

to bolus intraperitoneal injection of uniformly labeled 13C glucose, as a tracer, prior to

exercise. Potential biomarkers were used to assess both glucose tolerance/disposal as

well as how glucose metabolites may have been incorporated into lipids. At the

116

completion of this document in the summer of 2018, she received her Ph.D. from the

University of Florida.