Molecular Factors Influencing Feed Efficiency in Mature ...

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Molecular Factors Influencing Feed Efficiency in Mature Beef Cows

by

Katharine Melissa Wood

A Thesis

presented to

The University of Guelph

In partial fulfillment of requirements

for the degree of

Doctor of Philosophy

in

Animal and Poultry Science

Guelph, Ontario, Canada

© Katharine M. Wood, June, 2013

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ABSTRACT

MOLECULAR FACTORS INFLUENCING FEED EFFICIENCY IN MATURE BEEF COWS

Katharine Melissa Wood Advisors:

University of Guelph, 2013 Dr. Brian W. McBride & Dr. Kendall C. Swanson

Identifying molecular mechanisms regulating cellular energy utilization may lead to increased

understanding of maintenance energy cost and improved feed efficiency in beef cows. Three experiments

were conducted to characterize measures of residual feed intake (RFI) in pregnant beef cows; to examine

the effects of moderate dietary restriction on visceral organ mass and proteins relating to energy

metabolism; and to investigate the influence of pregnancy on visceral organ mass and proteins relating to

energy metabolism. The first experiment combined data from five experiments using 321 pregnant Angus

× Simmental cows. Including ultrasound fat measures and diet/management information increased the

feed intake prediction model R2 by 7.3% and > 20%, respectively. Individual experiment RFI models

varied greatly in accuracy. In the second experiment, 22 pregnant beef cows were fed at 85% (LOW;

n=11) or 140% (HIGH; n=11) of net energy requirements during mid- to late-gestation. Tissue samples

from liver, kidney, muscle, ruminal papillae, pancreas, and small intestinal muscosa were collected.

Western blots were conducted to quantify abundance of: proliferating cell nuclear antigen, ATP synthase,

ubiquitin, and Na/K+ ATPase for all tissues; peroxisome proliferator-activated receptor gamma,

peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α), and 5’-adenosine

monophosphate-activated protein kinase and phosphorylated-AMPK (pAMPK) for liver, muscle, and

rumen; phosphoenolpyruvate carboxykinase for liver and kidney; and uncoupling protein 2 for liver.

Cows fed HIGH had greater (P ≤ 0.04) ADG and final BW than cows fed LOW. Ubiquitin abundance in

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muscle was greater (P = 0.009) in cows fed LOW, and PCG-1 α in liver was greater (P = 0.03) in cows

fed HIGH. In the third experiment, 18 pregnant (PREG; n =9) or non-pregnant (OPEN; n=9) Angus ×

Simmental cows were fed for ad libitum intake during mid- to late-gestation. Tissues were weighed and

collected and analyzed for protein abundance as described in the second experiment. Liver mass was

lower (P ≤ 0.02), abundance of Na+/K+-ATPase was greater (P =0.04) and rumen pAMPK abundance

was increased (P = 0.006) in PREG cows. These experiments indicate that measuring RFI in pregnant

cows may pose some challenges, and nutrient restriction and pregnancy can influence molecular factors

influencing feed efficiency.

Keywords

Beef cows, cellular energy metabolism, feed efficiency, feed intake, pregnancy, visceral organ mass,

Advisory Committee: Dr. Brian W. McBride

Dr. Kendall C. Swanson

Dr. Stephen P. Miller

Dr. Carolyn Fitzsimmons

Dr. John P. Cant

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Dedication I would like to dedicate this dissertation to the farm.

I am very thankful for that fateful day over 50 years ago, when my Grandparents went looking for a place

to camp, and bought “Otonavista Farm” instead.

It has led me to fall in love with agriculture and taken me to where I am today.

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Acknowledgements Firstly, I need to thank my advisors for taking a chance on me, a quiet bovine enthusiast, who

likes to find out how things work. Not many people are lucky enough to find such supportive, passionate,

encouraging, patient and brilliant scientists; and I was lucky enough to find two of them. To Dr. Kendall

Swanson and Dr. Brian McBride, thank for your encouragement, your mentorship, your friendship.

There is an old African saying that says “it takes a village to raise a child.” The same saying

easily applies to raising a Ph D student. To my advisory committee: Dr. Carolyn Fitzsimmons, Dr. John

Cant and Dr. Steve Miller, each of you have taught me so much over the past three and a half years. I

sincerely appreciate your advice and insight into my work, my career and my passion for agriculture. To

Dr. Reynold Bergen, my CYL (tor)mentor. Thanks for teaching me so much about the beef industry. To

Tim Caldwell for all your help with the experiments and ultrasound analysis, Dr. Gord Vanderoort and

Dr. Margaret Quinton for statistical advice, Jing Zhang and Jeff Gross at the Genomics Facility, and

Linda Trouten-Radford for help with the O2 consumption measurement. Also a special thank you to

Charlie, Dave, Bob, John and others at the Elora Beef Research Station and to Hal and Leo at the New

Liskeard Agricultural Research Station, and Brian, Judy and Sam at the U of G meat lab, as well as

numerous undergraduate volunteers. None of this would be possible without your tireless efforts in

making our experiments a reality.

I need to thank all my lab mates past and present. In particular from the Swanson lab: Dr. Heba

Salim, Dr. Yajing Wang, Laura Martin, Simone Holligan, Dr. Basim Awda, Brock Smith and Dr. Yuri

Montanholi; and from the McBride lab: Dr. Mike Steele, Dr. Sabrina Greenwood, Dr. Ousama AlZahal,

Anne Laarman, Lauren Hopkins, Gail Ritchie and Dr. Louis Dionissopoulos. And to my officemates and

APS family, Dr. Julie Kim, Dr. Dan Columbus, Wilfredo Mansilla, Dr. John Doelman, and many others.

Thank you not only for your occasional helping hand, but also for your friendship.

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I wish to thank the financial backers of this project. The Canadian Beef Cattle Science Cluster,

through funding provided by the Beef Cattle Research Council and Agriculture and Agri-food Canada;

Agriculture Adaptation Council-Farm Innovation Program; Ontario Ministry of Agriculture, Food and

Rural Affairs; and the Ontario Cattleman’s Association. I would also like to thank the Ontario Graduate

Scholarship Program.

Finally I would like say thank you to my parents, brothers, both grandparents and my close friend

Laura Robson and her family. I would not be where I am today without your love and support.

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Table of Contents Dedication .................................................................................................................................................... iv

Acknowledgements ....................................................................................................................................... v

List of Tables ................................................................................................................................................. x

List of Figures .............................................................................................................................................. xii

List of Abbreviations ...................................................................................................................................xiii

Chapter 1: General Introduction ................................................................................................................... 1

Chapter 2: Literature Review ........................................................................................................................ 3

2.1 Residual Feed Intake ........................................................................................................................... 3

2.2 Feed efficiency and maintenance energy requirements in the beef cow .......................................... 4

2.3 Visceral organ mass and maintenance requirements ........................................................................ 6

2.4 Energetic demands in late pregnancy and energy repartitioning for pregnancy ............................... 8

2.5 Metabolic Pathways of interest .......................................................................................................... 9

2.5.1 PCNA and cell proliferation .......................................................................................................... 9

2.5.2 ATP Synthase .............................................................................................................................. 10

2.5.3 Na+/K+ - ATPase ......................................................................................................................... 11

2.5.4 Ubiquitin and protein degradation ............................................................................................ 12

2.5.5 PEPCK ......................................................................................................................................... 13

2.5.6 PPARγ ......................................................................................................................................... 14

2.6.7 PGC-1α ....................................................................................................................................... 15

2.5.7 AMPK and pAMPK ...................................................................................................................... 17

2.5.8 UCP2 ........................................................................................................................................... 18

2.6 Research Hypotheses and Objectives ............................................................................................... 19

Chapter 3: Characterization and evaluation of residual feed intake in mid to late gestation mature beef

cows ............................................................................................................................................................ 20

3.1 Introduction ...................................................................................................................................... 20

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3.2 Methods ............................................................................................................................................ 21

3.2.1 Animals and experiment design ................................................................................................. 21

3.2.2 Diets and feed sample analysis .................................................................................................. 22

3.2.3 Determination of traits, residual feed intake, and statistical analysis....................................... 23

3.3 Results and Discussion ...................................................................................................................... 24

3.4 Conclusions ....................................................................................................................................... 28

Chapter 4: The effect of moderate dietary restriction on visceral organ weight, hepatic oxygen

consumption, and metabolic proteins associated with energy balance in mature pregnant beef cows 1 41

4.1 Introduction ...................................................................................................................................... 41

4.2 Materials and Methods ..................................................................................................................... 42

4.2.1 Animals, Experimental Design and Dietary Treatments ............................................................ 42

4.2.2 Feed and sample analysis........................................................................................................... 43

4.2.3 Sample Collection and Carcass Measurements ......................................................................... 43

4.2.4 Protein Concentration, SDS-PAGE and Immunoblots ................................................................ 44

4.2.5 Oxygen consumption ................................................................................................................. 46

4.2.6 Citrate Synthase Activity ............................................................................................................ 47

4.2.7 Statistical Analysis ...................................................................................................................... 47

4.3 Results ............................................................................................................................................... 47

4.4 Discussion .......................................................................................................................................... 49

Chapter 5: The influence of pregnancy in mid-to-late gestation on circulating metabolites, visceral organ

mass, and abundance of proteins relating to energy metabolism in mature beef cows ........................... 66

5.1 Introduction ...................................................................................................................................... 66

5.2 Materials and Methods ..................................................................................................................... 67

5.2.1 Animals, experimental design and treatments .......................................................................... 67

5.2.2 Feed and sample analysis........................................................................................................... 68

5.2.3 Tissue collection and carcass measurements ............................................................................ 68

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5.2.4 Immunoblot and protein concentrations .................................................................................. 69

5.2.5 Statistical analysis ...................................................................................................................... 72

5.3 Results ............................................................................................................................................... 72

5.4 Discussion .......................................................................................................................................... 73

Chapter 6: General Conclusions .................................................................................................................. 91

Appendix 1: Relationships between measures of feed efficiency and circulating serum metabolites and

body parameter measures in mid to late gestating mature beef cows ................................................... 124

A1.1 Materials and Methods ................................................................................................................ 124

A1.1.1 Serum collection and analysis................................................................................................ 124

A1.1.2 Body Parameter Measures .................................................................................................... 124

A1.1.3 Analysis .................................................................................................................................. 125

A1.2 Results ........................................................................................................................................... 125

A1.2.1 Correlation between cow age, measures of performance and feed efficiency .................... 125

A1.2.2 Relationships between measures of feed efficiency and circulating serum metabolites ..... 126

A1.2.3 Relationships between measures of feed efficiency and body parameter measures .......... 128

A1.4 Conclusions ................................................................................................................................... 129

Appendix 2: Evaluation of using real-time ultrasound to predict total internal fat in the mature beef cow

.................................................................................................................................................................. 135

A2.1 Introduction .................................................................................................................................. 135

A2.2 Materials and Methods ................................................................................................................ 135

A2.3 Results and discussion .................................................................................................................. 136

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List of Tables

Table 3.1: Summary of contemporary group mature cow experiments included in the combined

dataset ............................................................................................................................................. 30

Table 3.2: Dietary analysis of rations fed to each contemporary group ......................................... 31

Table 3.5: Model fit statistics for RFI (DM intake) models tested within each contemporary group

of mature pregnant beef cows ......................................................................................................... 34

Table 3.5 continued: Model fit statistics for RFI (DM intake) models tested within each

contemporary group of mature pregnant beef cows ........................................................................ 35







Table 3.6: Descriptive statistics for the basic, greatest R2, and greatest BIC RFI models calculated

within each contemporary group of mature pregnant beef cows .................................................... 39

Table 3.7: Root mean squared prediction error for the basic, greatest R2, and greatest BIC RFI

models calculated within each contemporary group of mature pregnant beef cows....................... 40

Table 4.1. Diet composition and analyses ....................................................................................... 55

Table 4.2. Performance, real-time ultrasound and carcass characteristics of cows fed above or

below total net energy requirements ............................................................................................... 56

Table 4.3. Circulating serum metabolites of cows fed above or below total net energy

requirements. ................................................................................................................................... 57

Table 4.4. Organ weights (actual, relative to body weight and hot carcass weight) and total internal

fat weight (actual, relative to body weight and hot carcass weight) in cows fed above or below

total net energy requirements. ......................................................................................................... 59

Table 4.5. Hepatic oxygen consumption, protein concentration and citrate synthase activity in

cows fed above or below total net energy requirements. ................................................................ 61

Table 4.6. Abundance of proteins relating to energy balance in tissues of cows fed above or below

total net energy requirements. ......................................................................................................... 62

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Table 5.1. Diet composition and analyses ....................................................................................... 81

Table 5.2. Performance, real-time ultrasound and carcass characteristics of open and pregnant

cows ................................................................................................................................................ 82

Table 5.3. Circulating serum metabolites in pregnant or open cows at the start, day 56, and end of

trial .................................................................................................................................................. 83


fat weight (actual, relative to body weight and hot carcass weight) in cows .................................. 85

Table 5.5. Abundance of proteins relating to energy metabolism in tissues of open and pregnant

cows ................................................................................................................................................ 88

Table A1.1: Descriptive statistics for circulating serum metabolites and linear body measures for

combined dataset of mature pregnant beef cows. ......................................................................... 131

Table A1.2: Adjusted Peasron correlations between performance and feed efficiency measures in

mature pregnant beef cows1 .......................................................................................................... 132

Table A1.3: Corrected Pearson correlations between performance and feed efficiency measures

and circulating serum metabolites measured at the end of test in mature pregnant beef cows1 .... 133

Table A1.4: Corrected Pearson correlations between performance measures and linear body

parameter measures in mature pregnant beef cows1 ..................................................................... 134

Table A2.1: Pearson correlations between measures of ultrasound kidney fat and actual total body

fat. ................................................................................................................................................. 138

Table A2.2: Model equations to estimate total internal fat using ultrasound measures of kidney fat

depth in mature cows .................................................................................................................... 139

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List of Figures

Figure 4.1. Typical immunoblot of peroxisome proliferator-activated receptor gamma coactivator 1 alpha

(PGC-1α) in liver in mature beef cows fed 1.4 × total NE requirements (HIGH) or 0.85 × total NE

requirements (LOW) ................................................................................................................................... 65

Figure 4.2. Typical immunoblot of ubiquitin in muscle in mature beef cows fed 1.4 × total NE

requirements (HIGH) or 0.85 × total NE requirements (LOW).................................................................. 65

Figure 5.1 Representative immunoblot (top) and fast green stain (bottom) for Na+/K+ ATPase α1 in liver

tissue from pregnant (Pr) or non-pregnant (Op) mature beef cows. ........................................................... 90

Figure 5.2 Representative immunoblot (top) and fast green stain (bottom) for pAMPK in rumen papillae

from pregnant (Pr) or non-pregnant (Op) mature beef cows. ..................................................................... 90

Figure A2.1. Real-time ultrasound measures of three kidney fat depth measurements evaluated per

animal, From left to right: ventral of the abdominal muscles to the end of the kidney fat (uKFDe;

originally described by Riberio et al., 2008), ventral of the abdominal muscles to the ventral side of the

kidney (uKFDv), and ventral side of abdominal muscles to dorsal side of the kidney (uKFDd). ........... 140

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List of Abbreviations aBW actual back fat

ADF Acid detergent fibre

ADG average daily gain

AMPK 5’- adenosine monophosphate-activated protein kinase

AU arbitrary units

BIC Bayesian information criteria

BHBA β-hydroxybutyrate

BW bodyweight

cBF change in real-time ultrasound predicted back fat

CD36 cluster of differentiation 36

cKFD carcass measured kidney fat depth

CP crude protein

cRF Change in real-time ultrasound predicted rump fat

CV coefficient of variation

d day

DM dry matter

DMI dry matter intake

DNA deoxyribonucleic acid

EBRC Elora beef research centre

ERR estrogen-related receptor

F:G feed to gain

FCR feed conversion ratios

FOXO1 forkhead box protein O1

G:F gain to feed

GLUT4 glucose transporter 4

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GR glucocorticoid receptor

h hours

HCW hot carcass weight

HNF-4 hepatocyte nuclear factor 4

iBF initial real-time ultrasound predicted back fat

IGF insulin-like growth factor

iRF initial real-time ultrasound predicted rump fat

LM longissimus muscle

LMA longissimus muscle area

LPL lipoprotein lipase

MEF-2 myocyte enhancer factor-2

mTORC1 mammalian target of rapamycin complex 1

mTORC2 mammalian target of rapamycin complex 2

NDF neutral detergent fibre

NEFA non-esterified fatty acid

NE net energy

NEm net energy for maintenance

NLARS New Liskeard agricultural research station

NRF-1 nuclear respiratory factor 1

pAMPK phosphorylated 5’- adenosine monophosphate-activated protein kinase

pcADG pregnancy corrected average daily gain

pcBW pregnancy corrected mid-point body weight

PCNA proliferating cell nuclear receptor

PEPCK phosphoenolpyruvate carboxykinase

PGC-1α peroxisome proliferator-activated receptor gamma coactivator 1-α

PPARγ peroxisome proliferator-activated receptor gamma

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PVDF polyvinylidene fluoride

RFI residual feed intake

RMSE root mean square error

RMSPE root mean squared prediction error

RNA ribonucleic acid

SD standard deviation

SDS-PAGE sodium dodecyl sulphate-polyacrylamide electrophoresis

SIRT1 sirtuin 1

SNP single nucleotide polymorphisms

SREBP1 sterol regulatory element-binding transcription factor 1

T3 triiodothyronine

tFat total internal fat

TMR total mixed ration

TRMT contemporary group

U unit

uBF ultrasound measured back fat

UCP2 uncoupling protein 2

uKFD ultrasound predicted kidney fat depth

VFA volatile fatty acid

wk week

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Chapter 1: General Introduction As feed costs and the price of land continue to increase, livestock producers are finding financial

margins becoming increasingly narrower. In beef production in particular, feed costs usually represent the

single largest proportion of cost of production. Improving feed efficiency, even by a small percentage, has

the potential to represent large economic benefits to the producer. In addition to direct effects of feed

savings, Ahola and Hill (2012) outlined some of the broader benefits of increased feed efficiency to the

beef industry as whole: increased profitability and strengthening of rural economies, expansion of the

national cow inventory, reduction in end-product consumer price and increased competitiveness with pork

and poultry, and increased environmental benefits through reduction of manure and methane emissions

(Okine et al., 2001; Hegarty et al., 2007).

In more recent years, a renewed effort has been made into understanding feed efficiency in cattle.

Tremendous strides have been made into increased understanding of feed efficiency in growing cattle,

however very limited effort has been made into increasing understanding of biological feed efficiency in

mature beef cows (Carstens and Kerley, 2009).

In ruminant production, the cost of maintenance energy requirements represents approximately

half of the total gross energy required to produce beef (Dickerson, 1978). In the beef cow alone,

approximately ¾ of the total annual energy inputs are used for maintenance functions (Ferrell and

Jenkins, 1985). Although selection for increased growth, muscle development and meat quality has

occurred in the beef animal, almost no selection has been placed upon decreasing maintenance energy

requirements (Johnson et al., 2003).

In general, maintenance requirements can be classified as either: service functions; which are

responsible for respiration, functioning of the heart, kidney and liver, nervous function and removal of

wastes; or as cellular maintenance functions involved in protein and lipid turnover and ion transport.

Service functions represent 36 to 50% of total basal energy expenditure and cellular maintenance

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functions represent the remaining 40 to 56% of basal energy requirements (Baldwin et al., 1980). The

underlying mechanisms influencing cellular metabolism and are not well understood in ruminants. In the

present trial, a group of proteins were selected to examine effects relative to feed intake and physiological

status and to better understand their role in: protein turnover (ubiquitin), cell proliferation (proliferating

cell nuclear antigen; PCNA), ion pumping (Na+/K+ ATPaseα1 ), gluconeogenesis (phosphoenolpyruvate

carboxykinase; PEPCK) and cellular energy status (ATP synthase, uncoupling protein 2,UCP2;

peroxisome proliferator-activated receptor gamma, PPARγ; peroxisome proliferator-activated receptor

gamma coactivator 1 alpha, PGC-1α; 5’-adenosine monophosphate-activated protein kinase, AMPK; and

phosphorylated-AMPK ( pAMPK) in mature beef cows.

In ruminants, visceral organs account for a significant proportion of energetic cost, yet only

represent a small fraction of overall bodyweight (BW) (Burrin et al., 1989; McBride and Kelly, 1990;

Reynolds et al., 1991 ). With a large metabolic demand, monitoring visceral organ mass may provide

insights into key metabolic systems involved with maintenance energy requirements and feed efficiency

in beef cows.

Therefore the general objectives of this thesis are to firstly characterize and analyze the use of

residual feed intake (RFI) as a tool for identifying feed efficiency in pregnant beef cows; and secondly to

investigate the effect of limiting feed intake and pregnancy on select proteins relating to maintenance

energy requirements and energy metabolism, which may suggest future areas where research into

improvements in maintenance energy requirements and feed efficiency may occur.

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Chapter 2: Literature Review

2.1 Residual Feed Intake

There are numerous methods of measuring feed efficiency in cattle, each with their respective

advantages and disadvantages. Archer et al., (1999), and later summarized by Swanson and Miller (2008),

discussed the various approaches to the measurement of feed efficiency for beef cattle. Gross feed

efficiency is perhaps the most common and simplest measure of efficiency; calculated as a ratio of inputs

(usually feed) to outputs (in the beef industry, usually growth or weight gain) over a set length of time and

may be expressed as the inverse (gain:feed). This measure of efficiency poses a challenge as feed intake

and growth are not mutually exclusive, and this measure favours animals with potential for high growth

rates and high mature body weights. Other measures like partial efficiency of growth or maintenance

efficiency itself, account for energy required for maintenance, but can be extremely difficult to measure

accurately. Furthermore a whole systems approach may be used, which for cow/calf production may be

determined by accounting for total feed input per cow/calf unit per production cycle in relation to weight

of calf produced. Although this approach may reflect real economic value, as production in the cow/calf

industry is measured in the production of a calf, which is most often sold on a weight basis; measuring

feed intake over the whole production cycle may be costly and labour intensive.

In recent years, the concept of RFI or net feed efficiency has been gaining in popularity. It was

first described for use in beef cattle by Koch et al., (1963) and represents the difference between actual

measured feed intake and predicted intake, determined as a linear function of BW and gain. Therefore a

negative residual value represents the animal consuming less feed than predicted and a more efficient

phenotype. The measure of RFI is advantageous over traditional measures of feed efficiency, such as feed

conversion ratios (FCR) as it normalizes for growth rate and maturity patterns, and therefore is more

likely to reflect differences in metabolic rate (Nkrumah et al., 2006; Carstens and Kerley, 2009). Studies

have indicated that increased model accuracy can be obtained by accounting for animal variation in body

composition using ultrasound measures of fatness (Richardson et al., 2001; Montanholi et al., 2009) as

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differences in body composition have also been shown to correlate to feed efficiency (Basarab et al.

2003). Despite being independent of measures of growth, measures of RFI have been shown to have

strong correlations with FCR (Arthur et al., 2001; Basarab et al., 2003; Nkrumah et al., 2004). Measures

of RFI have also shown to be moderately heritable, ranging from 0.26 to 0.43 (Archer et al., 1999; Crews,

2005), and therefore may be advantageous for use for targeted genetic selection to improve production

efficiency.

Efficiency in the cow may also have significant influence on the progeny. Basarab et al., (2007)

measured RFI in progeny over 10 production cycles and found that efficiency in cows corresponded to

similar phenotypes in their offspring. Efficient cows also ate less and had a reduced twinning rate. Dams

of efficient progeny also maintained more back fat thickness pre-calving, pre-breeding, and at first and

second weaning in the production cycle. A much greater variation in RFI was noted in cows than as

measured in their progeny.

Although much progress in understanding of feed efficiency has been made in growing cattle,

knowledge regarding feed efficiency in mature cows is somewhat lacking (Carstens and Kerley, 2009).

Although a few studies have investigated measures of RFI classification as a heifer and then investigated

their subsequent phenotype as young cows (Archer et al., 2002; Arthur et al., 2005; Meyer et al., 2008),

less research has attempted to measure RFI on mature pregnant cows fed a forage diet.

2.2 Feed efficiency and maintenance energy requirements in the beef cow

Maintenance energy is defined as the nutritional energy cost required to sustain normal body

function, with no changes in body weight or body energy content. Maintenance energy requirements have

been generally attributed to body weight, which serves as a function of Max Rubner’s surface area law,

where fasting metabolism is lower in larger animal, due to lower body volume to surface area ratio

(Blaxter, 1967). Kleiber (1961) further developed the surface area law to generalizing that basal

metabolism is a function of BW 0.75

. Although Kleiber’s exponent is generally accepted as 0.75, this is in

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fact a generalization, and may not accurately represent differences in metabolic rate across individuals in

the same species, where differences in body weight between individuals are relatively small (Schmidt-

Nielson, K. 1970)

Level of feed intake is known to influence maintenance energy requirements or fasting heat

production as long term feed restriction is shown to reduce maintenance energy requirements (Blaxter et

al. 1966; Labussière et al., 2011). A study which investigated maintenance efficiency over several beef

and dairy breeds of open, non-lactating cows and found that all cows (except Red Polls) were most

efficient at the highest restriction level and decreased as feeding level increased (Taylor et al., 1986).

According to Baldwin et al. (1980) maintenance requirements can be divided into two major

groups: service functions; which are responsible for respiration, functioning of the heart, kidney and

liver, nervous function and removal of wastes and represent 36 to 50% of total basal energy expenditure.

Cellular maintenance functions represent the remaining 40 to 56% of basal energy requirements and can

be further sub-divided into protein turnover (9-12%), lipid turnover (2-4%), and ion transport (30-40%).

Estimates of variation in maintenance energy requirements between individual animals have been shown

to range from 5 to 35% of the mean (Johnson et al., 2003). Therefore this variation is of interest in

selection for reduced maintenance energy costs.

In the mature beef cow, maintenance requirements are of particular importance as maintenance

energy costs represent approximately 70 to 75 % of the total annual energy requirements (Ferrell and

Jenkins, 1985). When compared with other meat-producing livestock species, ruminants have the largest

proportion of total maintenance energy requirements per kg of edible meat (Dickerson, 1978). Although

selection for increased growth, muscle development and meat quality has occurred in the beef animal,

almost no selection has been placed upon decreasing maintenance energy requirements (Johnson et al.,

2003).

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When investigating factors contributing to variation in measures of RFI, it is suggested that

variation in activity, digestibility and heat increment of feeding each contribute approximately 10% of

variation, while variation in body composition and feeding behaviour each accounts for 5% and 2% of

variation, respectively. Protein turnover, tissue metabolism and stress are believed to account for an

estimated 37 %, with other unknown mechanisms accounting for another 27 %. (Richardson and Herd,

2004; Herd et al., 2004; Herd and Arthur, 2009). It has been suggested that inter-animal variation within

these cellular processes may greatly influence maintenance requirements and overall feed efficiency

(Bottje and Carstens, 2009; Carstens and Kerley; 2009; Herd and Arthur, 2009). Swanson and Miller

(2008) reviewed potential mechanism regulating feed efficiency in cattle and suggested that besides

whole animal factors (such as: level of intake; body composition, physical activity, nutrient

digestion/absorption/losses, health status, and systematic hormones), metabolic mechanisms such as:

differential cell turnover, ion transport, protein turnover, ATP synthesis/mitochondrial proton leak, urea

synthesis, nucleic acid and phosphlipid turnover, substrate cycling, as well as differences in expression of

regulatory proteins, may all contribute to differences in feed efficiency.

Little is known about the underlying cellular and molecular events involved in these processes in

relation to feed efficiency in the cow. In their review on the history of energetic efficiency research

Johnson et al. (2003) suggest that newer molecular tools may help accelerate progress in improving

energetic efficiency.

2.3 Visceral organ mass and maintenance requirements

The visceral organs, which encompass the portal-drained viscera, liver, pancreas and spleen,

serve the critical role of digestion and absorption of nutrients and play a major role in mediating post-

absorptive metabolism in other tissues (Huntington and McBride; 1988). Total visceral organ mass in

ruminants is estimated to be 6 to 10% of BW, yet it accounts for approximately 40 to 65% of total energy

expenditures (Burrin et al., 1989; Reynolds et al., 1991). Huntington and McBride, (1988) concluded that

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in the gut and liver, about 17.3% of whole body O2 consumption is associated with metabolic costs of

Na+/K+ -ATPase and protein synthesis alone, with urea synthesis accounting for an additional 5% and

protein degradation accounting for 2.7 % of whole body O2 consumption. It has been estimated that

increased energy use in visceral organs accounts for 70% of the increase in heat increment of

metabolizable energy intake above maintenance requirements (Johnson et al., 1990). In the

gastrointestinal tract, these changes in O2 consumption are likely due to changes in mass rather than organ

specific metabolic rate (Kelly et al., 2001)

Feed intake is also known to be positively correlated with visceral organ mass (Burrin et al.,

1990; Rompala et al., 1991; Fluharty and McClure; 1997; Swanson et al., 2000). Burrin et al. (1990)

suggest that increases in metabolic activity due to increasing feed intake appear to be a result of increased

visceral organ mass and not increases in tissue specific metabolic activity. In subsequent work, Burrin et

al. (1992) found that feeding above maintenance increased visceral RNA and protein abundance but not

DNA, suggesting that differences in visceral organ mass due to increased level of nutrition are due to

hypertrophic rather than hyperplastic growth. Besides gross level of energy intake, other dietary factors

can influence visceral organ size. Sainz and Bentley (1997) found that nutrient absorption drives

hypertrophy in the liver, while the forestomach responded more to fibre intake and intestines responded to

both diet type and nutrient supply.

Pregnancy has also been shown to impact visceral organ mass. Scheaffer et al. (2004)

investigated impacts of pregnancy and restrictive feeding in ewes and found that liver mass increased as d

of gestation increased, and restrictive feeding decreased liver mass, but there was no interaction between

d of gestation and feeding level. They also found similar results for small intestine.

Visceral organ mass differs between feed efficiency phenotypes, likely contributing to the

reduced cost of maintenance in efficient animals (Fluharty and McClure; 1997). Basarab et al. (2003)

found that low RFI (efficient) steers had smaller livers, small and large intestines, and total

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gastrointestinal tracts (intestine + stomach complex) than inefficient steers. Rompala et al. (1991) found

that the rumen mass of sheep selected for rapid growth/inefficiency was13 % greater than in control

sheep. The majority of research on visceral organ mass has been conducted in sheep, growing animals,

or dairy cows. Investigating differences in visceral organ mass in mature beef cows may lead to valuable

insights into understanding maintenance energy costs and feed efficiency.

2.4 Energetic demands in late pregnancy and energy repartitioning for

pregnancy

In the cow/calf industry, the cow must produce a calf every year in order for the producer to

remain profitable. In the ideal production cycle, a cow will calve and then approximately 60 days later

will be bred for the subsequent year. Thus, the cow will spend approximately ¾ of the year pregnant.

Nutrient requirements of a pregnant cow at the end of gestation are estimated to be approximately 75%

greater than in a non-pregnant cow of similar weight (Bauman and Currie, 1980). Although the nutrient

requirements for early gestation are relatively low, in mid- to late-gestation growth of the fetus and

associated tissues is observed (Ferrell, 1982; NRC, 1996), with dramatic increases commencing around d

180 of gestation. Nutrient requirements needed to sustain such growth also increase dramatically. In the

first 7 months of gestation, the fetus gains approximately 40% of its final weight. In the dairy cow, the

nutrient demanded to support the growth during the last 60 days of gestation is approximately equal to 3

to 6 kg of milk production per day (Bauman and Currie; 1980).

Partitioning of nutrients for pregnancy has been described as falling into two types of regulation:

homeostasis; responsible for maintaining equilibrium within the body; and homeorhesis, coordinated

control of metabolism throughout tissues used to support a physiological process, such as pregnancy or

lactation (Bauman and Currie; 1980). Many of the underlying mechanisms regulating the homeorhetic

drive are not well understood, but may potentially be mediated through endocrine changes associated with

pregnancy such as lactogen and estrogens (Bell and Bauman, 1997).

9

In ruminants, glucose is the primary source of energy for the conceptus (Bell and Bauman; 1997).

Glucose accounts for approximately 50 to 60% of fetal respiration (Hay et al., 1983) where acetate and

amino acids account for 10 to 15% and 30 to 40%, respectively (Bell, 1995). Since placental transport of

VFAs and ketones is limiting in ruminant fetuses (Bell et al.,1993), the majority of glucose must first be

produced by the dam before being utilized by the conceptus. Bell (1995) suggests that uterine uptake of

glucose is 46% of maternal supply and that of amino acids is 72% of maternal supply, whereas acetate is

only 12% of maternal supply. In order to meet these nutrient requirements, the dam may need to

dramatically shift basal metabolism. Steel and Leng, (1973) observed increased hepatic glucose

production in pregnant sheep fed for ad libitum intake as compared to non-pregnant sheep.

It has been suggested that late-gestating cows may be able to repartition energy towards

conceptus growth through metabolic adaptations and ultimately become more efficient and support

increased glucose production for the conceptus (Bell, 1995). The underlying processes to which this

occurs are not clear. However, Scheaffer et al. (2003) found that hepatic protein concentration was lower

and cellular proliferation and O2 consumption were decreased in the jejunum of pregnant heifers when

compared to non-pregnant heifers.

Understanding the influence of pregnancy is not only important to the production of healthy

calves, but investigating mechanisms involved in nutrient partitioning occurring during pregnancy may

identify cellular mechanisms which may play an important role in increasing efficiency.

2.5 Metabolic Pathways of interest

2.5.1 PCNA and cell proliferation

Visceral organs have been shown to have large energetic demands relative to size. Rates of

cellular regeneration rates in these tissues are also high and it has been suggested that partitioning of

energy towards protein synthesis for proliferation in visceral organs in the ruminant may contribute to

increased maintenance energy requirements (Hersom et al., 2004). An indicator of cellular proliferation

10

may be useful in identifying nutritional or physiological effects on rates of cell turnover and subsequent

increased energy demand for this process.

Proliferating cell nuclear antigen (PCNA) is a nuclear protein (36 kDa) involved in replication of

the leading strand of DNA and its expression peaks during the S phase of the cell cycle (Bravo and

Macdonald-Bravo, 1987). This protein has been used as an indicator of cell proliferation (Wang et al.

2009; Zheng et al., 1994; Swanson et al., 2000). Other markers (proliferative growth fraction; Ki67) for

cellular proliferation have been used in ruminants (Baldwin et al., 2004). However, in a survey of

different markers of cell proliferation, PCNA was suggested to be the most reliable (Iatropoulos and

Williams, 1996).

In growing beef steers, Wang et al. (2009) found that PCNA abundance increased linearly with

DMI in liver and quadraticly in pancreas, small intestinal mucosa and muscle, suggesting that cellular

proliferation is responsive to DMI, but may be maximized in some tissues at a moderate intake level.

However, Swanson et al. (2000) did not find significant differences in PCNA (total labelled area or

labeled nuclei) in intestinal tissues in growing wethers at high vs. low levels of intake. The role of PCNA

in the pregnant beef cow has not been investigated.

2.5.2 ATP Synthase

In cells ATP is the main unit of energy currency. In normal aerobic situations, the mitochondrial

bound protein complex ATP synthase is responsible for the majority of ATP synthesis (Das, 2003). ATP

synthase is the fifth (complex V) and final complex of the oxidative phosphorylation chain, and is

responsible for both ATP synthesis and dissipation of the mitochondrial proton gradient and is found on

the inner mitochondrial membrane. ATP synthase is comprised of two structures: the Fo structure, bound

in the mitochondria membrane and largely responsible for proton pumping; and the F1 structure composed

of 3 α and 3 β subunits alternatively arranged and responsible for ADP conversion to ATP. The ATP

synthase F1 complex is unique from a molecular perspective, as it functions like a rotary motor to convert

ADP to ATP. (Yoshida et al., 2001)

11

ATP synthase is coupled with approximately 70% of oxygen consumption in the mammalian cell

(Rolfe and Brown, 1997). Investigating animal differences in ATP synthase expression may increase

understanding of maintenance requirements in cows. A review by Das (2003) discussed regulation of

ATP synthase activity in a variety of tissues and species and concluded that ATP synthase regulation is

likely due to cellular energy demand, although specific signals are not well known. In fat-tailed Ghezel

lambs measured for feed efficiency, ATP synthase activity, along with all four other mitochondrial

respiratory chain complexes differed between low and high RFI lambs. ATP synthase activity was

correlated with RFI and FCR adjusted for metabolic BW (Rajaei Sharifabadi et al., 2012).

2.5.3 Na+/K+ - ATPase

Na+/K+ -ATPase is a membrane-bound protein (100 kDa) complex found ubiquitously across

most cell types that functions as an ATP-coupled pump used to maintain electrochemical gradients across

the plasma membrane. This heterodimeric protein is composed of a larger α-subunit with ten

transmembrane segments and a heavily glycosylated β-subunit (Kaplan, 2002). Although a variety of

isoforms exist, and they may be responsible for minor functional differences across tissues, the α1 isoform

is found ubiquitously in all tissues (Kaplan, 2002).

Activity of the Na+/K

+ - ATPase has been implicated as a major energy consuming processes in

the cell. McBride and Kelly (1990) estimated that Na+/K

+- ATPase in liver, small intestine and skeletal

muscle of ruminants accounts for approximately 20% of total maintenance energy demands. In the

gastrointestinal tract specifically Na+/K

+- ATPase coupled transport accounts for 25 to 60 % of O2

consumption in intestinal mucosa (McBride and Milligan,1985) and approximately 20% of O2

consumption in rumen papillae (Kelly et al., 1993). In skeletal muscle of pigs fed increasing dietary

protein, Na+/K

+- ATPase accounted for 22-25 % of O2 respiration (Adeola et al., 1989).

A variety of factors have been shown to influence Na+/K+ - ATPase activity and abundance.

Fasting has been shown to reduce Na+/K

+- ATPase-dependent O2 consumption in the liver. Fasted sheep

demonstrated a 62 % lower Na+/K

+- ATPase dependant O2 consumption than fed counterparts (McBride

12

and Milligan, 1985). Wang et al., (2009a) found that there was a linear relationship between hepatic

Na+/K

+- ATPase abundance and inclusion of forage in the diet, suggesting that diet may also influence

Na+/K

+- ATPase, perhaps through altered VFA profile. Beyond nutritional factors, it also has been found

that a variety of hormone signals can influence Na+/K+ - ATPase in tissues including insulin, thyroid

hormones, progesterone, cortisol, aldosterone, amongst others (Rossier et al., 1987; Ewart and Klip,

1995)

Na+/K+ - ATPase has been shown to be a large consumer of energy in a variety of cell types.

Baldwin et al., (1990) suggests that ion transport accounts for 30 to 40% of total basal energy

expenditures. Although not well researched, animal differences in Na+/K+ - ATPase may be partially

responsible for difference in underlying basal maintenance requirements. Increasing the understanding of

Na+/K+ - ATPase in mature cows may lead to increased understanding of maintenance requirements and

selection targets for improved feed efficiency.

2.5.4 Ubiquitin and protein degradation

In the body, protein degradation usually exceeds total daily protein intake and it is suggested that

at least 60% of protein required for protein remodelling in the body comes from amino acids mobilized

from tissues (Lobly, 2003). It is reported that 13.7% of cellular oxygen consumption is associated with

protein degradation in reticulocytes (Siems et al., 1984). Whole body fractional protein degradation rates

have been found to be positively correlated with maintenance requirements in steers (Castro Bulle et al.,

2007). Since protein degradation is an energetically expensive process, identifying animals with differing

protein turnover rates in tissues may improve our understanding of maintenance energy costs and

improved feed efficiency.

The ubiquitin mediated proteoytic pathway is the only ATP dependent pathway involved in

degradation of cytosolic, nuclear and myofibrillar proteins (Mitch and Goldberg, 1996; Davis et al.,

2012). Proteins slated for degradation are tagged with ubiquitin (7.6 kDA) through a process of ubiquitin

conjugation, where the ubiquitin protein is linked to lysine in target proteins. Ubiquitin is activated by the

13

E1 enzyme through an ATP-dependent reaction, where it is then transferred to a carrier E2 and then

finally to the target protein via a third catalytic enzyme E3. This process is repeated until a

polyubiquitinated chain is attached to the target protein. The 26S proteasome is a complex formed by the

19S complex and the 20S core proteasome. The 19S complex receives the polyubiquitinated protein,

cleaves the ubiquitin chain in an ATP-dependent reaction and the protein is degraded into peptides

through the 20S core proteasome and the ubiquitin molecules are then released and reused (Mitch and

Goldberg, 1996).

Ubiquitin abundance has previously been shown to be highly correlated (R = 0.924) with 20S

proteasome activity (Martin et al., 2002) which indicates that ubiquitin may be a useful marker for protein

degradation. Ubiquitin has previously been used as an indication of protein degradation in tissues of

ruminants (Mutsvangwa et al., 2004; Wang et al., 2009, Greenwood et al.,2009). Du et al. (2005) found

increases in protein ubiquitinylation in the muscle of feed restricted pregnant cows, but not in the muscle

of the fetuses, indicating the protein degradation and muscle atrophy of the pregnant cow may be a

mechanism to provide nutrients when intake is low. A microarray experiment investigating the effect of

restrictive feeding in muscle in Brahman steers found that pathways relating to protein turnover were

most affected by nutrient restriction (Byrne et al., 2005). Although it appears that protein degradation

marked by increased ubiquitin abundance has a close association with feed intake in cows, it is not well

known if ubiquitin mediated protein turnover is associated with feed efficiency in cows.

2.5.5 PEPCK

Phosphoenolpyruvate carboxykinase (PEPCK) is a gluconeogenic enzyme used in the conversion

of pyruvate to phosphoenolpyruvate. This protein is well known to be one of the rate controlling enzymes,

along with glucose-6-phosphatase and others, in gluconeogenesis (Rognstad, 1979; Davies et al., 1999).

In ruminants, PEPCK is present in the cytosol and in the mitochondria in approximately equal quantities

(Agca et al., 2002). Studies in dairy cows have shown that mRNA expression of the cytosolic fraction

increases at parturition (Agca et al., 2002) and the addition of monensin also increased cytosolic PEPCK,

14

suggesting that altered VFA supply may influence PEPCK gene expressssion (Karcher et al.,2007). In

fasted dairy heifers, PEPCK gene expression in increased (Doelman et al., 2012). Fasting has been shown

to increase PEPCK in the liver of mice, which appears to be mediated by cellular energy master

controllers PGC-1α and SIRT1 (Rodgers et al., 2005).

In non-ruminants, gluconeogenesis accounts for approximately 30% of liver metabolic rate, under

maximal glucose production conditions (Rolfe and Brown; 1997). In ruminants, about 18 to 30.3 % of

hepatic O2 consumption is devoted to substrate cycling, of this about 3.3 to 6.6% is associated with

PEPCK (McBride and Kelly, 1990). As ruminants are highly dependent on gluconeogenesis for glucose

production from propionate, lactate, and other VFAs, increasing abundance of PEPCK may mediate

increased glucose production and energy supply to the animal.

2.5.6 PPARγ

Peroxisome proliferator-activated receptor gamma (PPARγ) is a transcription factor involved in

the differentiation of adipocytes and lipid metabolism and is part of the PPAR sub-family of nuclear

receptors (Tontonoz and Spiegelman, 2008; Anghel and Wahli, 2007). PPARs bind to specific

peroxisome proliferator response elements and are then joined by transcriptional coactivators, increasing

transcription of a target gene (Berger and Moller, 2002). PPARγ has two main isoforms, PPARγ1 which

is found ubiquitously in most tissues and PPARγ2 which is primarily expressed in adipose tissues

(Anghel and Wahli, 2007). The primary role of PPARγ is that of a major regulator of adipogenesis,

involved in differentiation of preadipocytes into adipocytes and is thought to interact with downstream

target genes (PEPCK, LPL, GLUT4, CD36, and others) increasing triglyceride uptake and storage

(Tontonoz and Spiegelman, 2008). Activation of PPARγ has been shown to increase fatty acid uptake,

increase energy expenditure, and decrease gluconeogenesis in the liver and increase glucose uptake in the

muscle (Auwerx et al., 2003). In addition, PPARγ has been shown to have effects on energy homeostasis,

by decreasing UCPs and down-regulating leptin, although these mechanisms are not well understood

15

(Berger and Moller, 2002). More recently, PPARγ has been of interest in human medicine, as deletion of

PPARγ in mature adipocytes severely increases insulin resistance (Tontonoz and Spiegelman, 2008).

The role of PPARs in the ruminant has recently been reviewed by Bionaz et al. (2013). They

suggest that, in general, PPARγ expression and function is similar to that in other mammals, with adipose

followed by rumen having the greatest expression of PPARγ, and lowest in liver, kidney, pancreas, and

mammary gland. They also suggest that long-chain fatty acids have been shown to increase expression of

PPARγ and PPARα, with palmitate and stearate having the greatest effect. Glucose is also thought to

stimulate PPARγ expression, although this is not well-researched in ruminants. PPARγ may also play a

minor role in fatty acid oxidation, through increasing expression of the mitochondrial transporters

carnitine O- acetyltransferase and carnitine palmitoyltransferase 2.

One study investigated the consequences of β-oxidation by inducing BW loss in the muscle of

beef cows and found that mRNA expression of PPARγ, along with PPARα, PPARδ and UCP2 and UCP3

increased in beef cows losing weight (Brennan et al., 2009). This may suggest a coordinated cellular

response in order to mitigate increased nutrient stress. Increasing the understanding of PPARγ may prove

to be beneficial in understanding energy signalling in key tissues in beef cattle.

2.6.7 PGC-1α

Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α) is a

transcriptional coactivator of PPARγ and plays an energy sensing role through interactions with

mitochondrial biogenesis, fatty acid oxidation and gluconeogenesis (Wu et al., 1999; Houten and Auwerx,

2004; Liang and Ward, 2006). Regulation of PGC-1α is complex and poorly understood. Recent research

suggests that a variety of post-translational modifications may extensively regulate PGC-1α function

beyond transcriptional regulation, including methylation, phosphorylation, ubiquintation, and acetylation

(Fernandez-Marcos and Auwerx, 2011).

16

PGC-1α has been shown to have tissue specific responses. In brown adipose tissue, increased

PGC-1α has been shown after cold stress and adaptive thermogenesis; whereas in hepatic tissue, PGC-1α

expression increases gluconeogenesis during fasting; and in muscle, exercise increased PGC-1α -

mediated mitochondrial biogenesis and cellular respiration (Fernandez-Marcos and Auwerx, 2011).

Numerous splice variants of PGC-1α are known to exist and may also contribute to function and

specificity of the protein (Handschin and Spiegelman, 2006; Erkens et al., 2008) Fasting has been shown

to increase PGC-1α expression in rat hepatocytes which exerts dose dependant effects on

gluconeogenesis, and increases overall glucose output (Yoon et al., 2001)

PGC-1α was first identified as a coactivator of PPARγ in brown adipose tissue and is responsible

for uncoupling of mitochondrial respiration and brown adipocyte differentiation (Liang and Ward, 2006).

PGC-1α increases mitochondrial biogenesis through coactivation of nuclear respiratory factor 1and 2

(NRF-1 and NRF-2, respectively), which regulate expression of mitochondrial transcription factor –A

(Wu et al., 1999; Finck and Kelly, 2006). PGC-1α plays a role in glucose metabolism through the control

of hepatic gluconeogenesis and fatty acid oxidation in hepatic tissue through HNF-4, FOXO1

(gluconeogenesis) MEF-2 (glucose transporter) and the AMPK/SREBP1 pathways (Finck and Kelly,

2006; Jung et al., 2011) amongst others. PGC-1α is known to be linked to numerous upstream stimuli,

including cAMP, glucocorticoids (Yoon et al., 2001), S6 kinase (Lustig et al., 2011), thyroid hormones

(Weitzel et al., 2003), AMPK and SIRT1 (Cantó and Auwerx, 2009).

The role of PGC-1α is not well researched in cattle. One study using microarray techniques

identified that PGC-1α mRNA expression was increased in nutritionally ketotic Holstein cows (Loor et

al., 2007). Bottje and Carstens et al., (2009) found that hepatic PGC-1α abundance was increased in a

feed efficient line of broiler chickens. The role of PGC-1α as a highly integrated energy signalling

mechanism and its role in gluconeogenesis, mitochondrial biogenesis and fatty acid oxidation suggest that

PGC-1α may be a protein of interest in improving the understanding of energy metabolism and feed

efficiency in beef cows.

17

2.5.7 AMPK and pAMPK

5’-adenosine monophosphate-activated protein kinase (AMPK) and the activated form

phosphorylated-AMPK (pAMPK) is a highly conserved kinase, which plays a key role in cellular energy

homeostasis by sensing ADP:ATP ratio and phosphorylating major players in energy metabolism

(Pimentel et al., 2013). The AMPK protein is composed of three subunits: the catalytic α subunit, and the

regulatory β and γ subunits (Hardie, 2008). AMPK becomes activated by phosphorylation in the threonine

loop Thr172 on the α subunit (Viollet et al., 2009). pAMPK interacts with other downstream metabolic

pathways, either directly or indirectly, such as protein synthesis, fatty acid oxidation and

gluconeogenesis/glycolysis among others (Hardie, 2008). In general, activation of AMPK inhibits

anabolic pathways and stimulates catabolic pathways in cells.

Although previously thought to act as a response to acute energy changes with the cell, more

recent research suggests that AMPK may have coordinated responses to whole-body energy metabolism

(Andersson et al., 2004). AMPK is thought to be one of the downstream energy pathways influenced by

endocrine signalling (specifically ghrelin, leptin, adiponectin, insulin) through the hypothalamus, as

pharmacological stimulation of AMPK in the hypothalamus dramatically increased voluntary food intake

in rats (Andersson et al., 2004; Kim and Lee, 2005).

The role of AMPK has not been well researched in ruminants. However, Allen and Bradford,

(2005) suggest that AMPK may play an important role in the determination of voluntary feed intake in the

cow, similarly to rodent models. In hepatic tissue samples of Holstein cows that were fasted for 60 h and

suffering from fatty liver, Kuhla et al. (2009) found a significant increase in pAMPK abundance.

Although the authors were interested in downstream effects on VFA metabolism, this research does

indicate that AMPK is responsive to fasting in ruminants. This same group also demonstrated that AMPK

responds to acute feed restriction in the hypothalamus of Holstein cows (Kuhla et al., 2007). In feed-

restricted cows AMPK activity was shown to increase in muscle when compared to control cows (Du et

al., 2005). The role of AMPK in feed efficiency has not been investigated in cattle. However, protein

18

abundance of AMPK and pAMPK has been shown to be lower in the muscle of pig divergently selected

for low RFI (improved efficiency) (Faure et al., 2013).

Although the mechanisms of control and many of the downstream effects of AMPK are not yet

fully understood, the role of AMPK in feed intake regulation and whole-body energy sensing suggests

AMPK may be an important target for improving the understanding of feed efficiency in beef cattle.

2.5.8 UCP2

Uncoupling protein 2 (UCP2) is located in the inner mitochondrial matrix and is responsible for

partially uncoupling ATP synthesis (Spiegelman and Flier, 2001; Rousset et al., 2004) and dissipating this

energy as heat. This protein is found ubiquitously in all mammalian tissues, whereas UCP1 and UCP3 are

found primarily in brown adipose tissue and skeletal muscle respectively (Fleury and Sanchis, 1999).

Brennan et al., (2009) suggests that UCP2 mRNA expression is higher in cows losing body weight than

cows maintaining bodyweight. Mitochondrial uncoupling has been suggested as a possible biological

mechanism responsible for animal differences in feed efficiency in cattle and other livestock (Kolath et

al., 2006; Bottje and Carstens; 2009; Moore et al., 2009; Carstens and Kerley, 2009;).

These specific proteins were chosen as they represent key proteins in metabolic pathways relating

to ion pumping , uncoupled respiration, protein degradation, cell proliferation, and cellular energy status

sensing. These processes may play an integral role in energy metabolism and influence maintenance

energy requirements in the animal. Understanding differences between cows in these proteins may

provide valuable insights into understanding metabolic processes influencing maintenance energy

requirements and feed efficiency.

19

2.6 Research Hypotheses and Objectives

Increasing the understanding of cellular processes involved in maintenance energy requirements

may lead to increased understanding of feed requirements, and may provide opportunities for genetic

selection or feed management programs which may improve overall feed efficiency in the beef cow.

We hypothesized that:

1) Measures of residual feed intake may pose logistical challenges in the pregnant beef cow and may

not be a suitable measure for determining feed efficiency for mid-to-late gestation beef cows.

2) Limiting total intake will have impacts on the expression of proteins involved in regulating

cellular energy metabolism.

3) Changes in energy partitioning in pregnant cows will result in altered expression of proteins,

which may provide additional target molecular mechanisms and may suggest areas of further

study in selection or management for improved maintenance energy requirements and feed

efficiency

In order to evaluate these hypotheses, three experiments were conducted with the specific objectives to:

1) Investigate the use of RFI models to predict feed efficiency in mid to late gestating beef cows and

to identify variables which may reduce variability with these models.

2) Examine the impact of moderate energy restriction on tissue and molecular events which may

influence energy expenditure in pregnant beef cows during the wintering period.

3) Examine the impact of pregnancy on tissue and molecular events relating the energy metabolism

in mature beef cows.

20

Chapter 3: Characterization and evaluation of residual feed

intake in mid to late gestation mature beef cows

3.1 Introduction

Winter feed costs represent the greatest costs of production for cow/calf producers (Kaliel and

Kotowich, 2002). In conventional cow/calf production systems this period also coincides with mid-to late

gestation in Canada. Although adequate nutrition is needed for growth, pregnancy and reproduction and

maintenance of bodily functions, there may be large differences between animals in how energy and

nutrients are utilized which may enable producers to select for more feed efficient breeding females.

Traditionally feed efficiency measures are expressed as a ratio of input (feed) to output

(performance). These feed conversion ratios are not mutually exclusive and although are generally

inexpensive to measure, may not reflect true feed efficiency. The concept of net feed efficiency or

residual feed intake (RFI) was initially characterized for use in beef cattle by Koch et al. (1963) and

represents the difference between the actual feed intake and the predicted feed intake based upon the

regression of body weight and performance, usually growth, in terms of ADG. Negative RFI represents

efficient animals and positive RFI, inefficient animals (Kelly et al., 2010; Montanholi et al., 2009).

Although measures of RFI have gained increased interest in the research community in growing

animals, very little research has been conducted critically evaluating the measurement of RFI in the

pregnant beef cow. Measuring feed efficiency in cows in this phase of the production cycle may pose

many challenges, as output measures, such as body weight gain or loss, changes in body composition, or

growth of the conceptus are difficult to quantify. During this period cows may also maintain body weight,

having body weight gains close to zero or actually lose weight. This poses challenges to using RFI in the

mature beef cow. As well, little information is available analyzing measures of RFI in mature pregnant

beef cows fed forage- based diets, since the majority of RFI research has been conducted in a feedlot

setting where concentrate or pelleted feeds were used.

21

The objectives of this experiment were to investigate the use of RFI and variables that will reduce

variability in the measurement of RFI in the pregnant beef cow and to evaluate the fit of these models of

net feed efficiency.

3.2 Methods

3.2.1 Animals and experiment design

All experiments followed the recommendations of the Canadian Council on Animal Care (1993)

and met the approval of the University of Guelph Animal Care Committee.

A dataset was created by combining data from five different experiments and with different

treatment groups or replications, which created a total of nine (n = 9) separate contemporary groups. The

combined dataset contained 321 feed and performance records. A summary of all experiments can be

found in Table 3.1. All experiments utilized non-lactating pregnant multiparous beef cows fed over the

winter, leading up to parturition. All animals were primarily of Angus and Simmental crossbreeding and

were housed at the Elora Beef Research Station (EBRC; Elora, Ontario, Canada) or at the New Liskeard

Agriculture Research Station (NLARS; New Liskeard, Ontario, Canada). Cows were all individually fed

for ad libitum intake and dry matter intake was measured using Calan gates (American Calan Inc.,

Northwood, NH). Animals included in this dataset were all fed over the winter and remained on their

respective diets until approximately one wk prior to the earliest due date. In all experiments cows were

weighed on consecutive days at the start and at the end of the trial period to normalize for gut fill. All

cows were weighed every 28 d over their respective trial period and ultrasound measures obtained at the

start and end of the trial for rib fat (between the 12th and 13

th rib) and rump fat depth measurements, using

an Aloka SSD-500 ultrasound unit (Corometrics Medical Systems, Wallingford, CT). Cattle were

removed from the dataset for: carrying twins, premature births, aborting fetuses, or mastitis. A brief

description of each experiment is as follows.

22

The first experiment, containing contemporary groups one and two, examined the effect of

including different crop residues in a haylage-based total mixed ration (TMR) in wintering rations fed to

pregnant cows leading up to parturition (Wood et al., 2010). Cows were fed for 82 d leading up to the

earliest d of parturition. Contemporary group one was the control group from this experiment where cows

were fed haylage for ad libitum intake. Group two was fed a TMR consisting of haylage and 40% wheat

straw (DM basis).

The second experiment was designed as a replicated randomized complete block design

investigating different methods of restrictive feeding to pregnant cows over the winter (Wood et al.,

2010a). Contemporary groups three and four represent the control treatment from each of the replicates.

Cows were fed grass and alfalfa haylage for ad libitum intake for 105 d leading up to parturition.

The third and fifth experiment were conducted for investigating relationships between circulating

metabolites (for contemporary groups five, six, eight, and nine) and cow body parameter measurements

(contemporary groups five and six only) and RFI and other measures of performance and efficiency (see

appendix 1). Cows were randomly assigned to pen and fed for ad libitum intake a TMR containing

haylage and 30% wheat straw (DM basis). Contemporary group five and eight were conducted at EBRC

and groups six and nine conducted at NLARS.

The fourth experiment investigated moderate feed restriction on visceral organ mass and protein

expression within tissues associated with energy balance in pregnant beef cows (chapter 4). The data

included in contemporary group seven came from cows from the high intake group that were not selected

for slaughter. Cows were fed a TMR containing haylage and 20% wheat straw (DM basis) for 105 d

leading up to parturition.

3.2.2 Diets and feed sample analysis

Weekly TMR samples were collected from each experiment and frozen at -20ºC for future

analysis. Samples were later dried at 55ºC for 96 h to determine DM concentration and then ground to

23

pass through a 1-mm screen. All feed analysis was carried out at Agri-Food Laboratories Inc. (Guelph,

ON). Dry matter analysis was done in accordance with the Association of Official Analytical Chemists

guidelines (1990, Method 930.15.). Acid detergent fiber and NDF was determined using the methods of

Robertson and Van Soest (1981) using an Ankom fiber analyzer (Ankom Technology Corp., Fairport,

NY). Percent CP was determined by multiplying 6.25 by percent dietary nitrogen as determined by the

Leco Nitrogen analyzer (Leco Corporation, St. Joseph, MI). Dietary analysis for each contemporary

group is reported in Table 3.2.

3.2.3 Determination of traits, residual feed intake, and statistical analysis

Average daily gain and mid-point BW for the test period were calculated from monthly BW

measurements using regression over time using Proc GLM in SAS (SAS institute Inc. Cary, NC) .

Metabolic mid-point BW was calculated as mid-point BW 0.75

(Kleiber, 1961). In order to attempt to

remove the effects of conceptus and growth, pregnancy corrected mid-point body weight and ADG were

calculated by subtracting calculated conceptus weight from actual BW from at each corresponding stage

of gestation and then applying regression as above. Conceptus weight was calculated using the equations

outlined by Silvey and Haydock (1978) to estimate conceptus weight based on calf birth weight and d of

gestation.

Residual feed intake was calculated for each cow by subtracting actual DM intake from predicted

DM intake as previously described (Koch et al.,1963). Model parameters for predicted DM intake were

determined by linear regression using PROC GLM in SAS (SAS institute Inc. Cary, NC), the R2,

coefficient of variation and RMSE were recorded for each model. The basic model RFI contained

predicted DM intake model containing only mid-point BW and ADG (Koch et al. 1963). Models tested

included: metabolic BW, ultrasound measures of fatness, age, pregnancy corrections. In addition, models

examined over the whole dataset included effect of research station, contemporary group, and dietary

parameters. The model with the greatest R2 within each contemporary group was determined and used to

calculate the greatest R2 RFI (R

2 RFI) for each animal, and used in future correlations.

24

The bayesian information criterian was determined for each DM intake model using PROC

MIXED in SAS (SAS institute Inc. Cary, NC) to assess the fit of the RFI model. The DM intake model

with the lowest BIC was selected for each contemporary group to calculate lowest BIC RFI (BIC RFI)

used in future correlation analysis. To further assess fit of the basic, greatest R2 and least BIC models of

predicted DM intake within each contemporary group, mean square prediction errors (MSPE) was

calculated and decomposed into mean bias, slope bias and random error, were calculated according to the

method of Bibby and Toutenburg (1977).

Investigations into correlations of feed efficiency measures and circulating blood metabolites and

linear body measures can be found in Appendix 1.

3.3 Results and Discussion

As feed costs are increasing, identifying cattle that are more efficient is becoming increasingly

important. Although much research has investigated measures of RFI in growing steers, bulls and heifers,

very little research has been conducted in measuring RFI in beef cows specifically. In order to investigate

the contribution of various traits to variation among cows in measures of RFI, a combined dataset

consisting of nine contemporary groups and 321 mature cows was investigated. Descriptive statistics for

traits in the whole combined dataset can be found in Table 3.3.

Table 3.4 shows the goodness of fits (R2, CV, RMSE) for regression models used to predict DMI

for RFI calculations. The basic model for RFI, originally described by Koch et al. (1963), including only

BW and ADG , had much lower R2 than observed by other reports in growing animals (48 and 60% ;

Koch et al., 1963; 70% Arthur et al., 2003; 71 and 72%, Basarab et al., 2003; 68% Schenkel et al., 2004;

58% Montanholi et al., 2009; 77%, Kelly et al., 2010; 72-82% Kelly et al., 2011). Meyer et al., (2008)

measured RFI in growing heifers fed a high- forage diet and found a larger range in RFI than previously

reported in the literature. They suggested that increased variability is introduced when measuring RFI

with high forage diets, due to feed sorting, spillage and wasting feed, which is less prevalent in pelleted or

25

high grain rations. Since all animals were fed forage diets, feed wastage and spillage may contribute to

variability observed in the models (Table 3.4).

A large variation amongst the cows themselves may also be contributing to lower R2 observed in

the present study. It is suggested that controlling as many factors as possible is important in measuring

RFI (Arthur and Herd, 2008). Variation in age, type, size, etc. among groups of cows may be

considerably greater than among groups of growing steers, bulls or heifers. In Table 3.5, we see that

contemporary groups that had smaller CV also had some of the RFI models with the largest R2. Basarab

et al. (2007) also measured RFI in mid-gestation beef cows fed forage and also observed much larger CV

and SD in cows vs. RFI measures in their growing progeny. It may be more difficult to control for

variation in cow type, when measuring RFI in mature cows.

There was no improvement in R2 for the feed intake prediction model when metabolic BW was

used versus actual mid-point BW. Montanholi et al. (2009) also did not find any improvements of using

metabolic BW over actual midpoint BW. One of the critiques of the Kleiber(1961) ratio (BW 0.75

) for

metabolic body weight is that although it may be accurate across species, it may not accurately reflect

metabolic differences within each species which are minimally different in body weight (Schmidt-

Nielson, K. 1970). Relatively small differences in BW may not accurately reflect true variation in

maintenance between cows.

As BW gain in the pregnant cow is confounded with growth of the conceptus, pregnancy

corrected BW was calculated and then used to calculate pregnancy corrected ADG. However, pregnancy-

corrected BW or pregnancy-corrected ADG did not improve the overall fit of the model. This may be in

part due to the fact that the model described by Silvey and Haydock (1978) is an estimate of conceptus

growth and would have error associated with such a model, and may add variability rather than reduce

variability in the predicted DMI model used to calculate RFI. Accurately assessing conceptus growth in

the live animal may yield greater improvements in RFI.

26

Ultrasound measures of backfat and rumpfat, as well as change in rump fat or rib fat over the

feeding period, had variable impact on predicted DMI model fit. The model for DMI which contained

both initial backfat and rump fat as well as change in backfat and rump fat increased the R2 by 7.3% over

the basic model for predicted DMI, while BIC decreased. Others have shown that the addition of

measures of fatness have shown modest improvement in model fit (> 5% improvement Richardson et al.,

2001 and 3.9%, Basarab et al., 2003). Mader et al. (2009) also found positive correlations to RFI and

back fat measures, in addition to internal fat (kidney fat weight proportion). Perhaps the addition of

internal fatness measures will increase accuracy of RFI measures in pregnant cows (see appendix 2).

There were minimal differences between measures of backfat or ultrasound measures of rump fat when

included separately.

When research station was added to the model as a class variable, R2 of the predicted DMI model

increased 23.1% over the basic predicted DMI model and BIC decreased. This indicates that controlling

for environment and management play an import role in accurately determining RFI. However, when the

basic model for DMI was investigated within each research station, the R2 was very similar. This may

indicate that although management and environmental differences exist between research premise and

experimental design, diets, etc., overall variation within each research premises is consistent. When

contemporary group was included as a class variable, which accounts for both research station differences

as well as dietary treatment or replicate (if applicable), the greatest R2 of the models was achieved. The R

2

over the basic model was increased by 30.2% and the BIC was reduced. When ultrasound measures of fat

were added to the model containing only research station the R2 did not greatly improve, although BIC

decreased.

When dietary composition factors were included as continuous variables in the model for

predicted DMI, R2 was increased 25.4% over the basic model. This suggests that dietary factors play a

significant role in modeling RFI in the mature pregnant beef cows. Individually, CP, NDF, or NEm did

not greatly improve R2 (24 to 29%; data not shown) when added to the basic model of RFI. Herd et al.,

27

(2004) suggests that differences in digestion account for 14% of variation in RFI and heat increment of

feeding accounts for 9% of variation between animals in RFI. In addition, poorer fitting RFI models were

observed with animals fed lower quality diets (those containing ≥ 30% DM of wheat straw). This may be

due to greater ADF and dietary bulk, which may limit ad libitum intake due to increased gut fill.

Additionally, cows that were fed high quality rations (lower ADF) had less variability (Groups 1, 3, 4,

and 7), and higher gains (data not shown). As in this study, Meyer et al. (2009) also observed greater

variation in feed intake when measuring RFI on heifers fed a forage diet. However, one study found that

there was a strong correlation between RFI rank when heifers were fed a forage-based diet and when they

were fed a grain-based diet (Retallick and Faulkner, 2012), which suggests that overall major underlying

mechanisms (like metabolic factors) influencing feed efficiency may be partially independent of diet type

(Retallick and Faulkner, 2012). Perhaps feed value and digestion kinetics may play a more important role

in differences between cow RFI, when cows are fed a high forage diet.

Three models of predicted DMI intake, calculated within each contemporary group (Table 3.5)

were selected for use in correlations: the basic model containing only mid-point BW and ADG, the model

that resulted in the greatest R2 and the model that yielded the lowest BIC. None of the contemporary

groups resulted in the same model having both the greatest R2 and least BIC. Most often the model

containing the additional covariates for age, change in rib and rump fat and initial rib and rump fat

resulted in the greatest R2. The model for the least BIC model most often was that using metabolic BW

and ADG. Descriptive statistics for each of these models can be found in Table 3.6.

There was a tremendous range of accuracy in predicted DMI intake across contemporary groups.

Simply looking at the basic model, R2 ranged from 10% in the 6

th dataset to 69% in the 4

th dataset. The

addition of ultrasound measures of back or rump fat, as well as change in fatness also had varying results

across contemporary groups. In most cases, measures of fatness marginally improved R2 of the model

with minimal impact on BIC.

28

Further investigation into model fit using RMSPE to determine if slope and mean bias exists in

each of the DMI models used to calculated RFI is found in Table 3.7. The predicted DMI equations from

basic RFI, highest RFI and highest BIC RFI were calculated within each contemporary group. Significant

slope and mean biases were found in the basic RFI model in three of nine contemporary groups. In the

greatest R2 RFI models one of the contemporary groups had a large mean bias in addition to slope bias.

Similarly in the BIC RFI models one model was identified as having a mean bias. The presence of bias is

a concern as it further indicates poor fit for some of the DM intake models used to calculate RFI. Because

bias also occurred in models identified as either greatest R2 or greatest BIC, caution may be needed when

identifying the true best-fitting RFI model.

3.4 Conclusions

Measuring RFI in mature pregnant cows may pose numerous challenges that may hinder the

accuracy of the DM intake model. The results of the current study indicate that the use of RFI models to

determine feed efficiency have variable results when used in mature pregnant beef cows. In the most

fundamental form, Koch’s model of RFI measures “outputs”, while holding “inputs” constant. In the

mature, non-lactating beef cow measuring inputs may pose a greater challenge as cows are primarily

forage fed and intake may not be able to be precisely measured as with pelleted or high grain rations

(Meyer et al., 2008). Similarly measuring outputs in beef cows may pose similar challenges. In growing

animals, output is often characterized as growth or body weight gain. As cows have reached their mature

size, they have nominal body growth and weight changes more likely reflect changes in body

composition, differences in gut fill, and growth of the conceptus. As it is more difficult to measure these

parameters accurately in the live animal, fitting RFI models poses greater challenges.

In general, measures of body fat did moderately improve model fit, but results were variable. Low

nutrition and resulting minimal body weight gain (or loss) may result in lower model predictability. In

29

addition, caution should be used in choosing best fitting DMI model for RFI as mean and slope bias may

occur within the dataset.

Herd et al. (2004) suggested that approximately two-thirds of variation between animals that are

efficient and those that are inefficient relate to basal metabolic rate, cellular maintenance requirements,

and related energy lost as heat loss. As maintenance energy costs represent approximately 70 to 75% of

the total annual energy requirements for the mature beef cow (Ferrell and Jenkins, 1985), understanding

animal differences in maintenance requirements is of particular importance. Although heat production

was not measured in any of these experiments, it has been shown to improve accuracy in RFI models

(Montanholi et al., 2009, Colyn et al. 2010) and may prove beneficial to measures of RFI in mature,

pregnant cows.

Measuring feed efficiency in the mature, pregnant beef cows is complex. A large proportion of

variation between animals in RFI measures of feed efficiency remains unknown. Further investigation

into molecular mechanisms influencing maintenance energy costs and energy expenditure is warranted.

30

Table 3.1: Summary of contemporary group mature cow experiments included in the combined

dataset

1EBRC = Elora Beef Research Station; NLARS = New Liskeard Agriculture Research Station

Trial #

Treatment

(if applicable)

Research

Station1

Number

of

Cows

Days

on Feed

The effect of the inclusion of crop residues as

a winter feed source in haylage-based rations

on the performance of pregnant beef cows

1

Control -Full

Haylage EBRC 23 82

The effect of the inclusion of crop residues as

a winter feed source in haylage-based rations

on the performance of pregnant beef cows

2

Wheat Straw EBRC 21 82

The effects of restrictive feeding over the

winter on the performance of prepartum

crossbred beef cows

3

Control-

Full Haylage

replicate 1 NLARS 12 105

The effects of restrictive feeding over the

winter on the performance of prepartum

crossbred beef cows

4

Control-

Full Haylage

replicate 2 NLARS 12 105

Relationships between RFI and body

parameters and circulating metabolites 5

- EBRC 63 105

Relationships between RFI and body

parameters and circulating metabolites 6

- NLARS 54 112

The effect of moderate dietary restriction on

visceral organ weight, hepatic oxygen

consumption, and metabolic proteins

associated with energy balance in mature

pregnant beef cows.

7

High Intake

group EBRC 23 105

Relationships between RFI and circulating

metabolites 8

- EBRC 64 98

Relationships between RFI and circulating

metabolites 9

- NLARS 52 112

31

Table 3.2: Dietary analysis of rations fed to each contemporary group

Contemporary Group

Analysis1 1 2 3 4 5 6 7 8 9

DM, % 36.7 47.6 41.8 34.4 45.7 45.4 36.8 45.4 44.7

CP, % DM 18.3 11.7 15.4 14.9 9.7 12.1 12.2 10.3 9.6

NDF, % DM 49.5 64.5 47.9 50.8 61.1 53.2 58.2 62.0 59.6

ADF, % DM 42.2 50.6 39.6 42.9 41.1 39.0 39.4 44.2 40.8

NEm2, Mcal/kg 1.5 1.4 1.4 1.3 1.4 1.4 1.5 1.1 1.3

1Average of weekly samples.

2Calculated according to Weiss et al. (1992) and NRC (1996).

32

Table 3.3: Mean , standard deviation and number of data points for mature beef cows (n=321) used

in assessing measures of RFI.

Item Mean SD

Age, years 5.24 2.5

Initial BW, kg 703 92.8

Final BW, kg 793 92.9

Mid-point BW, kg 708 92.5

DMI, kg/d 12.97 2.05

ADG, kg/d 0.86 0.315

pcADG, kg/d 0.44 0.33

F to G, kg/kg 14.12 59.27

G to F, kg/kg 0.067 0.024

BW = body weight; DMI =Dry Matter Intake; ADG =Average daily gain; pcADG= pregnancy corrected

ADG (Silvey and Haydock, 1978); F to G = feed to gain ratio; G to F = gain to feed ratio.

33

Table 3.4: Model fit statistics for RFI (DMI models) with differing covariates over the entire

dataset of mature pregnant beef cows.

Across all contemporary groups

model covariates1 n R

2 CV R MSE BIC

mpBW ADG 321 0.236 13.85 1.796 1304.6

BW_75 ADG 321 0.236 13.86 1.795 1300.6

pcBW ADG 321 0.237 13.84 1.794 1304.1

mpBW pcADG 321 0.222 13.99 1.813 1310.7

BW_75 pcADG 321 0.222 13.98 1.812 1306.6

mpBW fBF ADG 321 0.240 13.81 1.791 1307.9

mpBW cBF ADG 321 0.242 13.81 1.791 1309.7

mpBW iBF cBF ADG 321 0.265 13.63 1.768 1302.7

mpBW fRF ADG 277 0.207 13.48 1.769 1125.0

mpBW cRF ADG 277 0.269 12.95 1.700 1101.7

mpBW iRF cRF ADG 277 0.278 12.89 1.691 1103.6

mpBW iRF cRF iBF cBF ADG 277 0.309 12.65 1.660 1101.1

mpBW AGE ADG 321 0.254 13.71 1.777 1301.3

mpBW Station ADG 321 0.467 11.58 1.502 1191.7

mpBW Station iRF cRF iBF cBF ADG 277 0.471 11.10 1.456 1030.6

mpBW NDF NEm CP ADG 321 0.490 11.37 1.474 1184.7

mpBW TRMT ADG 321 0.538 10.91 1.414 1148.5 1mpBW = mid-point BW, BW_75 = mid-point BW

0.75, pcBW = pregnancy corrected BW (Silvey and

Haydock, 1978), pcADG = pregnancy corrected ADG, fBF = final d of trial ultrasound back fat, cBF =

change in ultrasound back fat, iBF = initial ultrasound back fat, fRF = final ultrasound rump fat, cRF =

change in ultrasound rump fat, iRF = initial ultrasound rump fat, age= cow age in years, station =

research station (class variable), TRMT = contemporary group (class variable).

34

Table 3.5: Model fit statistics for RFI (DM intake) models tested within each contemporary group of mature pregnant beef cows

1mpBW = mid-point BW, BW_75 = mid-point BW

0.75, pcBW = pregnancy corrected BW (Silvey and Haydock, 1978), pcADG = pregnancy

corrected ADG, fBF = final d of trial ultrasound back fat, cBF = change in ultrasound back fat, iBF = initial ultrasound back fat, fRF = final

ultrasound rump fat, cRF = change in ultrasound rump fat, iRF = initial ultrasound rump fat, age= cow age in years

EBRC 2007 Full haylage EBRC 2007 40% Wheat Straw

Group 1 Group 2


2 CV R MSE BIC n R

2 CV R MSE BIC

mpBW ADG 23 0.498 9.3 1.22 83.1 21 0.175 19.65 2.117 96.8

BW_75 ADG 23 0.498 9.3 1.219 79.3 21 0.177 19.64 2.12 92.9

pcBW ADG 23 0.497 9.32 1.22 83.2 21 0.167 19.76 2.128 97

mpBW pcADG 23 0.428 9.93 1.302 86.1 21 0.156 19.88 2.141 97.3

BW_75 pcADG 23 0.428 9.93 1.302 82.2 21 0.158 19.86 2.14 93.4

mpBW fBF ADG 23 0.506 9.47 1.242 87.1 21 0.187 20.08 2.163 98.9

mpBW cBF ADG 23 0.515 9.39 1.231 85.4 21 0.192 20.02 2.157 97

mpBW iBF cBF ADG 23 0.515 9.64 1.264 88.8 21 0.225 20.2 2.176 98.2

mpBW cRF ADG NA . . . . NA . . . .

mpBW iRF cRF ADG NA . . . . NA . . . .

mpBW iRF cRF iBF cBF ADG NA . . . . NA . . . .

mpBW AGE ADG 23 0.539 9.146 1.199 83.8 21 0.175 20.222 2.178 97.5

mpBW AGE iBF cBF iRF cRF ADG NA . . . . NA . . . .

35

Table 3.5 continued: Model fit statistics for RFI (DM intake) models tested within each contemporary group of mature pregnant beef

cows

NLARS 2007/08 Restriction trial - haylage NLARS 2008/09 Restriction trial - haylage

Group 3 Group 4


2 CV R MSE BIC n R

2 CV R MSE BIC

mpBW ADG 12 0.382 7.5 1.123 42.1 12 0.688 6.71 1 41.3

BW_75 ADG 12 0.379 7.52 1.126 38.3 12 0.689 6.7 1 37.5

pcBW ADG 12 0.373 7.56 1.13 42.3 12 0.69 6.68 0.996 41.2

mpBW pcADG 12 0.41 7.34 1.099 41.4 12 0.714 6.42 0.957 40.3

BW_75 pcADG 12 0.405 7.36 1.102 37.6 12 0.715 6.413 0.956 36.5

mpBW fBF ADG 12 0.528 6.95 1.04 42.8 12 0.69 7.09 1.057 42.9

mpBW cBF ADG 12 0.549 6.79 1.017 40.5 12 0.695 7.04 1.049 42.8

mpBW iBF cBF ADG 12 0.588 6.95 1.04 42.7 12 0.696 7.51 1.119 43.2

mpBW fRF ADG 12 0.609 6.33 0.948 42.8 12 0.726 6.67 0.993 43.6

mpBW cRF ADG 12 0.821 4.29 0.642 35.2 12 0.704 6.93 1.033 42.8

mpBW iRF cRF ADG 12 0.867 3.95 0.591 38.4 12 0.726 7.12 1.062 44.9

mpBW iRF cRF iBF cBF ADG 12 0.886 4.31 0.646 41 12 0.741 8.19 1.22 45.8

mpBW AGE ADG 12 0.683 5.71 0.854 38.5 12 0.737 6.53 0.974 39.9

mpBW AGE iBF cBF iRf cRf ADG 12 0.892 4.7 0.704 42.1 12 0.984 2.3 0.343 30.9 1mpBW = mid-point BW, BW_75 = mid-point BW




36


cows

NLARS 2009/10 body parameter/RFI EBRC 2009/10 Body Parameter/RFI

Group 5 Group 6


2 CV R MSE BIC n R

2 CV R MSE BIC

mpBW ADG 54 0.294 10.84 1.516 197.1 63 0.1 11.02 1.288 223.3

BW_75 ADG 54 0.294 10.84 1.516 193.3 63 0.103 11.01 1.286 219.2

pcBW ADG 54 0.296 10.882 1.514 197 63 0.1 11.02 1.288 223.2

mpBW pcADG 54 0.298 10.81 1.512 197 63 0.11 10.92 1.28 223

BW_75 pcADG 54 0.298 10.81 1.511 193.2 63 0.11 10.97 1.282 219

mpBW fBF ADG 54 0.303 10.88 1.522 200.7 63 0.143 10.85 1.267 225.3

mpBW cBF ADG 54 0.295 10.94 1.53 200.2 63 0.101 11.11 1.298 226.5

mpBW iBF cBF ADG 54 0.304 10.99 1.538 203.4 63 0.165 10.8 1.262 226.6

mpBW fRF ADG 51 0.326 10.7 1.497 200.2 63 0.138 10.88 1.271 226.7

mpBW cRF ADG 51 0.33 10.67 1.49 198.6 63 0.103 11.1 1.3 227

mpBW iRF cRF ADG 51 0.34 10.71 1.497 202.9 63 0.145 10.928 1.277 229.6

mpBW iRF cRF iBF cBF ADG 51 0.344 10.92 1.527 208.5 63 0.166 10.99 1.283 234.7

mpBW AGE ADG 54 0.387 10.21 1.428 191.7 63 0.136 10.89 1.273 224.6





37


cows

EBRC 2010/2011 High intake

Group 7


2 CV R MSE BIC

mpBW ADG 23 0.653 6.64 0.742 62.7

BW_75 ADG 23 0.656 6.61 0.738 58.7

pcBW ADG 23 0.65 6.66 0.744 62.9

mpBW pcADG 23 0.63 6.85 0.765 64

BW_75 pcADG 23 0.632 6.83 0.762 60

mpBW fBF ADG 23 0.721 6.1 0.68 62.5

mpBW cBF ADG 23 0.653 6.81 0.76 64.5

mpBW iBF cBF ADG 23 0.723 6.25 0.698 64.3

mpBW fRF ADG 23 0.694 6.37 0.714 64.9

mpBW cRF ADG 23 0.659 6.74 0.753 65.3

mpBW iRF cRF ADG 23 0.699 6.51 0.727 67.7

mpBW iRF cRF iBF cBF ADG 23 0.73 6.53 0.73 71.2

mpBW AGE ADG 23 0.66 6.74 0.753 64.9

mpBW AGE iBF cBF iRf cRf ADG 23 0.74 6.63 0.74 73.1 1mpBW = mid-point BW, BW_75 = mid-point BW




38


cows

NLARS 2011/2012 RFI EBRC 2011/2012 RFI

Group 8 Group 9


2 CV R MSE BIC n R

2 CV R MSE BIC

mpBW ADG 64 0.251 11.19 1.44 240.6 52 0.3314 9.9 1.419 194.6

BW_75 ADG 64 0.252 11.18 1.44 236.6 52 0.33 9.91 1.42 190.9

pcBW ADG 64 0.249 11.2 1.44 240.7 52 0.331 9.91 1.419 194.6

mpBW pcADG 64 0.191 11.633 1.5 245.5 52 0.333 9.89 1.417 194.7

BW_75 pcADG 64 0.191 11.63 1.499 241.5 52 0.332 9.9 1.418 190.9

mpBW fBF ADG 64 0.308 10.84 1.398 239.5 52 0.34 9.93 1.422 195.9

mpBW cBF ADG 64 0.258 11.23 1.448 242.5 52 0.3364 9.97 1.428 195.5

mpBW iBF cBF ADG 64 0.325 10.8 1.393 240.6 52 0.344 10.02 1.435 196.9

mpBW fRF ADG 64 0.297 10.93 1.41 242.3 52 0.333 9.99 1.431 198.2

mpBW cRF ADG 64 0.251 11.28 1.454 243.9 52 0.332 10 1.433 196.9

mpBW iRF cRF ADG 64 0.297 11.02 1.421 245.6 52 0.333 10.01 1.446 199.8

mpBW iRF cRF iBF cBF ADG 64 0.328 10.97 1.414 248.5 52 0.369 10.04 1.439 199.4

mpBW AGE ADG 64 0.306 10.86 1.4 239.4 52 0.333 10 1.432 196.5





39

Table 3.6: Descriptive statistics for the basic, greatest R2, and greatest BIC RFI models calculated within each contemporary

group of mature pregnant beef cows

1 Within each contemporary group calculated RFI using the regression of ADG and midpoint BW (Koch et al., 1963)

2Within each contemporary group calculated RFI using the equation that yielded the greatest R2 (see table 6)

3 Within each contemporary group calculated RFI using the equation that yielded the greatest BIC (see table 6)

4 For description of each contemporary group see Table 3.1

Basic RFI1 R

2 RFI

2 BIC RFI

3

Contemporary

Group4 Mean SD Minimum Maximum Mean SD Minimum Maximum Mean SD Minimum Maximum

1 -0.82 1.40 -3.99 1.81 0.0003 1.09 -1.70 2.59 0.0003 1.14 -2.29 2.51

2 0 1.96 -3.06 4.52 0 1.89 -3.41 3.74 0 1.96 -3.07 4.53

3 0 0.97 -1.36 1.61 0 0.41 -0.50 0.93 0 0.52 -1.12 0.83

4 0 0.87 -1.72 1.74 0 0.20 -0.32 0.46 0 0.20 -0.32 0.46

5 -1.08 1.47 -3.49 6.12 0 1.32 -2.70 5.76 -0.99 1.39 -3.44 5.18

6 -4.75 6.44 -21.97 9.61 0 1.19 -4.06 2.34 0 1.25 -4.51 2.01

7 0 0.69 -2.27 0.88 0 0.60 -2.07 0.75 0 0.69 -2.26 0.87

8 0 1.41 -2.80 3.57 -5.14 2.76 -12.97 -1.04 0 1.41 -2.8 3.57

9 0 1.38 -2.41 4.96 0 1.34 -2.81 4.49 0 1.38 -2.23 4.66

40

Table 3.7: Root mean squared prediction error for the basic, greatest R2, and greatest BIC RFI models calculated within each

contemporary group of mature pregnant beef cows

1 Within each contemporary group calculated RFI using the regression of ADG and midpoint BW (Koch et al., 1963)

2Within each contemporary group calculated RFI using the equation that yielded the greatest R2 (see table 6)

3 Within each contemporary group calculated RFI using the equation that yielded the greatest BIC (see table 6)

4 For description of each contemporary group see Table 3.1

Basic RFI1 R

2 RFI

2 BIC RFI

3

Contemporary

Group4

RMSPE/

Mean

Mean Bias,

% of

RMSPE

Slope Bias,

% of

RMSPE

Random

Error,

% of

RMSPE

RMSPE/

Mean

Mean Bias,

% of

RMSPE

Slope Bias,

% of

RMSPE

Random

Error,

% of

RMSPE

RMSPE/

Mean

Mean Bias,

% of

RMSPE

Slope Bias,

% of

RMSPE

Random

Error,

% of

RMSPE

1 12.4 25.6 24.1 50.3 8.3 < 0.01 < 0.01 99.9 8.7 < 0.01 < 0.01 99.9

2 18.2 < 0.01 < 0.01 99.9 17.6 < 0.01 <0.01 99.9 18.2 < 0.01 < 0.01 99.9

3 6.5 < 0.01 <0.01 99.9 2.7 <0.01 < 0.01 99.9 3.5 < 0.01 < 0.01 99.9

4 5.8 < 0.01 <0.01 99.9 1.3 < 0.01 < 0.01 99.9 1.3 < 0.01 < 0.01 99.9

5 12.8 36.3 0.04 63.7 9.2 < 0.01 < 0.01 99.9 12 35.1 0.05 64.4

6 68.5 35.3 62.0 2.7 10.1 < 0.01 < 0.01 99.9 10.7 < 0.01 < 0.01 99.9

7 6.2 < 0.01 < 0.01 99.9 5.3 < 0.01 < 0.01 99.9 6.2 < 0.01 < 0.01 99.9

8 10.9 < 0.01 <0.01 99.9 45.2 77.7 16.3 6.0 10.9 <0.01 <0.01 99.9

9 9.6 < 0.01 < 0.01 99.9 9.3 < 0.01 < 0.01 99.9 9.6 < 0.01 < 0.01 99.9

41

Chapter 4: The effect of moderate dietary restriction on visceral

organ weight, hepatic oxygen consumption, and metabolic

proteins associated with energy balance in mature pregnant

beef cows 1

4.1 Introduction

Although adequate nutrition is needed for growth, reproduction and maintenance of bodily

functions, there are differences among animals in how energy and nutrients are utilized relative to feed

consumption. Herd et al. (2004) suggested that approximately two-thirds of variation among animals that

are efficient and those that are inefficient relates to basal metabolic rate, cellular maintenance

requirements, and related energy lost as heat. In mature beef cows, maintenance requirements represent

approximately 70 to 75% of the total annual energy requirements (Ferrell and Jenkins, 1985). Little is

known about the underlying cellular mechanisms involved in these processes in relation to feed efficiency

in the cow. Since chronic feed restriction has been shown to reduce basal metabolic rate, (Blaxter et al.,

1966; Labussière et al., 2011), we aimed to alter metabolic rate by feeding cows below and above

recommended total NE requirements.

The objective of this experiment was to investigate the impact of nutrient restriction of pregnant

beef cows during mid/late gestation on performance, organ mass, liver O2 consumption, citrate synthase

activity, and abundance of proteins relating to energy metabolism, namely; ATP synthase, Na+/K+

ATPase, proliferating cell nuclear antigen (PCNA), uncoupling protein 2 (UCP2), phosphoenolpyruvate

carboxykinase (PEPCK), peroxisome proliferator-activated receptor gamma (PPARγ), peroxisome

proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α), 5’-adenosine monophosphate-

activated protein kinase (AMPK), and the activated form phosphorylated-AMPK (pAMPK). By better

understanding cellular processes relating to maintenance energy requirements, cellular mechanisms may

be identified for further study such as nutritional manipulation or genetic selection to improve feed

efficiency in the mature cow.

1 J. Anim. Sci. 2013. Accepted

42

4.2 Materials and Methods

4.2.1 Animals, Experimental Design and Dietary Treatments

This experiment followed recommendations of the Canadian Council on Animal Care (1993) and

met the approval of the University of Guelph Animal Care Committee. Twenty-two (eleven per dietary

treatment; n = 11) non-lactating, mature pregnant beef cows, primarily of Angus and Simmental

crossbreeding were used in a randomized complete block design. This subset of cows was randomly

selected from a group of 72 cows (Thirty-six per dietary treatment; six cows per pen; data not included)

that were blocked by expected date of parturition, such that each block (n = 6) was slaughtered

approximately four weeks prior to expected date of parturition (approximately 250 d of gestation). Block

therefore accounted for d of gestation in initial and mid trial measures and time on feed in slaughter

measures. Cows were multiparous and averaged 3.23 ± 1.04 (mean ± SD) years old. Animals were

randomly assigned to pen (n = 12) and one of two dietary treatments: high level of feed intake (n = 11;

HIGH): formulated to be 1.4 × total NE requirements for maintenance and fetal growth (NRC, 1996),

equivalent to 2.1% of BW; and low level of intake (n = 11; LOW): to be 0.85 × total NE requirements for

maintenance and fetal growth, equivalent to1.25% of BW.

Dry matter intake was measured for individual animals using Calan gates (American Calan, Inc.,

Northwood, NH), with orts (if present) collected once per wk. Feed intakes were adjusted for individual

cows every 14 d based on BW to maintain a constant level of NE intake relative to treatment (1.4 or 0.85

× total NE requirements for maintenance and fetal growth) . The experiment took place over the winter,

beginning in December with the first block sent to slaughter at the end of February. The average daily

high and low temperature over this period was -4.0°C and -16.6°C, respectively (Environment Canada,

National Climate and Information Archive; 2013).

Rations were fed once daily as a total mixed ration (TMR) and contained haylage (79.5% of diet

DM; Table 4.1) primarily made up of mixed grasses along with wheat straw (20% of diet DM) and a trace

mineral and vitamin supplement (0.5% of diet DM; 35.8% NaCl, 14% Na, 12% Ca, 4% P, 1% Mg, 0.6%

43

S, 0.2% K, 2,369 mg/kg Mn, 1,000 mg/kg Cu, 3,000 mg/kg Zn, 2,294 mg/kg Fe, 58 mg/kg I, 25.5 mg/kg

Co, 16,2 mg/kg Se, 601.5 KIU/kg vitamin A, 100.5 KIU/kg vitamin D, and 2,000 IU/kg vitamin E).

Animals were weighed and ultrasounded using an Aloka SSD-500 ultrasound unit (Corometrics

Medical Systems, Wallingford, CT) for rib fat (between the 12th and 13

th rib) and rump fat at the start,

midpoint (d 56 of trial) and 3 to 5 d prior to slaughter. Blood samples were also obtained via jugular

venipuncture at these time points in the morning before feeding, for later analysis of plasma metabolites.

4.2.2 Feed and sample analysis

Weekly TMR samples were collected and frozen at -20ºC for future analysis. Samples were later

dried at 55ºC for 96 h to determine DM concentration and then ground to pass through a 1-mm screen.

All feed analysis was carried out at the Agri-Food Laboratories Inc. (Guelph, ON). Dry matter analysis

was done in accordance with the Association of Official Analytical Chemists guidelines (1990, Method

930.15.). Acid detergent fiber (expressed inclusive of residual ash) and NDF (assayed with heat stable

amylase and sodium sulphite and expressed inclusive of residual ash) was determined using the methods

of Robertson and Van Soest (1981) using an Ankom fiber analyzer (Ankom Technology Corp., Fairport,

NY). Percent CP was determined by multiplying 6.25 by percent dietary nitrogen as determined by the

Leco Nitrogen analyzer (Leco Corporation, St. Joseph, MI).

4.2.3 Sample Collection and Carcass Measurements

Four cows from blocks one through four and six (two from each treatment) and 2 cows from

block five (1 from each treatment) were slaughtered at the University of Guelph Meat Laboratory per

sample collection d. The first block of cows was slaughtered on d 83 relative to the start of dietary

treatments and the remaining blocks were slaughtered weekly thereafter, such that cows were sent to

slaughter at a common day of gestation (approximately d 250 of gestation). Final BW was obtained by

weighing the morning of slaughter. Hot carcass weight, grade fat (minimum fat depth over the last

quadrant of the LM), LM area (LMA), and subjective marbling score were determined as previously

described (Mandell et al., 1997; Laborde et al., 2002; Mader et al., 2009). Visceral organs (liver, kidneys,

44

heart, lungs, pancreas) were weighed and approximately 10 g samples of liver (mid-lobe), kidney (cortex

of the larger of the two kidneys) and pancreas (body), along with sternomandibularis muscle were rinsed

twice in 4°C saline (154 mM NaCl) and snap-frozen in liquid nitrogen for future analysis. The spleen,

esophagus, reticulorumen, and total lower gastrointestinal tract (small and large intestine, cecum, and

colon) were trimmed of fat, emptied, rinsed and weighed. Trimmed visceral fat, including kidney and

pelvic fat was also weighed. Samples of the rumen (ventral sac) and the small intestine (11th meter after

the pylorus) were rinsed twice in chilled saline (154 mM NaCl) and snap frozen in liquid nitrogen until

further analysis.

Blood samples were allowed to clot at room temperature for more than 30 min before storing on

ice, after which they were centrifuged at 3000 × g for 25 min and serum was separated and then frozen at

- 20ºC until further analysis. Serum samples were analyzed for serum urea, glucose, NEFA, beta-

hydroxybutyrate (BHBA) and total cholesterol at the University of Guelph Animal Health Laboratory

(Guelph, ON) using a Roche Cobas c311 and Immulite 1000 analyzers (Hoffmann- La Roche Ltd.,

Mississauga, ON, Canada). Serum samples were also analyzed by IDEXX Laboratory (Markham, ON.)

for serum triiodothyronine (T3) using a Siemens IMMULITE 2000 total T3 solid phase competitive

chemiluminescent immunoassay (Siemens Healthcare Diagnostics, Mississauga, ON, Canada).

4.2.4 Protein Concentration, SDS-PAGE and Immunoblots

Western blots were conducted to quantify abundance of: PCNA, ATP synthase, ubiquitin, and

Na/K+ ATPase for all tissues; PGC-1α, PPARγ, AMPKα and pAMPKα for liver, muscle, and rumen;

PEPCK for liver and kidney; and UCP2 for liver. One g of each tissue sample of liver, kidney, pancreas,

muscle, rumen papillae and scraped small intestinal epithelia (Matthews et al., 1996; Wang et al., 2009)

were homogenized in an ice-cold SEB solution [0.25mM sucrose, 10mM HEPES-KOH, 1 mM EDTA]

containing 10 μl/ml of a protease inhibitor cocktail (Pierce Protease Inhibitor Cocktail Kit, Pierce

Biotechnology, Rockford, IL, USA for all other samples) and then stored at -80°C until further analysis.

Protein concentration of homogenate was determined using a commercially available bicinchononic acid

45

kit (Pierce BCA Protein Assay Kit, Pierce Biotechnology, Rockford, IL) using bovine serum albumin as a

standard and measured on a PowerWave XS microplate spectrophotometer (BioTek Instruments Inc.,

Winooski, VT).

Twenty µg of total protein for ATP synthase and PCNA and 40 µg for all other protein targets

were loaded on to SDS-PAGE gels (8% for Na/K+ ATPase and PGC-1α; 18% for ubiquitin, and 10% for

all other proteins). Gels were electrophoresed according to methods described by Laemmli (1970) and

transferred to a PVDF membrane (0.2 μm; Millipore Corporation, Bedford, MA). Membranes were

blocked in a blocking solution containing 10 mM Tris-HCL, 200 mM NaCl, 1 mL/L Tween-20, and 50

g/L non-fat dry milk (Carnation Instant Skim Milk Powder, Markham, ON, Canada) for 1 h at room

temperature before incubation with primary antibodies. Primary antibodies were diluted in a blocking

solution containing 10mM Tris-CL, 200 mM NaCl, 1mL/L Tween-20, and 20 g/L non-fat dry milk. The

primary antibodies, concentrations, and incubation times used were: ATP Synthase (Complex 5;

F1F0ATPase; mouse anti-bovine monoclonal; #459240, Invitrogen, Camarillo, CA, USA; 1:5000

dilution; incubated for one hour); PCNA rabbit anti-human polyclonal (SC-7907, Santa Cruz

Biotechnology Inc., Santa Cruz, CA, USA; 1:1000 dilution; incubated for one hour); Na+/K+ ATPase α1

mouse anti-rabbit monoclonal (10R-N102A, Fitzgerald Industries International, Acton, MA, USA; 1:750

for liver, kidney, pancreas and rumen and 1:300 for small intestine and muscle, incubated overnight at

4˚C); Ubiquitin rabbit anti-human polyclonal (#3933, Cell Signalling Technology, Danvers, MA, USA;

1:1,000 dilution, incubated for 1.5 hours for liver, kidney, pancreas and 2.5 hours for small intestine and

rumen and overnight at 4˚C for muscle); PPARγ rabbit anti-human monoclonal (#2435, Cell Signalling

Technology, Danvers, MA, USA, 1:500 dilution, incubated overnight at 4˚C); AMPKα rabbit anti-human

monoclonal (#5831, Cell Signalling Technology, Danvers, MA, USA, 1:1,000 dilution, incubated

overnight at 4˚C); Phospho-AMPKα rabbit anti-human monoclonal (#2535, Cell Signalling Technology,

Danvers, MA, USA, 1:1000 dilution, incubated overnight at 4˚C); PEPCK2 rabbit anti-human polyclonal

(#6924, Cell Signalling Technology, Danvers, MA, USA, 1:750 dilution, for 15 hours); PGC-1α rabbit

46

anti-mouse polyclonal (AB3242, Millipore, Temecula, CA, USA, 1:750 dilution for liver and rumen and

1:500 dilution for muscle, incubated overnight at 4˚C); UCP-2 rabbit anti-human polyclonal (144-

157,Calbiochem, Darmsttadt, Germany, 1:750 dilution, incubated overnight at 4˚C). Donkey anti-mouse

immunoglobulin (1:5,000 dilution, Santa Cruz) and donkey anti-rabbit immunoglobulin for (1:5,000

dilution; GE Amersham) horseradish peroxidase-linked secondary antibodies were used in combination

with ECL Western Blotting Detection Reagents (GE Amersham, Baie d’Urfe, Quebec) for

chemiluminescent detection of immunoreactive proteins.

Apparent protein migration weights were determined using molecular weight markers (Precision

plus standards, 10- 250 kDa; Bio-Rad Laboratories Ltd., Mississauga, ON). Band intensities were

quantified using a FlourChem HD2 (Cell Biosciences/Proteinsimple, Santa Clara, CA, USA) imaging

system and Alphaview software (Alpha Innotech/Proteinsimple, Santa Clara, CA, USA) correcting for

local background intensity. To correct for unequal loading and/or transfer of proteins, membranes were

stained with fast green (Fisher Scientific, Ottawa, On) and a common predominant band was quantified

and used to normalize immunoblots (Howell et al. 2003, Wang et al., 2009). Band intensities are

expressed as corrected arbitrary units (AU).

4.2.5 Oxygen consumption

Hepatic tissue samples were collected immediately after slaughter for O2 consumption analysis as

descried previously (McBride and Milligan, 1985; Scheaffer et al., 2003). Liver samples were placed into

ice-cold Krebs-Ringer bicarbonate buffer fortified with sodium pyruvate (5 mM), sodium glutamate (5.0

mM), sodium acetate (4.5 mM), glucose (12.8 mM), and malic acid (4.5 mM) buffer at room temperature

and immediately transported to the laboratory. Tissue samples were sliced into 5 mm thick slices with a

Stadie-Riggs microtome (Thomas Scientific, Swedesboro, NJ), transferred into Petri dishes containing

buffer, and maintained at 37°C. Sliced tissue samples then were subsampled using an 8.0 mm biopsy

punch (Premier Uni-Punch, Plymouth Meeting, PA), and placed into 4 mL of buffer, and analyzed for in

47

vitro oxygen consumption, using a Clarke polariographic electrode (model 5300, Yellow Springs

Instruments, Yellow Springs, OH). Oxygen consumption was measured on duplicate samples over 3 min.

4.2.6 Citrate Synthase Activity

Liver tissue (0.1 g) was homogenized in 2 mL of pre-chilled CelLytic MT mammalian tissue

lysis/extraction reagent (Sigma-Aldrich, St. Louis, MO) using a Pyrex Ten Broeck Tissue Grinder

(Model: 7727-07; PYREX). Lysed samples (homogenate) were centrifuged (16,000 g × 10 min) and the

supernatant was used to measure citrate synthase activity as an indicator of mitochondrial biogenesis

(Morgunov and Srere, 1999). Citrate synthase activity was measured using a commercially available kit

(Citrate Synthase Assay Kit, Sigma-Aldrich,St. Louis, MO). Protein concentration (mg/g) was determined

using the BCA assay (Pierce, Rockford, IL) similar as described above. One unit (U) of enzyme activity

equals 1 mole product produced per min. Citrate synthase activity data are expressed as U/g wet tissue,

U/g of protein, kU/liver, and U/kg BW.

4.2.7 Statistical Analysis

Data were analyzed using PROC MIXED in SAS (2008). The model included the effect of

dietary treatment, cow age, pen and block. Pen nested within block and treatment was included as a

random effect. Results were considered significant at P ≤ 0.05.

4.3 Results

As designed, DMI was greater (P < 0.001; Table 4.2) in cows fed HIGH than those fed LOW.

Although initial BW did not differ (P = 0.90), final BW was greater (P = 0.04) for cows fed HIGH. This

resulted in a greater (P = 0.003) ADG for HIGH cows. Real-time ultrasound measures of rib fat and

rump fat were not different (P ≥ 0.26) between treatments at start of trial, middle (d 56 of restriction), or

before slaughter. Hot carcass weight did not differ (P = 0.07) between dietary treatments. Carcass

measures of rib-eye area and grade fat were not different (P ≥ 0.8) between treatments, nor was subjective

marbling score (P = 0.54).

48

Circulating BHBA, urea and total cholesterol were not different (P ≥ 0.06; Table 4.3) between

treatments at any time point. Circulating NEFA and T3 were not different (P ≥ 0.22) between dietary

treatment initially or at the middle timepoint, but at the final pre-slaughter time point circulating NEFA

concentrations were greater (P = 0.03) in cows fed LOW and T3 concentrations were greater (P = 0.01) in

cows fed HIGH. Glucose concentration was greater in HIGH during the initial (P ≤ 0.05) and mid-trial

time points, but did not differ (P = 0.17) at the final time point.

Mass of liver, kidney, lungs, heart, pancreas, spleen, and lower gastrointestional tract (intestinal +

caecum + rectum) weight (both actual and relative to BW and HCW) did not differ (P ≥ 0.07; Table 4.4)

between dietary treatments. Rumen mass was greater (P = 0.02) in cows fed HIGH than cows fed LOW,

although was not different (P ≥ 0.59) relative to BW or HCW. Total internal fat weight also was not

different (P ≥ 0.2) between treatments. Fetal weight averaged 31.4 kg in cows fed HIGH and 28.9 kg in

cow fed LOW and was not different between treatments (P = 0.54).

Liver in vitro O2 consumption per mg of tissue was not different (P = 0.12; Table 4.5) between

treatments, however O2 consumption per mg protein, liver weight or relative to BW was greater (P ≤

0.04) in cows fed HIGH than cows fed LOW. Citrate synthase activity (concentration or relative to BW)

did not differ (P ≥ 0.29) between treatments.

Abundance of PCNA, ATP synthase, and Na+/K+ -ATPase, did not differ (P ≥ 0.06; Table 4.6)

between treatments in all sampled tissues. Liver PGC1α was greater (P = 0.03; Figure 4.1) in cows fed

HIGH but did not differ (P ≥ 0.15) between cows fed HIGH and LOW in muscle or rumen. In liver,

rumen and muscle tissues, AMPK, pAMPK or PPARγ abundance did not differ (P ≥ 0.12) between

treatments. Liver and kidney PEPCK abundance, and liver UCP2 abundance was not different (P ≥ 0.32)

between dietary treatments. Muscle ubiquitin abundance was greater (P = 0.01; Figure 4.2) in cows fed

LOW, but did not differ (P ≥ 0.36) between treatments for other tissues.

49

4.4 Discussion

As the cost of feeding cattle continues to increase, the need to improve feed efficiency is

increasingly important. Fox et al. (2001) suggested that if feed intake remained constant and if efficiency

of ME use improved by 10%, profit for the producer would increase by 43%. Cellular maintenance

functions represent 40 to 56% of basal energy requirements and can be further sub-divided in to protein

turnover (9 to 12%), lipid turnover (2 to 4%), and ion transport (30 to 40%; Baldwin et al., 1980). By

better understanding maintenance energy requirements on a metabolic level, potential targets for

improved selection of metabolically efficient cows may be identified.

As we would expect from the design of the experiment, cows fed LOW consumed approximately

62% of the intake of cows fed HIGH. With this moderate rate of nutrient restriction, decreases in cow

performance (ADG and final BW) were also observed. Circulating metabolites indicate that cows fed

LOW were in a more catabolic state when compared to cow fed HIGH, as final (pre-slaughter) serum

samples had increased circulating NEFA concentrations. This would be an indication that cows fed LOW

mobilized more fat reserves to meet energy demands.

Circulating T3 concentrations were also increased in cows fed HIGH. The thyroid hormone T3 can

be used as an indication of overall resting metabolic rate (Cavallo et al., 1990; Rønning er al., 2009). In

broiler chickens, circulating T3 concentrations were greater in high RFI (inefficient) birds (Van Eerden et

al., 2006), indicating that high RFI birds have a greater metabolic rate. Level of feed intake may also have

an effect on T3 concentration. Results from this experiment are similar to those of others, which have

demonstrated that circulating T3 concentration is reduced when feed intake is restricted in beef steers

(Christopherson et al., 1979; Murphy et al.,1994).

In growing animals, dietary intake has been shown to have a direct influence on visceral organ

mass (Johnson et al., 1990; Reynolds et al.,1991; Rompala e tal.,1991; Lobley et al., 1994; McLeod and

Bladwin, 2000; Kelly et al., 2001) In the present experiment no differences were observed in visceral

50

organ mass, either actual or relative to BW or HCW, with the exception of actual rumen weight being

heavier in cows fed HIGH. This most likely is due to the effects of greater rumen fill which could

influence rumen mass. It is possible that nutrient restriction in the present study was not severe enough to

result in differences between dietary treatments in visceral organ masses.

In this experiment, no differences in fetal weight were observed. Although it is well known that

severe nutrient restriction can negatively impact fetal growth during late gestation (Wu et al., 2006;

Funston et al., 2010), other research also suggests that prepartum nutrition may not greatly influence birth

weight (Perry et al., 1991 and Stalker et al., 2006). It is likely that the level of feed restriction was not

severe enough to elicit any major changes in overall fetal growth. In previous work from our laboratory

group (Wood et al., 2010), when pregnant cows were fed a low quality diet that resulted in intakes similar

to those observed in the present experiment, calf birth weight was also not affected by treatment. Bassett

(1986) and Scheaffer et al. (2003) suggest that energy repartitioning may occur in the pregnant cow in

order to divert nutrients towards conceptus growth, although the underlying mechanisms are not well

understood.

The increase in oxygen consumption per g of tissue and total oxygen consumption relative to BW

suggests that the livers of cows fed HIGH were more metabolically active than those fed LOW. These

results are similar to those of Burrin et al. (1989), who found that sheep fed at maintenance had reduced

hepatic oxygen consumption when compared to those fed for ad libitum intake, and also suggests that the

liver responds more rapidly to changes in nutrition than other tissues of the portal drained viscera,

demonstrating the important role of the liver in understanding energy metabolism. Circulating T3 may also

be contributing to increases in hepatic oxygen consumption as it is known that increased circulating T3

increases resting metabolic rate (McBride and Early; 1989; Cavallo et al., 1990; Rønning er al., 2009) and

in rats has been shown to increase whole body oxygen consumption (Wang et al., 2000).

51

Citrate synthase is a key regulatory metabolic enzyme of the citric acid cycle (Winger et al.,

2000; Crumbley et al., 2012) which provides energy for cellular functions and plays a critical role in

energy production within the cell. Citrate synthase can act as an exclusive marker of the mitochondrial

matrix and act as a rough estimator of cellular mitochondrial abundance (Trounce et al., 1996; Morgunov

and Srere, 1998). Our results indicate that citrate synthase activity, both per g of tissue and relative to

hepatic tissue mass did not differ between dietary treatments. This would suggest that mitochondrial

concentration was not influenced by dietary restriction. Our results are similar to those found by Dumas et

al., (2004), who found that citrate synthase activity was not different in the liver of rats fed a 50%

restricted diet compared to non-restricted fed rats. Interestingly, other mitochondrial proteins (ATP

synthase, UCP2) measured in liver, in the present study as measured through Western blotting also were

not affected by dietary restriction.

In the present study, abundance of the transcriptional coactivator PGC-1α in liver was increased

in cows fed HIGH. This coactivator interacts with a variety of transcription factors relating to brown

adipocyte differentiation and uncoupling (PPAR-γ), mitochondrial biogenesis (NRF1, NRF2 and ERR-

α/β/γ), fatty acid oxidation (PPAR-α, PPAR-δ) and gluconeogenesis (GR, HNF-4α, FOXO1) among

others (Liang and Ward, 2006), and may play an important role in energy homeostasis and mitochondrial

function (Wu et al., 1999; Houten and Auwerx, 2004). Loor et al. (2007) found, using microarray

analysis, that gene expression of PGC-1α was upregulated in hepatic tissue from dairy cows with

nutritionally induced ketosis. The mRNA expression of PGC-1α has also been observed to be

differentially expressed in a variety of tissues in groups of cattle differing in milk yield and milk fat,

indicating that PGC-1α may play an important role in regulating performance traits (Weikard et al., 2005;

Weikard et al., 2012). Bottje and Carstens (2009) found that livers of low feed efficient broiler chickens

contained a greater abundance of PGC- 1α, which help support the results observed in the current

experiment. No treatment differences in rumen papillae or muscle PGC1-α abundance were observed.

However, many isoforms of PGC-1α exist and there may be numerous splice variants and post

52

transcriptional modifications which can greatly modify the function of PGC-1α and may also result in

tissue specific responses (Handschin and Spiegelman, 2006).

Research indicates that there is a strong relationship in endocrine signalling and PGC-1α

abundance, in particular the thyroid hormone T3 (Weitzel et al., 2003). In rats, T3 has been shown to

stimulate mRNA expression of PGC-1α by 13 fold in liver in rats (Wulf et al., 2008). As previously

discussed, circulating T3 concentrations were also increased in cows fed HIGH. It is possible that the

increased PGC-1α abundance observed in the present trial is as a result of increased T3. Our data indicates

that PGC-1α abundance in the liver is responsive to dietary influence, although much more research is

needed to better understand the mechanisms of regulation of this coactivator and subsequent influence of

other key downstream proteins in relation to metabolism.

Abundance of ubiquitin protein has been used as an indication of increased protein degradation,

through the ATP-dependent ubiquitin proteasome proteolytic pathway in cattle (Mutsvangwa et al., 2004;

Greenwood et al., 2008; Wang et al.2009). Variation in protein degradation in skeletal muscle among

cows may also play an important role in maintenance requirements and differences in feed efficiency

between animals. Bottje and Carstens (2009) found that ubiquitin abundance was lower in breast muscle

of highly feed efficient broilers, indicating that increased protein degradation may influence feed

efficiency. In the current study, sternomandibularis muscle tissue had increased abundance of ubiquitin in

feed restricted cows, indicating that dietary restriction may result in increased protein degradation in the

skeletal muscle of cows. These results concur with those observed by Du et al. (2005) who found that in

mid-gestation beef cows restricted to 68.1% of their NEm requirements or 86.7% of their metabolizable

protein requirements, abundance of skeletal muscle protein ubiquitinylation was increased along with

down regulation of mTOR signalling. This implicates muscle atrophy and degradation in feed restricted

cows.

53

Research has shown in other situations where the animal is in negative energy balance, such as in

periparturient dairy cows or sows at the onset of lactation, skeletal muscle protein may serve as a source

of energy. In dairy cows in negative energy balance, Kuhla et al. (2011) suggested that skeletal muscle

total protein content decreases and free amino acids increase in the muscle and unbalanced circulating

free amino acids result from negative energy balance post-partum indicating that skeletal muscle protein

catabolism occurs in instances of negative energy balance. Greenwood et al. (2009) also found that

mRNA expression of ubiquitin increases at onset of lactation (and associated negative energy balance) in

skeletal muscle of dairy cows, but not in hepatic tissue, which suggest that tissue specific responses exist

and that muscle may be preferentially degraded as a source of energy or amino acids.

In the present study total dietary restriction also would result in a reduction in total protein intake,

which may affect protein degradation. In a study investigating muscle protein loss over lactation in sows,

an increase in expression of genes involved with ubiquitin-ATP dependant proteasome pathway was

observed as well as a decrease in muscle RNA to DNA ratios, an indication of reduced capacity for

protein synthesis; furthermore these results were amplified in sows that were fed a reduced protein diet

(Clowes et al., 2005). Although more research is needed to better confirm preferential skeletal protein

degradation and the role and balance of protein synthesis in prepartum restricted fed beef cows, our data

suggest that this pathway may be influenced by level of intake and may play an important role in variation

in maintenance requirements and feed efficiency.

Although no other differences in protein abundance of PCNA, ATP synthase, Na+/K+ ATPase,

PEPCK, UCP2 or PPARγ were observed in tissues, it is possible that this moderate level of restriction

was not be severe enough to induce measurable differences in these proteins. The cows used in this trial

were also in moderate body condition at the beginning of the experimental period and therefore may have

been able to better cope with dietary restriction than cows in poorer condition. In addition, variation in

individual animal response to dietary treatment may contribute to variation observed within dietary

54

treatments. Future research is required to understand the regulation of these proteins in response to dietary

restriction.

In summary, the results of this study indicate that cows fed below total NE requirements had

decreased abundance of PGC1α in liver and increased ubiquitin abundance in muscle. Hepatic oxygen

consumption was also decreased in feed restricted cows. These results suggest that these pathways may be

important in regulating energy signalling and maintenance requirements in animals with different feed

intake, and suggests that future research is needed to better understand regulation and function of these

cellular mechanisms and potentially investigate these proteins in cows divergently selected for feed

efficiency traits. These proteins/genes may also be potential targets for investigation of possible SNPs

relating to feed efficiency in pregnant beef cows.

55

Table 4.1. Diet composition and analyses

Ingredient (% DM Basis) Amount

Grass Haylage 79.5

Wheat Straw 20.0

Mineral premix1 0.5

Analysis2

DM, % 36.8

CP, %DM 12.2

ADF, %DM 39.4

NDF, %DM 58.2

NEm, Mcal/kg3 1.5

1Contains: 35.8% NaCl, 14% Na, 12% Ca, 4% P, 1% Mg, 0.6% S, 0.2% K, 2369 mg/kg Mn, 1,000 mg/kg

Cu, 3,000 mg/kg Zn, 2,294 mg/kg Fe, 58 mg/kg I, 25.5 mg/kg Co, 16,2 mg/kg Se, 601.5 KIU/kg vitamin

A, 100.5 KIU/kg vitamin D, and 2,000 IU/kg vitamin E.



56

Table 4.2. Performance, real-time ultrasound and carcass characteristics of cows fed above or

below total net energy requirements.

Treatment1

Variable HIGH LOW SEM P-value

DMI, kg/d 10.9 6.8 0.119 <0.001

ADG, kg/d 1.11 0.59 0.066 0.003

Initial BW, kg 638.5 635.4 17.24 0.90

Final BW, kg 738.4 690.1 12.69 0.04

Initial US rib fat2, mm 7.5 7.8 1.74 0.90

Initial US rump fat2, mm 10.9 10.8 2.28 0.96

Mid-Trial US rib fat2, mm 8.72 8.08 1.68 0.80

Mid-Trial US rump fat2, mm 12.09 10.41 2.23 0.62

Final US rib fat2, mm 9.00 7.20 1.50 0.44

Final US rump fat2, mm 12.4 8.6 2.11 0.26

HCW2, kg 353.5 322.5 9.96 0.07

Grade Fat, mm 8.7 6.2 1.59 0.32

LM area, cm2 814.5 823.8 40.23 0.88

Marbling score3 5.51 5.57 0.169 0.80

1Values reported are LSM and SEM (n = 11). HIGH = cows fed at 1.4 × total NE requirements. LOW =

cows fed at 0.85 × total NE requirements.

2US= real time ultrasound; HCW = hot carcass weight

3LM scored subjectively for marbling using a 10-point scale (10= devoid, 9 = practically devoid,

8 = traces, 7 = slight, 6 = small, 5 = modest, 4 = moderate, 3 = slightly abundant, 2 = moderately

abundant, 1 = abundant)

57

Table 4.3. Circulating serum metabolites of cows fed above or below total net energy requirements.

Treatment1

Item HIGH LOW SEM P-value

Initial BHBA2 μmol/L 461.2 410.1 43.13 0.44

Mid-Trial BHBA2 μmol/L 131.6 158.0 18.12 0.35

Final BHBA2 μmol/L 243.8 319.1 37.03 0.21

Initial Cholesterol, mmol/L 3.40 3.26 0.171 0.58

Mid-Trial Cholesterol, mmol/L 3.14 2.67 0.136 0.06

Final Cholesterol, mmol/L 2.77 2.57 0.115 0.28

Initial NEFA3, mmol/L 0.09 0.13 0.022 0.27

Mid-Trial NEFA3, mmol/L 0.56 0.64 0.114 0.62

Final NEFA3, mmol/L 0.47 1.08 0.144 0.03

Initial Glucose, mmol/L 3.67 3.27 0.105 0.04

Mid-Trial Glucose, mmol/L 3.79 3.57 0.059 0.05

Final Glucose, mmol/L 3.70 3.53 0.074 0.17

Initial Urea, mmol/L 5.63 5.53 0.400 0.88

Mid-Trial Urea, mmol/L 3.31 3.64 0.016 0.19

Final Urea, mmol/L 3.30 3.87 0.175 0.07

Initial T34, pmol/L 2.34 2.30 0.099 0.79

Mid-Trial T34, pmol/L 1.93 1.74 0.096 0.22

58

Final T34, pmol/L 2.40 1.85 0.086 0.01



2BHBA = beta-hydroxybutyrate

4T3= triiodothyronine

59


fat weight (actual, relative to body weight and hot carcass weight) in cows fed above or below total

net energy requirements.

Treatment1

Item HIGH LOW SEM

P-value

Liver

Actual, g 6,209 5,375 259.7 0.07

Relative to BW, g/kg 8.49 7.79 0.399 0.27

Relative to HCW, g/kg 17.88 16.69 0.923 0.40

Kidneys

Actual, g 1,201 1,147 79.8 0.66



Lungs

Actual, g 5,793 5,897 215.6 0.75



Heart

Actual, g 2,467 2,362 106.3 0.52



Pancreas

Actual, g 468 380 36.3 0.14



Spleen

60

Actual, g 801 787 60.9 0.88



Rumen

Actual, g 12,227 11,491 167.0 0.02



Lower Gastrointestional Tract2

Actual, g 9,009 9,406 432.6 0.54



Total Internal Fat

Actual, g 26,902 20,575 3063.67 0.20



Fetal Weight

Fetal weight, kg 31.44 28.90 2.75 0.54



2Contains small and large intestine, cecum, and rectum.

61

Table 4.5. Hepatic oxygen consumption, protein concentration and citrate synthase activity in cows

fed above or below total net energy requirements.

Treatment1

Item HIGH LOW SEM P-value

O2 Consumption

μl/ mg/h 0.668 0.549 0.0453 0.12

ml/ g protein /h 12.47 9.59 0.717 0.04

ml/ Liver/h 4,050 2,945 183.2 0.01

ml/ Liver/BW/h 5.51 4.28 0.246 0.02

Liver Protein Concentration, mg/g 53.71 57.22 1.911 0.25

Citrate Synthase

U/g tissue 552.8 415.0 91.88 0.34

U/g protein 10,238 7,260 1766.1 0.29

KU/liver 3,448 2,268 590.0 0.22



62

Table 4.6. Abundance of proteins relating to energy balance in tissues of cows fed above or below

total net energy requirements.

Treatment1

Protein, AU2 HI LOW SEM

P-value

Liver

PCNA3 4.39 5.006 0.22 0.10

ATP Synthase 47.71 51.28 1.819 0.22

Na+/K+ ATPase 103.8 114.1 15.49 0.66

Ubiquitin 32.22 38.95 4.697 0.36

PEPCK4 302.6 339.0 22.97 0.32

AMPK5 61.13 58.01 4.636 0.65

Phospho-AMPK5 7.15 7.19 0.391 0.94

PPARγ6 18.11 17.16 1.609 0.69

PGC-1α7 125.07 73.84 12.09 0.03

UCP28 13.35 10.80 2.17 0.43

Kidney

PCNA3 10.47 9.18 0.783 0.30

ATP Synthase 15.59 13.85 1.059 0.30

Na+/K+ ATPase 698.8 628.0 60.56 0.45

Ubiquitin 39.74 39.08 3.206 0.89

PEPCK4 474.2 430.8 32.57 0.39

Pancreas

PCNA3 11.91 11.64 0.872 0.84

ATP Synthase 9.62 8.42 0.375 0.07

Na+/K+ ATPase 430.5 497.3 80.84 0.59

Ubiquitin 131.8 122.5 11.54 0.59

63

Rumen Papillae

PCNA3 306.2 328.4 24.33 0.55

ATP Synthase 10.92 13.59 1.223 0.19

Na+/K+ ATPase 284.2 326.7 15.46 0.06

Ubiquitin 128.1 135.1 14.96 0.76

AMPK5 5.01 5.88 0.326 0.12

Phospho-AMPK5 13.91 15.45 0.595 0.13

PPARγ16 461.1 561.5 58.34 0.27

PPARγ26 97.82 125.83 24.00 0.45

PGC1α7 120.2 154.2 14.25 0.15

Sternomandibularis Muscle

PCNA3 52.69 44.55 4.831 0.29

ATP Synthase 2.45 3.27 0.398 0.20

Na+/K+ ATPase 41.26 40.58 2.286 0.83

Ubiquitin 213.3 255.2 10.70 0.01

AMPK 5 51.13 28.78 13.04 0.28

Phospho-AMPK5 110.3 92.06 21.63 0.58

PPARγ16 119.0 104.04 6.263 0.15

PPARγ26 76.87 65.84 7.285 0.33

PGC-1α7 45.81 46.16 6.826 0.97

Small Intestinal Mucosa

PCNA3 40.86 59.62 9.364 0.21

ATP Synthase 1.92 2.29 0.227 0.29

Na+/K+ ATPase 236.1 260.0 104.2 0.88

Ubiquitin 46.53 50.64 3.473 0.44

64



2 Proteins expressed corrected arbitrary units

3PCNA= Peroxisome proliferator-activated receptor gamma

4PEPCK= Phosphoenolpyruvate carboxykinase

5AMPK = 5’-adenosine monophosphate-activated protein kinase

6PPARγ= Peroxisome proliferator-activated receptor gamma

7PGC-1α = Peroxisome proliferator-activated receptor gamma coactivator 1 alpha

8UCP2= Uncoupling protein 2

65

Figure 4.1. Typical immunoblot of peroxisome proliferator-activated receptor gamma coactivator 1 alpha

(PGC-1α) in liver in mature beef cows fed 1.4 × total NE requirements (HIGH) or 0.85 × total NE

requirements (LOW)

Figure 4.2. Typical immunoblot of ubiquitin in muscle in mature beef cows fed 1.4 × total NE

requirements (HIGH) or 0.85 × total NE requirements (LOW)

66

Chapter 5: The influence of pregnancy in mid-to-late gestation

on circulating metabolites, visceral organ mass, and abundance

of proteins relating to energy metabolism in mature beef cows 1

5.1 Introduction

Metabolism and energy partitioning during pregnancy has been described as a

combination of homeostasis – the energy needed to maintain bodily systems, and homeorhesis,

coordinating repartitioning of nutrients to support change in physiological state (Bauman and

Currie 1980). During the last trimester of gestation, fetal growth dramatically increases and

results in increased nutrient demand in order to support growth. (Ferrell, 1982; NRC, 1996). It

has been suggested that cows may be able to reduce maintenance energy costs in order to support

the energetic demands of the conceptus (Freetly et al., 2008).

Very little is known about how pregnancy impacts visceral organ mass and metabolism in

beef cows. It has been suggested that late gestation pregnant cows may be able to conserve

energy through altering metabolic adaptations (Bell, 1995) and modifications to cellular

physiology in the liver and small intestine (Scheaffer et al., 2003). It also has been suggested

that inter-animal variation in cellular maintenance functions (ion transport, lipid and protein

turnover) may influence maintenance requirements and feed efficiency (Bottje and Carstens,

2009; Carstens and Kerley, 2009; Herd and Arthur, 2009). Little is known about the underlying

cellular mechanisms involved in these processes in relation to pregnancy or feed efficiency in the

cow.

The objective of this experiment is to investigate the influence of pregnancy in mature

beef cows on visceral organ mass, or cellular mechanisms influencing metabolism. By

investigating target proteins relating to cellular energy metabolism, namely: ATP synthase,

1 Submitted to J. Anim. Sci. In review

67

Na+/K+ ATPaseα1, proliferating cell nuclear antigen (PCNA), uncoupling protein 2 (UCP2),

ubiquitin, phosphoenolpyruvate carboxykinase (PEPCK), peroxisome proliferator-activated

receptor gamma (PPARγ), peroxisome proliferator-activated receptor gamma coactivator 1 alpha

(PGC-1α), 5’-adenosine monophosphate-activated protein kinase (AMPK) and phosphorylated-

AMPK (pAMPK), cellular mechanisms may be identified which are impacted by pregnancy and

play important roles in energy partitioning in cows.

5.2 Materials and Methods

5.2.1 Animals, experimental design and treatments

This experiment followed the recommendations of the Canadian Council on Animal Care (1993)

and met the approval of the University of Guelph Animal Care Committee. Eighteen non-lactating mature

beef cows, nine pregnant (PREG; n = 9) and nine non-pregnant (OPEN; n = 9), primarily of Angus and

Simmental cross-breeding were used in a replicated randomized complete block design. Cows were 5.33

± 2.8 years old (mean ± SD) and went through at least one successful pregnancy prior to the start of this

trial. Cows were blocked according to expected d of gestation, such that PREG cows in each block were

slaughtered at approximately 4-5 wks prior to parturition and OPEN cows were randomly assigned to

each block. At the start of the trial, cows were approximately at 150-165 d of gestation. The first replicate

contained three blocks of four cows (two cows from each treatment), and the first block was slaughtered

on d 89 of the feeding period, with blocks 2 and 3 sent to slaughter 7 and 14 d (respectively) after block

1. The second replicate contained two blocks (block 1 and 3) and contained six cows, four cows (two per

treatment) in block 1 and two cows (one in each treatment) in block 3and were slaughtered on the same

schedule as the first replicate.

Prior to the start of this trial, cows were removed from pasture, calves were weaned, and cows

housed in a dry lot and fed grass haylage for at least 28 d prior to the start of the experiment. Cows were

fed once daily a total mixed ration (TMR) containing grassy haylage (69.5% DM basis Table 5.1), wheat

68

straw (30% of diet DM) and a commercially available trace vitamin and mineral supplement (0.5% of diet

DM; 35.8% NaCl, 14% Na, 12% Ca, 4% P, 1% Mg, 0.6% S, 0.2% K, 2369 mg/kg Mn, 1000 mg/kg Cu,

3000 mg/kg Zn, 2294 mg/kg Fe, 58 mg/kg I, 25.5 mg/kg Co, 16.2 mg/kg Se, 601.5 KIU/kg vitamin A,

100.5 KIU/kg vitamin D, and 2000 IU/kg vitamin E). Cows were fed ad libitum and individual feed

intakes were measured using Calan gates (American Calan, Inc., Northwood, NH), with orts measured at

least once per wk. Every 28 d, cows were weighed and on d 0 and 3-5 d prior to slaughter ultrasounded

using an Aloka SSD-500 ultrasound unit (Corometrics Medical Systems, Wallingford, CT) for rib fat

(between the 12th and 13

th rib) and rump fat depth measurements.

5.2.2 Feed and sample analysis

Weekly TMR samples were collected and frozen at -20ºC for future analysis. Samples were later

dried at 55ºC for 96 h to determine DM concentration and then ground to pass through a 1-mm screen.

All feed analysis was carried out at Agri-Food Laboratories Inc. (Guelph, ON). Dry matter analysis was

done in accordance with the Association of Official Analytical Chemists guidelines (1990, Method

930.15.). Acid detergent fiber and NDF was determined using the methods of Robertson and Van Soest

(1981) using an Ankom fiber analyzer (Ankom Technology Corp., Fairport, NY). Percent CP was

determined by multiplying 6.25 by percent dietary nitrogen as determined by the Leco Nitrogen analyzer

(Leco Corporation, St. Joseph, MI).

5.2.3 Tissue collection and carcass measurements

Cows were slaughtered at the University of Guelph Meat Laboratory. Final BW was

obtained by weighing the morning of slaughter. Hot carcass weight, grade fat (minimum fat

depth over the last quadrant of the LM), LM area (LMA), and subjective marbling score were

determined as previously described (Mandell et al., 1997; Laborde et al., 2002; Mader et al.,

2009). Visceral organs (liver, kidneys, heart, lungs, pancreas) were weighed and approximately

10 g samples of liver (mid-lobe), kidney (cortex of the larger of the two kidneys) and pancreas

(body), along with sternomandibularis muscle were rinsed twice in 4°C saline (154 mM NaCL)

69

and snap-frozen in liquid nitrogen for future analysis. The spleen, esophagus, reticulorumen, and

total lower gastrointestinal tract (small and large intestine, caecum, and colon) were trimmed of

fat, emptied, rinsed and weighed. Organ weights were expressed as actual, relative to BW and

relative to HCW in order to normalize for weight of the conceptus. Trimmed visceral fat,

including kidney and pelvic fat was also weighed. Samples of the rumen (ventral sac) and the

small intestine (11th

meter after the pylorus) were rinsed twice in chilled saline (154 mM NaCL)

and snap frozen in liquid nitrogen until further analysis. The uterus was trimmed of fat and

weighed in OPEN cows. In PREG cows, fat was trimmed, the fetus removed and weighed, and

the uterus and placenta weighed as total uterus weight.

Blood samples were taken in the morning prior to feeding via jugular venipuncture at d 0

(initial), 56 and 3-5 d prior to slaughter (final) into non-heparinized tubes. Blood was allowed to

clot at room temperature for greater than 30 min to allow for clotting. Blood samples were

centrifuged at 3000 × g for 25 min and serum was separated and then frozen at - 20ºC until

analyzed. Serum samples were analyzed for serum urea, glucose, NEFA, beta-hydroxybutyrate

(BHBA) and total cholesterol at the University of Guelph Animal Health Laboratory (Guelph,

ON) using Roche cobas c311 and Immulite 1000 analyzers (Hoffmann- La Roche Ltd.,

Mississauga, ON, Canada). Serum samples were analyzed by IDEXX Laboratory (Markham,

ON.) for serum triiodothyronine (T3) using a Siemens IMMULITE 2000 total T3 solid phase

competitive chemiluminescent immunoassay (Siemens Healthcare Diagnostics, Mississauga,

ON, Canada).

5.2.4 Immunoblot and protein concentrations

Western blots were conducted to quantify abundance of: PCNA, ATP synthase, ubiquitin,

and Na/K+ ATPaseα1 for all tissues; PGC-1α, PPARγ, AMPKα and pAMPKα for liver, muscle,

70

and rumen; PEPCK for liver and kidney; and UCP2 for liver. A known amount of each tissue

sample of liver, kidney, pancreas, muscle, rumen papillae and scraped small intestinal epithelia

(Matthews et al., 1996; Wang et al., 2009) were homogenized in an ice-cold SEB solution

[0.25mM sucrose, 10mM HEPES-KOH, 1 mM EDTA] containing 10 μl/mL of a protease

inhibitor cocktail (Pierce Protease Inhibitor Cocktail Kit, Pierce Biotechnology, Rockford, IL,

USA). Samples were homogenized on ice and then stored at -80°C until further analysis. Protein

concentration of homogenate was determined using a commercially available bicinchononic acid

kit (Pierce BCA Protein Assay Kit, Pierce Biotechnology, Rockford, IL) using bovine serum

albumin as a standard and measured on a PowerWave XS microplate spectrophotometer (BioTek

Instruments Inc., Winooski, VT).

Twenty micrograms of total protein for ATP synthase and 40 µg for all other protein

targets were loaded on to the sodium dodecyl sulphate-polyacrylamide (SDS-PAGE) gels. Gels

were electrophoresed according to methods described by Laemmli (1970) and transferred to a

PVDF membrane (0.2μm; Millipore Corporation, Bedford, MA). Membranes were blocked in a

blocking solution containing 10 mM Tris-HCL, 200mM NaCl, 1ml/L Tween-20, and 50g/L non-

fat dry milk (Carnation Instant Skim Milk Powder, Markham, ON, Canada) for 1 hour at room

temperature before incubation with primary antibodies. Primary antibodies were diluted in a

blocking solution containing 10 mM Tris-CL, 200 mM NaCl, 1ml/L Tween-20, and 20g/L non-

fat dry milk and incubated for 1.5 h at room temperature for ATP synthase and overnight at 4˚C

for all other proteins. The primary antibodies and concentrations used were: mouse anti-bovine

ATP Synthase (Complex 5; F1F0ATPase; #459240, Invitrogen, Camarillo, CA, USA; 1:5,000

dilution); PCNA rabbit anti-human polyclonial (SC-7907, Santa Cruz Biotechnology Inc., Santa

Cruz, CA, USA; 1:300 dilution); Na+/K+ ATPase α1 mouse anti-rabbit monoclonal (10R-

71

N102A, Fitzgerald Industries International, Acton, MA, USA; 1:300); Ubiquitin rabbit anti-

human polyclonal (#3933, Cell Signalling Technology, Danvers, MA, USA; 1:750 dilution);

PPARγ rabbit anti-human monoclonal (#2435, Cell Signalling Technology, Danvers, MA, USA,

1:500); AMPKα rabbit anti-human monoclonal (#5831, Cell Signalling Technology, Danvers,

MA, USA, 1:750 dilution); Phospho-AMPKα rabbit anti-human monoclonal (#2535, Cell

Signalling Technology, Danvers, MA, USA, 1:1,000 dilution); PEPCK2 rabbit anti-human

polyclonal (#6924, Cell Signalling Technology, Danvers, MA, USA, 1:750 dilution for liver,

1:500 for kidney); PGC-1α rabbit anti-mouse polyclonal (AB3242, Millipore, Temecula, CA,

USA, 1:500 dilution for muscle and 1:300 dilution for liver and rumen, incubated overnight at

4˚C); UCP-2 rabbit anti-human polyclonal (144-157,Calbiochem, Darmsttadt, Germany, 1:500

dilution, incubated overnight at 4˚C). Donkey anti-mouse immunoglobulin (1:5,000 dilution, GE

Amersham) and donkey anti-rabbit immunoglobulin for (1:5,000 dilution; GE Amersham)

horseradish peroxidase-linked secondary antibodies were used in combination with ECL Western

Blotting Detection Reagents (GE Amersham, Baie d’Urfe, Quebec) for chemiluminescent

detection of immunoreactive proteins.

Apparent protein migration weights were determined using molecular weight markers

(Precision plus standards, 10 to 250 kDa; Bio-Rad Laboratories Ltd., Mississauga, ON). Band

intensities were quantified using a FlourChem HD2 (Cell Biosciences/Proteinsimple, Santa

Clara, CA, USA) imaging system and Alphaview software (Alpha Innotech/Proteinsimple, Santa

Clara, CA, USA) correcting for local background intensity. To correct for unequal loading and/or

transfer of proteins, membranes were stained with fast green (Fisher Scientific, Ottawa, ON) and

a common predominant band was quantified and used to normalize immunoblots (Howell et al.,

2003, Wang et al., 2009) Band intensities are expressed as corrected arbitrary units (AU).

72

5.2.5 Statistical analysis

Prior to the start of the trial, a power of test (Berndtson, 1991) was conducted and

determined that a minimum of eight biological replicates were needed to detect a 20% difference

from control, with a CV of 10%, at 95% power. Data were analyzed using PROC MIXED in

SAS (2008). The model included the effect of treatment, age and block nested within replicate.

Results were considered significant at P ≤ 0.05

5.3 Results

Average DMI did not differ (P = 0.25; Table 5.2) between OPEN and PREG cows, nor

did ADG (P = 0.19). Initial BW and pre-slaughter BW were also not different (P ≥ 0.12)

between treatments. Real time ultrasound measures of both rib fat and rump fat did not differ (P

≥ 0.33) between OPEN and PREG cows initially or pre-slaughter. No differences (P ≥ 0.32) in

change in ultrasound rib or rump fat were observed, however, large variation within each

treatment was noted. Carcass measures of LM area, grade fat, subjective marbling score, or

HCW also did not differ (P ≥ 0.09) between treatments.

At the start of the trial, no differences (P ≥ 0.07: Table 5.3) were observed in circulating

levels of serum BHBA, total cholesterol, glucose, urea, NEFA, or T3. By d 56 of the feeding

period, PREG cows had lower (P = 0.05) total cholesterol and greater (P ≤ 0.04) circulating

BHBA, NEFA and urea concentrations. Glucose and T3 concentrations were not affected (P ≥

0.5) by treatment, for both d 56 and the pre-slaughter sampling period. At the pre-slaughter

period, BHBA, NEFA and urea concentrations remained greater (P ≤ 0.04) than OPEN, while

total cholesterol remained lower (P = 0.04) than OPEN.

73

Liver mass, both actual and relative to final BW or HCW was greater (P ≤ 0.02; Table

5.4) in OPEN cows. Pregnancy did not affect (P ≥ 0.08) the mass (actual, relative to BW or

HCW) of kidney, pancreas, heart, lung, spleen, omasum, abomasum and lower gastrointestinal

weight (small and large intestine). Although no differences (P ≥ 0.06) were observed in actual or

relative to HCW, rumen mass and rumen mass relative to BW was greater (P = 0.01) in OPEN

cows. Uterus weight (actual, relative to BW or HCW) was greater (P < 0.001) from PREG

cows. Kidney fat weight was greater (P = 0.04) in OPEN cows when expressed relative to BW,

but actual or relative to HCW was not (P ≥ 0.06) different between treatments. Total internal fat

(actual, relative to BW or HCW) was not affected (P ≥ 0.14) by pregnancy status. Average fetal

mass was 30.2 kg ± 6.19 (mean ± SD; n=10) and consisted of 4 females and 6 males and one set

of twins (data not shown).

Abundance of PCNA, ATP synthase, or ubiquitin was not affected (P ≥ 0.1; Table 5.5)

by pregnancy in all tissues analyzed. Abundance of Na/K+ ATPase increased (P = 0.04) in liver

of pregnant cows (Figure 5.1), but did not differ (P ≥ 0.11) in kidney, pancreas, rumen, muscle

or small intestine mucosa. In liver and kidney, PEPCK abundance was not influenced (P ≥ 0.08)

by treatment. Abundance of AMPK or phos-AMPK did not differ (P ≥ 0.17) in liver, muscle,

kidney, small intestine mucosa or pancreas, however rumen phos-AMPK was greater (P = 0.006)

in PREG cows (Figure 5.2). Liver, kidney or muscle PPARγ or PGC1-α abundance was not

affected (P ≥ 0.42) by pregnancy status, nor was hepatic UCP2 abundance (P = 0.14).

5.4 Discussion

In the beef industry, winter feed represents the greatest costs of production for cow/calf

producers (Kaliel and Kotowich, 2002), and is a point in the production cycle where cows

typically are in mid- to late-gestation. Understanding mechanisms which may improve nutrient

74

utilization may result in new opportunities for selection for improved feed efficiency. In the

pregnant cow, energy requirements required to support growth of the conceptus increase

exponentially leading up to parturition. Estimates of fasting heat production have been shown to

be approximately 4,600 kcal/d greater in pregnant beef heifers at 240 d of gestation than in non-

pregnant heifers (Ferrell et al., 1976). In late gestation, total nutrient requirements are

approximately 75% greater in the pregnant, than the non-pregnant cow (Bauman and Currie,

1980). A study by Freetly et al.(2008) investigated the effect of moderate feed restriction

followed by re-alimentation on energy metabolism in beef cows leading up to parturition and

found that efficiency (retained energy/ ME) was improved during re-alimentation compared to

cows fed consistently. This suggests that adaptive metabolic changes may occur to improve

nutrient utilization. However, in pregnant cows these adaptive changes remain largely unknown.

In the present study, ADG did not differ between PREG or OPEN cows, indicating that

OPEN cows were gaining weight not associated with the growth of the conceptus. Total DM

intake was also similar between OPEN and PREG cows, indicating that despite increased energy

demands, voluntary feed intake was likely maximized. Based on DMI and predicted NEm of the

diet, NEm intake for PREG cows was 14.7 Mcal/d. According to NRC (1996), maintenance

requirements for a 700 kg pregnant cow in a cold climate is approximately 13 Mcal/d. At 150 d

of gestation the NE requirement for pregnancy is approximately 0.6 Mcal/d and at 250 d of

gestation increases to approximately 3.6 Mcal/d. This would suggest that in the first part of the

feeding period nutrient requirements of the pregnant cow were met, while in the later part of the

experiment nutrient intake may have been limiting. As DM intake did not differ and overall

weight gain was not different, it is clear that repartitioning of nutrients occurred in order to meet

the metabolic demands of pregnancy.

75

At the start of the trial, circulating metabolites were similar amongst treatment groups.

However at d 56 and prior to slaughter, PREG cows had increased circulating BHBA and NEFA

and reduced circulating total cholesterol concentrations as compared to OPEN cows indicative of

greater fat catabolism and a more ketogenic metabolic state in PREG cows than in OPEN cows .

In late gestation, nutrient demand of glucose for the conceptus increases by approximately 50%

(Bell, 1995). Increased voluntary feed intake has the potential to meet this rising nutrient

demand, but in situations where nutrition is limiting, a decrease in glucose utilization by other

maternal tissues has been reported (Hough et al., 1985), and an increase in overall glucose

production observed in pregnant sheep at same level of intake (Wilson et al., 1983). Circulating

NEFA concentrations have also been found to rise in pregnant sheep (Petterson et al., 1994) and

cause increased production, production and oxidation of ketones in maternal tissues (Pethick et

al., 1983). Freetly and Ferrell (2000) found that net hepatic NEFA uptake increased over

gestation in pregnant ewes and suggested that this increase in NEFA entry likely results from

increased lipolysis from fat stores, sparing glucose and amino acids for conceptus nutrient

demands. In their study, feed intake decreased as gestation length increased. In the present study

it is possible that the medium to low quality of this diet created a situation where nutrient intake

was limited by the high NDF of the ration, forcing pregnant cows to homeorheticly direct

nutrients away from maintenance functions and towards supporting the increased glucose supply

needed for conceptus growth.

Although visceral organs account for approximately 10% of BW they contribute to about

50% of total energy costs (Reynolds et al., 1991) and can be attributed to increased maintenance

energy costs and energy expenditures (Ferrell, 1988; McBride and Kelly, 1990). It has been

suggested that visceral organ mass has a greater influence on whole body expenditure rather than

76

protein specific metabolic rate alone (Koong et al., 1985; Burrin et al., 1990; Kelly et al., 2001)

and has been shown to have a strong positive correlation with level of feed intake (Burrin et al.

1992; Sainz and Bentley, 1997; Swanson et al, 2000; Wang et al. 2009). Baldwin et al., (1980)

suggested that identifying animals that can minimize mass of high energy demanding tissues,

such as the liver, more independent of nutrient intake, may reduce apparent maintenance

requirements by 10 to 30%. Previous research has shown that visceral organ mass can be

influenced by both pregnancy and nutrient intake in pregnant sheep (Fell et al., 1972; Scheaffer

et al, 2004; Caton et al., 2009), beef cows (Meyer et al., 2010) and in rodents (Dai et al., 2011).

In the present study, liver mass relative to HCW was smaller in PREG cows despite no

differences in DMI, which may indicate that other mechanisms influence hepatic mass in

pregnant cows. There are very few studies investigating differences in visceral mass between

pregnant and non-pregnant ruminants. Contrary to the results of the present work, a study

investigating visceral organ mass in pregnant growing heifers found no differences in liver

weight or other visceral organ weights (Scheaffer et al., 2001). Meyer et al. (2010) found that

feed restriction, day of gestation, and the interaction between intake and d of gestation, resulted

in differences in liver mass; restriction decreased hepatic mass, but after re-alimentation liver

weight increased similarly to control cows. Liver weight also increased as gestation length

increased. Similarly in mature ewes, feed restriction resulted in smaller livers, however non-

pregnant ewes had smaller liver mass than pregnant ewes and liver mass increased with d of

gestation, with no interaction between nutrient level and reproductive status (Scheaffer et al.,

2004). Another study in pregnant ewes investigated the effect of high level of intake

(approximately twice of controls) and d of gestation on visceral organ mass and found that both

increased intake and d of gestation resulted in increased liver mass, with no significant effect of

77

interaction between stage of gestation and level of nutrition (Caton et al., 2009). This indicates

that nutrient intake plays a significant role in determination of liver mass. However, the influence

of pregnancy remains more elusive. It is possible that in the present study, nutrient intake was

sufficiently low to create a situation where the level of nutrition became limiting, inducing a

reduction in hepatic mass.

Decrease in rumen weight relative to BW is most likely a result of limiting space in the

body cavity due to late stage of pregnancy, which has been well researched in both cattle and

sheep (Gunter et al., 1990; Hanks et al., 1993; Scheaffer et al., 2001). However, when expressed

relative to HCW, significance was reduced. This may also have been a contributing factor to

limiting nutrient intake in the PREG cow group.

Fat surrounding the kidneys expressed relative to BW was greater in OPEN cows, and

total internal fat (which includes kidney and all visceral fat), was numerically greater in OPEN.

Since hot carcass weight was similar between OPEN and PREG cows, this indicates that weight

gain by OPEN cows was likely due to increasing fat stores. When kidney fat weight was

expressed relative to HCW, significance was reduced.

The protein Na/K+ ATPase is responsible for maintaining high intracellular K+

concentrations by actively transporting Na+ across the cellular membrane and it has been

suggested that this process accounts for greater than 20% of total maintenance energy costs

(Milligan and McBride, 1985; McBride and Early, 1989). Regulation of Na+/K+ is under control

of a wide variety of molecular pathways (Kaplan, 2002), and very limited research has

investigated Na+/K+ ATPase response to pregnancy. One study in rats found no differences in

hepatic Na+/K+ ATPase activity between pregnant and virgin rats (Zamora and Arola, 1987). In

78

our study, increased protein abundance of hepatic Na+/K+ ATPase was observed in PREG cows.

It is suspected that increased hepatic Na+/K+ ATPase is due to increased workload in the liver.

Hepatic oxygen consumption has been shown to increase with d of gestation in pregnant sheep

and is associated with feed intake (Freetly and Ferrell, 1997a). In fasted sheep Na/K+ ATPase

dependent O2 consumption in hepatocytes was 62% percent lower than in fed sheep (McBride

and Milligan, 1985). Gullans et al. (1984) investigated the relationship between gluconeogenesis

and Na+/K+ ATPase in renal proximal tubules and found that Na+/K+ ATPase activity varies

with VFA source. If PREG cows were in a state of negative energy balance during late

pregnancy, catabolism of bodily tissues may occur, resulting in increased circulating BHB and

NEFA concentrations and flux through the liver (Grummer, 1995; Drackley, 1999). This altered

VFA source, may also have an impact on Na+/K+ ATPase. A study (Wang et al., 2009)

investigating increasing levels of forage fed to growing steers, found a linear relationship

between hepatic Na+/K+ ATPase abundance and forage inclusion level and suggested that VFA

production may influence Na+/K+ ATPase abundance in the liver. However, further research is

needed to confirm the impact of VFA on hepatic Na+/K+ ATPase abundance. The sodium pump

has been shown to be influenced by a variety of endocrine signals including: thyroid hormones,

insulin, progesterone, cortisol, aldosterone, glucagon dexamethasone and others (Rossier et al.,

1987; Ewart and Klip, 1995) having various effects in different tissues. It is possible that altered

hormone levels between pregnant and non-pregnant animals play a role in observed differences

in Na+/K

+ ATPase abundance.

5’-adenosine monophosphate-activated protein kinase and the activated form, pAMPK is

a highly conserved kinase, which plays a key role in cellular energy homeostasis (by sensing

ADP:ATP ratio) and energy signalling (Pimentel et al., 2013). The phosphorylation of AMPK

79

interacts with numerous intermediates, which in turn alter a variety of metabolic processes

through decreased protein synthesis, glycogen synthesis, fatty acid synthesis lipolysis and

increased fatty acid oxidation and glucose transport among others (Hardie, 2003). AMPK has

been suggested as a cellular mechanism of regulating feed intake in ruminants (Allen et al.,

2005; Allen et al., 2009) and in rodents (Pimentel et al., 2012). In the present study, rumen

papillae pAMPK abundance was increased in pregnant cows, while AMPK was not different

between treatments. AMPK/pAMPK is known to have a co-ordinated response throughout

various tissues in the body (Kahn et al., 2005), however the signalling roll of AMPK in the

digestive tract is unclear. In the digestive tract, ghrelin is known to have stimulating effects on

AMPK activation and thought to be one of the molecular mechanisms linking ghrelin to appetite

regulation (Minokoshi et al., 2004; Kola et al., 2006;Xue and Kahn, 2006; Kojima and Kangawa,

2010), however the specific role of AMPK/ pAMPK in rumen tissue is not known. Protein

abundance of AMPK and pAMPK has been implicated as potential regulators of feed efficiency

in other species, where lower abundance of AMPK and pAMPK has been observed in the muscle

of low RFI (efficient) pigs (Faure et al., 2013).

These data indicate that PREG cows may metabolize energy reserves and alter their

metabolism in order to meet the energetic demands of the growing fetus, without altering DM

intake or overall growth. Increases in circulating NEFA, BHBA and urea, and decreasing total

cholesterol, accompanied by reduced kidney fat weight indicate that PREG cows may be

metabolizing energy reserves in order to meet metabolic demands of the growing conceptus.

Reduced hepatic mass in PREG suggests a reduction in hepatic maintenance costs, however

increased Na+/K+ -ATPase abundance in hepatic tissues of PREG cows may indicate increased

workload in those tissues. Increased pAMPK in rumen may be indicative of metabolic signaling

80

towards increasing energy intake. These data may lead to increased understanding of cellular

mechanisms involved in energetic repartitioning and may lead to a greater understanding of

metabolic processes contributing to differences in feed efficiency.

81

Table 5.1. Diet composition and analyses

Ingredient (% DM Basis) Ration

Grass Haylage 69.5

Wheat Straw 30.0

Mineral premix1 0.5

Analysis2

DM, % 45.42

CP, %DM 10.3

ADF, %DM 44.2

NDF, %DM 62.0

NEm, Mcal/kg3 1.10

1Contains: 35.8% NaCl, 14% Na, 12% Ca, 4% P, 1% Mg, 0.6% S, 0.2% K, 2369 mg/kg Mn,

1000 mg/kg Cu, 3000 mg/kg Zn, 2294 mg/kg Fe, 58 mg/kg I, 25.5 mg/kg Co, 16,2 mg/kg Se,

601.5 KIU/kg vitamin A, 100.5 KIU/kg vitamin D, and 2000 IU/kg vitamin E.



82

Table 5.2. Performance, real-time ultrasound and carcass characteristics of open and pregnant

cows

Treatment1

Variable OPEN PREG SEM P-value

DMI, kg/d 12.8 13.4 0.36 0.25

ADG, kg/d 0.75 0.90 0.08 0.19

Initial BW, kg 712 757 25.6 0.21

Final BW, kg 784 845 26.9 0.12

Initial US rib fat2, mm 11.2 12.2 1.18 0.53

Final US rib fat2, mm 12.2 12.2 1.10 0.98

Change in US rib fat, mm 1.04 -0.02 0.758 0.32

Initial US rump fat2, mm 18.3 16.6 3.0 0.67

Final US rump fat2, mm 21.4 17.3 2.98 0.33

Change in US rump fat2, mm 3.06 0.76 1.01 0.47

HCW, kg 399 413 15.3 0.51

Grade Fat, mm 13.9 12.3 1.35 0.40

LM area, cm2 886 916 38.8 0.57

Marbling score3 5.82 5.39 0.172 0.09

1Values reported are LSM and SEM (n = 9). OPEN = non-pregnant cows (n = 9); PREG =

pregnant cows (n = 9).

2US= real time ultrasound

3LM scored subjectively for marbling using a 10-point scale (10= devoid, 9 = practically devoid,

8 = traces, 7 = slight, 6 = small, 5 = modest, 4 = moderate, 3 = slightly abundant, 2 = moderately

abundant, 1 = abundant)

83

Table 5.3. Circulating serum metabolites in pregnant or open cows at the start, day 56, and end of

trial

Treatment1

Item OPEN PREG SEM P-value

Initial BHBA2 μmol/L 176.4 196.2 15.07 0.35

D 56 BHBA2 μmol/L 180.7 270.7 19.23 0.005

Final BHBA2 μmol/L 165.8 264.5 20.5 0.004

Initial Cholesterol, mmol/L 3.71 3.18 0.194 0.07

D 56 Cholesterol, mmol/L 3.26 2.73 0.181 0.05

Final Cholesterol, mmol/L 3.39 2.88 0.161 0.04

Initial NEFA, mmol/L 0.37 0.45 0.061 0.34

D 56 NEFA, mmol/L 0.21 0.44 0.072 0.04

Final NEFA, mmol/L 0.23 0.47 0.077 0.04

Initial Glucose, mmol/L 4.00 4.04 0.12 0.80

D 56 Glucose, mmol/L 3.71 3.64 0.082 0.50

Final Glucose, mmol/L 3.73 3.68 0.107 0.73

Initial Urea, mmol/L 5.14 4.44 0.45 0.34

D 56 Urea, mmol/L 3.48 3.96 0.118 0.01

Final Urea, mmol/L 3.47 4.00 0.16 0.03

84

Initial T33, pmol/L 1.87 2.01 0.218 0.65

D 56 T33, pmol/L 1.53 1.46 0.110 0.65

Final T33, pmol/L 1.57 1.60 0.122 0.85

1Values reported are LSM and SEM (n = 9). OPEN = non-pregnant cows (n = 9); PREG =

pregnant cows (n = 9).

2BHBA = beta-hydroxybutyrate

3T3= triiodothyronine

85


fat weight (actual, relative to body weight and hot carcass weight) in cows

Treatment1

Item OPEN PREG SEM

P-value

Liver

Actual, g 7,346 6,576 215.9 0.02

Relative to BW, g/kg 9.42 7.82 0.201 < 0.001


Kidneys

Actual, g 1,431 1,342 71.1 0.37



Lungs

Actual, g 6,884 6,944 412.6 0.92



Heart

Actual, g 2,719 2,765 103.2 0.75



Pancreas

Actual, g 612 561 40.3 0.36



Spleen

86

Actual, g 866 829 62.5 0.68



Rumen

Actual, kg 15.5 13.9 0.62 0.08

Relative to BW, kg/kg 0.020 0.016 0.0009 0.01

Relative to HCW, kg/kg 0.04 0.03 0.002 0.06

Abomasum

Actual, g 2,686 2,615 219.6 0.81



Lower Gastrointestional

Tract2

Actual, g 9,619 10,001 426.5 0.52



Uterus3

Actual, kg 2.2 17.8 1.22 <0.001

Relative to BW, g/kg 2.45 20.96 1.114 <0.001

Relative to HCW, g/kg 5.04 43.09 2.90 <0.001

Kidney Fat

Actual, kg 13.2 10.8 1.08 0.13



Total Internal Fat

87

Actual, kg 41.6 36.8 4.11 0.40



1Values reported are LSM and SEM. OPEN = non-pregnant cows (n = 9); PREG = pregnant

cows (n = 9).

2Contains small and large intestine.

3 Uterus contains total uterus = fetal membranes (for PREG cows)

88

Table 5.5. Abundance of proteins relating to energy metabolism in tissues of open and pregnant

cows

Treatment1

Protein, AU2 OPEN PREG SEM

P-value

Liver

PCNA3 67.51 73.35 4.937 0.40

ATP Synthase 25.68 26.22 1.651 0.81

Na+/K+ ATPase 21.09 31.28 3.17 0.04

Ubiquitin 23.75 28.52 5.15 0.50

PEPCK4 43.70 37.53 2.41 0.08

AMPK5 37.95 40.62 3.58 0.59

Phospho-AMPK5 34.04 41.44 4.58 0.25

PPARγ26 85.23 81.40 7.10 0.70

PGC-1α7 23.23 25.27 2.76 0.59

UCP28 68.54 95.37 12.76 0.14

Kidney

PCNA3 51.55 53.99 2.62 0.50

ATP Synthase 36.02 39.06 3.03 0.47

Na+/K+ ATPase 39.26 33.26 3.14 0.18

Ubiquitin 54.32 60.11 5.50 0.45

PEPCK4 17.02 17.16 22.50 0.97

Pancreas

PCNA3 13.50 14.01 1.44 0.80

ATP Synthase 16.91 17.87 0.84 0.41

Na+/K+ ATPase 31.87 33.31 3.84 0.79

Ubiquitin 11.08 10.22 0.60 0.31

Rumen Papillae

PCNA3 11.12 11.32 0.59 0.79

ATP Synthase 15.19 15.34 0.54 0.84

Na+/K+ ATPase 41.95 25.37 7.14 0.11

Ubiquitin 49.16 58.41 5.05 0.20

AMPK5 52.85 29.00 5.82 0.69

Phospho-AMPK5 16.81 25.20 1.81 0.006

PPARγ6 13.63 13.76 1.14 0.93

PGC1α7 43.52 44.85 20.36 0.96

Sternomandibularis Muscle

PCNA3 65.20 70.31 10.78 0.73

ATP Synthase 24.02 27.34 2.07 0.26

Na+/K+ ATPase 12.34 12.44 1.01 0.94

Ubiquitin 9.67 9.26 1.11 0.79

AMPK 5 10.22 13.28 2.13 0.31

Phospho-AMPK5 41.54 46.16 2.32 0.17

PPARγ16 15.38 17.87 2.19 0.42

PPARγ26 19.18 17.29 2.66 0.61

PGC-1α7 33.70 30.02 3.55 0.46

89

Small Intestinal Mucosa

PCNA3 27.96 39.29 4.78 0.10

ATP Synthase 26.20 31.13 5.35 0.51

Na+/K+ ATPase 9.93 18.03 6.99 0.41

Ubiquitin 16.41 16.67 1.55 0.90 1Values reported are LSM and SEM. OPEN = non-pregnant cows (n = 9); PREG = pregnant

cows (n = 9).

2 Proteins expressed corrected arbitrary units

3PCNA= Peroxisome proliferator-activated receptor gamma

4PEPCK= Phosphoenolpyruvate carboxykinase

5AMPK = 5’-adenosine monophosphate-activated protein kinase

6PPARγ= Peroxisome proliferator-activated receptor gamma

7PGC-1α = Peroxisome proliferator-activated receptor gamma coactivator 1 alpha

8UCP2= Uncoupling protein 2

90

Figure 5.1 Representative immunoblot (top) and fast green stain (bottom) for Na+/K+ ATPase α1 in liver

tissue from pregnant (Pr) or non-pregnant (Op) mature beef cows.

Figure 5.2 Representative immunoblot (top) and fast green stain (bottom) for pAMPK in rumen papillae

from pregnant (Pr) or non-pregnant (Op) mature beef cows.

91

Chapter 6: General Conclusions As feed costs continue to rise, more financial stress is being placed on livestock producers.

Research results into understanding feed efficiency and maintenance energy costs may help to make

selection and managements decisions which would help mitigate some of the large cost of feeding

livestock. While in recent years much of the research conducted in feed efficiency has focused on

growing steers, heifers, or bulls, research into feed efficiency in mature cows has been lacking in the

literature. The first objective of the current study was to investigate measuring net feed efficiency in

mature, pregnant beef cows. As a large percentage of total energy inputs for the mature beef cow are used

to fulfil maintenance energy requirements (Ferrell and Jenkins, 1985), animal differences in maintenance

requirements may reflect differences in feed efficiency. In the present study, proteins relating to

maintenance energy expenditure and cellular energy signalling were investigated in two models: mature

cows fed at differing levels of dietary intake and cows with differing physiological state (pregnancy).

As mentioned in the first experimental chapter of this thesis, measuring feed efficiency in mature

cows can present many challenges and may require changes or alternative approaches for measuring feed

efficiency in the mature cow. This chapter combined the data from five experiments in mature pregnant

beef cows, creating a dataset with 321 records. Analysis revealed that using RFI as a measure of feed

efficiency can be highly variable and models may need to be revised. Minimal body weight gains

measured in cows, may suggest that weight gain may not be an ideal choice for use in linear models

predicting DMI. Correcting for predicted conceptus gain did not improve model fit. In general, including

ultrasound measures of body fat did slightly improve the fit of the RFI model. Contemporary group, as

well as dietary composition effects were also important factors that lead to improved model fit. When RFI

models were investigated within each contemporary group there were instances of mean and slope bias in

models for the basic model, the greatest R2, and least BIC models of DMI. In general, much of the

variation in RFI in mature cows remains unknown, but is likely due to differences in basal metabolic rate

(Herd et al., 2004).

92

In the second study, liver O2 consumption was reduced in restricted fed cows, indicating

that the liver responds to changes in energy status. In muscle, cows fed the restricted diet had

increased ubiquitin, a protein involved in protein turnover in cells, abundance in muscle tissue.

These results indicate that rates of protein synthesis and degradation may play important roles in

energy metabolism and maintenance requirements in mature cows. In liver of restricted fed

cows, abundance of peroxisome proliferator-activated receptor gamma coactivator 1-alpha

(PGC1-α), a protein involved in energy regulation through regulation of functions related to

mitochondrial biogenesis amongst others, was lower than in cows fed at a high level of intake.

This may be related to the reduced thyroid hormone triiodothyronine (T3) concentrations, as T3 is

a known stimulator of PGC1-α. These proteins may be specific targets for future studies aimed at

improving feed efficiency in cows through nutrition, management, or genetic improvement

programs.

The third study investigated the influence of pregnancy on energetics showed that

pregnant cows had smaller livers and smaller rumens. This reduction in rumen size may

influence free choice intake resulting in pregnant cows having decreased intake in late gestation.

This may result in cows not receiving enough energy to meet requirements which is supported by

our blood analysis results with increased serum NEFA, BHB and urea concentrations. The

protein phosphorylated 5’adenosine monophosphate-activated protein kinase, an enzyme

important for energy sensing in the cell, was increased in rumen papillae tissue in pregnant cows,

which may be a signal for the need to increase energy intake. In liver, Na+/K+ ATPase, a

cellular ion pumping protein, was also increased in pregnant cows. This may be a result of

decreased liver size, increased demand on the liver to help support pregnancy, and hormonal

interactions. Although more research is needed to better understand these observed differences,

93

these results do indicate that pregnant cows repartition energy to support fetal growth and that

specific cellular proteins are likely involved in regulating the use of energy by different tissues.

Overall this work identifies numerous areas where further research may lead to improvement in

feed efficiency in cows. Firstly, the results of the study examining the use of RFI as a measure of feed

efficiency in cows suggests that alternative measures of feed efficiency should be sought. The American

Angus Association has developed a measure of residual daily gain for use in calculating expected progeny

differences (EPD), which uses feed intake as an input, among other phenotypic measures (AAA, 2010).

Similarly, Berry and Crowley, (2012) describe the use of residual gain, as a measure of feed efficiency,

which regresses BW and DMI in a similar method to Koch et al. (1963). Although, this may reduce

variation with DMI, similar troubles with low or negative gains may occur. Perhaps a more mechanistic

and dynamic model may be needed to more accurately model feed efficiency in mature beef cows. Hoch

and Agabriel (2004 ; 2004a) describe using a mechanistic model for measuring growth and carcass

composition in beef steers and bulls using metabolizable energy as the main input. This model accounts

for synthesis and degradation within the water, ash, protein, and lipid pools of the carcass and non-carcass

tissues within the animal. A model similar to this type may more accurately reflect maintenance energy

costs and better account for repartitioning within the pregnant beef cow, and more accurately select

metabolically efficient beef cows. Perhaps investigation into a measure of residual partitioning of the

protein and lipid pool (and perhaps conceptus pool) may be a more suitable approach.

The energy signalling pathways of PGC-1α and AMPK, found to be responsive to treatment in

the second and third experiments, respectively, suggest further research in to their role in feed intake,

metabolism and feed efficiency. It would be of interest to evaluate these proteins in populations

divergently selected for differences in feed efficiency. Similarly, results from the second experiment and

those in appendix 1, suggest that protein metabolism may be influential in efficiency in mature cows, as

increased ubiquitin abundance was observed in restricted fed cows and positive correlations with RFI and

circulating urea were observed. As mentioned above, perhaps feed efficiency measures in mature beef

94

cows are better described by modeling changes in body composition or protein and lipid pools than by

determination based on body weight gain alone. Further research is needed to determine if dietary protein

supplementation changes protein catabolism and lead to improved feed efficiency in mature cows.

Decreased liver mass observed in pregnant cows in the third experiment suggests repartitioning of energy

towards a more efficient state, however increased Na+/K+ -ATPase may indicate increased work load.

Mechanisms signalling this repartitioning are not clear.

One area that warrants further investigation in to the control and regulation of energy metabolism

and maintenance expenditure in mature beef cows is endocrine control and regulation, which has only

begun to be examined with the analysis of thyroid hormone and metabolites by the work in this thesis.

Glucocorticoids, although variable throughout the day may show relationships with feed efficiency

(Montanholi et al., 2013). Leptin, insulin, and the IGF family of compounds have also been implicated in

feed efficiency and maintenance requirements in beef cattle (Nkrumah et al. 2005; Richardson et al.,

2004; Kelly et al., 2011) and may interact with proteins investigated in this study.

Additionally, the proteins relating to energy metabolism investigated in this thesis are by no

means a complete list of possible proteins that may have significant influence on maintenance costs in

beef cows. Numerous other proteins, including those up- and down-stream from those investigated in

these experiments may also play pivotal roles in regulating energy metabolism in ruminants. As protein

metabolism may be important in energetic efficiency in cows, investigations into additional proteins in

the ubiquitin-proteasome pathway (such as 26S proteasome, E1, E2, E3) or other markers of protein may

provide additional insights. Markers for protein synthesis (mTORC1 and associated proteins) may also be

important. Uncoupling proteins, although UCP2 was not significantly different in liver in these studies,

may play an important role in other tissues, such as muscle. In terms of energy signalling, NRF proteins

downstream of PGC-1α, as well as HNF-4 may also be involved in cellular energy regulation. Other

members of the PGC-1 family, PGC-1β may also be important, as PGC-1β may play a greater role in

lipogenesis and interacts strongly with SREBP (Lin et al., 2005). Another metabolic sensor, SIRT1, a

95

protein highly sensitive to NAD+, has regulatory effects on PGC-1 and PPARγ, mTORC2, among others,

may have regulatory effects on energy homeostasis (Li, 2013).

Results from these studies have identified proteins that are responsive to restrictive

feeding and to pregnancy in mature beef cows. These results indicate that these pathways may be

important in energy signalling and maintenance requirements for beef cows. Although more

research is needed to determine how these proteins are controlled, they do identify possible key

targets for possible genetic selection, identification of novel SNPs or control through dietary,

management or pharmacological manipulation.

96

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Appendix 1: Relationships between measures of feed efficiency

and circulating serum metabolites and body parameter

measures in mid to late gestating mature beef cows

The objectives of this experiment were 1) investigate relationships between measures of

feed efficiency and circulating serum metabolites and 2) investigate relationships between

measures of feed efficiency and phenotypic body measurements in mature pregnant beef cows.

A1.1 Materials and Methods

(Supplemental to Chapter 3)

A1.1.1 Serum collection and analysis

To investigate relationships between common circulating serum metabolites and

measures of feed efficiency and performance in cows, serum samples were obtained from cows

from contemporary groups four, five, eight, and nine. Blood samples were obtained prior to

feeding at approximately 0900 via jugular veinipuncture into non-heparinized tubes and allowed

to stand at room temperature for at least 30 min to allow clotting before being stored on ice.

Samples were centrifuged for 25 min at 3000 x g and serum separated and frozen at -20˚C until

further analysis. Serum samples were analyzed at the University of Guelph Animal Health

Laboratory (Guelph, ON) for serum urea, glucose, NEFA, beta-hydroxybutyrate (BHBA) and

total cholesterol using Roche cobas c311 and Immulite 1000 analyzers (Hoffmann- La Roche

Ltd., Mississauga, ON, Canada).

A1.1.2 Body Parameter Measures

To investigate relationships between measures of feed efficiency and objective measures

of cow body phenotype, cows from contemporary group number five and six were measured at

the start and final day of the feeding period and the average measurement used to investigate

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relationships with efficiency and performance traits. Measures included: body length; from the

point of shoulder to end of the rump, hip height; from ground to base of tail head, hip width;

parallel to ground across pin bones, heart girth; circumference around the midsection caudal to

shoulder, mid-girth; circumference around middle over navel, and girth at flank; circumference

around the middle at the flank and cranial to the udder. Body length was measured using a metal

tape measure and girth measures were obtained using a fabric measuring tape. Hip height was

measured using a livestock height measuring stick and hip width measures obtained using

calipers. All measurements were taken by the same individual at each research station.

A1.1.3 Analysis

Pearson correlations were conducted to investigate the relationships between measures of

feed efficiency and performance. Models of RFI were calculated as previously described above,

and calculated within each contemporary group. Correlations included mid-point BW, DM

intake, ADG, pregnancy corrected ADG, F:G, G:F, basic RFI, R2 RFI and BIC RFI . Benjamini

and Hochberg (1995) adjustment for false discovery rate was applied using PROC MULTTEST

(SAS institute Inc. Cary, NC).

A1.2 Results

A1.2.1 Correlation between cow age, measures of performance and feed efficiency

Table A 1.1 shows the descriptive statistics for circulating metabolites and body

measures. Table A1.2 shows correlations between cow age, performance measures and measures

of feed efficiency. As expected DM intake was positively correlated with ADG and pcADG as

well as the three RFI models. Feed to gain ratio may not be suitable measure to use in measures

of performance in mature pregnant cows, as cows who had ADG close to zero resulted in very

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large F:G. Feed conversion expressed as gain to feed may be more appropriate for use in cows.

Gain to feed was negatively correlated (P ≤ 0.05) with all three RFI models. The base model of

RFI was correlated with BIC RFI model but not R2 RFI model.

A1.2.2 Relationships between measures of feed efficiency and circulating serum

metabolites

Relationships between circulating metabolites and cow age, performance measures and

measures of feed efficiency are found in Table A1.3. Serum measures were taken on the final

day of the feeding period.

Serum glucose was not correlated (P ≥ 0.05) with age, DM intake, ADG or pcADG or

measures of feed efficiency. Similar to our results, Kelly et al., (2010) also found no

relationships between feed efficiency measures and glucose concentration. Glucose was

positively correlated with mpBW.

Circulating urea concentration was positively correlated (P ≤ 0.05) with DM intake,

ADG, pcADG, G:F and all models of RFI. This suggests that protein metabolism may play an

important role in regulating feed efficiency in the pregnant beef cow. Circulating urea has been

used as an indicator of protein status in the animal, and largely represents the degradation of

protein sources, either endogenous (muscle catabolism) or exogenous (from feed) (Sniffen et al.,

1992). Kelly at al., (2010, 2011) also found positive correlations between serum urea and DM

intake in growing heifers and bulls and feed conversion ratio in growing heifers, but not with

RFI models. However, Richardson et al. (2004) found that while RFI was positively correlated

with circulating urea in young animals (post-weaning), this relationship disappeared as the

animal matured. Research from the same lab group found that efficient (low RFI) steers were

leaner both entering and exiting the feedlot (Richardson et al., 1998, 2001) indicating that greater

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body protein accretion is a desirable efficient phenotype. In pigs divergently selected for RFI,

metabolic pathways (calpain activity and 20S proteasome activity), have been shown to have

reduced protein turnover rate in muscle (Cruzen et al., 2012). Protein synthesis and degradation

are known to be energy demanding processes (Gill et al., 1989, Kelly et al., 1991). Further

research into understanding protein metabolism in the mature cow may be beneficial to

understanding feed efficiency.

Circulating NEFA and BHBA concentrations were negatively correlated (P ≤ 0.01 ) with

DM intake, ADG, pcADG and R2 RFI. In growing heifers Kelly et al.,(2010) reported negative

correlations between NEFA and DM intake, feed conversion and their base model of RFI, while

BHBA showed positive relationships between DM intake, feed conversion ratio and basic and

complex models of RFI. In bulls, no relationships were found between BHBA and NEFA with

DMI, ADG, F:G or RFI (Kelly et al., 2011). Since circulating NEFA and BHBA concentration

represent catabolism of body fat and ketone production, respectively (Wathes et al, 2007), may

suggest that the ability of the pregnant cows to mobilize fat may play an important in feed

efficiency in mature pregnant beef cows.

Total circulating cholesterol concentrations were negatively correlated (P ≤ 0.04) with

cow age, mpBW and DM intake as well as basic model of RFI. Conversely, a positive

relationship (P =0.02) between total cholesterol concentration and R2 RFI model was observed.

In summary, these data suggests that metabolic pathways involved with protein

metabolism and lipid metabolism may play key roles in feed efficiency in pregnant beef cows.

Future research is needed to identify key regulatory steps in these metabolic pathways, which

may be used to improve efficiency in pregnant beef cows.

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A1.2.3 Relationships between measures of feed efficiency and body parameter

measures

Linear measurements of body dimensions may provide insight into changes in

maintenance energy requirements and surface area and gut capacity (Kleiber, 1961) Correlations

between linear body parameter measurements and measures of performance and feed efficiency

can be found in Table A1.4. Hip height was not significantly (P ≥ 0.05) correlated to any

measure of age, performance or efficiency measurement. This may indicate that frame size does

not play a role in measures of efficiency in the mature pregnant cow. In growing animals,

Basarab et al., (2003) and Kelly et al. (2010) found no significant correlations between hip height

or wither height, respectively, and RFI. Hip width was positively correlated (P ≤ 0.05) with DM

intake and mid-point BW. Hip width may reflect differences between animals in muscularity in

addition to pelvic area.

Body length was also found to be positively correlated (P < 0.001) with mid-point BW

and DM intake and also was positively correlated (P = 0.003) with cow age. Body length was

negatively correlated (P = 0.03) with BIC RFI. This differs from results in growing heifers,

where no relationships between ADG or RFI and body length were observed (Kelly et al., 2010),

however relationships to DMI were similar to what was observed in the present experiment.

Measures of the animal’s girth, particularly the heart girth have been shown to have

strong correlations with BW (Heinrichs et al., 1992). As expected, all three girth measures were

strongly positively correlated (P < 0.001) with mid-point BW.

Feed intake was positively correlated (P < 0.001) with heart and mid-girth measurements

but not the flank measurement of girth circumference (P = 0.15). Since this measurement is taken

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caudal to the stomach complex, it may not be as greatly influenced by gut fill, where heart and

mid-girth measurements may reflect increased rumen fill with increased DM intake.

Measures of ADG, pcADG, and F:G were not correlated (P ≥ 0.05) with hip height or

width, body length or heart or mid-girth circumference. Girth at the flank was negatively

correlated with ADG and pcADG and positively correlated with F:G (P ≤ 0.05). All three girth

measures were negatively correlated (P ≤ 0.05) to G:F. This may be in part driven by intake in

the case of heart and mid-girth and ADG in flank girth. With the exception of body length and

BIC RFI, there were no significant correlations with the RFI models investigated. Similarly,

Kelly et al. (2010) did not find any correlations with linear measures of body characteristics and

models of RFI in growing heifers.

Overall, this data indicates that linear measurements of mature pregnant beef cows may

not be useful in identifying efficient phenotypes. The measure of girth at flank may be useful as

it was correlated with BW, ADG and G:F, but appears not to be associated with DMI. Other

linear measures may be useful when examining traits such as BW. Feed efficiency is more

complex than cow size and dimensions.

A1.4 Conclusions

Further investigations into correlations between feed efficiency measures and metabolic

measurements are warranted. However, measures of girth at the flank may be of interest as a

potential linear body measure as it was correlated with BW, and feed conversion ratio, but

independent of DMI. Correlations between circulating metabolites and feed efficiency traits

suggest that protein metabolism may play an important role in maintenance energy metabolism

as circulating urea was correlated with G to F and all three RFI models examined. However,

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body parameter measures were not significantly correlated with RFI models tested and likely do

not contribute to animal variation in RFI measures of feed efficiency in mature pregnant cows.

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Table A1.1: Descriptive statistics for circulating serum metabolites and linear body measures for

combined dataset of mature pregnant beef cows.

Item n Mean SD

Glucose, mmol/L 227 3.39 0.385

Urea, mmol/L 227 3.28 0.63

NEFA, mmol/L 227 0.66 0.456

BHBA, µmol/L 227 306 128.1

Total Cholesterol,

mmol/L 227 2.71 0.432

Hip Height, cm 114 143.9 57.13

Hip Width, cm 114 58.4 25.97

Body Length, cm 114 156.1 8.15

Heart Girth, cm 114 212.6 11.17

Mid-Girth, cm 114 258.9 13.68

Girth at Flank, cm 114 229.6 12.82

NEFA = non-esterified fatty acid; BHBA = β-hydroxybutyrate

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Table A1.2: Adjusted Peasron correlations between performance and feed efficiency measures in mature pregnant beef cows1

Age Mid-point BW DM Intake ADG pcADG2 F to G

3 G to F

4 basic RFI

5 R

2 RFI

6 BIC RFI

7

Age . 0.53 0.21 -0.09 -0.1 0.07 -0.2 0.01 -0.31 0.14

Mid-point BW . 0.19 -0.14 -0.16 0.06 -0.25 0.001 -0.12 -0.02

DM intake . 0.42 0.39 0.17 0.02 0.35 0.25 0.55

ADG . 0.97 0.04 0.9 0.03 -0.11 0.02

pcADG . 0.05 0.89 0.02 -0.14 0.01

F to G . 0.04 0.09 0.06 0.13

G to F . -0.12 -0.21 -0.19

basic RFI . 0.02 0.35

R2 RFI . 0.34

BIC RFI . 1 Values in bold and red font indicate significance (P ≤ 0.05)

2 Pregnancy corrected ADG calculated using estimates for conceptus growth from the equation described by Silvey and Haydock, 1978

3Feed to gain

4 Gain to feed

5Within each contemporary group calculated RFI using the regression of ADG and midpoint BW (Koch et al., 1963)

6 Within each contemporary group calculated RFI using the equation that yielded the greatest R2 (see Table 3.6)

7 Within each contemporary group calculated RFI using the equation that yielded the greatest BIC (see Table 3.6)

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Table A1.3: Corrected Pearson correlations between performance and feed efficiency measures

and circulating serum metabolites measured at the end of test in mature pregnant beef cows1

Item Glucose Urea NEFA BHBA

Total

Chol.

Age 0.13 -0.1 0.02 0.004 -0.35

Mid-point BW 0.3 -0.2 0.11 -0.009 -0.26

DM Intake 0.04 0.4 -0.3 -0.23 -0.15

ADG 0.02 0.36 -0.21 -0.19 0.05

pcADG2 0.04 0.34 -0.24 -0.23 0.07

F to G3 0.04 -0.14 0.05 0.05 -0.11

G to F4 0.002 0.22 -0.1 -0.09 0.13

basic RFI5 -0.002 0.24 -0.05 0.01 -0.15

R2 RFI

6 -0.12 0.18 -0.35 -0.25 0.17

BIC RFI7 0.008 0.15 0.05 -0.04 -0.12

1 Values in bold and red font indicate significance (P ≤ 0.05)

2 pcADG = Pregnancy corrected ADG calculated using estimates for conceptus growth from the equation

described by Silvey and Haydock, 1978

3 Feed to gain

4 Gain to feed

5 Within each contemporary group calculated RFI using the regression of ADG and midpoint BW (Koch

et al., 1963)

6 Within each contemporary group calculated RFI using the equation that yielded the greatest R2 (see

Table 3.6)

7 Within each contemporary group calculated RFI using the equation that yielded the greatest BIC (see

Table 3.6)

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Table A1.4: Corrected Pearson correlations between performance measures and linear body

parameter measures in mature pregnant beef cows1

Hip Height Hip Width Body Length Heart Girth Mid-Girth

Girth at

Flank

Age 0.03 0.05 0.3 0.45 0.37 0.56

Mid-point BW 0.11 0.23 0.62 0.85 0.82 0.89

DMI 0.13 0.21 0.43 0.39 0.46 0.16

ADG -0.04 0.06 0.09 -0.15 -0.006 -0.35

pcADG2 -0.05 0.07 0.06 -0.19 -0.04 -0.36

F to G3 0.02 -0.03 0.06 0.01 0.08 0.26

G to F4 -0.08 -0.02 -0.09 -0.33 -0.2 -0.44

basic RFI5 0.04 0.06 0.13 0.05 0.08 -0.04

R2 RFI

6 0.1 0.03 -0.08 0.04 0.03 -0.006

BIC RFI7 0.06 -0.12 -0.23 -0.1 -0.1 0.01

1 Values in bold and red font indicate significance (P ≤ 0.05)

2 Pregnancy corrected ADG calculated using estimates for conceptus growth from the equation described

by Silvey and Haydock, 1978

3 Feed to gain

4Gain to feed

5Within each contemporary group calculated RFI using the regression of ADG and midpoint BW (Koch

et al., 1963)

6 Within each contemporary group calculated RFI using the equation that yielded the greatest R2 (see

Table 3.6)

7Within each contemporary group calculated RFI using the equation that yielded the greatest BIC (see

Table 3.6)

135

Appendix 2: Evaluation of using real-time ultrasound to predict

total internal fat in the mature beef cow

A2.1 Introduction

Degree of fatness in cattle can have implications for animal performance, reproductive success and

maintenance energy costs. Body fat depots can generally be divided into omental and mesenteric (pelvic),

perirenal (kidney), muscular and associated fatty tissues, subcutaneous and other minor sources (such as

bone). As body weight gain is increased in terms of fat accumulation, relative proportions of fat type also

change (Wright and Russel, 1984). While body condition scoring and real-time measures of back fat

provides a good estimation of subcutaneous fat, determination of internal fat depots in the live animal is

considerable more challenging. Studies by Ribeiro et al. (2008) and Ribeiro and Tedschi (2012) used real-

time ultrasound measurements of kidney fat to estimate total physically separable internal fat in growing

steers, heifers and bulls, and accurately predicted total internal fat using real-time ultrasound measures of

kidney fat. This technique has not yet been evaluated in mature cows. Mature cows may contain

considerably more internal fat and may pose greater variation in fatness than growing animals. The

objective of this trial was to evaluate the use of this technique first described by Ribeiro et al., 2008 for

use in mature beef cows to predict total internal fat.

A2.2 Materials and Methods

Twenty-four mature Angus and Simmental crossbred beef cows that were selected as culls from

the research herd at the Elora Beef Research Station were used. Animals were culled for reproductive

failures, chronic mastitis, poor feet and legs, non-docile behaviour or poor calf performance. Three d prior

to slaughter, cows were weighed and ultrasounded for backfat and kidney fat measures using an Aloka

SSD-500 (Tokyo, Japan) ultrasound unit equipped with a 172 mm: 3.5 MHz transducer probe (model

5044; Corometrics Medical System, Wallingfort, CT, USA). Ultrasound backfat (uBF) was obtained over

136

the 12th and 13

th rib. Kidney fat images were obtained as described by Riberio et al.(2008).. Briefly,

kidney fat images were obtained from the animal’s right side, directly caudal to the 13th rib and

approximately 20 cm from the midline (spine). Ultrasound images were interpreted using AUSKey

software (Amarillo, TX, USA) to quantify three kidney fat depth measurements per animal using

the following reference points: 1) ventral of the abdominal muscles (iliocostalis, obliquus

abdominis interni, and obliquus abdominis externi), to the end of the kidney fat (uKFDe;

originally described by Riberio et al. (2008); Fig.A2.1 ), 2) ventral of the abdominal muscles to

the ventral side of the kidney (uKFDv), and 3) ventral side of abdominal muscles to dorsal side

of the kidney (uKFDd). At slaughter, actual kidney fat depth was measured on the right side of

the carcass prior to removal of the kidneys, using a measuring tape. Right and left kidney fat

depots were removed and weighed. Remaining visceral organs and gastrointestinal tract was

removed and trimmed of fat to obtain a total internal fat weight. Actual backfat depth (aBF) was

also measured on the LM of the carcass.

Pearson correlations were conducted in PROC CORR in SAS (2008) and linear models

were constructed using PROC GLM (SAS; 2008). Significance was declared at P ≤ 0.05.

A2.3 Results and discussion

Determining internal fatness in vivo, may provide a easily obtained and inexpensive measure of

the body fat pools and may have applications in improving body composition estimates which may

improve accuracy of RFI models. Basarab et al. (2003) found that low RFI steers had smaller kidney fat

deposits than high or moderate RFI groups. Table A2.1 shows correlations between ultrasound kidney fat

measures and actual measured kidney fat depth and total internal fat mass. All three ultrasound kidney fat

measures were correlated (P ≤ 0.05) with actual carcass kidney fat depth. Total fat was correlated with

uKFDe and uKFDv but not uKFDd. Actual carcass KFD was highly correlated with total fat. Table A2.2

137

shows linear models developed to predict total internal based on BW and ultrasound measures of fatness.

Overall model fit was poor, as the greatest R2 was 37.5%. Using uKFDe improved R

2 by 4.5%. In general

models underpredicted actual kidney fat depth (data not shown).

In many of the images the landmarks to determine the end of the kidney fat were very difficult to

identify and may have reduced the accuracy of determining the true length of the kidney fat. As many of

the landmarks were approaching the end of the field of view it is possible that in cows that had very large

fat deposits/ large kidney diameters, that these landmarks may have been beyond the field of view. Using

uKFDv, with clearly visible landmarks in the ultrasound image may be more desirable, although

relationships to total internal fat were not as strong as uKFDe. In addition using this technique requires

the animals to stand calmly and relaxed in order to obtain a clear image. Animals that were nervous,

fidgety or tense standing in the chute were challenging in obtaining a useable image.

In conclusion, this technique may require further refinement in order to accurately determine the

total internal body fat in mature cows in vivo. However, since cKFD was highly correlated with tFAT,

using depth of fat over the kidney may prove to be a suitable measure to determine total internal fat.

138

Table A2.1: Pearson correlations between measures of ultrasound kidney fat and actual total body

fat.

aBF uBF uKFDe uKFDv uKFDd cKFD tFat

BW 0.46 0.43 0.52 0.58 0.26 0.64 0.52

aBF . 0.88 0.32 0.31 0.33 0.41 0.41

uBF . . 0.31 0.27 0.25 0.28 0.33

uKFDe . . . 0.93 0.78 0.60 0.51

uKFDv . . . . 0.71 0.58 0.43

uKFDd . . . . . 0.45 0.36

cKFD . . . . . . 0.81

aBF = actual back fat measured on carcass

uBF = real-time ultrasound estimate of back fat

uKFDe = Ultrasound kidney fat measure, ventral of the abdominal muscles (iliocostalis, obliquus

abdominis interni, and obliquus abdominis externi), to the end of the kidney fat.

uKFDv = Ultrasound kidney fat measure, ventral of the abdominal muscles to the ventral side of

the kidney.

uKFDd = Ultrasound kidney fat measures ventral side of abdominal muscles to dorsal side of

the kidney.

cKFD = Actual kidney fat depth measure

tFat = actual measured internal fat

139

Table A2.2: Model equations to estimate total internal fat using ultrasound measures of kidney fat

depth in mature cows

Model # Equation1 RMSE R

2 , %

1 tFAT = -13036.9 + 62.9(BW) 9170.1 26.5

2 tFAT= -3701.0 + 3208.9(uKFDe) 9176.5 26.4

3 tFAT = 4227.3 + 3305.3(uKFDv) 9571.2 20

4 tFAT= -11955.9 + 2927.6(uBF) + 55.9(BW) 9289.8 28

5 tFAT= -5088.2 + 4079.3(uBF) + 2842.7(uKFDe) 9185.6 29.6

6 tFAT= -14092.4 + 2808.5(uBF) + 1608.4(uKFDv) + 40.7(BW) 9311.2 31.1

7 tFAT= -19562.6 + 2165.3(uBF) + 2027.4(uKFDe) + 37.0(BW) 9004.8 35.6

8 tFAT= -17355.5 +5053.6(aBF) + 1966.3(uKFDe) + 32.4(BW) 8868.1 37.5

1BW = body weight; uBF = real-time ultrasound estimate of back fat; uKFDe = Ultrasound

kidney fat measure, ventral of the abdominal muscles (iliocostalis, obliquus abdominis interni,

and obliquus abdominis externi), to the end of the kidney fat; uKFDv = Ultrasound kidney fat

measure, ventral of the abdominal muscles to the ventral side of the kidney; uKFDd =

Ultrasound kidney fat measures ventral side of abdominal muscles to dorsal side of the kidney.

140

Figure A2.1. Real-time ultrasound measures of three kidney fat depth measurements evaluated per

animal, From left to right: ventral of the abdominal muscles to the end of the kidney fat (uKFDe;

originally described by Riberio et al., 2008), ventral of the abdominal muscles to the ventral side of the

kidney (uKFDv), and ventral side of abdominal muscles to dorsal side of the kidney (uKFDd).

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