Targeted Gene Expression Profiling in Beef Cattle under Finishing Conditions in the South 
Eastern United States: Identification of regulatory factors involved in metabolic efficiency, 
energy partitioning, and intramuscular fat deposition 
 
by 
 
Derris Deon Burnett 
 
 
 
 
A dissertation submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Doctor of Philosophy  
 
Auburn, Alabama 
May 3, 2014 
 
 
 
 
Keywords: Beta-Agonist, Efficiency, Marbling, Gene-Expression, Transcription-Factor 
 
 
Copyright 2014 by Derris Deon Burnett 
 
 
Approved by 
 
Werner G. Bergen, Chair, Professor of Biochemistry and Nutrition 
Keith A. Cummins, Professor of Dairy Cattle Management and Canine Nutrition 
Ya Xiong Tao, Associate Professor of Physiology 
Kevin Huggins, Associate Professor of Nutrition and Dietetics 
 
 
 
 
 
 
 
 ii 
Abstract 
 
 
 
Three studies were conducted in order to examine the molecular events associated with 
metabolic efficiency in specific skeletal muscle and adipose tissue depots in finishing beef cattle.  
In the first study a total of 15 male cattle (n = 7 Bulls and 8 Steers) were selected from the initial 
progeny of a Residual Feed Intake (RFI) selection program to determine the relationship between 
RFI and the transcriptomic signature of performance efficiency in finishing beef cattle. Calves 
were individually fed twice daily for 84 days and refusals were collected once daily. Feed intake 
data was collected for each animal and used to determine residual feed intake (RFI). At day 84, 
skeletal muscle and adipose biopsies were collected for analysis of regulatory gene expression 
related to protein turnover and lipid metabolism. Overall, the results from this study indicate that 
RFI appears to be related to genes involved in protein turnover and to a lesser extent, other 
metabolic genes; however genes involved in AT metabolism did not appear to be related. It is 
possible that the range of RFI values in bulls and steers was too narrow to ascertain a 
relationship between fattening and feed efficiency and future studies should utilize more 
divergent cattle populations with respect to RFI.   
In a second study was conducted to determine the effect of days on feed and beta-agonist 
administration on the expression of regulatory genes in skeletal muscle and adipose tissue of 
finishing heifers. Seventy-one crossbred heifers were stratified according to height and height 
and assigned to one of six pens (12 cattle per pen). Cattle had continuous access to automatic 
water troughs and each pen contained 12 Calan Gates? to allow for individual feed provision 
 iii 
and intake determination. Treatment groups consisted of days on feed (DOF, n= 16 per group) 
with the following assignments: 79, 100, 121, and 142 DOF. For each DOF group, half (n=8) of 
the animals were treated with ractopamine hydrochloride (RAC) 300mg/hd/d for the final 35d 
prior to harvest while the other half served as controls (CON). At slaughter, skeletal muscle and 
adipose samples were collected to determine regulatory gene expression. These data did not 
identify coordinated regulation of metabolic pathways in response to RAC administration in any 
of the DOF groups however temporal patterns of gene expression were observed that are 
consistent with the order and priority of tissue development in finishing cattle.   
The final study looked at the effects of feed restriction and re-feeding on gene expression 
patterns in muscle and adipose tissue of forage fed beef. Temporal regulation of gene expression 
was observed between biopsy dates and plane of nutrition differentially regulated growth initially 
however poor forage growth led to a lack of robustness in the treatment groups as the experiment 
proceeded and weight gains began to equalize between groups. This led to a lack of coordinated 
regulation of the selected genes between treatment groups. As a whole, this body of work 
provides the basis for future studies and has established that transcriptomic/gene expression 
profiling in beef cattle determined by quantitative RT-PCR of skeletal muscle and adipose tissue 
samples can be an effective approach when used in conjunction with more elaborate approaches 
to identify and manipulate the molecular distinctions that are inherent to more efficient animals. 
This will eventually lead to innovations to improve production efficiency and carcass value by 
augmenting the growth of specific tissues independently of less valuable ones. 
 
 iv 
Acknowledgements 
I would first like to thank my advisor and mentor Dr. Werner G. Bergen. Under his 
tutelage I have learned the principles to conducting quality research with vigor and progressive 
thinking. Dr. Bergen?s guidance and mentorship have left an invaluable impact on me as a 
person and as a scientist and have prepared me to become a dynamic contributor in my field of 
study and the scientific arena at large. I consider it both a privilege to have matriculated under 
his guidance, and a responsibility to represent his creed as I aspire in my scientific endeavors.  I 
would like to extend a special thanks to my committee members Dr.?s Tao, Cummins, and 
Huggins. I appreciate their participation in my PhD program. Each of you has impacted me as a 
scientist and I am grateful for your guidance over these past few years. To the faculty and staff in 
the Auburn University Department of Animal Sciences I greatly appreciate your assistance on 
my research projects and your camaraderie during my time at Auburn. Your fellowship made my 
time at Auburn very memorable and enjoyable. I would like to extend a special thanks to the 
department head Dr. Wayne Greene who has been a staunch supporter and advocate for my 
success throughout my tenure at Auburn. Without his support achieving this degree would not 
have been possible.   
I would next like to thank my mother Ms. Arda B. Burnett for her unwavering support 
and inspiration. Her disdain for mediocrity in all of her children has driven us to the heights of 
academic achievement and made each of us productive contributors to society. To my siblings 
Kendrick, Nielah and Aaryn, you are each so very special and talented and your support has been 
integral to my achievements. To my sisters Tekedra and Dawnielle I would like to thank you for 
 v 
giving me a home and family away from home during my collegiate career. Your love and 
support helped get me through my undergraduate years and I am truly grateful. To Dr. Shecoya 
White I would like to extend my appreciation for your friendship, companionship, and support 
throughout my Masters and PhD programs. As a colleague and a friend your presence has been 
instrumental to my success for the last 7 years. Lastly, to all my friends and extended family I am 
eternally grateful for your prayers and support throughout the years. Your inspiration has been 
vital and cannot be overstated. Once again thank you all. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 vi 
Table of Contents 
 
 
Abstract ......................................................................................................................................... ii 
Acknowledgments ....................................................................................................................... iv 
List of Figures .............................................................................................................................. ix 
List of Tables ............................................................................................................................ xvii 
Chapter 1.  Introduction ................................................................................................................ 1 
Chapter 2.  Literature Review ....................................................................................................... 5 
Feed Efficiency   ........................................................................................................................... 5 
   Measures of Feed Efficiency   ........................................................................................ 6 
   Residual Feed Intake   ..................................................................................................... 6 
   Molecular Basis of Residual Feed Intake   ..................................................................... 8 
Metabolic Modifiers  .................................................................................................................... 9 
   Modifiers in Beef Production  ........................................................................................ 9 
Beta-Adrenergic Agonists   ......................................................................................................... 10 
    Beta-Adrenergic Receptors   ........................................................................................ 11 
    Beta-Adrenergic Receptor Subtypes   .......................................................................... 12 
    Beta-Adrenergic Agonist?s Effects in Skeletal Muscle and Adipose Tissue   ............. 14 
    Beta-Adrenergic Agonists in Livestock Production   .................................................. 16 
Ractopamine Hydrochloride   ..................................................................................................... 17 
      Effectiveness of Ractopamine and other Beta-Adrenergic Agonists in Livestock  .. 19 
 vii 
      Duration of Effects   ................................................................................................... 21 
      Future of Beta-Adrenergic Agonists in Livestock   ................................................... 23 
Adipose Tissue   .......................................................................................................................... 23 
      Economic Value of Adipose Tissue in Beef Production   ......................................... 24 
      Cellular Composition of Adipose Tissue   ................................................................. 25 
      Adipocytes   ............................................................................................................... 25 
Adipogenesis   ............................................................................................................................. 27 
      Regulation of Adipogenesis   ..................................................................................... 28 
      Peroxisome Proliferator Activated Receptors   .......................................................... 29 
      CCAAT-Enhancer-Binding Proteins   ....................................................................... 30 
      Preadipocyte Factor 1   .............................................................................................. 31 
      Zinc-finger Protein 423  ............................................................................................. 32 
      Carbohydrate Response Element Binding Protein  .................................................... 33 
      Stearoyl-CoA Desaturase  .......................................................................................... 34 
      Fatty Acid Binding Proteins ...................................................................................... 35 
      Sterol Regulatory Element Binding Protein .............................................................. 35 
      Adipogenesis in Beef Cattle  ..................................................................................... 36 
Adipose Tissue Depots   ............................................................................................................. 37 
      Depot Specific Metabolism of Adipose Tissue   ....................................................... 37 
      Adipose Tissue Distribution in Beef Cattle   ............................................................. 41 
Intramuscular Fat   ...................................................................................................................... 42 
 viii 
      Intramuscular Fat is a Distinct AT Depot   ................................................................ 43 
      Marbling   ................................................................................................................... 45 
      Fat in Meat Products .................................................................................................. 48 
Post-Genomic Era Livestock Production   .................................................................................. 49 
Summary   ................................................................................................................................... 52 
Chapter 3.  The Relationship between Residual Feed Intake and the Transcriptomic Signature of 
Performance Efficiency in Finishing Beef Cattle  ?????????????????....54  
 
Chapter 4.  The Effect Of Days On Feed and Beta-Agonist Administration On The Expression 
Of Regulatory Genes in Skeletal Muscle and Adipose Tissue of Finishing Heifers? .? ..??..98  
 
Chapter 5. The effect of compensatory growth on gene expression patterns in the loin muscle and 
subcutaneous adipose tissue of forage fed beef...??????????????.???...182  
 
Chapter 6 Summary of Experiments?? ??? ?????????..????????..256  
 
References   ............. ???????????????????????..????... 26 5 
  
 ix 
List of Figures 
 
Figure 3.1 Contributions of biological mechanisms to variation in residual feed intake as 
determined from experiments using divergently selected cattle???????????? ...75      
 
Figure 3.2 Overview of TaqMan? gene expression chemistry???????????? .?82  
 
Figure 3.3 Average daily gains of individual bulls and steers on feed for 84 days??? ..?. ...83 
 
Figure 3.4 Comparison of average daily gains of bulls vs. steers on feed for 84 days? ?... ?.. 84 
 
Figure 3.5 Dry matter feed conversion for individual bulls and steers on feed for 84 days? ?.85  
 
Figure 3.6 Dry matter feed conversion for individual bulls and steers on feed for 84 days?.?86  
 
Figure 3.7 Calculated residual feed intakes for individual bulls and steers on feed for 84 
days???????????????????????????????????....87  
 
Figure 3.8 Relative expression of PPAR-? in SM biopsies of finishing bulls vs. steers on feed 
for 84 days? ????????????????????????????..???.90  
 
Figure 3.9 Relative expression of UCP-2 in SM biopsies of finishing bulls vs. steers on feed for 
84 days??????????????????????????????????...91  
 
Figure 3.10 Relative expression of Ub-E2 in SM biopsies of finishing bulls vs. steers on feed for 
84 days????????????????????????????????.??92  
 
Figure 3.11 Relative expression of poly-ubiquitin in SM biopsies of finishing bulls vs. steers on 
feed for 84 days???????????????????????????????93  
 
Figure 3.12 Relative expression of CPT-1b in SM of finishing bulls vs. steers on feed for 84 
days???????????????????????????????????..94  
 
Figure 3.13 Relative expression of FAS in AT of finishing bulls vs. steers on feed for 84 
days???????????????????????????????????..95  
 
Figure 3.14 Relative expression of Leptin in AT of finishing bulls vs. steers on feed for 84 
days???????????????????????????????????..96  
 
Figure 3.15 Relative expression of PPAR-? in AT of finishing bulls vs. steers on feed for 84 
days??????????????????????????????????..?97  
 x 
 
Figure 4.1 General structure of beta-adrenergic agonists including common aromatic 
substitutions????????????????????????????????.1 29 
 
Figure 4.2 Chemical structure of Optaflexx? (Ractopamine HCl)???????????130  
Figure 4.3 Putative ?-adrenergic agonist mechanism of action via ?-adrenergic receptor??131  
 
Figure 4.4 ?2-adrenergic receptor mRNA expression in rump adipose tissue from finishing 
heifers??????????????????????????????????..139  
 
Figure 4.5 ?2-adrenergic receptor mRNA expression in gastrocenimus muscle from finishing 
heifers??????????????????????????????????..140  
 
Figure 4.6 Relative PPAR gamma Expression in SM of Control vs. Ractopamine treated 
heifers?????????????????????????????????...?141  
 
Figure 4.7 Relative PPAR gamma expression in SM of control heifers harvested at 79 and 142 
days???????????????????????????????????..142  
 
Figure 4.8 Relative PPAR gamma Expression in SM of RAC heifers harvested at 79 and 142 
days??????????????????????????????.?????.143  
 
Figure 4.9 Relative PREF-1 expression in skeletal muscle tissue harvested from Control and 
Ractopamine treated finishing heifers??????????????????.????144  
 
Figure 4.10 Relative Pref-1expression in skeletal muscle tissue of finishing heifers harvested at 
79 and 142 days??????????????????..????????????.145  
 
Figure 4.11 Relative Expression of PREF-1 in Longissimus dorsi and gastrocnemius muscle 
tissue harvested from finishing heifers??????..???????????????..146  
 
Figure 4.12 Relative Expression of PREF-1 in skeletal muscle tissue harvested from Control and 
Ractopamine treated heifers at 79 and 142 days on feed??????????????.....147  
 
Figure 4.13 Relative Expression of PREF-1 in gastrocnemius and longissimus muscle samples 
harvested from Control and Ractopamine treated heifers???????????????148  
 
Figure 4.14 Relative expression of glycerol-phosphate acyl transferase in skeletal muscle tissue 
collected from heifers in Control vs. Ractopamine treated finishing heifers???????..149  
 
 xi 
Figure 4.15 Relative expression of glycerol phosphate acyl transferase in gastrocnemius vs. 
longissimus muscle collected from finishing heifers??????.??????????.150  
 
Figure 4.16 Relative expression of glycerol-phosphate acyl transferase in skeletal muscle tissue 
collected from finishing heifers harvested at 79 and 142 days?????????????151  
 
Figure 4.17 Relative expression of CPT-1b in skeletal muscle tissue collected from control vs. 
ractopamine treated finishing heifers?????????????????????.?..152  
 
Figure 4.18 Relative expression of CPT-1b in gastrocnemius and longissimus muscle collected 
from finishing heifers????????????????????????????...153  
Figure 4.19 Relative expression of CPT1b in skeletal muscle tissue collected from finishing 
heifers harvested at 79 and 142 days on feed???????.?????.??????...154  
 
Figure 4.20 Relative expression of UCP2 in skeletal muscle tissue collected from control vs. 
ractopamine treated heifers????????????????.?.????.??..?.?155   
 
Figure 4.21 Relative expression Of UCP2 in gastrocnemius and longissimus muscle collected 
from finishing heifers??????????????????..????.????..?..156  
 
Figure 4.22 Relative expression Of UCP2 in skeletal muscle tissue collected from finishing 
heifers harvested at 79 and 142 days???????????.??????????..?157  
 
Figure 4.23 Relative expression of PPAR-? in skeletal muscle tissue collected from control vs. 
ractopamine treated finishing heifers?????????????????????..?.158  
 
Figure 4.24 Relative expression of PPAR-? in gastrocnemius and longissimus muscle tissue 
collected from finishing heifers????????????????????????...159  
 
Figure 4.25 Relative Expression of PPAR?? in skeletal muscle tissue collected from finishing 
heifers harvested at 79 and 142 days?????????? ??????????...??160  
 
Figure 4.26 Relative expression of polyubiquitin in SM of control and ractopamine treated 
finishing heifers???? ??????????????????????????...161  
 
Figure 4.27 Relative expression of polyubiquitin in skeletal muscle of finishing heifers 
harvested at 79 and 142 days?????????????...???????????162  
 
Figure 4.28 Relative expression of polyubiquitin in gastrocnemius and longissimus dorsi muscle 
samples harvested from control and ractopamine treated heifers at 79 and 142 days? .? ...?163  
 
 xii 
Figure 4.29 Relative expression of ubiquitin E2 in skeletal muscle of control and ractopamine 
treated finishing heifers ? ??????????????????????????..164  
 
Figure 4.30 Relative expression  of ubiquitin E2 in skeletal muscle tissue of finishing heifers 
harvested at 79 and 142 days??????? ?????.. ???????????? .?165  
 
Figure 4.31 Relative expression of ubiquitin E2 in gastrocnemius and longissimus dorsi muscle 
samples from control and ractopamine treated heifers harvested at 79 and 142 days????.166  
 
Figure 4.32 Relative expression of PSMD11 in skeletal muscle tissue collected from control vs. 
ractopamine treated finishing heifers?????????????????????...?167  
 
Figure 4.33 Relative expression of PSDM11 in gastrocnemius and longissimus muscle collected 
from finishing heifers????????????????????????...????168  
 
Figure 4.34 Relative expression of PSDM11 in skeletal muscle tissue collected from finishing 
heifers harvested at 79 and 142 days??????????????????????...169  
 
Figure 4.35 Relative expression of PPAR-gamma in adipose tissue of control vs. ractopamine 
treated finishing heifers????? ???................????????????????170  
 
Figure 4.36 Relative expression of PPAR-gamma in adipose tissue of finishing heifers harvested 
at 79 and 142 days??????????????????????????????171  
 
Figure 4.37 Relative expression of Pref-1 in SC AT of finishing heifers at 79 and 142 
days???????????????????????????????????..172  
 
Figure 4.38 Average Relative Expression of Pref-1 in SC AT of control and ractopamine-treated 
finishing heifers??????????????????????????????...173  
 
Figure 4.39 Relative Expression of Pref-1 in SC AT harvested from control and ractopamine-
treated finishing heifers at 79 and 142 days?????????????.???????174  
 
Figure 4.40 Relative Expression of FABP4 in adipose tissue collected from control vs. 
Ractopamine treated heifers?????????????????????????.?175  
 
Figure 4.41 Relative expression of FABP4 in rump and deep subcutaneous adipose tissue 
collected from finishing heifers????? ???????????????????...176  
 
Figure 4.42 Relative expression of FABP4 in adipose tissue collected from finishing heifers 
harvested at 79 and 142 days?????????????????????????...177  
 xiii 
 
Figure 4.43 Relative Expression of UCP-2 in adipose tissue of control vs. ractopamine  
treated finishing heifers????????????????????????????178  
 
Figure 4.44 Relative expression of UCP 2 in rump and deep deep subcutaneous adipose tissue of 
finishing heifers??????????????????????????????...179  
 
Figure 4.45 Relative expression of UCP 2 in adipose tissue collected from finishing heifers 
harvested at 79 and 142 days?????????????????????????...180  
 
Figure 5.1 Grazing Assignments and Treatment Phases for Steers for the 126d Feeding 
Trial??????????????????????????????????.?215  
 
Figure 5.2 Effect of treatment group on total weight gain in forage-fed steers for 126d trial 
period??????????????????????????????????..220  
 
Figure 5.3 Effect of treatment group on average daily gain in forage-fed steers for 126d trial 
period????????????????????????? ?????????..221  
 
Figure 5.4 Effect of treatment on average daily gain of individual forage-fed steers for 126d trial 
period??????????????????????????????????...222  
 
Figure 5.5 Effect of treatment group on total weight gain in forage-fed Steers from 63d to 84d of 
the trial period???????????????????????????????..223  
 
Figure 5.6 Effect of treatment group on average daily gain in forage-fed steers from 63d to 84d 
of the trial period??????????????????????????????..224  
 
Figure 5.7 Effect of treatment on average daily gain of individual forage-fed steers from 63d to 
84d of the trial period???????????????????????????.?..225  
 
Figure 5.8 Effect of treatment group on total weight gain in forage-fed steers from 84 to 105 of 
the trial period??????????????????????????????.?.226  
 
Figure 5.9 Effect of treatment group on average daily gain in forage-fed steers from 84d to 105d 
of the trial period?????????????????????????????.?.227  
 
Figure 5.10 Effect of treatment group on total gain in forage-fed steers from 105 to 126d of the 
trial period???????????????????????????????..?..228  
 
 xiv 
Figure 5.11 Effect of treatment group on average daily gain in forage-fed steers from 105 to 
126d of the trial period??????????????????????????..?229  
 
Figure 5.12 Effect of treatment on average daily gain of individual forage-fed steers from 105 to 
126d of the trial period????????????????????????????230  
 
Figure 5.13 Effect of biopsy date on relative expression of CPT-1b in steer skeletal muscle 
biopsies??????????????????????????????????231  
 
Figure 5.14 Effect of biopsy date on relative expression of PPAR-? in steer skeletal muscle 
biopsies???????????????????????????????..?.?.232  
 
Figure 5.15 Effect of biopsy date on relative expression of UCP2 in steer skeletal muscle 
biopsies??????????????????????????????...???.233  
 
Figure 5.16 Effect of biopsy date on relative expression of CEBP/? in steer skeletal muscle 
biopsies??????????????????????????????????234  
 
Figure 5.17 Effect of biopsy date on relative expression of FABP4 in steer skeletal muscle 
biopsies?????????????????..????????????????..235  
 
Figure 5.18 Effect of biopsy date on relative expression of PSDM11 in steer skeletal muscle 
biopsies??????????????????????????????????236  
 
Figure 5.19 Effect of biopsy date on relative expression of Pref-1 in steer skeletal muscle 
biopsies??????????????????????????.?????.??.237  
 
Figure 5.20 Effect of biopsy date on relative expression of PGC-1? in steer skeletal muscle 
biopsies??????????????????????????????.?.??.238  
 
Figure 5.21 Effect of treatment on relative expression of CEBP/? in steer skeletal muscle 
biopsies????????????????????????????????.?..239  
 
Figure 5.22 Effect of treatment on relative expression of CPT-1b in steer skeletal muscle 
biopsies?????????????????????????????????...240  
 
Figure 5.23 Effect of treatment on relative expression of UCP-2 in steer skeletal muscle 
biopsies?????????????????????????????????...241  
 
 xv 
Figure 5.24 Effect of treatment on relative expression of FABP4 in steer skeletal muscle 
biopsies????????????????????????????????.?..242  
 
Figure 5.25 Effect of treatment on relative expression of Pref-1 in steer skeletal muscle 
biopsies????????????????????????????????.?..243  
 
Figure 5.26 Effect of treatment on relative expression of PPAR-? in steer skeletal muscle 
biopsies??????????????????????????????????244  
 
Figure 5.27 Effect of treatment on relative expression of PGC-1? in steer skeletal muscle 
biopsies for the 126d trial period???????????????.?????????245  
 
Figure 5.28 Effect of biopsy date on relative expression of GPAT in steer adipose tissue biopsies 
collected at 84 and 126d?.??????????????????????????.246  
 
Figure 5.29 Effect of biopsy date on relative expression of ZFP423 in steer adipose tissue 
biopsies collected at 84 and 126d??????????????.????????.....247  
 
Figure 5.30 Effect of biopsy date on relative expression of FABP4 in steer adipose tissue 
biopsies collected at 84 and 126d?????????????????..??????..248  
 
Figure 5.31 Effect of biopsy date on relative expression of UCP2 in steer adipose tissue biopsies 
collected at 84 and 126d???????????????????????????..249  
 
Figure 5.32 Effect of biopsy date on relative expression of Leptin in steer adipose tissue 
biopsies??????????????????????????????????250   
 
Figure 5.33 Effect of treatment on relative expression of ZFP423 in steer adipose tissue 
biopsies??????????????????????????????????251  
 
Figure 5.34 Effect of treatment on relative expression of FABP4 in steer adipose tissue 
biopsies???????????????????????? ??????................252  
 
Figure 5.35 Effect of treatment on relative expression of UCP-2 in steer adipose tissue 
biopsies??????????????????????????????????253  
 
Figure 5.36 Effect of treatment on relative expression of GPAT in steer adipose tissue 
biopsies??????????????????????????????????254  
 
 xvi 
Figure 5.37 Effect of treatment group on relative expression of Leptin in steer adipose tissue 
biopsies??????????????????????????????????255  
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 xvii 
List of Tables 
 
Table 3.1 Experimental diet composition?.????????????????????76  
 
Table 3.2 High-capacity cDNA synthesis reaction components???...?????????77  
 
Table 3.3 cDNA synthesis reaction conditions????????.???????????78  
 
Table 3.4 TaqMan gene-specific primer and TaqMan probe sequences for the genes and tissues 
of interest?????? ???????????????????????????...79  
 
Table 3.5 Reaction components for Real-Time PCR gene expression analysis??...????80  
 
Table 3.6 Real-Time PCR thermocycler reaction conditions??????..????? .?? 81 
 
Table 3.7 Arbitrary classification of cattle as high, average, or low RFI based on individual 
animals? deviation from the mean RFI for the population???????????????88  
 
Table 3.8 Pearson correlation coefficients relating the relative expression of each gene with 
estimated RFI for steers on feed for 84 days???????????????????.?89  
 
Table 4.1 Composition of diets fed to animals during the feeding trial period??????..132  
 
Table 4.2 High-capacity cDNA synthesis reaction components????????????.133  
 
Table 4.3 cDNA synthesis reaction thermocycling conditions????????????...134  
 
Table 4.4 Individual Reaction Components for Real-Time PCR Gene Expression 
Analysis???????? ?????????????????????????....135  
 
Table 4.5 Real-Time PCR thermocycler reaction 
conditions????????? ??? ???????????????????.??136  
 
Table 4.6 Gene-Specific primer and TaqMan? probe sequences for the genes and tissues of 
interest??????? ???????????????????????????..137  
 
Table 4.7 Animal performance during the treatment period (final 35 DOF for each group)?.138  
 
Table 5.1 Reaction components for cDNA synthesis from total RNA from skeletal muscle and 
adipose tissue biopsies from grazing????????????????...??????216  
 
 xviii 
Table 5.2 Thermocycling conditions for cDNA synthesis from total RNA extracted from skeletal 
muscle and adipose tissue biopsies from grazing steers?.?????????????.?217  
 
Table 5.3 Reaction components for gene expression analysis using quantitative real-time 
PCR?????????????????????????????????.??.218  
 
Table 5.4 Thermocycling conditions for quantitative Real-Time PCR analysis??????.219  
 
 
 
 
 
1 
 
Chapter 1. Introduction 
 
Feed represents the single largest input cost in most animal production systems 
comprising about 70% of the input costs for commercial beef cattle operations (Herd at 
al., 1998; Liu et al., 2000; Arthur et al., 2001; Basarab et al., 2002). A major issue 
threatening the sustainability of the beef industry is trying to maintain and improve 
product quality in the midst of increasing feed-costs. For beef cattle, 60 to 75% of the 
feed provided is used to meet maintenance requirements (Ferrell and Jenkins, 1984; 
NRC, 1996). There is considerable variation in the cattle maintenance requirements and 
genetic variation appears to be moderately heritable (Carstens et al., 1989) suggesting an 
opportunity to improve on this trait by selecting for more efficient cattle.  
Profitability in the beef industry is determined by both inputs (e.g. feed intake) 
and outputs (e.g. lean meat produced), therefore improvement of the ratio of outputs to 
input would provide significant economic benefits to beef producers. For years animal 
scientists and producers have sought to improve the various desirable aspects of meat 
quality that are determined by consumer preference and minimize or eliminate factors 
that may cause health concerns. Researchers have focused on improving the efficiency of 
livestock production while providing a product of acceptable quality in an increasingly 
global and diverse marketplace. This has included efforts to improve rate of gain, feed 
efficiency, and manipulating the composition of gain in an economically relevant fashion.  
There are some inherent conflicts in obtaining these goals and some factors are 
often sacrificed at the expense of others. The selection for increasingly lean animals has 
led to increases in production efficiency and consumer preference, in many cases at the 
detriment of intramuscular fat (IMF) content in various meat products. Despite some of 
2 
 
the detrimental impacts, intense selection strategies based on animal science research 
have led to relative improvements in overall production efficiency and decreased 
environmental impact of beef production.  
Profitable beef production in the modern era will require producers to take 
advantage of emerging technologies and utilization of strategic management practices to 
minimize operation costs. Compared to other meat-producing animals, beef cattle are the 
least efficient at converting feed to gain with current estimates indicating that cattle have 
a feed conversion ratio (FCR) of about 7.7 (unit feed: unit gain). This is compared to a 
FCR of 1.1, 2.0, and 2.5 to 3.0 in farm raised fish, poultry, and swine, respectively 
(Bergen, 2008; Roux, 2006).  It is estimated that a 5% improvement in feed efficiency 
has an economic impact equivalent to a 20% improvement in average daily gain (Gibb 
and McAllister, 1999). Therefore feed efficiency will continue to be one of the primary 
determinants of profit or loss in beef production systems and improvements in 
performance that maximize the conversion of feed to usable products will be vital to 
long-term industry sustainability. Understanding the molecular determinants of efficient 
feed utilization will provide valuable genetic targets and cues for implementing 
management strategies to optimize production efficiency.  
Increased consumer health awareness has led to efforts to manipulate the amount, 
quality, and distribution of adipose tissue (AT) in beef cattle and other meat species. The 
flow of energy into and out of AT has significant economic implications in beef 
production because the amount and distribution of body fat are primary determinants of 
beef carcass quality and yield grades according to the USDA grading system (USDA, 
1996). Increased IMF can have positive impacts on beef quality and eating 
3 
 
characteristics. On the other hand, wasteful deposition of fat in low-value depots 
represents an inefficient use of feed energy and has a negative impact on overall 
production efficiency. Selective enhancement of valuable depots (e.g. IMF) is a 
promising strategy to improve carcass quality and efficient use of nutrients by directing 
them into economically valuable depots. However, the knowledge of depot specific 
adipose tissue metabolism, particularly molecular regulatory components, as related to 
beef cattle production is currently lacking. 
Metabolic modifiers are a group of compounds that improve production efficiency 
in part by increasing lean protein deposition and decreasing AT accretion. Several of 
these exogenous agents have been developed with varying efficacies and species 
specificity. Beta-adrenergic agonists (BAA) are part of a class of metabolic modifiers 
known as energy repartitioning agents, which increase protein accretion and decrease 
adiposity in various livestock species. Major hurdles that inhibit the widespread 
incorporation of many of these useful compounds in commercial operations include 
consumer acceptance, long withdrawal periods, and variable effectiveness. Determination 
of specific mechanisms of action may lead to improved compounds that have more 
specific targets and reduced undesirable effects.   
The lack of a comprehensive understanding of the molecular determinants of feed 
efficiency, energy partitioning, and marbling accumulation have hindered our ability to 
manipulate the beef carcass beyond the means of traditional genetic selection tools. 
Livestock production in the post-genomic era is poised to benefit from innovative 
application of novel technologies originally designed for the sequencing of the human 
genome. In the current, post-genomic era there are a plethora of tools, technologies, and 
4 
 
genomic information databases that are becoming available and affordable for application 
to improve livestock production. Identification of measurable regulatory factors will lead 
to more specific targets for pharmacological intervention and will help optimize 
management strategies to improve product quality, lower production costs, and reduce 
the environmental burden of the beef cattle industry.  
In the current project we have utilized a targeted transcriptomic approach aimed at 
mapping the expression patterns of putative and novel regulatory factors involved in 
energy metabolism, protein turnover, and adipogenesis in beef cattle. These categories 
relate directly to muscle and fat deposition and overall beef carcass composition and 
quality. Three independent studies were conducted to observe the gene expression 
profiles as they relate to beef cattle under finishing conditions in the Southeastern US 
(SE). In the first study we sought to investigate genes associated with residual feed intake 
in finishing steers and bulls to identify potential molecular targets to improve this trait. In 
a second study we investigated the effect of Ractopamine HCl (Optaflexx, Elanco 
Animal Health) on the expression of genes involved in adipogenesis, lipid metabolism 
and energy metabolism. In a final study we looked at the effect of compensatory growth 
and a forage-based finishing system on the expression of key regulatory metabolic genes 
in loin muscle and subcutaneous adipose tissue using serial biopsies obtained during the 
finishing phase.  
 
 
 
 
 
5 
 
Chapter 2. Literature Review 
 
Feed Efficiency 
 With feed representing the major cost of beef production (Herd et al., 2003), 
improving the output of beef per unit feed is the primary objective of feed efficiency 
research and forms the basis for genetic selection for feed efficiency (FE). In the past 50 
years the livestock industry, in general, has become more efficient in the face of rising 
feed costs, increased competition for land and resources, and increased consumer product 
demand.  
Poultry and swine producers have dramatically improved production efficiency. 
Genetic selection has nearly maximized the biological potential of broiler production and 
has left little room for further improvement. Havenstein et al. (2003) estimated that 
today?s broilers require less time and less feed to reach a market weight of 1.8kgs. They 
also concluded that the majority (80-90%) of the improved production efficiency was the 
result of intense genetic selection for more efficient birds that grew larger faster, while 
improved nutrition (e.g. vitamin and mineral supplementation) played a nominal role.   
Cattle have also experienced substantial improvement in overall efficiency 
although much of this has come as a result of grain feeding and other nutritional 
regimens, as well as reproductive and pharmaceutical technologies (Elam and Preston, 
2004).  While there is considerable variation for FE in beef cattle and this variation 
appears to be moderately heritable, there has been little improvement in FE or 
maintenance energy requirements during the past 50 years (Archer et al., 1999).  
 Gibbs and McAllister (1999) reported that increasing FE by 5% would have an 
economic impact four times greater than a 5% enhancement in average daily gain (ADG). 
6 
 
With 60% of the total cost to achieve a marketable steer, estimated to be attributed to the 
feed (Ritchie, 1992), the cumulative effects of improved efficiency over the entire 
production system would be of economically beneficial to the beef industry as a whole 
and particularly the pasture based SE beef industry. Furthermore, 70 to 75% of the energy 
provided in feed is used to meet maintenance energy requirements and this appears to be 
a moderately heritable trait. The benefits of more efficient cattle include: decreased 
maintenance requirements, decreased feed intake (FI) with similar weight gain, increased 
product yield per unit feed, and decreased manure and methane emissions (Nkrumah et 
al., 2006; Hegarty et al., 2007)  
 
Measures of Feed Efficiency 
 Feed efficiency can be described as a ratio of some form of inputs to outputs. In 
beef cattle, feed conversion ratio is defined as units of feed consumed to units of product. 
While this term is useful for determining the effects of various feeds and management 
practices on production efficiency, there are some confounding factors that preclude feed 
conversion ratios (FCR) as a useful trait for genetic improvement. Another measure of 
efficiency is partial efficiency of gain, which is a ratio of ADG to feed used for growth 
minus the predicted feed used for maintenance (Arthur et al., 2001). While these 
measures of efficiency have their merits, they also have their limitations in that many fail 
to account for various contributing factors such as mature body size.  
 
Residual Feed Intake 
Residual feed intake (RFI) was first investigated by Byerly (1941) who examined 
what he called net efficiency in laying hens. In 1963, Koch et al. observed that there were 
7 
 
differences in the ability to gain and maintain body weight amongst growing beef cattle 
impacting feed requirements. These factors are related to the net energy requirements for 
maintenance (NEm) and gain (NEg) which varies between individual animals.   They 
suggested that the feed requirements could be adjusted for body weight (BW) and weight 
gain by partitioning feed intake into an expected component which is the amount of food 
that an animal would be expected to eat based on the estimated requirements for a given 
level of performance, and a residual component which is the actual amount of feed intake 
above or below the expected intake levels. More efficient animals would show a lower 
(more negative) RFI value compared to their less efficient counterparts (Koch et al., 
1963).  
While selection for animals that consume less and maintain a similar body weight 
and growth rate as their contemporaries is an attractive option to improve sustainability 
and profitability of beef production, adoption of RFI as a practical efficiency parameter 
has been met with limited success for various reasons.  One of the primary factors that 
inhibit widespread adoption of RFI is the cost and difficulty of measuring individual feed 
intake and BW on large numbers of cattle over an extended period of time (Moore et al., 
2009). Selection for improved (lower or more negative RFI) may also have some 
unexpected consequences on the overall efficiency of the beef operation.  Shaffer et al 
(2010) reported that for each 1 unit increase in RFI, a decrease of 7.5 d in age at puberty 
was observed. They concluded that differences in body fat and rate of metabolism 
associated with RFI could be responsible for the delay in reproductive maturity. 
Therefore, it is imperative to understand the long-term impact of selection for RFI on 
metabolic and reproductive efficiency.  
8 
 
 
Molecular Basis of Residual Feed Intake 
Genetic and molecular mechanisms that control feed intake and feed efficiency 
are currently not very well understood. Knowledge of biological contribution to 
production efficiency is lacking, but the post-genomic era has ushered in new tools that 
can facilitate novel approaches to understanding feed efficiency and its molecular 
regulatory mechanisms (Bolormaa et al., 2011; Hill and Azain, 2009; Moore et al., 2009). 
The ability to predict RFI using genomic tools provides an attractive alternative to 
circumvent the need for these costly and labor intensive measurements and may lead to 
more widespread acceptance of RFI (Moore et al., 2009). 
Kolath et al. (2006) showed that the rate of mitochondrial respiration is increased 
in steers with low RFI versus those with high RFI. This is similar to results in poultry and 
rats, which have identified a relationship between mitochondrial respiration and feed 
conversion ratio (Bittke et al., 2002; Lutz and Stahly, 2003). Lkhagvadorj et al. (2009) 
identified common differentially expressed genes in the liver and AT of pigs subjected to 
caloric restriction (CR) or selected for feed efficiency (RFI). These genes included the 
transcription factors (TFs) PPAR alpha, PPAR-gamma, and CREB. It is known that 
several transcription factors are responsible for mediating the physiological response to 
CR (Desvergne et al., 2006) and this data indicates that a common energy conservation 
mechanism may exist in CR and low RFI pigs.  
While there has been considerable research on defining variation between types of 
cattle, there has been little progress on identifying the underlying molecular components 
that regulate metabolic efficiency. Richardson and Herd (2004) listed some sources that 
9 
 
may contribute to variation in RFI including feed intake, digestion, body composition, 
rate of gain, etc. They estimated that 37% of the variation in RFI can be attributed to 
tissue metabolism, protein turnover and stress. Even with their comprehensive assessment 
it can be said that the exact source of a substantial amount of the variation in RFI has yet 
to be elucidated.  
 
Metabolic Modifiers 
Modifiers in Beef Production 
Small profit margins are a continuing threat to the long-term stability of the beef 
cattle industry (Herd, 2003). Feed provision is a major cost; therefore, researchers and 
producers are constantly looking for ways to improve feed efficiency and increase lean 
tissue deposition while simultaneously decreasing excessive fat cover in beef cattle. 
Genetic selection and an increased understanding of various environmental and 
nutritional factors have led to marked improvements in carcass quality, composition and 
production efficiency. In addition, several exogenous compounds known as metabolic 
modifiers have been developed to improve the profitability of animal production 
(Dikeman, 2007).  
Metabolic modifiers are defined as agents that can be fed, injected, or implanted 
in animals to improve rate of gain, feed efficiency, dressing percent, yield percent, or 
sensory characteristics of meat (Dikeman, 2007). In the livestock industry, the use of 
metabolic modifiers is focused on improving the efficiency of meat and milk production, 
as well as improving the yield and composition of these products (Beerman et al., 2005).  
These compounds generally work by improving feed efficiency, increasing muscle 
deposition, decreasing AT accretion, or some combination of the above (Dunshea et al., 
10 
 
2005). Metabolic modifiers can be separated into various functional categories. These 
include but are not limited to: anabolic steroids, somatotropins, beta-adrenergic agonists, 
designer lipids, and other dietary additives such as vitamins and minerals that have been 
shown to have specific modulatory effects on energy and protein metabolism (Dunshea et 
al., 2005; Dikeman, 2007).  
The United States Food and Drug Administration (FDA) have approved the use of 
various metabolic modifiers for a number of species. The use of these compounds dates 
back to 1956 with the introduction of anabolic implants for use in beef cattle (Preston, 
1999). There are tangible benefits for the use of metabolic modifiers in beef production 
and this is evidenced by the widespread use of implants and feed additives in the US beef 
industry (Beerman et al., 2005). Their advantages include increased production efficiency 
for producers and increased lean meat yield for packers; translating to the availability of 
leaner, less expensive meat for consumers.  The magnitude of these improvements is 
influenced by: the dosage and duration of the treatment, the compounds used, and target 
species (Beermann, 1993; Mersmann, 1998; Moody et al., 2002).  
 
Beta-Adrenergic Agonists 
One class of modifiers that has received much attention is the beta-adrenergic 
agonists (BAAs) also known as phenethanolamine repartitioning compounds. ?-
adrenergic agonists bind to and, activate the ?-adrenergic receptor (BAR), which is 
expressed in various tissues and cell types across mammalian and avian species 
(Lefkowitz, 2007; Mersmann, 1995; Coutinho et al., 1992). The physiological BAAs are 
epinephrine and norepinephrine and numerous synthetic compounds have also been 
produced to stimulate or block the activity of BAR. Because the receptors they activate 
11 
 
control a wide array of physiological functions, much of the interest in these compounds 
has been driven by potential biomedical applications as bronchodilators, modulators of 
cardiovascular function and other therapeutic purposes.  The physiological activity of a 
BAA is dependent on several factors including receptor activity, rate of absorption, rate 
of metabolism, and rate of elimination as well as receptor distribution with respect to 
target tissues (Smith, 1998).  
 
Beta-Adrenergic Receptors 
Beta-adrenergic receptors (BAR) are ubiquitous in all cells of the body and across 
all mammalian species. BAR are membrane bound proteins that belong to the seven-
transmembrane class of cell surface receptors also known as G-protein coupled receptors 
(GPCRs). These receptors represent the largest family of plasma membrane receptors 
with over 1000 genes, and play a role in nearly every mammalian physiological process 
(Lefkowitz, 2007). They control a wide array of physiological and metabolic functions, 
ranging from heart-rate regulation to lipolysis and these properties have peaked 
pharmacological interests in these receptors for decades making them the most common 
targets of currently prescribed drugs (Lefkowitz, 2007; Ma and Zimmel, 2002; 
Mersmann, 1998).  
A physiological response is induced when a BAA binds to a BAR and the degree 
and effect of this response is dependent on a combination of factors. The mechanism of 
BAR action is mediated through the G-stimulatory (Gs) protein-signaling pathway. Once 
bound by a suitable agonist, Gs protein activity causes activation of adenylate cyclase, 
which leads to the production of cyclic adenine nucleotide monophosphate (cAMP), a 
well-characterized intracellular second messenger.  cAMP activates protein kinase 
12 
 
resulting in the subsequent phosphorylation of various downstream target proteins and 
modulation of metabolic pathways. 
There are several considerations that account for the overall response to a BAA 
including dosage duration, receptor subtype targeted, and the specific milieu of 
downstream effectors that are modulated in response to receptor activation. Together 
these reasons, along with age, genetics, and environmental factors are responsible for the 
various physiological effects seen when using different BAA within and across species. 
Therefore, it cannot be assumed that a given agonist will induce a prototypical response 
across species or within a species under different conditions and treatment regimens 
(Mersmann, 1998). 
 
Beta-Adrenergic Receptor Subtypes 
There are at least three BAR subtypes that have been identified and these are B1-
AR, the B2-AR, and the B3-AR. Beta-Adrenergic receptors are distributed universally on 
mammalian cells across species and the distribution of these subtypes differs between 
tissues and across species (Strosberg, 1997; McNeel and Mersmann, 1999; Mersmann, 
1996). The rat heart contains more than 90% B1-AR while the rat adipocytes contain 
greater than 90%B3-AR. Porcine adipocytes in contrast contain less than 10% B3-AR 
(McNeel and Mersmann, 1999).  While these tissues represent extremes with respect to 
subtype composition, other tissues have more equal distribution of the B1-AR and B2-
AR receptor subtypes. The B3-AR subtype shows a more limited distribution being 
restricted primarily to the gut and AT. In addition, the various subtypes exhibit variable 
responses to different BAA.  In addition to across species and across-tissue differences, 
the distribution and expression of the BAR subtypes changes with growth/age of the 
13 
 
animal, which can influence the response to a given BAA in vivo (Mersmann, 2002; 
Feeve et al., 1991). 
Although there is considerable homology among the subtypes across species, 
amino acid sequence variation imparts another layer of complexity that causes 
differential ligand-binding and physiological responses to a given agonist. The amino 
acid sequence homology between subtypes is 80-90% with the transmembrane segments 
sharing many conserved residues (Mills and Mersmann, 1995). BAR subtypes were 
originally classified based on the ability of agonists or antagonists to stimulate or inhibit a 
physiological response when bound to the receptor. In addition, these subtypes originally 
appeared to be localized in a specific tissue. It has since been determined that this 
specificity is relative and not absolute, and that agonist and antagonist can bind to 
multiple receptor subtypes making the classification system somewhat ambiguous 
(Mersmann, 1995).  
There is only limited knowledge of the specific BAR subtypes on skeletal muscle 
(SM) and AT of meat producing species. These are the primary tissues that are 
responsive to administration of oral BAA (Mersmann, 1995).  Tissue distribution can be 
determined by using selected agonists and antagonists (Coutinho et al., 1991; Mersmann, 
2002). Using this approach Coutinho et al. (1991) determined that 45% of the BAR 
present in adipocytes isolated from crossbred barrows showed high affinity for the B1-
AR selective agonists ICI 89,406 and thus were classified as B1-ARs. Low affinity 
binding sites comprised 55% of the total, and were tentatively classified as B2-AR.  
Transcripts for the B1-AR, B2-AR, and B3-AR have been identified on bovine 
and porcine adipocytes (Mills et al., 2002; Mersmann, 1998). In terms of BAR 
14 
 
distribution, 75% of the BAR in SM and AT of beef cattle have been identified as the 
BR2 subtype. This is in contrast to pigs where BR1 is the predominant subtype (Mills, 
2002).  The actual expression patterns and functionality of these receptor subtypes is not 
conclusive in all tissues. Because the BARs are distributed on almost all mammalian cell 
types, there are complex mechanism of actions depending on the BAA used, the BAR-
subtype population, and the distribution of BAA to various tissues. 
 
Beta-Adrenergic Agonist?s Effects in Skeletal Muscle and Adipose Tissue 
It is known that the hydrolysis of stored triglycerides in AT is regulated by 
endogenous catacholamines that bind the BAR to activate the lipolytic enzymes. Binding 
of the BAA to the BAR causes an increase in cAMP levels resulting in protein kinase 
activation and subsequent activation of hormone sensitive lipase. The liberated TAG can 
then be exported and oxidized in other tissues to meet energy requirements and protein 
can be spared for lean deposition. In this manner carcass adiposity can be decreased and 
leanness increased with the use of BAA. Adipocytes express all three of the BAR 
subtypes on their cell surface, although the role for each subtype is not clearly understood 
(Mills et al., 2003). In pigs, the predominant subtype is the B1-AR, which comprises 
nearly 80% of the total.  
 One of the more pronounced responses to oral BAA administration is the rapid 
and preferential increase in SM mass. In beef animals this translates to more dissectible 
lean and can lead to more efficient production of meat products. Beerman (2002) 
summarized the effects of BAA on skeletal muscle growth in laboratory and farm 
animals. The anabolic effects of BAA administration in SM include hypertrophy of 
muscle fibers, changes in muscle fiber type, and differential rates of nucleic acid and 
15 
 
protein accretion. Bergen et al. (1989) reported that the effects of BAA on SM 
hypertrophy could be attributed to changes in the muscle hypertrophy and fractional 
protein accretion rates in finishing pigs. Grant et al. (1993) studied the effects of 
Ractopamine HCl on the expression of SM alpha-actin and insulin-like growth factor I 
mRNA in crossbred barrows. Their results indicated that the BAA-induced growth of 
skeletal muscle was the result of increased myofibrillar gene expression including strong 
induction of SM alpha actin, and that the response is maximal with short-term 
administration of the agonist. This is similar to data reported by Gunawan et al. (2007) 
who found that myosin heavy chain genes are differentially regulated with BAA 
treatment and that the effects of BAA on muscle hypertrophy may be mediated through 
changes in muscle fiber type-specific gene expression patterns.  Studies have also shown 
that metabolic maturation of SM tissues may be necessary for maximum efficacy of 
BAA; therefore, receptor distribution and density are important issues.  
Insulin sensitivity has been shown to be decreased in adipocytes from pigs (Liu 
and Mills, 1990) and rats (Hausman et al., 1989) treated with RAC, while no effect was 
reported on SM sensitivity in the hindquarters of lambs despite a 50% decrease in 
circulating insulin concentrations (Beerman, 1987). This appears to indicate opposing 
effects on insulin sensitivity in AT and SM which may contribute to the effects seen with 
BAA administration.  Clenbuterol, a potent BAA, has also been shown to induce transient 
and chronic insulin insensitivity in cattle (Sternbauer et al., 1998). Other studies using 
endocrine altered animals have provided evidence that opposes the involvement of 
indirect endocrine mechanisms acting on SM. Theil et al. (1987) conducted a study using 
hypophysectomized rats and showed that SM growth was significantly increased in 
16 
 
animals receiving BAA treatment compared to controls. Similarly, in animals with a 
genetic growth hormone deficiency, BAA was still capable of augmenting SM growth.  
While stimulation of growth may also be a result of indirect action on SM tissue 
involving various endocrine mechanisms, the contribution of indirect mechanisms 
appears to be negligible (Beerman, 2001). 
 
Beta-Adrenergic Agonists in Livestock Production 
Because of the perpetually high cost of feed, considerable attention must be paid 
to input cost of beef production in order to maintain profitability (Herd et al., 2003). 
Reduction of input cost via improved feed efficiency and effective nutrient partitioning 
are the primary economic incentives of BAA use in livestock. BAA are the most recent 
compounds to gain approval for use in feedlot cattle (Winterholler et al., 2007) and these 
compounds have been shown to effectively increase ADG, Gain: feed, and hot carcass 
weight (Laudert et al., 2004). BAA?s specifically enhance lean protein deposition 
(Bergen et al., 1989; Grant et al., 1993) and decreases AT accretion by modifying 
metabolic flux such that there is a decrease in nutrients directed towards adipogenesis and 
an increase in lean growth (Lopez-Carlos et al., 2011; Bergen, 2001; Anderson et al., 
1991). The net result of administering BAA includes some combination of: increased rate 
of gain, improved feed efficiency, increased leanness, and increased dressing percentage 
(Moody et al., 2000).   
There are some limitations and drawbacks to the use of BAA in livestock. These 
compounds are generally only effective for the final 20 to 40 DOF as the effects begin to 
plateau and additional exposure does not render additional benefit. There are also 
disparate responses to various BAA between livestock species making it difficult to 
17 
 
extrapolate across species and across studies (Mersman, 1995). Cattle and sheep show 
greater responses and chickens are the least responsive (Moody et al., 2000). Turkeys and 
pigs appear to be intermediate. One possible explanation may be the fact that chickens 
have been under such intense selection pressure that they may be close to reaching their 
maximal biological potential for growth rate and as such have much less room for 
improvement via exogenous compounds compared to less efficient species. Elucidation 
of the molecular factors responsible for BAA action will be critical to understanding 
these differential responses and effectively tailoring management strategies to maximize 
the benefit of these compounds.  
 
Ractopamine Hydrochloride 
The use of Ractopamine HCl (RAC) in farm animals has been studied extensively 
for the last 20 years. Mills (2002) provides an excellent summary characterizing the use 
of RAC in these species.  Ractopamine hydrochloride is a phenethanolamine derivative 
that was originally approved for use in swine in 1999 and subsequently for use in beef 
cattle in 2003. RAC was developed and patented by Eli Lily in 1986 and is currently 
patented by Elanco Animal Health (Greenfield, Indiana) and marketed under the trade 
names Paylean and Optaflexx for the swine, and beef cattle formulations, respectively. 
RAC is classified as a phenethanolamine agent, which is structurally or functionally 
similar to the endogenous catecholamine epinephrine and norepinephrine (Beerman, 
2002; Hancock et al., 2006), and like other BAA, act as a repartitioning agent with the 
ability to increase lean protein and decrease adiposity in pigs and cattle.  
 
Structure and Function 
18 
 
The biological activity of a given BAA is a function of its specific chemical 
composition. The first description of BAA mechanism of action comes from Easson and 
Stedman (1933) who proposed that BAA interact with the BAR at three contact points: 
the ?-hydroxyl group, the aliphatic positively-charged nitrogen group, and the aromatic 
ring. Substitution or alteration of these groups can have profound effects on binding and 
activity of BAA.  In addition to these common groups, there is a bulky substituent that 
confers specificity for the BAR (Carlstrom et al., 1973; Weiner, 1980). These are 
elements that are common to all BAA. While they share some general characteristics, 
specific substitutions change the activity and efficacy of the BAA compounds (Figure 
4.1). In addition, rate of clearance, uptake, and tissue specificity are determined by the 
specific substitutions about the aforementioned chemical groups. These are chemical 
elements also contained in the physiological BAAs epinephrine and norepinephrine with 
the exception of the bulky group on the aliphatic nitrogen (Smith, 1998).  
Stereochemistry of BAA is important in determining biological activity due to the 
presence of multiple chiral centers on these molecules. RAC is sold as mixture of its 
component stereoisomers that arise as a result of 2 chiral centers, one provided by the 
beta-hydroxyl group and a second that arises in the R-group (See Figure 4.1). This 
chirality produces four stereoisomers two of which (RR and RS) are leveratory and 2 of 
which (SR and SS) are dextrorotary. Ricke et al. (1999) determined that the RR 
stereoisomer is responsible for the biological activity of RAC. In terms of BAR subtypes, 
RAC binds the B1-AR with higher affinity than the B2-AR (Hancock et al., 2006).  Some 
agonists can bind both alpha and BAR. In addition, an agonist for one receptor subtype 
19 
 
may function as an antagonist for another subtype adding yet another level of complexity. 
This is not the case with RAC, which binds almost exclusively to the B1-AR.  
 
Effectiveness of Ractopamine and other Beta-Adrenergic Agonists in Livestock  
Halsey et al. (2011) investigated the effect of RAC on gene expression in 
subcutaneous AT of finishing pigs. RAC was fed for 12, 28, or 42d and expression of 
adipogenic and lipogenic TFs was determined by Northern blot analysis. They 
determined that RAC supplementation caused reduction in lipogenic genes including 
PPAR-g, SREBP-1, and FAS after 42 days.  These data indicate that RAC mechanism of 
action in AT has to do with attenuation of the processes of adipogenesis and lipid filling 
in this tissue although the specific mechanisms remain undefined. While it is likely that 
prolonged administration of RAC causes down regulation of the BAR, the anti-
adipogenic effects observed at 42d indicate that alternative mechanisms may be 
responsible for the effects of RAC on SC AT. 
Reiter et al. (2007) used divergently selected lean and obese pigs to examine the 
role of genetic background on metabolic pathways associated with anabolic and catabolic 
lipid metabolism in liver, skeletal muscle, and AT in animals supplemented with RAC for 
52 days. While these animals showed differential expression of lipogenic and adipogenic 
genes and TFs between genotypes, treatment with RAC resulted in depressed anabolic 
and increased catabolic lipid-metabolism related gene expression irrespective of genetic 
background. This is similar to results by Mimbs et al. (2005) who reported enhanced 
growth performance in finishing lean and obese pigs fed RAC regardless of their degree 
of adiposity at the initiation of the trial.  
20 
 
In heifers RAC has been shown to improve average daily gain (ADG), while 
either decreasing or having no effect on dry matter feed intake (Gruber et al., 2007; 
Walker et al., 2006). Consumption of less feed with equivalent lean growth results in 
fewer days to market and improved feed and production efficiency making RAC a 
promising agent to improve the performance of feedlot cattle. 
Winterholler et al. (2007) conducted a study to determine the effects of RAC in 
finishing heifers. They showed a 4.6% increase in ADG and increased hot carcass weight 
without any effects on marbling score or feed intake when RAC was fed at 200mg/hd/d. 
Winterholler et al. (2007) reported ADG, G:F, and DMI decreased and HCW, USDA 
Quality Grade, and Yield Grade increased as DOF increased from 150 to 192d. 
Winterholler et al. (2007) observed that RAC fed at a rate of 200 mg/hd/d for 28d 
increased ADG by 4.6% and HCW by 8 kg in heifers. Schroder and others (2004) 
determined Optaflexx? improved steer ADG by an average of 25.7% when compared to 
control steers. They also reported a 20.4% improvement in ADG was observed in heifers. 
No effects in feed intake were observed indicating improved feed efficiency in both 
groups across three treatment levels of Optaflexx (100, 200, and 300 mg/hd/d) and two 
treatment durations of 28 and 42d.         
Zilpaterol HCl (ZH; Zilmax, Intervet Schering Plough Animal Health, DeSoto, 
KS) was recently approved for oral administration in beef cattle by the United States 
Food and Drug Administration (FDA) in 2006. Like RAC, this BAA is approved for use 
during the finishing phase and label uses include improved FE and increased carcass 
leanness in cattle when fed during the final 20-40 DOF. Treatment with ZH requires a 
minimum of a three day withdrawal period. Approval of ZH use in the US was preceded 
21 
 
by approval in South Africa, Mexico, and more recently Canada (Delmore et al., 2010). 
One negative impact of ZH is the perceived increase in Warner-Bratzler Shear Force 
(WBSF) in steaks from animals receiving ZH during the finishing period.    
Vasconcelos et al. (2008) fed Zilpaterol HCl (ZH) to finishing steers for the final 
twenty days with a three day withdrawal period. They observed an increase in ADG and 
decreased DMI. They showed that feeding steers for more than twenty days did not give 
further advantage to ADG. They also observed a 9kg increase in BW and a 15kg increase 
in hot carcass weight resulting in changes in dressing percentage. They hypothesized that 
treatment with ZH resulted in partitioning of nutrients away from non-carcass 
components and towards valuable carcass depots (Elam et al., 2009). In this instance the 
BAA improved both the FCR as well as the yield of useable product thereby enhancing 
the overall efficiency of production.         
  Lopez-Carlos et al. (2011) recently conducted a study feeding RAC or ZH 
to finishing lambs in order to directly compare the effects of these two BAA. 112 
crossbred lambs were treated with either RAC or ZH for the final 14, 28, or 42 DOF. 
While there was similar growth performance between lambs receiving ZH or RAC, lambs 
receiving either BAA had 9.6% higher final BW and 24% higher ADG compared to 
controls. Carcass characteristics were also improved including a 3.9% and 3.8% greater 
HCW and dressing percentage, respectively. Most notably perhaps was the 20% decrease 
they observed in fat thickness in animals fed BAA indicating that these compounds were 
effective at partitioning energy metabolism away from AT and towards SM resulting in 
observable changes in carcass composition. 
 
Duration of Effects 
22 
 
The intensity or maximal response of a receptor to an agonist can diminish over 
time despite the continued presence of the agonists. This inherent regulatory mechanism 
serves to mediate the amplitude, frequency and duration of the ligand-induced response 
(Sun et al., 2003). One such mechanism is known as receptor desensitization and is a 
phenomenon that occurs with the BAR under chronic BAA administration (Lefkowitz, 
2007). In the classical model of receptor desensitization G-proteins mediated signaling is 
subject to receptor desensitization and internalization in a signaling pathway involving 
arrestins and GPCR-related kinases (GRks, Clark et al., 1999). This occurs because 
receptor activation also induces regulatory processes that attenuate the duration of 
signaling via ligand-induced receptor internalization or ligand-induced desensitization of 
the receptor (Clark et al., 1999). Whether or not these classical paradigms apply to BAA-
induced desensitization of the BAR in animals fed RAC is yet to be determined.  
Smith (1989) determined that chronic administration of BAA can lead to temporal 
changes, down regulation, or desensitization of BAR.  Kim et al. (1992) reported 
decreased BAR density in muscles from rats fed cimaterol. Likewise, Mills et al. (1990) 
found that porcine adipocytes isolated from pigs fed RAC were less sensitive to RAC-
induced lipolysis in vitro. Similarly, Spurlock et al. (1994) looked at the effect of RAC on 
BAR density and affinity in porcine SM and AT. While RAC treatment did not affect the 
affinity of the BAR for RAC, and did not affect the maximal binding (Bmax) in SM 
tissue, there was a reduction in the Bmax in AT which may account for the diminished 
lipolytic response and plateau effect of chronic BAA administration (Spurlock et al., 
1994). Be it receptor desensitization, down-regulation, or a yet undetermined 
23 
 
phenomenon, long-term diminishing effects have precluded effectiveness of these 
compounds over extended amounts of time or after previous exposure. 
The use of RAC and other repartitioning agents is generally limited to the 
finishing phase. Optaflexx is approved for use in beef cattle for 28 to 42d prior to harvest 
and is to be administered at a rate of 70 to 430 mg/hd/d. Strategic timing of Optaflexx 
administration may be a valuable tool for managing and marketing of finished cattle. For 
example, it may be possible to group cattle according to expected end weights to increase 
effectiveness and efficiency of Optaflexx administration in finisher rations. With the high 
cost of feed, the number of days on feed can significantly affect the profitability of the 
enterprise and the relative value of administering BAA or other metabolic modifiers 
making it imperative to identify molecular cues that are indicative of optimal windows 
for treatment with these compounds. 
 
Future of Beta-Adrenergic Agonists in Livestock 
Historically, research with these compounds has been focused on optimizing 
dosage, duration, and timing of treatment to maximize the tangible outcomes. Less 
research has focused on understanding the molecular intricacies that are responsible for 
the action of these compounds. Besides an overall all effect on lean deposition/protein 
synthesis and lipid accretion, lipogenesis and lipolysis (Bergen, 2001), a clearer 
understanding of the molecular events responsible for the specific mechanism of action in 
finishing heifers is lacking and such understanding will contribute to optimizing 
management strategies for finishing cattle under SE conditions. With the advent of 
genomic platforms tailored for livestock production, the molecular underpinnings of 
energy partitioning and feed efficiency appear on the verge of elucidation. This will form 
24 
 
the basis for future technologies and management strategies toward effective 
implementation of these and other metabolic modifiers in beef cattle production.  
 
Adipose Tissue 
Adipose tissue (AT) is present to some extent in all mammals, and other 
organisms from C. elegans to Drosophila have also developed similar ways to store 
excess energy in the form of fat (Pond, 1992; Ashrafi, 2007; Gesta et al., 2007). In terms 
of energy metabolism the role of AT is to store energy in the form of triglycerides during 
periods of nutritional excess and to release this currency when nutrient availability is low 
or energy demand is high.  In addition to its role in energy storage, AT is a dynamic 
organ with major impact on the organism?s metabolic disposition (Waki and Totonez, 
2007). Once thought of as simply a storage vat for excess dietary energy with lesser roles 
as a cushion and insulator in the body, we now know that AT participates in endocrine, 
paracrine, and autocrine signaling networks and that it plays critical roles in energy 
homeostasis, immune function and in reproductive processes (Kim and Moustaid-
Moussa, 2001). AT also serves as the primary site for de novo lipogenesis (DNL) in 
livestock species such as pigs, and ruminant species; which is not the case for humans 
and rodents (Bergen and Mersmann, 2005). The role of AT in thermoregulation has also 
been defined with white AT serving as an insulator to maintain body temperature and 
brown AT being involved in thermogenesis via uncoupling of mitochondrial respiration.  
Novel physiological roles for AT are constantly being identified and the contribution of 
AT to the whole-body energy metabolism and cell-signaling in humans and animals has 
begun to garner much more appreciation. 
 
25 
 
Economic Value of Adipose Tissue in Beef Production 
Aside from its physiological roles, AT is an economically important tissue when 
it comes to beef cattle production. AT physiology has been of interest to animal scientists 
and livestock producers for years due to the direct impact of this tissue on economic and 
quality aspects of meat production. A longstanding goal of animal scientists has been to 
minimize wasteful AT accretion because it represents a waste of dietary energy, making 
over-fattening of livestock an undesirable and inefficient process (Hausman et al., 2008; 
Dodson et al., 2009).  While excessive trim fat decreases carcass value and production 
efficiency, the beef industry paradoxically rewards producers with premium prices for 
carcasses with high IMF or marbling (Schroeder et al., 2002). The issue of excess fat 
cover is not a trivial one because genetic selection for carcass leanness can come at the 
expense of IMF and thus decrease carcass quality and value. Therefore, understanding 
molecular intricacies of AT biology in general, as well as the regional peculiarities 
between specific AT depots are both imperative to understanding the contribution of AT 
to metabolic and production efficiency (Hausman et al., 2009).   
 
Cellular Composition of Adipose Tissue 
At the histological level AT can be described as an innervated loose connective 
tissue composed primarily but not exclusively of adipocytes, which originate from the 
mesenchymal stem cell lineage. The pluripotent cells that give rise to the population of 
preadipocytes present in AT also give rise to myocytes and chondrocytes among other 
cells (Gesta et al., 2007). AT itself is a heterogeneous tissue composed of preadipocytes, 
endothelial cells, macrophages and other cell types. Mature adipocytes capable of storing 
and releasing lipids make up about one-third of the cellular population. The heterogeneity 
26 
 
of the cells that comprise AT imparts a dynamic and depot-specific microenvironment 
within the tissue and thus AT exhibits a high degree of responsiveness and plasticity. In 
addition, AT is highly innervated and vascularized all of which are indicative of the 
dynamic role of AT in whole body metabolism (Hausman and Richardson, 2004; Jacobi 
et al., 2006). 
 
Adipocytes 
While they are not the exclusive residents in the AT depot, adipocytes are the 
primary functional cellular components of AT. Mature adipocytes are responsible for 
energy cycling (energy storage and release) and they serve as the primary source of the 
various factors synthesized and secreted by AT (Mohamed-Ali, 1998; Kim and Moustaid-
Moussa, 2000). In addition, these adipocytes are responsive to local and distant metabolic 
cues allowing them to have an impact on whole body metabolism and contribute to 
energy homeostasis (Flier and Maratos-Flier, 1998).  
Mammalian adipocytes can be classified into two types: those that give rise to 
white adipose tissue (WAT) and those that give rise to brown adipose tissue (BAT; 
Spiegelman and Flier, 2001). These cell types can be distinguished morphologically and 
functionally. WAT contain a large unilocular lipid droplet, which displaces the nucleus of 
the cell towards the periphery. BAT has multilocular lipid droplets and an increased 
proportion of mitochondria, which are responsible of the brown appearance of these cells. 
In terms of energy homeostasis, white adipocytes are responsible primarily for 
triglyceride synthesis and energy storage while brown adipocytes contain the cellular 
machinery for fatty acid metabolism and thermogenesis, and participate in energy 
dissipation via uncoupling of mitochondrial electron transport from ATP production.  
27 
 
Because these cell types act as counter actors in energy metabolism, 
understanding the molecular components that distinguish BAT and WAT has become 
increasingly important to understanding AT biology and overall energy homeostasis. 
While BAT has been thoroughly investigated in rodents and more recently humans, the 
evidence and role of BAT in livestock species has been scarcely studied (Martin et al., 
1999; Smith et al., 2004). The balance of BAT and WAT can contribute to systemic 
energy balance (Spiegelman and Flier, 2001), and as such may warrant future 
investigation in livestock production efficiency research.  Unless otherwise stated, the 
following discussion will deal exclusively with WAT and the adipocytes that comprise 
this tissue.   
 
Adipogenesis 
Adipogenesis is an inclusive term that describes the cellular events responsible for 
the commitment of pluripotent stem cells to the adipocyte lineage to form pre-adipocytes, 
and the proliferation and differentiation of these pre-adipocytes into mature adipocytes 
capable of assimilating and storing lipids and responding to various external stimuli 
(Rosen et al., 2000; Hausman et al., 2009). The process of adipogenesis begins in mid to 
late gestation in ruminant animals (Feve, 2005; Gnanalingham et al., 2005; Muhlhausler 
et al., 2006) and this process continues into adult life in accordance with normal cell 
turnover, as well as the expansion of AT depots to accommodate caloric excess. De novo 
AT development occurs in multiple distinct sites including between the muscle and 
dermis (SC AT), and around the kidneys and heart.  These events are accompanied by 
functional and morphological changes that occur at the cellular level and lead to the 
formation and accretion of distinct AT depots in vivo (Hausman et al., 2001; Novakofski, 
28 
 
2004). Postnatal growth of AT can be attributed to adipogenic processes that lead to 
hyperplastic and hypertrophic expansion of the depot during the course of growth and 
development. The timing of this process differs between species and breeds, within 
species, as well as between depots in individual animals.  
Numerous detailed studies on the stages and indices of adipogenesis have been 
conducted due to the agricultural and biomedical ramifications of AT biology and 
pathology, particularly with respect to excessive AT accretion. Much of our 
understanding of these events has been established using in-vitro techniques and various 
cell-lines (Reviewed in Novakofski, 2004; Poulos et al., 2010), which form the basis for 
hypothesis driven in vivo experimentation. The advent of various molecular techniques 
and platforms has allowed substantial progress in understanding how cellular 
heterogeneity and local micro-environment can impact the capacity and extent to which 
adipogenesis occurs in an ordered and depot-specific manner.   
 
Regulation of Adipogenesis 
The process of adipogenesis occurs as a series of temporally regulated events that 
are responsible for the development and expansion of AT in specific depots.  Several 
studies have helped to determine that this process is under the control of specific 
transcription factors (TFs) that respond to environmental and developmental cues to 
establish and maintain adipogenic gene-expression paradigms (Christy et al., 1989; 
Totonoz et al., 1993; Yeh et al., 1994; MacDougald and Lane, 1995; Farmer, 2006). The 
gene products targeted by these TFs include metabolic proteins, lipid transport proteins, 
and those that impart hormone responsiveness on the adipocyte (Kim and Moustaid-
Moussa, 2000; Fruhbeck et al., 2001). The expression of these TFs and the genes that 
29 
 
they regulate follows a specific pattern that allows one to track the progression of the 
differentiation process and may provide molecular targets to enhance or depress the 
development of specific AT depots in beef cattle.  
The ultimate fate of adipocyte precursor cells is determined by several factors 
including the immediate endocrine/paracrine environment, adhesion to the extracellular 
matrix, the specific milieu of transcription and regulatory factors, and nutrient availability 
(Poulos et al., 2010; Hausman et al., 2009). The balance between pro- and anti-
adipogenic factors present in the local environment dictate the commitment to, and 
progression through the adipogenic cascade. A discussion of some of the well-
characterized adipogenic TFs and other markers of differentiation follows below. 
 
Peroxisome Proliferator Activated Receptors (PPARs) 
PPARs belong to a family of ligand-dependent nuclear receptor transcription 
factors and the role of these TFs as master regulators of adipogenesis is supported by 
both in vivo and in vitro studies. Members of the PPAR family included PPAR-alpha, 
PPAR-beta/delta, and PPAR-gamma (PPAR-?).  
In terms of adipogenesis, the regulatory transcription cascade is centered on the 
expression of PPAR?? (Wu et al., 1996; Chinetti et al., 2000; Rosen et al., 2000; Rosen 
and Speigleman, 2001).  PPAR??is a lipid activated nuclear receptor that is considered to 
be both necessary and sufficient to initiate the entire program of adipogenesis 
(Fernyhough et al., 2007; Rosen et al., 2000; Rosen and Speigleman, 2001; Wu et al., 
1999). PPAR? is expressed as two isoforms designated PPAR?1 and PPAR?2, which are 
PPAR?1 with the exception of an additional 30 amino acid segment at the N-terminus of 
30 
 
the peptide (Meirhaeghe et al., 2003). While the PPAR?1 isoform is expressed in various 
tissues, PPAR?2 expression appears to be restricted to AT. 
Thiazolidinediones are a class of pharmacological compounds that have insulin-
sensitizing effects and have been shown to be ligands for PPAR-
Komers et al., 1998; Chinetti et al., 2000). These compounds are effective at improving 
insulin sensitivity in part by the PPAR-? induced changes in expression of lipid 
metabolism and adipogenesis related genes including glucose transporters, FABPs, and 
lipoprotein lipase among other PPAR-? targets (Weng et al., 2006).  TZD?s have been 
shown to effectively induce adipogenesis in several in vitro systems including rodent, 
human, bovine, and porcine adipose tissue stromal-vascular cell cultures (Reviewed in 
Hausman et al., 2007) While these compounds are widely used in treatment of type-2 
diabetes, their usefulness in livestock species has garnered recent attention and warrants 
further consideration.      
 
CCAAT-Enhancer-Binding Proteins (C/EBPs)  
CCAAT-Enhancer-Binding Proteins (C/EBPs) are a family of TFs, which consists 
of six members (C/EBP? to C/EBP?). The expression of each of the C/EBP proteins 
occurs at specific time points during adipogenesis, indicative of their distinct roles in the 
process.  CEBP/? and CEBP/? are expressed early in adipogenesis and are responsible 
for initiating the differentiation of preadipocytes through eventual activation of PPAR?. 
Tang et al. (2003b) provided evidence for the role of CEBP/??in the adipogenic process. 
Using 3T3-L1 cells and murine embryonic fibroblasts (MEFs) they determined that 
CEBP/? is involved in regulating mitotic clonal expansion of preadipocytes which is 
31 
 
required for adipogenesis to occur, and precedes the expression of the TFs that give rise 
to the mature adipocyte phenotype (Tang et al., 2003a).  
 C/EBP? is also known as master regulator of adipogenesis and cooperates with 
PPAR? to establish and maintain the adipogenic program. In cell culture, expression of 
C/EBP? occurs relatively late during adipogenesis after the induction of PPAR? (Wu et 
al., 1998; Salma et al., 2006).  Rosen et al. (2002) determined that while important, 
C/EBP? is not required to drive adipogenesis in the same manner that PPAR? does. This 
suggests that PPAR? and C/EBP? induce adipogenesis using a single pathway with 
PPAR? playing the predominant role and C/EBP? acting in a feed-forward loop to drive 
PPAR??expression. Terminal adipogenesis appears to be dependent on C/EBP? because 
failure to express C/EBP? results in insulin resistance and failure to accumulate WAT in 
vivo suggesting that C/EBP? is required to maintain PPAR? expression which in turn 
contributes to insulin sensitivity and AT accretion (Wu et al., 1999).   
 
Preadipocyte Factor 1/DLK-1 
The differentiation of adipocyte progenitor cells to adipocytes is subject to 
induction by transcriptional activators and suppression by transcriptional inhibitors 
(Rosen and Speigleman, 2000). The balance of inhibitors and activators is responsible for 
the rate and extent of adipogenesis among a population of progenitor cells. Preadipocyte 
factor 1 (Pref-1) also known as delta-like kinase homolog 1 (DLK-1) is a member of the 
epidermal growth factor-like protein family that was identified during differential 
screening of cDNAs expressed in differentiating murine 3T3-L1 adipocytes (Smas and 
Sul, 1993). Pref-1 expression is high in preadipocytes and is down-regulated during the 
32 
 
conversion of preadipocytes to adipocytes. Smas and Sul (1993) reported that Pref-1 was 
important in maintaining the preadipocyte state and that overexpression of Pref-1 or 
addition of the soluble ectodomain of Pref-1 to 3T3-L1-cell culture was sufficient to 
prevent adipose conversion in these cells. Pref-1 also has an inhibitory effect on 
expression of PPAR-? and CEBP/?. Pref-1 mRNA and protein levels are high in 3T3-L1 
preadipocytes, but Pref-1 expression decreases during adipocyte differentiation and is 
absent in mature adipocytes. The role of Pref-1 as an anti-adipogenic TF has since been 
characterized in beef cattle and other livestock species with the bovine PREF1 gene being 
mapped to chromosome 21q24 (Minoshima et al., 2001) and its importance in 
adipogenesis particularly in the development of intramuscular fat has recently gained 
attention.   
Multipotent mesenchymal cells commit and differentiate into various cell lineages 
including myocytes, adipocytes, osteoblasts, and chondrocytes (Gesta et al., 2007). Pref-1 
is capable of regulating both the commitment of multipotent MSCs to a particular cell 
lineage and the maintenance of these committed cells in an immature state (Wang and 
Sul, 2009). Using Pref-1 knockout and transgenic murine embryonic fibroblasts, Wang 
and Sul (2009) were able to elucidate some details on Pref-1 mechanism of action. They 
showed that Pref-1 inhibits the differentiation of adipocytes by upregulating the 
expression of Sox9 which is expressed in progenitor cells that give rise to osteocytes and 
chondrocytes and promotes chondrogenic commitment. Cells that eventually give rise to 
chondrocytes and adipocytes come from a common source of MSCs; Sox9 inhibits 
adipogenesis while promoting chondrogenesis in this pool of progenitor cells. In light of 
these findings they concluded that down regulation of Sox9 is required for adipocyte 
33 
 
differentiation and that Pref-1 inhibits adipocyte differentiation through upregulation of 
Sox9 expression. Sox9 directly binds C/EBP?/? promoters and inhibits their expression 
thereby preventing adipocyte differentiation. Additionally they showed that Pref-1 
promotes chondrogenic determination of MSCs using a combination of Pref-1 null and 
overexpression mouse models. This provides in vitro evidence that Pref-1 directly targets 
and promotes Sox9 expression and that Pref-1 is capable of inhibiting MSC 
differentiation into adipocytes and directing them to chondrogenic lineage. 
Zinc-finger Protein 423 
Zinc finger protein 423 (Zfp423) is another recently identified regulator 
adipogenesis and preadipocyte determination that, unlike Pref-1, appears to promote the 
adipogenic events. Zfp423 is enriched in preadipogenic fibroblasts relative to non-
adipogenic fibroblasts and its expression occurs upstream of PPAR? (Gupta et al., 2010). 
Ablation of Zfp243 expression via short-hairpin RNA reduces PPAR? expression and 
impairs differentiation in 3T3-l1 preadipocytes. In undifferentiated cells, over-expression 
of Zfp423 results in PPAR-? induction and adipogenic differentiation of these cells 
(under appropriate differentiation conditions). Zfp423 has also been implicated as a 
regulator of neurologic development; therefore other specifying factors are likely to be 
involved in obtaining adipogenic competency in non-committed precursor cells. Taken 
together these data indicate a permissive role of Zfp243 in commitment of precursor cells 
to the adipogenic fate, and because Zfp243 is not expressed in non-adipogenic fibroblasts 
(Gupta et al., 2010) it may serve as a potential molecular marker to identify and define 
committed preadipocyte populations in vivo. 
 
Carbohydrate Response Element Binding Protein  
34 
 
Carbohydrate response element binding protein (ChREBP) is a regulatory 
transcription factor that enhances the expression of lipogenic enzymes in response to 
cellular carbohydrate metabolite levels. Studies have shown that targeted disruption of 
ChREBP results in aberrant lipid metabolism in vitro and in vivo. Lizuka et al. (2004) 
reported that mRNA levels for all of the major lipogenic enzyme genes, as well as for 
hepatic pyruvate kinase, were significantly lower in ChREBP-null mice fed a high-starch 
diet compared to those in WT mice. In addition these mice showed decreased liver TAG 
and total body lipogenesis providing compelling evidence for the role of the ChREBP TF 
in glucose regulation of lipid metabolism in vivo. Ishii et al. (2004) also provided 
evidence for the direct role of ChREBP in lipogenic gene expression. Using various 
reporter-constructs containing ACC, FAS, or LPK gene promoters they showed 
ChREBPs were responsive to glucose when transfected into WT but not ChREBP null 
hepatocytes. In addition glucose transactivation of the constructs in ChREBP null 
hepatocytes was restored by co-transfection with a functional ChREBP expression 
plasmid. Any role of ChREBP is acetate driven lipogenesis (as in ruminants) is not clear. 
 
Stearoyl-CoA Desaturase 
Stearoyl-CoA desaturase (SCD) catalyzes the delta-9 desaturation of saturated 
fatty acids to form monounsaturated fatty acids (MUFAs). It has been studied extensively 
in the biomedical community as it pertains to metabolic syndrome and other disorders 
(Biddinger et al., 2005). In mouse 3T3-L1 adipocytes, SCD mRNA expression is 
regulated by various factors including sterol regulatory element binding protein 1 which 
is considered a main regulator lipid metabolism (Le Lay et al., 2002; Horton et al., 2002). 
Ntambi et al. (1988) reported that SCD gene expression of 3T3-L1 preadipocyte is 
35 
 
increased in the early stage of adipocyte differentiation, suggesting an important role for 
SCD in adipogenesis and subsequent lipid metabolism. Mouse SCD1 and human SCD 
show conserved binding sites for the TFs SREBP1 and C/EBP-? in their promoter 
regions (Bene et al., 2001). In addition, these elements in the promoter region of bovine 
SCD are highly homologous to that of mice and humans (Keating et al., 2005), 
suggesting that SREBP1 and C/EBP-? are also important transcriptional factor of bovine 
SCD. In addition, Taniguchi et al. (2004) reported that SCD genotype is associated with 
the fatty acid composition in Japanese Black cattle. SCD is therefore related not only to 
biomedical issues but also to quality aspects of beef production. 
 
Fatty Acid Binding Proteins 
Fatty acid binding proteins (FABPs) are intracellular lipid-binding proteins 
involved in cell signaling and metabolism. These are small proteins that reversibly bind 
hydrophobic ligands including saturated and unsaturated fatty acids, eicosanoids, and 
other lipids. They are abundantly expressed in a tissue specific manner and found across 
all species from Drosophila melanogaster to humans and livestock demonstrating strong 
evolutionary conservation and apparent importance of these proteins in cell physiology 
(Zimmerman and Veerkamp, 2002). Adipocyte fatty acid binding protein (aFABP) also 
known as aP2 or FABP4 is an adipocyte specific FABP that has been used as reliable 
indicator of terminal differentiation and lipid metabolism. This is a predominant cytosolic 
protein that makes up about 5% of the total cellular protein content in mature adipocytes 
and is indicative of active lipid metabolism within the cell (Boord et al., 2002). aFABP 
contains a PPAR response element in its promoter region and has been shown to be a 
target of PPAR-g in the adipogenic cascade (Shin et al., 2009). aFABP has also been 
36 
 
implicated in the development of Metabolic Syndrome (MetS) and related maladies and 
mice deficient in A-FABP are protected from development of diet-induced and genetic 
obesity and he associated hyperinsulinemia, hyperglycemia, and insulin resistance (Uysal 
et al., 2000; Hotamisligil et al., 1996).    
 
Sterol Regulatory Element Binding Protein 
Sterol Regulatory Element Binding Proteins (SREBPs) are helix-loop-helix TFs 
involved in adipogenesis and lipid metabolism (Horton et al., 2002; Shimano et al., 2001; 
Tontonoz et al., 1993). Tontonoz et al. (1993) described what was known as adipocyte 
determination and differentiation factor-1 (ADD1) as a basic helix loop helix domain 
containing protein which was expressed in brown adipose tissue and showed increased 
expression during differentiation of adipocytes in cell culture. They also showed that 
ADD1 could bind and effect expression of the Fatty acid synthase (FAS) gene, but was 
incapable of binding to other bHLH domain containing genes indicating the sequence 
specificity of this TF.   
 
Adipogenesis in Beef Cattle 
The process of adipogenesis in beef cattle has been well studied by animal 
researchers seeking to improve production efficiency and product quality. In cattle early 
adipogenesis occurs during fetal development and during the early postnatal period (Du 
et al., 2010). Adipocytes are recruited from pluripotent mesenchymal stem cells (MSCs) 
that are induced to commit to the adipocyte lineage. Skeletal muscle has an abundant 
supply of these MSCs the majority of which are destined to become myocytes; however, 
a variable portion of these cells are committed to become intramuscular adipocytes and 
37 
 
give rise to IMF. Cell culture studies have provided information on the specific 
chronology of TF expression. As discussed previously, this process occurs as the result of 
several coordinated events and is characterized by differentiation (basically by the well 
know canonical mechanism) of preadipocytes to mature adipocytes capable of storing 
and metabolizing lipids, as well as secretion of various adipocyte specific proteins that 
affect whole body energy homeostasis.  
Adipose Tissue Depots 
AT is widely distributed throughout the body in mammals and can be separated 
into anatomically and physiologically distinct depots. Unlike the liver or other 
morphologically defined organs, AT is rather amorphous which can lead to an 
underestimation of the anatomical and functional organization of the tissue. In terms of 
anatomical location, at least 16 distinct AT depots have been identified in mammals. 
These depots have differential responses to hormonal, metabolic, and environmental cues 
(Pond, 1992). In the simplest of classification schemes, the two major anatomic 
subdivisions are the visceral AT (VAT) and the subcutaneous AT (SC) depots. These 
subdivisions can be further divided into subcategories. The VAT can be divided into 
intraperitoneal and retroperitoneal, the latter of which can be again divided into the 
omental and mesenteric AT. SC AT can be divided into the superficial SC and deep SC, 
which is considered to be more metabolically active.  In addition to the SC and VAT 
depots, there are also fat depots that surround the kidney, pelvis and heart (KPH), 
genitalia, and within and between muscle bundles (intramuscular and intermuscular fat, 
respectively). Several studies have identified differential metabolic and physiological 
characteristics between various AT depots and these depot-specific differences provide 
38 
 
opportunities to manipulate AT depots independently of one-another to manipulate beef 
carcass quality and provide consumers with a consistent and desirable product in an 
economically efficient manner. 
 
Depot Specific Metabolism of Adipose Tissue    
It is now generally accepted that not all AT depots are created equal with respect 
to their impact on energy partitioning, production efficiency, and meat-quality.  AT 
depots do not always exhibit uniform patterns of metabolism or response to external 
stimuli (Goodpaster et al., 2005; Kissebah and Krakower, 1994; Mersmann, 1998). 
Moreover, depot-specific differences in the presence and number of fat cell precursors 
may contribute to the differential rates of AT growth between depots, which can have 
economic implications. Temporal and developmental changes in AT metabolism have 
been recognized for some time now; however, spatial differences that result in marked 
physiological disparities between depots are less clearly defined. Because early studies of 
AT cellularity and physiology were conducted with rodent models and more specifically 
with the epididymal fat pad in these animals, regional differences in AT metabolism are 
only recently being understood and appreciated.  
In humans and rodents it has been shown that visceral adipose tissue (VAT) is a 
primary contributor to complications from diabetes and metabolic syndrome (MetS), 
while the subcutaneous adipose tissue (SAT) appears to play a neutral or protective role 
(Kisseba and Krakower, 1994; Giorgino, 2005). In addition several TFs and regulatory 
genes are differentially expressed in a depot specific manner, including C/EBP and 
carnitine palmitoyl transferase-1b (CPT-1b). These data indicate that the location and not 
the amount of fat per se is the causative agent resulting in the various maladies associated 
39 
 
with obesity and MetS. It has been shown that preadipocytes derived from subcutaneous 
AT show more accumulation of TAG compared to those derived from visceral AT.  
Like humans and rodents, AT depots in meat animals possess different cellular, 
metabolic, and developmental signatures, all of which have an impact on the amount and 
distribution of fat in meat products. The AT depots of beef cattle are likely to exhibit 
differential expression of regulatory factors in a depot-specific and adipogenic state-
specific manner which varies as the processes of growth and development occur. 
Targeting these factors is a potentially viable approach to independently manipulate AT 
depots to reduce overall carcass adiposity and simultaneously enhance IMF percentage in 
an economically efficient manner.  
In vitro evidence for depot-specific metabolism also exists. Chen et al. (2010) 
conducted a molecular comparison of adipocytes derived from porcine visceral and IMF 
AT using cell culture techniques. Mature adipocytes were isolated and purified from the 
respective depots and allowed to undergo dedifferentiation and redifferentiation in vitro. 
They observed that early in the differentiation process, both visceral and IMF adipocytes 
demonstrated similar abilities to accumulate lipid, however by day 10 post-
redifferentiation, the cells derived from IMF accumulated lipid at a much higher rate 
compared to visceral AT. The increased accumulation and metabolism of lipid was also 
supported by increased expression of lipogenic TFs including CEBP/? and PPAR-?. 
These data support the paradigm of depot specific metabolism and may have biomedical 
and agricultural implications in terms of therapeutic interventions and management 
strategies, respectively.  
40 
 
Torii et al. (1998) conducted a study to determine the role of PPAR-? in 
adipocytes within the longissimus muscle of beef cattle. Fibroblast-like cells were 
isolated from the longissimus muscle and cultured with known activators of murine 
PPAR-?. Using Oil Red O staining the authors observed dose dependent induction of 
differentiation using T-174, a thiazolidinedione compound that is a specific activator of 
PPAR-?. This study showed that bovine SM has adipose precursor cells that are 
responsive to PPAR-? ligands and can be induced to differentiate into mature adipocytes 
in vitro. 
Ohyama et al. (1998) reported increased differentiation of perirenal and IMF 
derived S-V cells isolated from Japanese Black cattle. Similarly, Wu et al. (2000) 
determined that treatment with PPAR-? agonists caused induction of differentiation 
although it was to a greater extent in omental derived S-V cells compared with those 
derived from SC AT once again highlighting differential depot-specific responses to the 
local hormonal and regulatory milieu. 
Pickworth et al. (2010) investigated differentiation state-specific gene expression 
as it related to carcass adiposity in steers selected for four levels of IMF. Their data 
indicated that differentiation state specific gene expression was not affected by the level 
of carcass adiposity; however, they observed differences between the SC and IMF depots 
indicating that adipogenesis in these distinct depots occurs independently. The expression 
of Pref-1 was higher in IMF versus SQ AT while PPAR-?, FAS, LPL, and FABP4 were 
higher in SC versus IMF. 
Grant et al. (2008) conducted a study to determine the differentiation capacity of 
stromo-vascular (S-V) cells isolated from bovine IMF and SC AT in response to 
41 
 
dexamethasone (DEX, a glucocorticoid analog) and troglitazone (TRO, a PPAR-? 
agonist). Cells were cultured in differentiation media with added TRO, DEX, or both. 
Control cells had no added TRO or DEX. The addition of TRO and/or DEX caused 
enhanced differentiation of S-V cells derived from both SC and IMF compared to control 
cells. Morphological assessment indicated nearly a two-fold increase in the proportion of 
adipogenic colonies. Interestingly, they identified inherent differences in the overall 
capacity to differentiate between the cell types with the percentage of differentiated cells 
being 6.4 fold higher in SC versus IMF-derived cells.      
Because AT metabolism is inextricably tied to whole-body energy metabolism, 
the dynamics of depot-specific adipose tissue metabolism are essential components to 
understanding metabolic efficiency and how it relates to overall production efficiency in 
beef cattle. The current understanding of depot specific adipose tissue metabolism in beef 
cattle is scarce although the advent of several molecular and genomic tools is expediting 
the rate at which information is acquired and analyzed.  
 
Adipose Tissue Distribution in Beef Cattle  
AT in beef cattle is separated into discreet depots that have varying economic 
impact on the value of the carcass (Dodson et al., 2010a; Dodson et al., 2010b). These 
include subcutaneous AT (SC which is located beneath the skin), visceral AT which is 
located around internal viscera (VAT), intermuscular fat which is located between 
muscles, and IMF located within the perimysium of an individual muscle. In terms of 
developmental order, VAT is generally quoted as developing first followed by SC, 
intermuscular and intramuscular fat. While the presence of at least some AT in various 
depots is obligatory for normal physiology, excessive partitioning of dietary energy into 
42 
 
expendable AT depots represents inefficient use of feedstuffs and has a negative impact 
on production efficiency. Strategies to selectively enhance the marbling depot will 
depend on using contemporary and classic tools to develop an improved understanding of 
depot-specific physiology and metabolism, as well as a more comprehensive 
understanding of the region-specific differences in adipocyte and preadipocyte biology 
(Dodson et al., 2010; Basu et al., 2009; Hausman et al., 2009). 
 
 
Intramuscular Fat 
Intramuscular fat (IMF) develops within the perimysial connective tissue that 
surrounds the myofibrillar bundles in SM tissue (Moody and Cassens, 1968). As 
mentioned before, this is distinct from intermuscular fat, which is the fat that is located 
between individual muscles of the same cut (Hoquette et al., 2009). The presence of 
intramuscular fat is responsible for important meat quality traits that have implications on 
beef cattle profitability and consumer preference. A longstanding goal of animal 
scientists has been to understand the processes that lead to IMF accumulation in attempts 
to manipulate these factors to improve product quality. The accumulation of 
intramuscular fat gives rise to the commercial trait known as ?marbling?, which has a 
direct impact on carcass value under the most meat quality grading systems.  
One of the confounding issues that have increased total fat content of meat 
products is the drive to increase IMF as a result of both consumer preference and the 
emphasis of IMF in the USDA quality grading system. While development of the IMF 
depot occurs in pre-natal and early post-natal life, accumulation of fat in the IMF depot 
occurs after other AT depots have reached a plateau. During this ?finishing phase? the 
43 
 
IMF depot expands and the animal attains a higher quality grade according to the USDA 
scale. The concomitant expansion of the earlier maturing subcutaneous and intermuscular 
fat depots prior to and during this finishing phase results in an undesirable increase in 
carcass adiposity and inefficient use of dietary energy. This has spearheaded efforts to 
enhance IMF development independently of other AT depots to improve production 
efficiency while producing a desirable and valuable product (Dodson et al., 2010; 
Pickworth et al., 2010; Smith et al., 2008; Sweeten et al., 1990).  
Intramuscular Fat is a Distinct Adipose Tissue Depot 
Regulation of IMF adipogenesis occurs at the cellular level and is under the 
control of the various TF and regulatory factors that govern metabolism in other AT 
depots (Hausman et al., 2009). Various studies have sought to understand the molecular 
components of marbling development in vitro and in vivo. While IMF is histologically a 
bona fide AT depot exhibiting the signature markers, extracellular matrix, and metabolic 
machinery present in other AT, it represents a distinct depot with specific differences in 
metabolism and responsiveness to external cues (Smith and Crouse, 1984; Hausman et al, 
2009).  
About 80% of the TAG in muscles is stored within the resident adipocytes that 
make up the IMF depot (Gondret et al., 1998). Intramyocellular lipid makes up the 
remaining portion of muscle TAG and contributes to the total fat content within the 
muscle; however, this is not discernible macroscopically and does not contribute to the 
quality grading to the extent that IMF AT does.  Rates of fatty acid (FA) synthesis in 
marbling are generally depressed relative to subcutaneous AT (SC; Hood and Allen, 
1978). In addition glucose appears also to be an important contributor to FA synthesis in 
44 
 
IMF compared to SC AT, which uses acetate as the primary carbon source (Smith and 
Crouse, 1984). Indeed feeding ruminant animals diets high in carbohydrates that escape 
rumen fermentation has been shown to increase visual marbling fat (Pethick et al., 1997). 
Adipocyte differentiation in skeletal muscle initiates from multipotent 
mesenchymal stem cells (MSCs), which are abundant in skeletal muscle at early 
developmental stages. In addition to providing the source of progenitor cells for 
myogenesis, a portion of this same population of MSCs gives rise to intramuscular 
adipocytes, which serve as the sites for the accumulation of IMF (Tong et al., 2009). 
While adipogenesis occurs during fetal and early post-natal life, accumulation of muscle 
lipid as a result of energy partitioning occurs later in life in beef cattle and other species 
(Du et al., 2010). 
Taniguchi et al. (2007) demonstrated that several genes are switched on and off 
during events associated with early adipogenesis and the authors suggest that a bovine 
specific gene network (distinct from that observed from cell lines derived from other 
species) was responsible for the adipogenic program in bovine IMF adipocytes. They 
showed that over 100 genes were differentially expressed and that these genes were 
involved in metabolic and cell signaling pathways. Of particular interest was the fact that 
typical adipogenic genes were upregulated during early differentiation of the 
preadipocytes. 
In a subsequent study the same group (Taniguchi et al., 2008) conducted 
experiments using non-transformed adipofibroblasts that were derived from de-
differentiation of mature adipocytes isolated from perimuscular fat tissue. These 
adipocytes were cultured with the appropriate media to induce differentiation and were 
45 
 
harvested daily for RNA isolation and gene expression analysis. They determined that the 
expression of PPAR-? and SREBP-1 along with the downstream targets of these TFs was 
co-expressed at day 2 post-differentiation. Among the genes co expressed were SCD, 
ELOVL6, FABP4, LDLR, and FAS.  
While it is generally accepted that adipocyte hyperplasia and hypertrophy is 
responsible for depot expansion, one unanswered question is the source of the progenitor 
cells that undergo adipogenesis. A recently discovered source of adipocytes that has been 
identified in vitro arise as a result of de-differentiation of mature adipocytes to form 
proliferation competent daughter cells capable of population expansion.  It is estimated 
that 1 in 100 adipocytes is capable of undergoing de-differentiation, proliferation, and 
then redifferentiation (Fernyhough et al., 2004) making this phenomenon a potentially 
viable target for inducing independent expansion of the IMF depot (Dodson et al., 2010). 
 
Marbling 
Marbling is a cattle industry term that refers to the appearance of white flecks of 
fat between myofibrillar bundles. Economically, marbling is a major determinant of 
carcass value in the United States (USDA, 1996), and consumers are often willing to pay 
a premium price for guaranteed quality beef (Hocquette et al., 2009). Carcasses receive a 
marbling score based on the subjective evaluation of the amount and distribution of IMF 
at the cut surface of the rib eye between the 12th and 13th rib. High marbling is 
appreciated by the consumer because of the perceived influence it has on sensory 
qualities such as tenderness and juiciness of meat products (Platter et al., 2005). The high 
value placed on marbling scores provides incentive and motive to manipulate the IMF 
depot to maximize profitability. At the cellular level, marbling can be enhanced through 
46 
 
the enlargement of existing adipocytes (hypertrophy) and through an increase in the 
number of adipocytes (hyperplasia) in the IMF depot (Du et al., 2010). These are 
physiological processes affected by genetics, nutrition, and environmental cues therefore 
elucidating the molecular regulatory network specific to the IMF depot is essential to 
improving efficiency of beef production (Harper and Pethick, 2004).  
There are several factors that affect IMF accumulation, which impacts the 
marbling scores and carcass value. The extent of marbling development varies between 
sexes and between different breeds of cattle. Some breeds have the genetic propensity to 
develop significant amounts of marbling and their carcasses are highly valued in markets 
around the world. Selection of sires and breeds of cattle that produce offspring with the 
genetic potential to direct energy into intramuscular adipocytes early in the growth period 
is a promising management strategy (Mir et al., 1999). Japanese Black cattle (JB) have 
the genetic potential to accumulate large amounts of IMF comprised of a high proportion 
of mono-unsaturated fatty acids (MUFA). These animals can accumulate IMF up to 40 
months of age with the typical finishing period beginning around 10 months and 
continuing for another 20 months. These cattle also deposit fat at a rate that exceeds that 
of less marbled breeds such as the Japanese Brown and Holstein breeds (Zembayashi et 
al., 1995).  The high MUFA content meat cuts from these animals a characteristic 
marbling composed of so called ?soft-fat,? due to the lower melting point of these fatty 
acids and this property is considered desirable to many consumers. 
Oshaki et al. (2007) isolated stromal-vascular cells from perirenal adipose tissues 
of Japanese Black and Holstein steers to investigate gene expression profile during 
bovine preadipocyte differentiation. They reported a 10.8 and 6.3-fold increase in the 
47 
 
level of SCD mRNA in Japanese Black and Holstein, respectively, on day 1 of the cell 
culture. Differential SCD expression between the two breeds may reflect the phenotypic 
differences in the fat development and fatty acid profile between the breeds. SREBP1 and 
C/EBP-alpha are supposed to regulate SCD expression; however, in their study 
expression of these two factors was not completely consistent with that of SCD 
expression. 
While it is known that cattle breeds have a differential propensity to develop 
regional adiposity, particularly IMF, the molecular cues responsible for this phenotypic 
variation is only recently being investigated. The high IMF in JB cattle appears to be due 
to an increase in the number of intramuscular adipocytes relative to other breeds; 
however an increase in adipocyte size was also reported (Gotoh et al., 2009). Wang et al. 
(2005) conducted microarray studies to compare the expression of genes in the 
longissimus muscle (LM) of JB and Holstein (HOL) cattle over an extended intensive 
feeding period. Using a consecutive biopsy approach they identified 335 genes that were 
differentially expressed between the breeds, which have differential propensities for 
adipose accretion. Genes that were upregulated in the LM of JB cattle included 
Adiponectin, SCD, FABP4, and LPL all of which are involved in unsaturated FA 
synthesis, adipogenesis and thyroid hormone pathways, while in HOL cattle, the genes 
involved in connective tissue and SM development were differentially expressed. The 
authors suggest that these data support the notion that at any given developmental time-
point, JB cattle have more active IMF development than HOL cattle on the same diet. 
Jurie et al. (2007) reported significant correlations between expression of A-FABP and 
TAG content in steers of different genotype that showed high or low levels of marbling. 
48 
 
Studies of this nature give important clues to the molecular basis for the unique 
intramuscular adiposity of the Japanese Black breed and shed light on important 
phenomena including the onset and rate of adipose tissue development, metabolic 
differences between breeds, and signaling pathways involved in lipid metabolism in beef 
cattle.  
Marbling development can also be altered by nutrition and management practices. 
Dietary roughage level has been shown to affect carcass adiposity in beef cattle. Yamada 
et al. (2009) subjected Wagyu steers to high, medium, and low roughage diets (HR, MR, 
and LR, respectively). They reported that dietary roughage levels induced differential 
expression of putative adipogenic TFs in mesenteric and intramuscular fat depots, while 
there were no effects of treatment on the SC depot. In mesenteric adipose tissue, the 
expression of C/EBP? in the LR and MR groups was significantly higher than that in the 
HR group.  Adipocytes in the LR and MR group had intramuscular adipocytes that were 
significantly smaller than in the HR group. These results indicate that nutritional 
management can affect the expression of key adipogenic genes and the cellularity of AT 
in a depot specific manner in beef cattle.   
In another study, Graugnard et al. (2009) looked at adipogenic gene networks in 
the longissimus lumborum muscle of Angus and Angus x Simmental cattle fed high- or 
low starch diets. They determined that various TFs and markers of mature adipose tissue 
differentiation were present during the early growing phase in these cattle. The genes 
identified appear to be part of a transcriptional network driven by the putative regulator 
of adipogenesis, PPAR-?. They concluded that this network is responsible for 
coordinating IMF metabolism and lipid filling during the early growth phase. 
49 
 
Understanding these and other gene networks that contribute to IMF maturation will 
provide strategies tailored to maximize the marbling trait and avert excess carcass 
adiposity (Hausman et al., 2009).  
 
Fat in Meat Products 
In addition to the direct effects on efficiency and the economic implications, there 
are concerns about the impact of animal fats including those contained in meat products, 
on consumer health (Kouba and Mourot, 2011; Colmenero, 2000; Wood et al. 1999; 
Colmenero, 1996; Bergen and Merkel, 1991). While red meat has been shown to be a 
valuable source of dietary protein and other essential nutrients including B-complex 
vitamins, iron and zinc, the fat content of diets containing red meat have been linked to 
the development of obesity and heart disease (Kontogianni et al., 2008; Kelemen et al., 
2005). Chronic overconsumption of dietary energy, particularly in the form of animal fat 
has been implicated in the onset of various metabolic diseases although it cannot be said 
definitively that the meat itself is the causative agent for this association (McAfee et al., 
2010). In response to these increasing pressures to eliminate animal products as a source 
of excess dietary fat, the amount and distribution of AT in meat animals has become a 
concern to animal researchers, industry stakeholders, and end product consumers. The 
burgeoning obesity epidemic has prompted studies to develop a more comprehensive 
understanding the molecular aspects of AT biology both in animals to minimize excess 
fat content in meat products, and in humans in order to identify biological markers and 
potential targets for therapeutic intervention (Hausman et al., 2009; Vernon et al., 1999). 
 
Post-Genomic Era Livestock Production 
50 
 
Livestock production in the post-genomic era is poised to benefit from innovative 
application of novel technologies originally designed for the sequencing of the human 
genome, which was completed in 2001 (Lander et al., 2001; Venter et al., 2001).  The 
pre-existing infrastructure and advanced stage of the available reagents will speed the rate 
at which livestock genomic data is obtained and made useful (Rothschild and Plastow, 
2007). With the DNA sequences of many livestock species available, discoveries in the 
laboratory are rapidly making their way to farms and commercial operations.  The Bovine 
Sequencing and Analysis Consortium were responsible for the publication of the 
sequence, annotation, and comparative analysis of the cattle genome in 2009.  
The already rapid adoption and innovation of these available platforms and 
technologies into livestock production is a testament to their effectiveness and usefulness 
in these systems. Indeed, several dedicated technologies for cattle, pigs, and other species 
have been developed to further improve economically relevant traits in these animals 
(Tsai et al., 2006; AffyMetrix, Santa Clara CA). In addition to the direct advantages to 
agriculture, another driving force for the improved technologies is the usefulness of 
livestock species as appropriate models suited for biomedical research (Ireland et al., 
2008; Gibbs et al., 2004).  
Zhao et al. (2010) looked at proteome differences associated with fat deposition in 
bovine SC AT. Using crossbred steers with different back fat thickness they sought to 
identify protein markers associated with fattening in beef cattle. Their result indicated 
that annexin 1 a protein was highly expressed in tissues of animals with increased back 
fat thickness identifying this protein as a potential marker for assessing this trait in beef 
cattle at various stages.  
51 
 
Barendse et al. (2007) conducted a study on 1472 cattle from various breeds to 
determine the association between single nucleotide polymorphisms in the bovine 
genome and phenotypic measures of and feed efficiency. Using this whole genome 
association approach they determined that DNA variants in or near proteins associated 
with energy usage were an order of magnitude more common compared to those 
affecting body mass and appetite.  The largest group of polymorphisms consisted of 
genomic regions containing promoter regions, micro-RNA motifs and other non-coding 
elements. This suggests that a substantial portion of the phenotypic variation in feed 
efficiency is due to differences in the regulatory aspects of gene expression.   
The use of genomic data to understand the molecular components of metabolic 
regulation will help identify targets to improve biological and production efficiency. 
Economically relevant traits such as lean muscle deposition and lipid accumulation are 
complex biological traits under the control of interacting gene networks (Wu and Lin, 
2006). These networks act coordinately to control and integrate energy metabolism, 
adipocyte differentiation, and protein turnover. Transcriptional regulation of key 
metabolic enzymes is under the control of various transcription factors which themselves 
belong to coordinately regulated gene networks. Metabolic pathways in higher organisms 
require transcriptional regulation as a long-term mechanism to control the levels of 
regulatory genes and enzymes (Desvergne et al., 2006). It has been shown that there is a 
high correlation between mRNA expression of target genes and the recruitment of 
transcription factors, nuclear receptors, and their co-regulatory factors.  This suggests that 
gene expression analysis is suitable for inferring transcriptional activity in various tissues. 
52 
 
With respect to coding genes, phenotypic changes or observations are attributable 
to gene expression (mRNA abundance) in so far as the changes in gene expression 
translate to changes in functional protein products. Efficiency of translation machinery 
can affect the rate at which proteins are translated and proteins are also subject to post-
translational modifications and proteolysis by various regulatory and housekeeping 
mechanisms adding an additional layer of complexity when trying to extrapolate from 
gene expression to functional protein expression.  Therefore, it is better to corroborate all 
three-genomic, transcriptomic and proteomic data in order to make a comprehensive 
assessment and deduction, although this is not always feasible, be it economically or 
otherwise. Taken all together, gene expression analysis has become a viable technique for 
understanding the dynamics of molecular regulation of metabolic pathways and for 
generating the basis for hypothesis driven and proof of function research.  
 
Summary 
It is evident that achieving maximum production efficiency while producing a 
desirable and valuable phenotype or product is paramount to the success of any beef 
cattle operation. There are several approaches, which can be taken ranging from selective 
breeding to nutritional management and administration of metabolic modifiers, all of 
which are aimed at minimizing production costs and increasing the yield of desirable 
product. The advent of genomics and the emergence of the post-genomic era have 
provided a viable avenue towards improving beef cattle production by allowing us to 
identify molecular components that are responsive to the various management and 
selection strategies being employed, and understanding those that are responsible for the 
fruition of profitable phenotypes. Elucidating and targeting these regulatory factors can 
53 
 
achieve rapid improvement in efficiency and profitability ensuring the long-term success 
and viability of the beef cattle industry. 
Changes in gene networks that control metabolic pathways such as adipogenesis 
and energy metabolism are controlled by regulatory factors that can be used to 
manipulate AT depots. Transcriptional networks coordinate adipocyte differentiation and 
energy metabolism in rodents and other species (Desvergene et al., 2005). The 
recruitment of lipogenic transcription factors and nuclear receptors to promoter regions is 
highly correlated with the mRNA expression of the genes they target (Bennet et al., 
2008). This suggests that gene expression (i.e. mRNA abundance) is useful for inferring 
transcriptional activity in vivo. Skeletal muscle tissue is a heterogeneous tissue composed 
of varying cell types (myocytes, non-differentiated stem cells, satellite cells, pre-
adipocytes, adipocytes, and components of the extracellular matrix).  Messenger RNA 
(mRNA) expression is but one of many regulatory factors to be considered when 
studying the complex molecular networks working simultaneously in a heterogeneous 
tissue such as SM. While care must be taken to avoid over simplification of the various 
mechanisms, gene expression profiling provides valid information to aid in future study 
design and identification of potential biomarkers. In addition cell specific makers may be 
used to elucidate the individual contribution of the constituent cells to the functionality of 
the network and the physiology of the tissue.  
  
54 
 
Chapter 3. Relationship between Residual Feed Intake and the 
Transcriptomic Signature of Performance Efficiency in Finishing Beef Cattle 
 
Introduction 
Feed represents the single largest input cost in most animal production systems 
and comprises about 70% of the input costs for commercial beef cattle operations (Herd 
at al., 1998; Liu et al., 2000; Arthur et al., 2001; Basarab et al., 2002). With the cost of 
feed continuing to rise, improvement in the efficiency of animal production remains one 
the highest priorities of animal science research and a necessity to ensure the viability of 
the beef cattle industry. Gibbs and McAllister (1999) reported that increasing FE by 5% 
would have an economic impact four times greater than a 5% enhancement in average 
daily gain (ADG). Swine and poultry producers have dramatically reduced the time and 
resources necessary to produce marketable animals through intense genetic selection for 
more efficient animals, and improvement in nutrition and management practices 
(Havenstein et al., 2003). During this time cattle have also experienced substantial 
improvement in overall efficiency although much of this has come as a result of grain 
feeding and other nutritional regimens, as well as reproductive and pharmaceutical 
technologies (Elam and Preston, 2004). While there is considerable variation for FE in 
beef cattle and this trait appears to be moderately heritable, there has been little 
improvement in FE or maintenance energy requirements during the past 50 years (Archer 
et al., 1999).  
Residual Feed Intake (RFI) is a measure of FE that is independent of growth and 
bodyweight, factors, which are confounding to other measures of feed efficiency making 
RFI a more reliable and attractive measure than traditional feed conversion ratios. Koch 
55 
 
et al. (1963) suggested that the feed requirements could be adjusted for body weight 
(BW) and weight gain by partitioning feed intake into an expected component which is 
the amount of food that an animal would be expected to eat based on the estimated 
requirements, and a residual component which is the actual amount of feed intake above 
or below the expected intake levels. RFI partitions the drivers of feed intake into two 
categories: the feed expected for a given level of performance, and a residual component. 
RFI can then be defined as the difference between the actual and expected feed intake for 
a given level of performance with more efficient animals having a lower (more negative) 
RFI compared to less efficient ones. Considerable variation exists for RFI within animal 
breeds and genetic strains, and the trait has been shown to be moderately heritable (Fan et 
al., 1995). There are several biological factors responsible for variation in RFI of which 
protein turnover and tissue metabolism have been implicated as major contributors 
(Richardson and Herd, 2004; Figure 3.1), although the specifics of how these pathways 
are involved are less understood.  
The need for improved FE along with the confounding factors for other measures 
of this parameter have driven interest and research in RFI into prominence (Hill and 
Azain, 2009). RFI is however difficult to measure due to the large number of animals 
required and the cost and labor involved in measuring individual animal weight and feed 
intake over a long period of time. These factors have led to a lack of adoption of RFI as a 
FE measure despite its advantages over other FE parameters. Predictive molecular 
markers for RFI have recently gained interest as an alternative to direct measurement, 
which would aid in the more practical implementation of RFI in management practice. 
Identification of predictive biomarkers for RFI will circumvent the need for individual 
56 
 
animal measurements and facilitate the use of RFI in breeding and management systems 
in order to improve the overall efficiency of animal production. 
 
Objective 
The objective of this study was to examine the expression of a panel of surrogate 
marker genes involved in lipid metabolism, energy balance, protein turnover and 
mitochondrial function and associate them with a measure of feed efficiency in finishing 
cattle.  The selected cattle were the initial progeny of a RFI selection program expected to 
produce divergent phenotypes with respect of RFI. Therefore, we sought to determine the 
relationship between the selected GE profiles and the individual RFI of these animals in 
an effort to identify specific pathways or genes that can be used as indicators of RFI 
and/or targeted to improved feed efficiency. These efforts would reduce the costs and 
labor associated with RFI measurement and will lead to expedited improvement in terms 
of selecting for efficient animals. 
 
Materials and Methods 
 
Animals:   
All procedures were conducted in accordance with the Auburn University 
Institutional Animal Care and Use Committee (IACUC No. 2004-0783).  A total of 15 
male cattle (n = 7 Bulls and 8 Steers) were selected from the initial progeny of a RFI 
selection program being established at the Alabama Black Belt Research and 
Experimentation Station.  All cattle were Angus sired and their dams were Angus x 
Simmental crosses. Calves were individually fed twice daily for 84 days and refusals 
were collected once daily (See Table 3.1 for diet composition). Animals were housed in a 
slatted floor barn and were allowed nose to nose contact for socialization. Feed intake 
57 
 
(FI) data was collected for each animal and used to determine residual feed intake (RFI) 
according to methods outlined by Okine et al. (2004). In addition for FI data, animals 
were weighed every two weeks for the duration of the experiment. Average daily gain 
and total weight gain were determined.  
 
Sample Collection: 
Adipose tissue and skeletal muscle biopsy samples were collected from each of 
the steers and bulls in the study. The cattle were restrained in a cattle chute and a 12 cm x 
12 cm area centered over the loin area just caudal to the 13th rib was shaved and 
scrubbed in preparation for aseptic excision of skeletal muscle biopsy samples. Lidocaine 
hydrochloride 2% (15ml) was infused with a sterile 20 gauge x 1 ? inch needle along the 
cranial and dorsal edges of the surgically prepared area to achieve local anesthesia.  The 
incision was extended ventromedially into the longissimus dorsi muscle. A 1g sample of 
the dorso-lateral aspect of the longissimus dorsi muscle was removed by sharp dissection. 
The skin incision was closed with #3 braided coated nylon sutures (POLYWEB, Webster 
Veterinary Supply, Sterling, MA) in a cruciate pattern. For AT sample collection, a skin 
incision was made in the tail head and approximately 1g of subcutaneous adipose tissue 
was surgically excised from the area. Again, the skin incision was closed with #3 braided 
coated nylon sutures. Samples were placed in labeled aluminum foil wrappers and 
immediately snap-frozen in liquid nitrogen until they could be transferred into a -80 ?C 
freezer for long-term storage and gene expression analysis.  
 
RNA Isolation: 
 Total RNA was isolated from the SM an AT biopsy samples using TriZol reagent 
58 
 
(Invitrogen Corp., Carlsbad, CA) following the manufacturers recommended protocol. 
The samples were removed from the -80 ?C freezer and placed into a container filled with 
liquid nitrogen. For each tissue sample, approximately 500mg of tissue was removed 
from the labeled foil wrapper and placed into a freezing cold metal mortar and pestle. The 
sample was then crushed hitting the pestle with a hammer and then the crushed tissue was 
placed into a tube containing 2ml of TriZol. The sample was then homogenized in this 
tube using a mechanical homogenizer (Polytron). Chloroform was then added to the 50ml 
tube; the tube was shaken for 15 seconds and then allowed to sit for 10 minutes. The 
contents were then transferred into a glass centrifuge tube which was then capped and 
placed into the chilled centrifuge maintained at 4 ?C. The samples were spun at 10,000x g 
for 10 minutes and then the upper aqueous supernatant was removed and placed into 
another sterile 50ml tube. The remaining portions placed into a labeled hazardous 
material disposal jar until it could be appropriately discarded.  
 A second extraction was then performed on the layer by adding 1ml of TriZol and 
1ml of Chloroform to the sterile tube containing the upper aqueous extract. Once again, 
samples were shaken for 15 seconds, allowed to sit for 10 minutes and then centrifuged 
again at 10,000 x g for 10 minutes. The supernatant was removed, placed into a sterile 
glass centrifuge tube and 1 volume of isopropanol was added. The samples were inverted 
to remove any precipitate that formed at the top of the tube and the samples were then 
centrifuged at 10,000 x g for 8 minutes. The isopropanol was gently discarded being 
careful not to disturb the nucleic acid precipitate which had formed on the side and 
bottom of the glass tube. Five ml of ice cold 70% ethanol was then added to the tube 
before a final spin at 10,000 x g. The ethanol was discarded and the RNA precipitate was 
59 
 
then re-suspended in 75-100?l of nuclease free water and placed into labeled, 2ml micro-
centrifuge tubes for storage and subsequent use.  
DeoxyRiboNuclease (DNAse) Treatment        
  Total RNA was subjected to deoxyribonuclease (DNAse) treatment using 
Turbo DNA Free (Kit No. AM1907M; Applied Biosystems/Ambion, Austin TX.).  20?l 
of the RNA isolate was transferred into a nuclease free tube and treated with 2?l of the 
supplied 10X reaction buffer and 1?g of DNAase (2 Units/?l). Samples were incubated at 
37 ?C for 30 minutes and then the DNAase reaction was stopped using 0.1 volumes 
(approximately 2.2?l) of DNAse inactivation reagent. Samples were allowed to sit for 2 
minutes at room temperature and then centrifuged to pellet the inactivation reagent and 
DNAase enzyme. The RNA supernatant was collected via pipette and placed into a new, 
labeled, micro-centrifuge tube.  DNAse treated samples were then purified using RNeasy 
mini RNA purification kits (Kit No. 74102; Qiagen, Vencia, CA). RNA quality was 
assessed using the 260nm/280nm absorbance determination observed via NanoDrop ND-
1000 spectrophotometer (Thermo Fisher Scientific, Willmington, DE). Acceptable 
quality RNA displaying a 260nm/280nm ratio of between 1.8 and 2.0 was then used for 
subsequent downstream procedures. 
 
cDNA Synthesis: 
Complimentary DNA (cDNA) was synthesized from the purified RNA isolate 
using high capacity cDNA Reverse Transcription kits (Kit # 4368814, Applied 
Biosystems, Foster City, CA). One microgram of DNAse treated total RNA was 
subjected to cDNA synthesis according to the manufacturer?s protocol and as follows.  
 
60 
 
cDNA Synthesis Master Mix 
The cDNA synthesis kit is supplied by the manufacturer as individual components 
that are mixed to create a 1x 20?l reaction (see table 3.2). These components were 
allowed to thaw on ice and then the appropriate volumes were mixed for the RT reaction. 
A single reaction calls for adding 2?l of 10x RT buffer to a sterile micro-centrifuge tube 
followed by 4.2?l nuclease free water, 0.8ul of 25x deoxy nucleoside tri-phopsphates 
(dNTP) mixture, 2.0?l of random RT primers and finally 1.0?l of multiscribe reverse 
transcriptase (added last to prevent premature or non-specific polymerization). For 
consistency, a sufficient amount of master-mix containing reaction buffer, dNTP?s, 
random oligonucleotide primers, and reverse transcriptase (RT) was prepared for each set 
of tissue samples. Once the master mix was created it was stored on ice. 
 
Diluting RNA 
For each tissue/RNA sample, one-microgram of RNA was diluted to 10?l using 
certified nuclease-free water. This 10?l RNA solution was the transferred into a labeled, 
0.2ml PCR tube and 10?l of the previously prepared master-mix was added to make for a 
20?l reaction. The reaction was gently mixed by pipetting and then centrifuged to remove 
air bubbles and bring all reaction components to the bottom of the tube. The tubes were 
then placed back into a PCR cooler to maintain the temperature while the other tubes 
were centrifuged. 
 
 cDNA Synthesis 
The tubes containing the 20?l reaction were then placed in the PCR thermocycler 
and subjected to the 3-step thermocycling protocol recommended by the manufacturer 
61 
 
(Table 3.3). The 1st step is an initial primer annealing step (25 ?C for 10 Minutes). This 
primer incubation step is necessary to maximize primer-RNA template binding when 
using oligo d (T) primers for first-strand cDNA synthesis. This step was then followed by 
an elongation step (37 ?C for 120min) and finally an enzyme deactivation step (85 ?C for 
5 Min). Upon completion of these steps, samples were held at 4 ?C in the thermocycler 
until they were transferred to a -20 ?C freezer for storage. The resulting cDNA (20ng/?l) 
was used for quantitative Real-Time PCR (qRT-PCR) to determine the expression of 
specific target genes. 
 
Gene Expression Assays 
 
Assay Design 
Custom TaqMan gene expression assays (Applied Biosystems, Foster City, CA) 
were designed for each of the genes of interest (see table 3.4) using the proprietary 
Applied Biosystems Custom TaqMan? Assay Design Tool (Applied Biosystems, Foster 
City, CA). Available sequence data was obtained from the National Center for 
Biotechnology Information database (NCBI; http://www.ncbi.nlm.nih.gov/nucleotide) for 
each gene and the online tool utilized the genomic input information, specifically mRNA 
sequence data, to generate custom primers and a sequence-specific fluorescent probe for 
real-time gene expression analysis (see Table 3.4). 
  
TaqMan Gene Expression Assay 
Gene expression analysis was conducted using TaqMan? Gene Expression assay 
chemistry (Figure 3.2), which uses a pair of custom, unlabeled PCR primers to target the 
gene of interest. In addition, a sequence-specific custom TaqMan? probe with a 6-
62 
 
carboxyfluorescein (FAM?) fluorescent-dye label and minor groove binding (MGB) 
moiety on the 5' end, and 6-carboxy-tetramethyl-rhodamine (TAMRA) non-fluorescent 
quencher (NFQ) dye on the 3' end is used to monitor the accumulation of amplified 
product as the PCR cycles progress. This probe makes use of a NFQ and the principles of 
fluorescence resonance energy transfer (FRET) to suppress the fluorescence emitted by 
the FAM reporter dye while they are held in close proximity at the ends of the intact 
probe. After primer and probe annealing, polymerization occurs and the intrinsic 5? 
exonuclease activity of the Taq polymerase causes cleavage of the 5? reporter dye from 
the probe resulting in a fluorescent signal due to displacement from the non-fluorescent 
quencher. The Real-Time machine then detects and records the fluorescent signal once 
during each PCR cycle. The use of this custom fluorescent probe enables the specific 
detection of the target gene product as it accumulates during the polymerase reaction 
because the fluorescent signal increases as the exponential amplification of the target 
gene occurs.  
 
Threshold Cycle (Ct) 
The cycle at which the fluorescent signal surpasses a pre-determined level is 
known as the threshold cycle (Ct). The Ct is recorded and used in the quantification 
calculations to determine relative expression between samples and tissues. The 
fluorescence detected at each cycle, including the Ct, is directly proportional to the 
amount of fluorophore released during amplification and is indicative of the amount of 
cDNA template present at any given cycle. With that said the Ct value should be 
proportional to the initial amount of target transcript present in the sample and reflect the 
relative mRNA abundance when compared to the Ct from other samples.  
63 
 
Graphically, the threshold cycle for a given sample is defined as the point where 
the threshold line intersects the amplification plot which is readily observable using the 
real-time monitoring platform. The threshold, which can be manually adjusted, should be 
set above the baseline of the plot which includes the early cycles in which exponential 
amplification is not occurring. It should also fall within the exponential region of the 
curve in which the target sequence is being amplified exponentially. The SDS Software 
(v2.0.3 Applied Biosystems, Foster City, CA) automatically calculates the baseline and 
threshold values for a detector based on the assumption that the data exhibit the ?typical? 
amplification curve with optimal amplification efficiency. In each experiment, 
amplification curves were observed and it was determined whether or not the automatic 
threshold value was occurred with the exponential portion of the curve. If not the 
threshold was manually adjusted to ensure proper quantification. 
 
Relative Quantification Protocol 
Relative quantification (RQ) determines the change in expression of a nucleic 
acid sequence(target) in a test sample relative to the same sequence in a calibrator sample 
by comparing the Ct values between these samples and adjusting for the initial cDNA 
loading concentrations using an internal control gene (Pfaffl, 2003). The calibrator 
sample can be a pooled control sample, an untreated sample, a sample at zero time-point 
etc. (Pfaffl, 2003; Livak and Schmittgen, 2001). The calibrator has its expression set to 1 
or 100% and each of the other samples are quantified and expressed as a function of the 
calibrator sample. In the current study a pooled cDNA sample, representative of all 
treatments groups, was used as the calibrator sample for each qPCR run.  
 
64 
 
qPCR Reaction Set-up 
For each 20?l multiplexed reaction the reaction components and PCR cycling 
conditions were as indicated in table 3.5 and Table 3.6, respectively. For each reaction 
1?l (50ng) of cDNA synthesis reaction product was diluted to 8?l in a 2ml nuclease-free 
micro-centrifuge tube and placed on ice.  A sufficient amount of master mix containing 
enough TaqMan gene expression master mix (Part No. 4369016; Applied Biosystems 
Foster City, CA), 18S probe, and the custom gene expression assay was prepared for the 
tissue/gene of interest and then placed on ice. For each sample 12?l of master mix was 
added to the tube containing the 8ul cDNA solution. The reactants were gently mixed and 
then the 20?l reaction was pipetted into individual tubes in 0.2ml MicroAmp? Optical 8-
Tube Strips (Part No. 4316567; Applied Biosystems, Foster City, CA) that were resting 
in a chilled PCR cooler. The strips were then capped with MicroAmp? Optical 8-Cap 
Strip (Part No. 4323032 Applied Biosystems, Foster City CA) and centrifuged gently to 
remove air bubbles and ensure all components of the reaction were at the bottom of the 
tube. For each tissue sample, the multiplexed assay was run in duplicate and this process 
was repeated for each gene/tissue combination. 
 
Quantitative Real-Time PCR 
Quantitative Real-Time PCR was conducted to determine relative mRNA 
abundance for each of the genes of interest using an ABI7500 thermocycler and the 
relative quantification protocol of the SDS software package. The individual 8-tube strips 
containing the multiplexed reaction were placed into the sliding tray of the PCR machine 
and then sample location and identification were entered into the protocol. The samples 
were then subjected to qRT-PCR using the thermocyling conditions outlined in Table 3.6.  
65 
 
 
Quantitative Real-Time PCR Efficiency Determination 
The RQ method of quantification used by the SDS software system assumes 
optimal reaction efficiency or doubling of template cDNA during each reaction cycle.  
The manufacturer purports 100% reaction efficiency from properly designed TaqMan 
gene expression assays, however efficiency can be affected by various factors including 
the presence of PCR inhibitors or other contaminants thus the PCR efficiency was 
determined for each assay using comparative Ct analysis for serial dilutions from a 
pooled control cDNA sample. For each assay, cDNA dilutions of 10?g, 5?g; 1?g, and, 
0.1?g were prepared from a pooled sample of cDNA. Real-time analysis was then 
conducted and Ct values determined for each of the dilutions. A plot of the log cDNA 
input versus the Ct value was generated and the slope of the line determined. Assuming 
100% efficiency, the value of this slope should be -3.32. This indicates that it takes 3.32 
cycles to increase the Ct value by a factor of 10. This stands to reason because assuming 
that the amount of template doubles each cycle the relative amount of template can be 
determined using the equation: X*2^Y=n where X=initial cDNA input and n=amount of 
template at cycle Y. In the case of optimal efficiency as determined by dilution 2^3.32 is 
equal to 10. In other words, for each 10-fold change in cDNA input, there is a 3.32 
change in Ct value. The actual efficiency (E) of the reaction can be determined using the 
equation E= 10 (-1/slope)-1. All assays showing efficiencies of equal to or greater than 1.9 
were deemed acceptable for data analysis.  
 
Data Normalization 
66 
 
In order to ensure accurate quantification of mRNA abundance, data must be 
normalized to an internal control gene to account for variation in initial cDNA input 
amounts. TaqMan chemistry allows for multiplexed PCR reactions when probes are 
designed using different reporter dyes. In the current experiment 18s ribosomal RNA was 
used as the endogenous control gene in a multiplexed real-time reaction. The 18s rRNA 
assay was designed with a separate, sequence-specific VIC?-labeled fluorescent probe 
and the cDNA?s for the target gene and the 18s rRNA were co-amplified for each sample. 
The Ct values were recorded for simultaneously for both the 18s and target gene 
amplification and the ABI 7500 sequence detection system software (SDS; v2.0.3, 
Applied Biosystems, Foster City, CA) software calculates the relative expression value 
based on the normalized expression data from the multiplexed reaction. 
 
RFI Calculations: 
RFI values for this study were estimated as outlined by Okine et al., (2004) and 
modified by Anderson et al (Auburn University;Personal Communication). RFI for bulls 
and steers were calculated for each sex, respectively using the regression equation of:  
Equation 1:    Daily DMI = ?0 + ?1 (ADG) + ?2(MidWT0.75) +RFI,      
where daily DMI is the average daily feed intake, ?0 is the regression intercept, ?1 is the 
partial regression coefficient of daily intake on ADG and ?2 is the partial regression 
coefficient of daily intake on body weight. ADG and MidWt0.75 were used as regressors 
on daily DMI (SAS Inst., Inc., Cary, NC, 2003).  RFI is the error term of the model.  
 
Statistical Analysis: 
67 
 
The data were analyzed using the GLM Procedure (SAS Inst. Inc., Cary, NC) as a 
completely randomized design, with animal as the experimental unit.The mean relative 
gene expression (RQ) for each gene was compared with RFI each animal. For simple 
means comparisons of animal performance, gene expression, and RFI the PROC GLM 
procedure of SAS was used (SAS Inst. Inc., Cary, NC). The Proc CORR procedure of 
SAS was used to determine correlation coefficients between performance traits and 
average relative expression for each gene.  All Differences were considered significant at 
P < 0.05. 
 
Results and Discussion 
 
Animal Performance and Residual Feed Intake: 
 
The animals used in the current experiment were the initial progeny of a breeding 
experiment designed to select for RFI under production conditions in the SE US. 
Determination of RFI provides a measure of how animals perform relative to their 
contemporaries under the same circumstances and may be a more accurate measure of 
feed efficiency compared to other determinations. It has been shown that castration has 
an effect on animal feed efficiency and markedly increases IMF in LM of beef cattle 
(Park et al., 2002).  Sixteen male progeny of the initial calf crop were used, one half of 
them castrated (Steers) and the rest intact (Bulls). Animals were fed a corn and soy based 
ration (Table 3.1) and daily feed intake and refusals were determined.  Residual feed 
intake was calculated for each of the animals in the trial using equation 1. As expected 
using the regression equation, the mean RFI for the animals within the contemporary 
group was equal to zero (Table 3. 7) with some animals having lower or higher RFIs.  
68 
 
Our hypothesis was that bulls would have lower (more efficient) RFI compared to 
steers however there was no segregation by reproductive status. The range of RFI values 
varied within and between groups (Figure 3.7) with both having some high and low RFI 
animals indicating varying efficiency between animals regardless of reproductive status.  
We did not have a wide range of segregation of RFI values between animals (Figure 3.7). 
An arbitrary classification scheme was utilized to categorize animals into low, medium or 
high RFI based on their deviation from the mean (Table 3.7). Most cattle (9 out of 15) 
had RFI values that fell within one standard deviation of the mean (Table 3.7; standard 
deviation = 0.51). Based on this classification scheme, 9 animals were classified as 
average RFI. One bull and two steers were designated low RFI indicating greater 
efficiency in these animals compared to their average and high RFI contemporaries. 
Three of the animals were classified as high RFI (inefficient). Once again this was an 
arbitrary classification scheme in order to further mine our data based on biological 
efficiency of the individual animals.  
One possible explanation for the lack of segregation in RFI is the small amount of 
animal numbers used in this preliminary experiment. In addition, as mentioned in the 
methods, these cattle were the 1st generation progeny of an RFI selection program 
intended to generate a divergent population of cattle with respect to RFI. In such a 
system, subsequent generations of progeny would be expected to achieve more 
divergence in RFI and may have resulted in a more segregated population of animals with 
respect to this efficiency parameter. Between, and within contemporary groups, there 
appeared to be a large amount of inter-animal variation indicating variable performance 
with respect to RFI. There was no effect of sex on RFI therefore we considered all 
69 
 
animals as one group when doing correlation analysis. We found that when taken on 
whole, the expression of some target genes was correlated with RFI and as such may 
warrant further research to determine exactly which components of their corresponding 
pathways are potentially related to RFI. These data are discussed below. 
 
Gene Expression 
Richardson and Herd (2004) suggested that up to 37% of the variation in RFI 
observed in beef cattle was due to tissue metabolism, protein turnover, and stress (Figure 
3.1). The panels of selected genes in the current experiment represent regulatory sentinels 
responsible for governing the flux of nutrients to and from economically relevant tissues 
and orchestrating energy metabolism within these tissues. These are putative and novel 
factors that have been implicated in the dynamics of AT accretion, SM metabolism, 
mitochondrial function, and energy partitioning which are important processes that affect 
metabolic efficiency and carcass characteristics and production efficiency. We expected 
that RFI would be related to the expression patterns of these regulatory factors and that 
these expression profiles would be informative in terms of the molecular mechanisms that 
contribute to variation in RFI.  
 
Adipose Tissue 
In Adipose tissue, the expression of FAS, leptin and PPAR?2 was determined. 
These genes are involved in AT metabolism and indicative of metabolic activity in the 
tissue. Correlation analysis indicated that these genes were unrelated to RFI (Figure 3.8; 
P > 0.05). There were no differences in expression of FAS (Figure 3.13) or PPAR?2 
(Figure 3.15) between bulls and steers and this was not correlated with RFI. Interestingly, 
70 
 
AT Leptin GE was higher (P < 0.05) in bulls compared to steers (Figure 3.14). This may 
be the result of increased AT deposition in bulls as leptin serves as an indicator of AT 
abundance. Considering the effect of castration on carcass adiposity, this is an interesting 
observation given that bulls generally produce leaner carcasses than steers and leptin is 
an indicator of fat cell size and fat cover in cattle (Delavaud et al., 2002). In many species 
including cattle, circulating levels of leptin and expression of the leptin (ob) gene are 
indicative of adipocyte size and volume (Delavaud et al., 2007; Chilliard et al., 2005; 
Delevaud et al., 2002; Soukas et al., 2000).  There are several short and long term 
regulatory mechanisms that impact leptin expression which may have played a role in the 
current experiment. Of particular interest is body fatness which regulates leptin and its 
response to other factors such as feeding level and physiological status. Because of 
infrastructure limitations we did not look at carcass adiposity in this study; however, this 
would have been very valuable in order to relate our leptin expression data to a known 
factor that regulates leptin expression.  
 
Skeletal Muscle 
 
In SM, UCP-2 and E2 were positively correlated with RFI (Table 3.7; P < 0.05). 
These genes are involved in energy oxidation and protein turnover, respectively.  
Mitochondria serve as the primary site for cellular energy production accounting for 
upwards of 90% of cellular ATP production. Because of the predominant role of 
mitochondria in energy balance, it has been hypothesized that differences in 
mitochondrial function may be responsible for disparities in FE amongst groups and 
breeds of animals. Differences in mitochondrial function have been reported pigs, 
broilers, and steers divergently selected for RFI (Grubbs et al., 2013; Kolath et al., 2006a; 
71 
 
Lancaster et al., 2007). In the current study, expression of UCP-2 was positively 
correlated with RFI (Table 3.7; P = 0.02) and with the expression of poly-ubiquitin (P = 
0.006) and E2 Conjugase indicating that more efficient animals had lower expression of 
the genes involved in skeletal muscle protein turnover and mitochondrial uncoupling.  
In 2006 Kolath et al., conducted a study to determine the relationship between 
RFI and mitochondrial function in finishing Angus steers. In their study they conducted a 
feeding trial to identify low and high RFI steers and used only these animals in 
subsequent analysis. There were no differences in expression of UCP2 and 3 in high and 
low RFI steers and it was determined that the expression (mRNA abundance) of these 
proteins was not related to RFI in finishing steers. It is worth noting that in the Kolath 
study the author?s collected LM samples post-slaughter as opposed to the biopsy samples 
that were collected from live animals in the current study.  
Kelly et al. (2011) conducted a study to determine the effect of phenotypic 
ranking for RFI on the expression of genes involved in the mitochondrial respiratory 
chain and TFs responsible for mitochondrial biogenesis. Beef heifers were subjected to 
contrasting feeding regimens (High forage vs. High Concentrate) and RFI was 
determined. Similar to the Kolath study, these authors selected animals that were either 
feed inefficient (High RFI, low FE; n=10) or feed efficient (Low RFI, High FE, n=10) to 
represent phenotypic extremes. They determined that UCP3 expression tended to be up-
regulated (2.2-fold, P = 0.06) for in high-RFI versus low-RFI animals. PGC-1? which is 
a master regulator of mitochondrial biogenesis was 1.7-fold higher (P = 0.01) in low 
compared with high-RFI animals. These data along with the data from the current study 
suggest an important role for mitochondria in improving feed efficiency.  
72 
 
Much of skeletal muscle metabolism is devoted to the dynamics of protein 
synthesis and degradation. In meat producing animals, protein metabolism is important 
because the net differences between protein synthesis and protein degradation is the 
determining factor in the amount of marketable product (Bergen, 2008). Additionally, the 
process of protein synthesis requires dietary energy therefore the rates of synthesis and 
degradation affect the efficiency of animal production. Ubiquitin (Ub) is a regulatory 
protein that is located in nearly all-eukaryotic cells as its name implies. Ub can exist as a 
monomer or in the form of Polyubiqutin (UB), which is a homo-polymer of ubiquitin 
molecules. Among its various signaling functions, ubiquitin is best recognized for its role 
in targeting proteins to the UPS for degradation and recycling. (Pickart and Fushman, 
2004). Ubiquitin Conjugating Enzymes (E2) performs the second step in the 
ubiquitination reaction that targets proteins for degradation via the ubiquitin-proteasome 
system (UPS). While UB expression was not correlated with RFI (Table 3.7) it was 
highly correlated with E2 GE. This is to be expected, as these are two components of the 
ubiquitin proteasome protein degradation system and would be expected to be auto-
correlated. An autocorrelation of this nature makes physiological sense and may help to 
validate the technical soundness of the approach despite large amounts inter-animal 
variation within and between bulls and steers. 
Energetic homeostasis is determined by the balance between energy consumed 
and stored versus energy requirements and oxidation in an organism. Energy oxidation, 
particularly FA oxidation is an important component of the energy equation and the 
balance between storage and oxidation can determine the net accretion of AT in beef 
cattle. To determine the energetic disposition of the finishing animals we looked at 
73 
 
oxidative gene expression as indicators of oxidative metabolism. CPT-1b is the rate-
limiting enzyme controlling the oxidation of FA in skeletal muscle mitochondria. There 
were no differences between the expression of CPT-1b in bulls and steers (Figure 3.12) 
and there was no significant correlation with RFI (Table 3.7). Similarly, PPAR??another 
indicator of cellular energy oxidation was not correlated with RFI (Table 3.7). Because 
energy expenditure is a major component of the energy balance equation, it was 
interesting to note that there were no differences in the energy oxidation genes between 
bulls and steers in the current experiment. Again, variable performance between groups 
may be responsible for the lack of a clear expression pattern with respect to these genes, 
but we expected that these genes might be associated with RFI which was not the case. 
While these genes may be involved in the overall energy equation, the lack of segregation 
in performance efficiency that we observed may have diluted the relationship of these 
genes with RFI.  
Adipose tissue genes and genes for fat deposition appeared to be minimally 
related to RFI and FE (Table 3.7). It is possible that the range of RFI values in bulls and 
steers was too narrow to ascertain a relationship between fattening and feed efficiency. 
Our results do suggest that feed efficiency, measured as RFI is related to the expression 
of genes involved in SM metabolism as well as weakly correlated with other metabolic 
genes (Table 3.7). The current project utilized only 15 animals and future work will be 
focused on identifying other genes related to RFI in an effort to find predictive 
biomarkers that can be used to determine RFI without the need for intense and laborious 
data collection. Such studies should include a divergently selected (High vs. Low RFI) 
population of animals so as to have a more robust model of RFI phenotypes.  
74 
 
Identification of putative molecular markers of RFI will also make the cost of 
determining RFI more reasonable which can lead to this efficiency parameter being more 
readily adopted as a selection tool.  
Serial tissue sampling may also be warranted to garner a longitudinal perspective 
of the transcriptomic signature as the animal progresses through the finishing process. 
This would be informative in terms of determining how ?highly-efficient (low RFI) 
animals? profile compared to their ?less-efficient (high RFI)? counterparts. Furthermore, 
this may elucidate early developmental time points at which low- vs. high- RFI animals 
can be identified, which would be the ultimate practical use of this methodology. The 
goal of this work will be to provide an improved method of more rapidly identifying 
efficient animals and to maximize the potential for RFI to reduce the overall cost and 
environmental impact of beef cattle production. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
75 
 
Chapter 3 Figures 
 
Figure 3.1 Contributions of biological mechanisms to variation in residual feed 
intake as determined from experiments using divergently selected cattle1  
 
 
 
1Circled area represents pathways controlled by modulations in gene expression 
and other factors; Determined from experiments using divergently selected cattle; 
Modified from Richardson, E. C., and R. M. Herd. 2004. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76 
 
Table 3.1 Experimental diet composition1 
 
Ingredient Percentage 
Cracked Corn 28% 
Cotton Seed Meal 7% 
Soyhull pellets 60% 
Syrup 3% 
Bovitec Minerals 2% 
 
1Experimental Diet composition only; Proximate analysis data not 
presented; Diet was fed ad libitum with refusals collected daily 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
77 
 
           Table 3.2 High-capacity cDNA synthesis reaction components1  
 
 
 
 
 
 
 
 
1Individual Reaction components for complementary DNA synthesis from total 
RNA isolate. For each tissue a master mix containing sufficient quantities of the 
individual reaction components was created and cDNA synthesis was conducted 
for each RNA sample; RT- Reverse Transcription; dNTP- deoxynucleoside 
triphosphates. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Component Volume/Reaction (?l) 
10x RT Buffer 2 
25x dNTP Mix (100mM) 0.8 
10X Random RT Primers 2 
MultiScribe Reverse Transcriptase 1 
Nuclease Free Water 4.2 
Total Reaction Volume 10 
78 
 
 Table 3.3 cDNA synthesis reaction conditions1  
 
  Step 1 Step 2 Step 3 Step 4 
Temperature (?C) 25?C 37?C 85?C 4?C 
Time 10min 120min 5min Hold 
 
1Reaction conditions for the 4 step cDNA synthesis reaction; Temperatures were 
controlled in PCR thermocycler; upon completion, reactions were held at 4?C 
until samples were removed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79 
 
Table 3.4 TaqMan gene-specific primer and TaqMan probe sequences for the genes and tissues of interest 
 
Gene of 
interest Accession No.
 Forward Primer Reverse Primer TaqMan Probe Tissue 
FAS NM_00102669 CCTGGCCTTCGTGAGCAT AGCACGGCGTAGCCA CAGCAGCCATGCCCTT AT 
Leptin NM_173928 GCCTTCCAGAAATGTGGTCCAAATA CGGCCAGCAGGTGGA CCTCCGGGACCTTC AT 
PPAR-gamma Y12420 CCGCTGACCAAAGCAAAGG AGTTCATGTCATAGATAACAAACGGTGAT TTTCCCGTCAAGATCG AT 
Poly-Ubiquitin NM_174133 ACCTGGTCCTCCGTCTGA CCTCCAGGGTGATGGTCTTG CCGGTCAGGGTCTTCA LM 
Ubiquitin E2 LOC100138178 AGTTCATGCATAGATA GCCTTCCAGAAATGTGGTCCA  LM 
CPT-1b NM_001034349 GACTGGCAGCCCTCACT CTTGTTCTTGCCAGAGCTGAAG CCCACTCCACTCTTCC LM 
PPAR-alpha AF229356 TTGCCGGGAAGACCAACA CCATACACAGCGTCTCCATGTC ACAACCCCGCCTTTCT LM 
UCP-2 AF127029 GGACTCTGGAAAGGGACATCTC CCAGCTCAGCACAGTTGACA TCGCTCGCAATGCCAT LM 
      
 
1Accession No. = NCBI database accession number (http://www.ncbi.nlm.nih.gov/); FAS- Fatty Acid Synthase; PPAR-gamma - 
Peroxisome proliferator activator protein gamma; Ubiquitin E2 ? E2 Ligase; CPT-1b - Carnitine Palmitoyl Transferase 1b; PPAR-
alpha - Peroxisome proliferator activator protein alpha; UCP-2 ?Uncoupling protein 2; AT- Tail Head Adipose Tissue Biopsies; LM- 
Longissimus dorsi Muscle Tissue Biopsies. 
80 
 
Table 3.5 Reaction components for Real-Time PCR gene expression analysis 
  
Component Volume 
TaqMan Gene Expression Master Mix 8?l 
Vic-Labeled 18S rRNA Probe1 1?l 
Custom TaqMAn Gene Expression Assay 1?l 
cDNA2 + H20 10?l 
Total Reaction Volume 20?l 
 
118S Vic Labeled 18S ribosomal RNA probe for data normalization in multiplexed 
quantitative Real-Time PCR reaction; cDNA = Complementary DNA. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
81 
 
Table 3.6 Real-Time PCR thermocycler reaction conditions 
 
Step Description Time Temperature 
1 AMPerase UNG Activation 2 min 50?C 
2 Taq Activation/UNG Inactivation 10 min 95?C 
3 Polymerization 15 sec 95?C 
4 Primer Annealing/Detection 1 min 60?C 
5 Repeat Steps 3-4 for 40 cycles NA NA 
 
1UNG = AMPerase Uracil N Glycosylase- Prevents Carryover Contamination; Taq =  
Thermostable Taq DNA Polymerase; Fluorescence Detected step 3.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
82 
 
Figure 3.2 Overview of TaqMan? gene expression chemistry1 
 
  
1Description: Unlabeled PCR primers anneal to template DNA. In addition a 
fluorophore-labeled probe containing a quencher moiety also anneals to the target 
sequence.  Polymerization and exonuclease activity of Taq Polymerase results in 
displacement of the reporter from the probe causing a detectable fluorescent signal 
proportional to the amount of amplified DNA present. (Adapted from Applied 
Biosystems.com)  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83 
 
Results 
 
 
Figure 3.3 Average daily gains of individual bulls and steers on feed for 84 days1-3 
 
  
1Range of Average Daily Gains for Individual bulls and steers in the feeding trial. 
2Bulls are represented by diamonds and steers represented by squares 
3Average Daily Gain (kg) calculated for 84 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
Av
era
ge
 Daily
 Gain
 (k
g) 
Individual Animal 
Average Daily Gain of Bulls and Steers 
Bulls
Steers
84 
 
Figure 3.4 Comparison of average daily gains of bulls vs. steers on feed for 84 days1 
 
  
1Animal Group = Bulls vs. Steers; Average daily gain determined for 84 days on feed; data are 
presented as Mean +/- SEM; a,bMeans with different superscripts differ (P<0.05).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Av
era
ge
 Daily
 Gain
 (k
gs)
 
Bulls                             Steers 
Animal Group 
Average Daily Gain of Bulls vs Steers 
Bulls
Steers
a 
b 
85 
 
Figure 3.5 Dry matter feed conversion for individual bulls and steers on feed for 84 days1 
 
  
1Dry matter feed conversion for individual animals on feed for 84 days; Individual bulls are 
represented by diamonds and steers are represented by squares. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
1
2
3
4
5
6
7
8
9
10
Fe
ed
 Con
ve
rsion
 Rat
io 
Individual Animal 
Feed Conversion Ratio for Bulls and Steers 
(Feed:Gain) 
Bulls
Steers
86 
 
Figure 3.6 Dry matter feed conversion for individual bulls and steers on feed for 84 days1 
 
  
1Animal Group = Bulls vs. Steers; Dry matter feed conversion (kg fed: kg weight gained) 
determined for 84 days on feed; data are presented as Mean +/- SEM; a,bMeans with different 
superscripts differ (P<0.05).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
1
2
3
4
5
6
7
8
9
Fee
d Con
ver
sion
 Ra
tio
 
Bulls                                      Steers 
Animal Group 
Feed Conversion Ratio (Feed:Gain) for Bulls and Steers 
Bulls
Steers
a 
b 
87 
 
Figure 3.7 Calculated residual feed intakes for individual bulls and steers on feed for 84 days1 
 
  
1Residual Feed Intake in individual animals on feed for 84 days; Individual bulls are represented 
by diamonds and steers are represented by Squares. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Resid
ual 
Fee
d In
tak
e 
Individual Animal 
Residual Feed Intake of Individual Steers and Bulls 
Bulls
Steers
88 
 
Table 3.7 Arbitrary classification of cattle as high, average, or low RFI based on 
individual animals? deviation from the mean RFI for the population1 
 
Sex 
Individual 
RFI 
Classification based on 
STDV from the mean 
Bull -0.06 Average 
Bull -0.59 Low 
Bull 0.79 High 
Bull -0.29 Average 
Bull -0.38 Average 
Bull 0.61 High 
Bull -0.08 Average 
Steer -0.05 Average 
Steer 0.05 Average 
Steer -0.28 Average 
Steer -0.59 Low 
Steer 1.1 High 
Steer -0.55 Low 
Steer 0.35 Average 
Steer -0.03 Average 
Mean RFI 0 
 STDV 0.51 
  
1Arbitrary Classification Scheme based on Calculated Individual Residual Feed Intake; 
Low RFI- More efficient; Individual RFI is more than one standard deviation lower than 
the mean RFI; Average RFI- Average Efficiency compared to contemporaries; Individual 
RFI is within one standard deviation of the mean RFI; High RFI- Less efficient; 
Individual RFI is more than one standard deviation higher than the mean RFI; Mean RFI 
= Average RFI for the Entire Group of Animals (Bulls and Steers); STDV = Standard 
Deviation. 
 
 
 
 
 
 
 
 
 
 
 
 
 
89 
 
Table 3.8 Pearson correlation coefficients relating the relative expression of each gene 
with estimated RFI for steers on feed for 84 days1 
 
Gene Pearson Corr. Coefficient p-value 
PPAR?2 -0.18 0.58 
FAS -0.23 0.52 
Leptin 0.1 0.76 
PPAR? -0.2 0.44 
CPT-1b -0.48 0.15 
Ubiq-E2 0.51 0.05 
Polyubiquitin 0.18 0.53 
UCP-2 0.59 0.02 
 
1PPAR-?2 = Peroxisome Proliferator Activator Protein Gamma 2; FAS = Fatty acid 
Synthase; PPAR? = Peroxisome Proliferator Activator Protein; CPT-1b = Carnitine 
Palmitoyl Transferase- 1b; Ubiq-E2 = Ubiquitin E2 Ligase; UCP-2 = Uncoupling Protein 
2. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
90 
 
Gene Expression  
 
Figure 3.8 Relative expression of PPAR-? in SM biopsies of finishing bulls vs. steers on 
feed for 84 days 
 
  
a,b Mean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls (Black) vs. Steers (Grey); SM = Skeletal Muscle; PPAR-? = Peroxisome 
Proliferator Activator Receptor-alpha. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
1
2
3
4
5
Rel
ati
ve 
Ex
pressi
on
 (A
rbi
trar
y Un
its)
 
Animal Group 
Bulls
Steers
a 
b 
91 
 
Figure 3.9 Relative expression of UCP-2 in SM biopsies of finishing bulls vs. steers on 
feed for 84 days 
 
  
a,bMean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls (Black) vs. Steers (Grey); SM = Skeletal Muscle; UCP-2 = Uncoupling 
Protein 2. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
1
2
3
4
5
6
Re
lati
ve
 Ex
pre
ssi
on
 (A
rbi
trar
y Uni
ts)
 
Animal Group 
Bulls
Steers
a 
a 
92 
 
Figure 3.10 Relative expression of Ub-E2 in SM biopsies of finishing bulls vs. steers on 
feed for 84 days 
 
  
a,b Mean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls vs. Steers; SM = Skeletal Muscle; UB-E2 Ubiquitin E2 Ligase. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 3.11 Relative expression of poly-ubiquitin in SM biopsies of finishing bulls vs. 
steers on feed for 84 days 
 
  
a,b Mean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls vs. Steers; SM = Skeletal Muscle. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 3.12 Relative expression of CPT-1b in SM of finishing bulls vs. steers on feed for 
84 days1-4 
 
  
a,b Mean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls vs. Steers; SM = Skeletal Muscle; CPT1-b = Carnitine Palmitoyl 
Transferase 1-b. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 3.13 Relative expression of FAS in AT of finishing bulls vs. steers on feed for 84 
days 
 
  
a,b Mean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls vs. Steers; AT = Adipose Tissue; FAS = Fatty acid synthase. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 3.14 Relative expression of Leptin in AT of finishing bulls vs. steers on feed for 
84 days 
 
  
a,b Mean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls vs. Steers; AT = Adipose Tissue. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 3.15 Relative expression of PPAR-? in AT of finishing bulls vs. steers on feed for 84 
days 
 
  
aMean +/- SEM; Means not sharing a common superscript differ (P < 0.05); Animal 
Group = Bulls vs. Steers; AT = Adipose Tissue; PPAR-? ? Peroxisome Proliferator 
Activator Receptor- gamma. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Chapter 4. The Effect of Days on Feed and Beta-Agonist Administration on the Expression 
of Regulatory Genes in Skeletal Muscle and Adipose Tissue of Finishing Heifers 
 
 
Introduction 
Rising feed costs and less than optimal production efficiency are major factors that have a 
marked impact on the bottom line of any beef cattle operation. These factors also affect the 
overall economic and environmental sustainability of the beef cattle industry in the Southeastern 
United States (SE). Feed represents 60 to 70% of total input costs for commercial beef 
production (Basarab et al., 2002; Arthur et al., 2001; Herd et al., 1998; Ritchie et al., 1992) and 
beef cattle are amongst the least efficient of livestock species (Rosegrant et al., 1999). Heifers 
are even less efficient than their steer counterparts making them less desirable in a finishing 
situation and highlighting the need for continuing research to improve beef production 
efficiency. Heifers comprise about one-third of the annual beef harvest in the US (USDA, 2010) 
and generally have poorer feed efficiencies, lower hot carcass weights, and produce less tender 
beef than steers (Tatum, 2007; Choat et al., 2006). These problems have been a driving force for 
researchers and producers to develop and apply various management and selection strategies to 
improve production efficiency and product quality in finishing heifers (Beerman, 2002; Mills, 
2002). Advances in genetics and animal nutrition have made substantial improvements in 
production efficiency, but considerable variation still exists with regard to maintenance energy 
requirements (Archer et al., 1999) which dictates the relative efficiency individual animals and 
has a marked impact on the efficiency of the operation.  
In addition to genetic selection and nutritional management, several exogenous agents 
have been developed and proven safe and effective for improving meat animal productivity and 
product quality (Dikeman, 2007; Beerman et al., 2005). Ractopamine HCl (RAC) is a member of 
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the beta-adrenergic agonist (BAA) group of growth modifying repartitioning agents, which are 
chemically and pharmacologically similar to the endogenous catecholamine?s epinephrine and 
norepinephrine (Figure 4.1, Figure 4.2; Smith, 1998). Historically BAAs have been used 
therapeutically in human medicine due to their effects on smooth muscle, their potential as anti-
obesity agents, and other medicinal uses (Tattersfield, 2006). Interest in livestock production is 
due to their effectiveness in repartitioning dietary energy away from peripheral fat synthesis and 
increasing the rate and efficiency of protein deposition in lean muscle tissue (Dikeman, 2007; 
Beerman et al., 2002; Mersmann, 1998). This results in increased muscle mass and lean muscle 
percentage (Beerman, 2002; Mills 2002) without increasing feed intake making these compounds 
effective repartitioning agents and valuable tools for improving production efficiency.  In 1999, 
the USDA approved the use of RAC under the trade name ?Paylean (Ractopamine HCl; Elanco 
Animal Health, Deerfield IN) for finishing swine and subsequently in 2003 approved Optaflexx 
(Ractopamine HCl; Elanco Animal Health, Deerfield IN) as a formulation of RAC for use in 
finishing cattle. These compounds have since been incorporated to varying degrees in the swine 
and cattle industries. Currently RAC is approved for use for the final 28-42 days on feed at a 
dosage of 70-430mg/hd/d in finishing cattle. 
The phenotypic effects of RAC in livestock species have been well documented and the 
proposed benefits include increased feed efficiency, leaner carcasses, and decreased wasteful AT 
accumulation (Walker et al., 2006; Beerman et al., 2002; Mersmann, 1998). The mechanisms 
that accomplish these phenotypic changes are less clearly understood. While some evidence have 
pointed to indirect actions resulting in changes in concentration of circulating hormones and 
target cell sensitivity (Beerman, 2002; Mersmann, 1998), it is generally accepted that direct, 
receptor mediated changes are responsible for BAA action in vivo (Figure 4.3; Winterholler et 
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al., 2007; Beerman, 2002). The presence of BAR subtypes on cells in AT and SM provide 
evidence for the direct effect of RAC on these tissues and binding of BAA to these receptors has 
been shown to induce metabolic responses in cell lines and animal models (Lefkowitz, 2007; 
Bergen, 2001; Peterla and Scanes, 1990).  Many of the studies that have reported on the effect of 
RAC on metabolite concentrations are confounded by the inclusion of various steroidal implants 
(Winterholler et al., 2008; Sissom et al., 2007) which are common in the beef cattle industry, but 
can affect the metabolic response to RAC or other agents making the independent mechanism of 
BAA action difficult to determine in production scenarios.  
RAC is purported to increase hot carcass weight, rib eye area, and dressing percentage 
and decrease carcass fat (Winterholler et al., 2008; Schroeder et al., 2005). The basis for these 
effects lies the metabolic activity of AT and SM which are of economic importance to producers 
and consumers alike. Of particular interest is the ability of these compounds to produce leaner 
carcasses and decrease waste fat accretion. In pigs fed BAA increased rates of lipolysis and 
decreased lipogenic enzyme activity has been reported (Perterla and Scanes, 1990; Merkel et al., 
1987). In various animal and cell models modulation of lipolytic enzyme activity has been 
observed in response to BAA administration (Hausman et al., 2009; Halsey et al., 2011; Reiter et 
al., 2007; Leftkowiz 2007; Bergen 2001; Mersmann, 1998; Peterla and Scanes, 1990) and 
lipolytic/lipogenic enzyme activity is known to be regulated by a myriad of transcription factors 
(Desvergne et al., 2006; Griffin and Sul, 2004; Sul and Wang, 1998). Halsey et al. (2011) and 
Reiter et al. (2007), each reported decreased lipogenic gene expression in SC AT of pigs treated 
with RAC however similar studies in cattle are limited. 
In AT the response to RAC is transient and may be affected by down-regulation of BAR 
during chronic administration of the BAA (Liang and Mills, 2002; Liu et al., 1994). Spurlock et 
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al. (1994) showed that the density of the BAR is decreased in SC AT in pigs administered RAC 
which may account for the diminished lipolytic response to chronic RAC dosages. This fleeting 
effect makes it essential to determine the optimal timing of administration to maximize the 
benefit of using these compounds with respect to distribution of carcass fat. Additionally, 
because AT development is regulated in a temporal fashion and in particular the IMF depot is 
late maturing, it is necessary to determine the regulatory mechanisms affected by RAC 
administration in order to avoid any detrimental impact on the IMF depot for the sake of 
increasing lean tissue. The response of AT to RAC in has been fairly well characterized using a 
variety of biochemical and molecular studies in pig models (Halsey et al., 2011; Reiter et al., 
2007; Liang and Mills, 2002; Dunshea et al., 1993, 1998; Liu et al., 1994), however to date, 
these studies have not been widely conducted in beef cattle.  
RAC administration leads to muscle cell hypertrophy, increased muscle mass and more 
lean tissue for meat products. Several studies have shown increase in individual muscle weights 
with administration of various BAA in sheep, cattle, and poultry (See et al., 2004; Rehfeldt et al., 
1997; Bergen et al., 1989; Wheeler and Koohmarie 1992; Claeys et al., 1989) The mechanism of 
muscle hypertrophy induced by RAC is related to its ability to cause a shift in the metabolic 
activity of muscle fibers and induce a switch from type-I to type-II muscle fibers (Gonzalez et 
al., 2007, 2008). RAC also affects the dynamics of protein metabolism via increased rates of 
protein synthesis, decreased rates of protein degradation or a combination of both contributing to 
SM hypertrophy (Grant et al., 1993; Bergen et al., 1989; Mersmann, 1998; Sainz et al., 1993). 
Also, it has been shown that administration of BAA results in increased net uptake of AA in SM, 
along with lowered muscle fractional degradation rates and increased fractional accretion rates 
(Wheeler and Koohmaraie, 1992). These basic metabolic changes are governed by regulatory 
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signals which must be responsive to BAR activation by RAC, but their identity and mechanism 
of action are not clearly understood. 
The effects of BAA on muscle protein metabolism are also transient and mediated by 
direct binding of the BAA to specific isoforms of the BAR, but the downstream target and 
effectors of the pathway are less clear. Moreover BAAs have been shown to be less efficacious 
in younger beef cattle with the maximum benefits occurring in the finishing phase of production 
although the physiological mechanisms for this phenomenon have not been identified. These are 
likely tied to temporal growth patterns and protein dynamics in young versus older animals 
(Beerman, 2002; Mills, 2002). These processes are governed by genes regulating energy 
metabolism and protein turnover and thus the identification of regulatory factors implicated in 
these pathways may provide molecular targets for improving beef cattle production (Wang et al, 
2009). Understanding the molecular mechanisms will allow for more tailored management 
strategies to optimize the timing of administration and maximize the animal response without the 
potential deleterious effects of these compounds. 
Increased dry matter feed efficiency is another proposed benefit of RAC. Schroeder et al., 
(2004) reported increases of 25 and 20% in steers and heifers fed RAC, respectively, without an 
increase in daily feed intake. Similarly, Gruber et al. (2007) and Walker et al. (2006) reported 
increased ADG without an increase in feed intake indicating increase efficiency with RAC 
administration. Avendano-Reyes et al. (2006) reported decreased dry matter feed intake in 
heifers treated with RAC compared to control animals. By increasing protein deposition and 
decreasing fat deposition, RAC increases efficiency of production by changing body composition 
in an economically favorable manner. Accomplishing this repartitioning at without affecting feed 
intake levels further increases efficiency due to the decreased resources necessary to achieve a 
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marketable phenotype (Williams et al., 1994) but it is not clear how these compounds are 
apparently able to manipulate energy flux in a manner that improves production efficiency.  
Other than the improvement in feedlot performance and feed efficiency, there are modest 
and variable changes in carcass composition in cattle compared to that noticed in pigs. This may 
be due to the distribution of BR subtypes on target tissues between the two species. Winterholler 
et al., 2007 reported heaver hot carcass weights but noticed no difference in rib eye area, yield 
grade, and indicators carcass adiposity in finishing heifers. Similar results have been reported for 
heifers treated with RAC. Still, others have reported no differences in HCW or loin area in 
heifers treated with RAC (Walker et al., 2006) highlighting a disparate response to RAC in fed 
cattle under various conditions. Furthermore Gonzalez et al. (2008) reported a differential 
response by different muscles to RAC administration which adds another layer of complexity 
because various muscles may respond differently to RAC treatment. These disparities must be 
addressed and considered in order to effectively incorporate RAC into beef cattle management 
plans and garner consistent and reproducible results.    
While metabolic modifiers have been used in livestock production for the last half 
century, recent interest and our understanding of these compounds has been aided by advances in 
livestock genomics and the advent of innovative molecular strategies (Etherton et al., 2003). 
These advances have allowed researchers to track the signaling cascades induced by various 
exogenous agents and to identify putative and novel mediators of biological efficiency as 
determined by the pathways they effect (Riedmaier et al., 2011; Bionaz and Loor, 2007; Reiter et 
al., 2007). The goal of this project was to determine the effect of days on feed and ractopamine 
administration on the expression of genes involved in regulation of energy and protein 
metabolism and to relate the expression of these genes to performance and carcass data for 
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finishing heifers under SE conditions. This is in an effort to monitor the expression patterns of 
these regulatory genes and determine how they are affected by duration of the finishing phase 
and BAA supplementation. These data may provide a framework for optimal inclusion of BAA 
in management strategies based on molecular events and determinants of energy metabolism and 
partitioning.  Optimal administration timing can be developed to coordinate with the temporal 
patterns of growth and development of valuable tissues and will help to mitigate unexpected and 
undesired effects of these compounds.  In addition, identification of downstream targets for the 
BAA will lead to potential target genes for improving production efficiency and product quality. 
This would augment the economic and environmental benefits of these exogenous agents and 
improve the efficiency and sustainability of beef cattle production in the SE and elsewhere. 
 
Materials and Methods 
Animal Care and Use 
All procedures were approved of by the Auburn University Institutional Animal Care and 
Use Committee for the use of live animals in experiments (IACUC Approval Number: 2007-
1273). 
 
Animal Trial 
 Seventy-one crossbred heifers were purchased via tele-auction in 2007. These heifers 
were co-mingled after purchase and were backgrounded for 66 days on summer a perennial 
Bermuda grass and Bahia grass pasture mix supplemented with soy hull pellets (3.2kg/hd/d). The 
heifers were then transported to the Auburn University Beef Cattle Evaluation Center where they 
were housed for the duration of the feeding trial. Cattle were assigned to one of six pens (12 
cattle per pen) and were stratified according to height and weight to minimize social dominance.  
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Cattle had continuous access to automatic water troughs and each pen contained 12 Calan 
Gates? (American Calan, Northwood, NH) to allow for individual feed provision and intake 
determination. A 21d warm-up period was used to train the heifers to use the Calan Gates?. 
During this period the heifers received a corn based diet (Table 4.1) at 2% of BW, and had 
access to hay ad libitum. After the warm-up period, hay was removed and the heifers had ad 
libitum access to the corn-based diet. Orts were recorded daily, and MGA (0.5 mg/hd/d) was 
added to the diets to suppress estrus. 
Treatment groups consisted of days on feed (DOF) with the following assignments: 79 
(n=16), 100 (n=16), 121 (n=16), and 142 (n=16) DOF. Furthermore, for each DOF group, half 
(n=8) of the animals were treated with ractopamine hydrochloride (RAC; Optaflexx?; Elanco 
Animal Health, Greenfield IN) at 300mg/hd/d for the final 35d prior to harvest. The other half 
(n=8 per DOF group) did not receive treatment and served as controls for the experiment (CON).  
 
Sample Collection 
Heifers were harvested at the Auburn University Lambert-Powell meats laboratory under 
USDA inspector supervision. Heifers were transported to the meats laboratory 24 hours prior to 
harvest and were humanely euthanized beginning at 8:00 am on each harvest day. During the 
normal slaughter and dressing process, skeletal muscle tissue samples were collected from the 
gastrocnemius and longissimus muscle (13th Rib for consistency) immediately upon hide 
removal. Subcutaneous AT was collected from the rump area and a deep SC AT sample was also 
collected from the site of LM collection. All tissue samples were approximately 2-5 grams and 
care was taken to ensure consistent collection sites between animals. Upon collection, tissues 
were placed in labeled aluminum foil wrappers and immediately snap frozen in a portable liquid 
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nitrogen container before being transported and stored in a minus 80 degree C freezer until 
subsequent use. 
 
RNA Isolation 
Total RNA was extracted from frozen tissue samples using TRIzol reagent (Invitrogen, 
Carlsbad, CA). Approximately 0.5 grams of tissue was removed from the minus 80 degree 
freezer and placed in a portable liquid nitrogen container. The frozen tissue was then crushed 
using a metal mortar, and the crushed tissue was placed in 5ml of TRIzol. The sample was then 
homogenized using a polytron? (PT 1200, Kinematica; Bohemia, NY) tissue homogenizer and 
then the extraction carried out as per the manufacturers protocol (Invitrogen, Carlsbad, CA). The 
total RNA isolate was subjected to RNA purification using RNeasy mini elute columns (Kit 
74102; Qiagen, Vencia, CA).  RNA quality was assessed electrophoretically under denaturing 
conditions. Samples were deemed accessible if they showed a prominent 28s band and a lesser 
18s band along with minimal evidence of degradation as previously described in our laboratory 
(Reiter et al., 2007). RNA concentration was determined using a Nano drop (ND-1000) 
spectrophotometer (Thermo Fisher Scientific; Wilmington, DE).  
 
Complimentary DNA synthesis 
Complimentary DNA (cDNA) was synthesized from the purified RNA isolate using high 
capacity cDNA Reverse Transcription kits (Kit # 4368814, Applied Biosystems; Foster City, 
CA). For each reaction, one microgram of DNAse treated total RNA was subjected to cDNA 
synthesis according to the manufacturer?s protocol. Table X and Table X show the cDNA 
reaction components and thermocycling conditions for cDNA synthesis, respectively. The 
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resulting cDNA (50ng/?l) was used for quantitative Real-Time PCR (qRT-PCR) to determine 
the expression of specific target genes. 
 
Selected Genes of Interest 
The panel of selected genes for this study included genes involved in lipogenesis (FAS, 
CEBP/a, and PPAR-g), Lipid Oxidation (CPT-1b, PPAR-a), mitochondrial function (PGC-1a, 
UCP2), Protein turnover (PolyUb, UbE2, and PSDM 11), and energy balance (leptin). For this 
study genes expression was assayed in loin muscle (LM) and gastrocnemius muscle (GM) and in 
two subcutaneous AT depots (Rump AT and Deep SC AT). Expression was measured across 3 
DOF groups (79, 121 and 142) and two treatment groups (CON vs. RAC). Table 4.6 shows the 
genes that were assayed for each tissue and treatment.  
 
Gene Expression Assays 
Assay Design 
Custom TaqMan gene expression assays (Applied Biosystems; Foster City, CA) were 
designed for each of the genes of interest using the proprietary Applied Biosystems Custom 
TaqMan? Assay Design Tool (www.appliedbiosystems.com; Applied Biosystems; Foster City, 
CA). Available sequence data was obtained from the NCBI database (www.ncbi.nih.nlm.gov) for 
each gene and the online assay design tool utilized the genomic input information, specifically 
mRNA sequence data, to generate custom primers and a sequence-specific fluorescent probe for 
real-time gene expression analysis.  For some genes of interest, there were available made-to-
order kits which utilize existing/available sequence to design proprietary assays for commonly 
studied and/or thoroughly vetted genes. 
 
qPCR Reaction Set-up 
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For each 20ul multiplexed qPCR sample the reaction components and PCR cycling 
conditions were as indicated in Table 4.4 and Table 4.5, respectively. For each reaction 1ul 
(50ng) of cDNA synthesis reaction product was diluted to 8ul in a 2ml nuclease-free micro 
centrifuge tube and placed on ice.  A sufficient amount of master mix containing enough 
TaqMan gene expression master mix (Part No. 4369016; Applied Biosystems; Foster City, CA), 
18S probe, and the custom gene expression assay was prepared for the tissue/gene of interest and 
then placed on ice. For each sample 12ul of master mix was added to the tube containing the 8ul 
cDNA solution. The reactants were gently mixed and then the 20ul reaction was pipetted into 
individual tubes in 0.2ml MicroAmp? Optical 8-Tube Strips (Part No. 4316567; Applied 
Biosystems; Foster City, CA) that were resting in a chilled PCR cooler. The strips were then 
capped with MicroAmp? Optical 8-Cap Strip (Part No. 432303; Applied Biosystems; Foster 
City, CA) and centrifuged gently to remove air bubbles and ensure all components of the 
reaction were at the bottom of the tube. For each tissue sample, the multiplexed assay was run in 
duplicate and this process was repeated for each gene/tissue combination. 
 
Data Normalization 
Gene expression data was normalized to an internal control gene to account for variation 
in initial cDNA input amounts. 18s ribosomal RNA was used as the endogenous loading control 
in the multiplexed real-time reactions. The 18s rRNA assay was designed with a separate, 
sequence-specific VIC?-labeled fluorescent probe and the cDNA?s for the target gene and the 
18s rRNA were co-amplified in a single, multiplexed reaction for each sample. Relative 
quantification (RQ) was determined using the ABI 7500 sequence detection system software 
(SDS; v2.0.3, Applied Biosystems, Foster City, CA) using the normalized expression data from 
the multiplexed reaction. 
109 
 
Statistical Analysis 
Statistical analysis was done using the GLM procedure of SAS (SAS Inst. Inc., Cary, 
NC). The least square means statement was used to compare average relative gene expression. 
Differences in least square means were considered significant at P < 0.05. Tissue (LM, GM, 
Rump Fat, LM Fat) Treatment (RAC vs. Control), Days on feed (DOF) and 
tissue*treatment*DOF interactions were analyzed.  The main effects of tissue, treatment, and 
harvest date were considered in the model. The interaction of these effects was also considered. 
 
Tissue Collection and Gene Expression Analysis 
 In the current project we profiled the expression of individual regulatory genes involved 
in protein turnover, AT development, and energy metabolism as heifers progressed through the 
finishing process. Additionally, the effect of Optaflexx?, a member of the BAA class of 
metabolic modifiers, on expression of these genes was determined. Skeletal muscle and adipose 
tissue samples were collected immediately after animals were harvested and were used for gene 
expression analysis. Heifers were transported to the USDA- inspected Auburn University 
Lambert-Powell meats laboratory and fasted for 24 hours prior to harvest. In retrospect, it may 
have been more prudent to collect biopsy samples prior to transport or fasting the heifers because 
these processes may affect gene expression patterns over the course of the 24-hour period. 
Changes in gene expression occur over the course of hours to days, and this should be considered 
with respect to our sampling process. Robust changes in expression could be diluted by 
molecular changes to accommodate the stress of transport and the shifts in the metabolic 
paradigm bought about by the 24hr fast. Of course these changes and stresses due to the 
sampling process cannot be completely eliminated even with the use of biopsy sampling etc., but 
steps can be taken to mitigate confounding factors particularly when metabolic genes are of 
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interest. In future studies this should be taken into consideration to obtain a more accurate 
snapshot of tissue-specific GE expression profile while the animal is on feed. With that said, 
there were still some notable changes in expression with respect to tissue, treatment, and DOF 
that will be discussed individually below. 
 
SM Energy and Lipid Metabolism 
Skeletal muscle growth and metabolism are essential to the efficient production of meat 
products from beef cattle. The longissimus muscle (LM) is a supportive muscle located along the 
spinal cord and is responsible for some of the more valuable retail cuts of beef including rib eye, 
top loin and strip loin. LM area is an important parameter used in selection programs as an 
indicator of muscling potential in breeding animals (Wilson et al., 1995) as well as an estimate of 
carcass composition in finishing animals. Additionally, LM area is used as an adjusting 
coefficient for USDA yield grading (USDA, 1996). There is a substantial amount of variation for 
IMF content between different muscles in the same animal (Brackebush et al 1991; Johnson et 
al., 1973), however there is a high correlation between the IMF content of the LM and other 
muscles over a range of IMF content (Brackebush et al., 1991) thus the LM has become the 
customary location for IMF assessment in beef cattle. Ultrasound techniques have been 
developed to predict marbling scores based on the IMF content of the LM (Newcome et al., 
2002; Hassen et al., 2001; Wilson et al., 2001; Hassen et al., 1999; Amin et al., 1997). The LM 
therefore contributes to both the quality and yield grades according to the USDA system (USDA, 
1996) and AT and protein metabolism within this muscle is important to production efficiency 
and profit margins.  
Gastrocnemius muscle (GM) was selected because it provided a readily accessible 
muscle that differed physiologically and anatomically from the LM. The GM is a located in the 
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lower leg and is responsible for the bulge shape in the calf muscle. This locomotive muscle is 
involved in standing, running, walking, and jumping and does not have the retail value of the 
LM. Gonzalez at al., (2008) determined that there is a differential response to RAC between SM 
of different location and function and that RAC may be able to improve the value of certain low 
value cuts (Gonzalez et al., 2008). This also shows that results cannot simply be extrapolated 
from muscle to muscle. 
Skeletal muscle is one most metabolically demanding tissues in the body. The goal of 
meat production is to maximize SM growth and optimize the nutrient content to produce 
valuable meat products. These objectives are governed by tightly regulated metabolic pathways. 
Physiologically these pathways are responsible for nutrient partitioning, protein turnover, energy 
homeostasis etc., but from a production standpoint these pathways determine the growth and 
composition of SM tissue and affect profit margins. Given the two functionally, anatomically, 
and economically distinct muscles (LM and GM), we profiled the expression of genes involved 
in protein turnover and adipogenesis/lipid metabolism to gain a perspective into how days on 
feed and RAC affects metabolism in these tissues. 
 
Skeletal Muscle Gene Expression 
Lipid Metabolism: Triglycerides (TG) are a primary storage form of neutral lipids in 
adipocytes, and in the SM serve as an important source of energy for SM contraction and 
function. Increase in IMF is generally due to increased accumulation of TG (Gao and Zhao et al., 
2009) making TG metabolism an attractive target for biomarkers of IMF accumulation (Gondret 
et al., 2008).  In meat animals TG accumulation in the SC depot results in the wasteful deposition 
of costly feed energy, and accumulation in the IMF results in expansion of the valuable marbling 
depot. Thus depending on the tissue of interest, TG accumulation can represent a paradoxical 
112 
 
phenomenon. We profiled the expression of genes related to adipogenesis and lipid metabolism 
in skeletal muscle tissue from the finishing heifers in the current project the current project as 
these factors likely regulate the extent to which lipid accumulation occurs in response to days on 
feed and a BAA repartitioning agent. 
PPARg expression did not differ between control and RAC treated heifers (P > 0.05; 
Figure 4.6). BAAs enhance production efficiency by decreasing AT accumulation and directing 
nutrients to SM for protein synthesis. It is possible that the adipocytes in the IMF are not as 
responsive to RAC as in other depots. It has been shown that the IMF depot shows differential 
response to nutritional and hormonal stimuli compared to the SC and other AT depots (Hausman 
et al., 2009; Dodson et al., 2010). These metabolic distinctions could lead to a lack of response of 
IMF to the BAA compounds.  
Interestingly, relative expression of PPARg decreased from 70 to 142 DOF in the SM of 
control heifers (P < 0.05; Figure 4.7). PPARg drives the adipogenic program and we expected 
increased PPAR gamma expression in SM tissue as DOF increases as this would be indicative of 
enhanced lipid metabolism and AT accumulation during this timeframe. It is not clear why 
expression would decrease in SM of control heifers as DOF progressed. PPARg was increased in 
RAC heifers at 142 DOF compared to 72 DOF (P < 0.05; Figure 4.8). This may indicate that 
RAC was more effective at limiting adipogenesis in heifers harvested at the earlier portions of 
the finishing period as opposed to the latter phases. Because of the effects of age and maturity on 
fattening, the temporal aspects of RAC on AT development warrant further investigation. 
Additionally, because IMF is a metabolically distinct depot that is inherently late developing, the 
optimal timing of BAA administration must be determined to mitigate potential interference with 
the critical time window for marbling development. 
113 
 
Pref-1 expression was highest in the SM of control animals at 79 DOF compared to RAC 
treated heifers (P < 0.05; Figure 4.12). There were no differences between the two treatment 
groups at 142 DOF (P > 0.05 Figure 4.12).  RAC treated heifers had lower Pref-1 expression 
compared to control heifers (P < 0.05; Figure 4.9), and GM showed higher expression levels 
compared to LM (P < 0.05 Figure 4.11). For Pref-1 there was a significant interaction effect of 
treatment and SM tissue (P < 0.05; LM vs. GM). Expression of Pref-1 was much higher in the 
GM of control animals compared to the LM of these same animals and compared to LM and GM 
of RAC treated heifers (P < 0.05). We found no difference between LM and GM in RAC treated 
animals in the current study (Figure 4.13; P > 0.05).    
 Glycerol-3-phasphate acyl transferase (GPAT) is involved in esterification of acyl 
groups to glycerol-3-phosphate in the process of TG biosynthesis, which is a metabolic 
characteristic of mature adipocytes actively synthesizing and depositing lipid. Tissue and 
treatment did not have an effect on GPAT expression (Figure 4.14 and 4.15; P > 0.05).We found 
that GPAT expression was nearly 3 times higher at 142d compared to 72d (Figure 4.16; P < 
0.05). This may indicate that deposition of lipid increased as the IMF depot matures which is 
consistent with the general school of thought regarding marbling maturation (Du et al., 2010.) 
Jeong et al., 2012 determined the expression of lipid deposition and lipolytic genes in LM of 
Korean cattle and then developed a predictive model to determine IMF based on gene 
expression. Among 9 fat deposition genes they determined that GPAT showed the highest 
correlation with IMF (r = 0.74; P < 0.001). They also showed that adipose tissue triglyceride 
lipase (ATGL), a lipolytic gene, was negatively correlated with IMF in these cattle (r = -0.68; P 
< 0.001). These data show that metabolic gene expression is important in determining the IMF 
phenotype and that expression profiling may be a viable tool in predicting IMF content.  The 
114 
 
cattle in the current experiment did not show substantial differences in marbling scores between 
treatment groups (Grubbs et al., 2009) which is also reflected in our gene expression analysis for 
GPAT.  
Carnitine palmitoyl transferase 1b (CPT1b) is rate-limiting for the oxidation of long chain 
fatty acids via mitochondrial beta oxidation in SM. The function of CPT-1b is to facilitate the 
transfer of LCFA from the cytoplasm across the mitochondrial membrane to access the 
machinery for ?-oxidation. For CPT-1b expression only the effect of harvest date was significant 
(P < 0.05; Figure 4.19). Loin muscle tended to have higher expression of CPT-1b but this was 
not significant (P = 0.12; Figure 4.18). RAC and control heifers did not differ (P > 0.05; Figure 
4.17). From a metabolic standpoint it was interesting to note that while GPAT expression was up 
at 142 DOF, CPT-1b expression was lower. This could possibly indicate a metabolic shift 
towards lipid accumulation and away from oxidation as days on feed increased. CPT-1b 
expression was more than 3 times higher in muscle tissue samples taken at 79d compared to 
142d (P < 0.05; Figure 4.19). Given its role in energy oxidation, CPT-1b expression may be 
used to infer the metabolic disposition in a given tissue. Finishing animals are expected to 
deposit lean muscle and as a result of dietary energy provision also deposit fat in specific depots. 
High CPT-1b expression at the earlier harvest date in the IMF depot may indicate the use of IMF 
as an energy source for the surrounding SM as protein deposition occurs. Later in the finishing 
phase when relative rates of protein deposition decline and IMF deposition occurs (Du et al., 
2010; Hausman et al., 2009; ), decreased CPT-1b expression could allow for accumulation of the 
IMF responsible for the all-important marbling trait in beef cattle. It would be interesting to 
profile the histological and biochemical activities of the IMF depot and relate it to gene 
expression data to understand the metabolic progression of this depot during the finishing period.  
115 
 
 While the IMF depot is generally cited as the last major AT depot to mature, Pethick et 
al. (2004) argue that IMF is not a late maturing AT depot and that it develops at a similar rate to 
other AT depots. They believe this common misconception arises because as an animal matures 
the rate of fat deposition will increase relative to lean deposition, and the visual appearance of 
IMF increases. Hence, the commercial trait ?marbling? is indeed late maturing; however this 
does not necessarily reflect IMF development (adipogenesis and lipid filling) which occurs at a 
time and rate comparable to other depots.  They also contend that an important determinant of 
final levels of IMF after finishing is the initial amounts of IMF (i.e. number and size of 
preadipocytes/adipocytes present in the depot) before entering the feedlot. With the heifers in the 
current study being relatively advanced composition-wise, it may have been difficult to observe 
more concrete differences in expression patterns due to the metabolic shifts that occur as the 
animal matures.  Aoki et al. (2001) reported that IMF does not increase beyond a ?maximum 
value? point due to decreased feed intake and less changes in carcass composition as animals 
reach maturity.  
Our data support the notion of a temporal pattern of AT metabolism with changes in GE 
for key genes however due to limited time points and sample size did not reveal any discernible 
mechanism for lipid metabolism with respect to treatment and DOF. A more complete temporal 
landscape representing animals at different ages and compositional phenotypes is necessary to 
make comprehensive assessments of GE profiles and relate them the patterns of growth and 
development. This is true both with respect to natural maturation patterns as well as with the use 
of BAA which have been shown to have differential effects on younger versus older animals. 
Also, because there are metabolic distinctions between AT depots, it can also be assumed that 
the specificity and proportion of BARs would also vary between depots. It would be interesting 
116 
 
to focus on isolated intramuscular adipocytes and determine their complement of ?-receptors. 
This may provide some insight into the molecular basis of the RAC response on a depot specific 
basis with the ultimate goal of being able to target and manipulate these depots independently of 
one another.  
Skeletal muscle is a heterogeneous tissue that has a complex assortment of cell types 
(including adipocytes) all contributing to the anatomy and physiology of the tissue. mRNA 
abundance is one of many ways to assess the metabolic disposition of the tissue however like 
most techniques it does not stand alone as a comprehensive tool. Still, this approach of molecular 
profiling in the IMF depot is novel and will prove to be valuable in ascertaining the regulatory 
factors that govern this depots expansion. In a complex tissue such as SM, metabolic control 
depends on the activity of transcription factors which modulate nutrient flux through various 
metabolic pathways (Desvergne et al, 2001) and the mRNA abundance for these transcription 
factors may be useful in determining their activity in vivo (Bennett et al., 2008). The presence of 
the IMF depot within the larger SM tissue both necessitates and facilitates specific methods for 
inferring metabolic activity and adipogenesis within this tissue. The molecular tools for this 
assessment in beef cattle are becoming more widely available and, while their potential for 
commercial application must be thoroughly evaluated, their possibility of depot specific 
intervention is highly coveted (Dodson et al., 2010a; Dodson et al., 2010b; Hausman et al., 
2009).  
 
Mitochondrial Function: Mitochondria are responsible for 80-90% of cellular ATP 
production (Szewczyk and Wojtczak, 2002) therefore we also profiled mitochondrial genes that 
have roles in energy oxidation and metabolism. Mitochondrial uncoupling protein 2 (UCP-2) is a 
member of the larger UCP family which function to dissipate the mitochondrial proton gradient 
117 
 
and separate mitochondrial oxidation from ATP production. UCP-2, which is expressed in 
bovine adipocytes and skeletal muscle, has been shown to be involved in feed efficiency in 
poultry (Raimbault et al., 2001), however Kolath et al. (2006) did not see differences in UCP 
expression in loin muscle from high versus low RFI cattle. In skeletal muscle, there were no 
effects of treatment tissue, or harvest date on UCP2 expression (P > 0.05; Figures 4.20, 4.21, and 
4.22).  
 
Protein Turnover: Protein turnover is the sum of protein synthesis and degradation and 
the balance of these processes determines net protein accumulation within a cell or tissue. 
Because these processes are energy dependent, they have a direct impact on the efficiency and 
profitability of livestock production (Bergen, 2008). Additionally, the efficiency of muscle 
growth is inextricably tied to meat production and it is critical to understand the dynamics of 
protein turnover to maximize beef cattle productivity (Bergen, 2008). This is important not only 
to SM protein metabolism as it pertains to lean tissue accretion, but also because protein 
metabolism is closely tied to energy metabolism. Peptide bond formation is estimated to require 
4 ATP equivalents and degradation is estimated to require one. Given this energy requirement, 
increased rates of protein turnover can prove to be energetically demanding and detrimental to 
production efficiency and whenever repeated rounds of protein turnover can be avoided, there is 
a net savings on metabolic energy (Bergen, 2008; Bergen and Merkel, 1991).  Metabolic 
redundancies or inefficiencies identified in this process are likely to contribute to variations in 
production efficiency in cattle and other species (Bergen 2008; Bergen and Merkel, 1991). BAAs 
have been shown to increase lean protein deposition by modulating the balance of protein 
degradation and synthesis in favor of net accretion of protein. 
118 
 
Protein degradation has traditionally been determined by differences between 
measurements of fractional protein accretion and synthesis rates, by measurement of urinary 3-
methyl histidine excretion, or by measurement of the activity of known proteases within the SM 
tissue (Goll et al., 1998). Skeletal muscle contains three primary systems for proteolysis. These 
include the ATP-dependent ubiquitin proteasome system, a lysosomal system, and a calcium 
dependent (calpain/calpastatin) system. It should be stated that more diverse functions for 
ubiquitin in cell signaling have recently been identified including protein trafficking and signal 
transduction (Welchman et al., 2005) but it is generally accepted that the much of SM proteolysis 
is accomplished via the UPS system (Attaix et al., 1998; Solomon and Goldberg, 1996).  
Changes in the activity of components of the UPS is primarily responsible for protein turnover 
dynamics under different physiological conditions including periods of disease induced 
proteolysis, autophagy and sepsis (Goll et al., 2008; Reinstein and Ciechanover, 2006; Minnard 
et al., 2005; Hasselgren et al., 1997); however the dynamics of this system in growing, healthy 
animals are less clearly understood (Bergen, 2008).   
The 76 amino acid protein ubiquitin is regulatory protein that targets proteins for 
degradation via the UPS. There were no differences in polyubiquitin expression between control 
and RAC treated animals (P > 0.05; Figure 4.26). We observed a 20% decrease in polyubiquitin 
expression from 79 to 142 DOF in SM tissue (P < 0.05; Figure 4.27). There was substantial 
variation in both treatment groups however the average relative expression between the groups 
was very similar. There was no significant interaction between kill date and treatment for 
polyubiquitin expression (P = 0.15). There was no difference in ubiquitin expression between 
control and RAC heifers at 142 DOF (P > 0.05; Figure 4.28).  
119 
 
Ubiquitin E2 enzymes (UbE2) also known as ubiquitin conjugating enzymes are 
responsible for the second step in the covalent attachment of ubiquitin to target proteins destined 
for proteolytic degradation by the UPS. Ubiquitinated proteins are then recognized and 
hydrolyzed by the regulatory and core particles of the 26S proteasome, respectively. There were 
no differences in expression UbE2 between harvest dates (Figure 4.29 P > 0.05) and treatment 
did not affect UbE2 expression in SM samples (Figure 4.30 P > 0.05).  
While ubiquitin and E2 are responsible for targeting proteins to the proteasome, PSDM11 is a 
non-ATPase regulatory subunit of the 26S proteasome itself. Ubiquitinated proteins are targeted 
to and recognized by the regulatory subunits of the proteasomes which are found in the cytosol 
and nucleus of various cells. As a regulatory unit, the expression of PSDM11 can be used to 
monitor the activity of the UPS system. Ractopamine did not affect the expression of PSMD11 
(Figure 4.32; P > 0.05) however expression tended to be higher in LM vs. GM (Figure 4.33 P < 
0.10). As mentioned earlier, these muscles represent physiologically distinct muscle groups 
which have different fiber type composition and different metabolic profiles. The functional 
demands of these different muscles may be responsible for differential protein metabolism 
between these two tissues. At 142 DOF, expression of PSDM11 was increased by nearly 3 fold 
compared to at 79 DOF (P < 0.05; Figure 4.34). Increased protein turnover can potentially be 
detrimental to muscle growth if rates of protein synthesis are not comparably increased. 
Additionally, individual animal variation in PSMD11 may contribute to variable efficiency and 
response to BAA administration. Lower levels of protein turnover at earlier time points may be 
indicative of rapid net protein accretion in these growing/finishing animals earlier in the 
finishing period. Unfortunately we did not assay for parameters of protein synthesis which would 
120 
 
have provided a complementary data set and a more comprehensive analysis of SM protein 
metabolism in these finishing heifers.   
There are several examples of UPS modulation in response to physiological status in 
farm animals. During lactation induced SM proteolysis, transcriptional upregulation of UPS 
components has been observed in dairy cattle (Clowes et al., 2005). UPS expression has also 
been monitored in growing chickens (Harper et al., 1999); however it is unclear how these relate 
to growth rate and efficiency. Given the known effects of RAC on lean tissue accretion and the 
purported effects on protein turnover we expected to see marked differences between control and 
treatment animals with respect to UPS marker genes. The effects of RAC and DOF on protein 
metabolism were not evident with the panel of marker genes we selected and may occur through 
other mechanisms affecting protein turnover. There were no differences in expression of 
ubiquitin E2 conjugase between treatment groups or across days on feed. These results are 
mainly consistent with polyubiquitin expression which was not different between treatment 
groups or DOF with the exception of the 142DOF control animals which appeared to have 
slightly lower expression of polyubiquitin compared to other groups. This would seem to 
indicate increased protein turnover by way of the ubiquitin proteasome system, however we did 
not observe significant increases in other components of the UPS that me measured. It is unclear 
the extent to which this modest decrease in expression effected protein turnover in these tissues 
and more extensive biochemical analyses should be done to further investigate this process. 
We did not observe marked down regulation of the components of the UPS that we tested 
however and it may be necessary to look at specific ubiquitin ligases such as those mentioned 
above before making an affirmative conclusion on the involvement of the UPS in the RAC 
response in finishing heifers. 
121 
 
The exact mechanism by which BAAs increase net protein deposition in cattle is not 
clearly understood. We looked primarily at genes involved in the UPS to compare rates of 
protein turnover (in the form of degradation); however this is only one component of the protein 
turnover equation. We did not measure protein synthesis in the current experiment, but it should 
be mentioned because despite not observing marked changes in proteolytic machinery, it cannot 
be conclusively stated that the overall dynamics of protein turnover where not shifted in response 
to DOF and/or treatment group. In addition, as mentioned previously, there are 2 other major 
proteolytic systems responsible for protein degradation in the cell. Aspects of these systems 
could be contributors to the apparent enhanced protein accretion observed with RAC 
administration. For example Pascual et al. (1993), observed increased cathespin A activity with 
administration of a mixed, non-selective BAA in SM of rats treated with. Cathespin A is an 
indicator of lysosomal protein degradation and upregulation of this system may be responsible 
for the effects of RAC however we did not measure expression of genes involved in lysosomal 
proteolysis. Wheeler and Koohmarie (1991) treated cattle with L644,939 and they attributed the 
hypertrophic effects of this BAA to increased activity of calpastatin which is the inhibitory 
component of the calpain/calpastatin system of protein degradation. They observed improved 
feed efficiency, reduced urinary nitrogen excretion, and increased calpastatin activity in BAA 
treated animals. This increased calpastatin activity would decrease the activity of this proteolytic 
system. While our current study used RAC, it could be possible that components of the 
calpain/calpastatin system were involved with the effect of RAC on protein turnover although 
one must be careful in generalizing results between different BAAs. 
BAAs have been shown to increase cross sectional area of muscle fibers contributing to 
the hypertrophy of SM tissue (Gonzalez et al., 2008; Aalhus et al., 1991). Several studies have 
122 
 
looked at expression of myofibrillar genes to explain the increased protein synthesis in SM in 
response to BAA administration. ?-actin mRNA abundance increased in pigs (Bergen et al., 
1989) and sheep (Koohmaraie et al; 1991) treated with BAA. These effects have not been 
directly linked to elevated cAMP in response to BAR activation, however they point to increased 
capacity for protein synthesis in these models. Smith et al. (1989) conducted a study in which 
they used Northern blot analysis to assay the expression of a putative myosin heavy chain 
(MHC) cDNA clone and determined that RAC either increased expression of this MHC gene or 
increased the stability of the mRNA either of which could lead to increases in SM protein 
synthesis.  Anderson et al. (1990) incubated cultured rat myotubes with RAC to determine the 
effect on protein synthesis. They showed that RAC stimulated protein synthesis and increased 
the apparent rate of MHC synthesis. Direct measurements of protein synthesis were not taken in 
the current study but the lack of differences between control and RAC treated heifers suggests 
that other dynamics of protein turnover are likely in play with respect to the mechanism of RAC 
action on SM tissue in beef cattle.  We did not look at MHC expression or muscle fiber 
morphometrics however these are important considerations when making conclusions about 
muscle protein metabolism.   
In farm animal species there is a wide range of efficiency with respect to protein 
synthesis (Bergen, 2008). Theoretically the efficiency of protein synthesis is projected to be 
around 80%, however much lower efficiencies have been estimated (Bergen, 2008; Bergen and 
Merkel, 1991). Increased protein turnover can potentially be detrimental to muscle growth and 
profits if rates of protein synthesis are not comparably increased. While metabolic processes such 
as protein synthesis and degradation have fixed thermodynamic efficiencies, the aggregate 
impact of these processes is likely amenable to genetic selection and therapeutic intervention 
123 
 
once the molecular basis of these processes are more thoroughly understood.  It would be 
interesting to compare our gene expression results with measures of protein synthesis (i.e. 
RNA/Protein ratio, or other translational machinery) to gain a comprehensive perspective on the 
rates and extent of protein synthesis as days on feed progressed and as affected by RAC. 
Emerging techniques in proteomics and other molecular innovations will allow for more 
sophisticated assessments of protein turnover in efforts to regulate this process and enhance the 
efficiency of muscle growth in meat producing animals (Bergen, 2008).  
There are currently very few reports regarding direct measurements of protein turnover in 
cattle receiving RAC. These data would be valuable both in ascertaining the direct mechanism of 
RAC action in cattle, but also in corroborating molecular data on the regulation of the process in 
specific target tissues. Walker et al. (2011) conducted a study to determine the effect of gender 
and RAC administration on protein turnover in steers and heifers. They determined that there 
was a differential effect of RAC on biceps femoris and LM muscle and that RAC decreased 
protein degradation rates in LM but did not affect rates in biceps femoris. They also reported that 
RAC did not affect protein synthesis rates isolating its effects to protein degradation. Their study, 
not unlike many preceding it, utilized various measures of protein synthesis/degradation to 
determine rates of protein turnover. We utilized a targeted gene expression approach which is 
relatively novel and does not calculate direct rate of synthesis and/or breakdown but utilizes 
molecular information to infer the activity of the governing pathways. This data should be 
reconciled with biochemical protein turnover assays to determine and optimize the efficacy of 
this approach. Identification of sentinel genes that can report on protein turnover will provide an 
innovative tool to determine the efficiency of protein metabolism which directly impacts muscle 
growth and the production efficiency in the beef cattle industry.  
124 
 
Adipose Tissue Metabolism: RAC treated heifers showed PPARg expression levels that 
were twice that of the control animals (P < 0.05). This profile is contrary to what would be 
expected given the purported mechanism of BAA action on adipose tissue which includes 
increased lipolysis and decreased AT accumulation in treated animals. PPARg increased, 
although not significantly from 70 to 142 DOF (P < 0.10). This trend although not significant, is 
expected as animals would increase fattening as DOF increase. Additionally we saw an opposite 
pattern in FABP4 expression which is peculiar because FABP4 is a target of PPARg (Hausman 
et al., 2009) and expression is increased as adipocytes reach terminal differentiation. Therefore 
PPARg should coordinately regulate FABP4 expression as demonstrated by Graugnard et al. 
(2009). In their study Graugnard et al. (2009) fed cattle high or low starch diets to produce 
divergent metabolic profiles. They showed that PPARg regulates a host of target genes that are 
involved in adipogenesis and lipid accumulation. One possible reason for the expression patterns 
observed in the current study may be due to the physiological stage of the heifers compared to 
the young calves used in the Graugnard study which would likely influence the dynamics of lipid 
metabolism. The steers used in the Graugnard study were at a much younger compositional and 
chronological age and this may be responsible for the more robust expression patterns observed 
in their study with respect to days on feed. 
RAC treated heifers had showed PPARg expression levels that were double that of the 
control animals. This profile is contrary to what would be expected given the purported 
mechanism of BAA action on adipose tissue which includes increased lipolysis and decreased 
AT accumulation in treated animals.  
RAC heifers had lower expression of FABP4 compared to control heifers (P < 0.05; 
Figure 4.40). The decrease in FABP4 could be a result of decreased lipid accumulation in the 
125 
 
subcutaneous AT depot. These expression data should be compared to carcass data to determine 
if the phenotypes substantiate the expression profiles. There were no effects of tissue or harvest 
date on FABP4 Expression (P > 0.05; Figure 4.40, 4.41, and 4.43). There was substantial 
variation in FABP4 expression within and between treatment groups. This may have contributed 
to the lack of significant differences in FABP4 expression patterns.  
Expression of UCP-2 was affected by treatment and by harvest date.There was 
substantial variation within each harvest date but expression at 79 DOF was low relative to 142 
DOF which was nearly two-fold higher (Figure 4.43; P < 0.05).  Animals treated with RAC had 
higher expression of UCP2 compared to control animals (Figure 4.43; P < 0.05). This result is 
interesting because increased UCP-2 activity in AT could lead to oxidation of FA and reduced 
AT accumulation although it is difficult to reconcile this with increased feed efficiency. SC AT 
location (Deep SC vs. Rump) did not affect UCP2 expression (Figure 4.44, P > 0.05).  
 
Summary and Conclusions 
Much of the role of energy substrates and how they provide fuel for SM have been 
investigated in human and rodent species and driven by interests in exercise physiology and 
more recently the impacts of ectopic lipid accumulation on risk factors for diabetes etc. (Watt et 
al., 2004; Watt et al., 2002). In meat animals, interest in energy metabolism is driven by the 
desire to manipulate energy partitioning towards valuable tissues and away from less valuable 
ones. This longstanding objective has been assisted with the application of metabolic modifiers 
and repartitioning agents; however there is still a dearth of information on the specific 
mechanisms of action and effects of these compounds on meat quality. The molecular processes 
involved have been scarcely evaluated in cattle but interest in these mechanisms as tools to 
improve beef production is emerging.  The current experiment did not identify specific 
126 
 
mechanisms that are responsible for the repartitioning effect of the BAA Optaflexx but we did 
observe differential expression of some of the target genes in response to DOF and RAC 
treatment as well as distinct expression between depots and tissues.  
Fattening is affected by nutrition, gender, and age in cattle (Hausman et al., 2009). The 
heifers in this study were relatively advanced on their growth and compositional curve (Grubbs 
et al., 2008) which may have impacted the gene expression profiles observed both with respect to 
days on feed and response to Ractopamine treatment. Marbling is considered to be a late 
developing trait and some believe adipogenesis in this depot occurs later than in the SC depot 
(Hausman et al., 2009) so profiling during the latter phases of the finishing process are certainly 
informative in terms of the molecular regulation of the expansion of the IMF depot. However, 
populations of progenitor cells in the IMF and other AT depots are determined earlier in the 
developmental process (Du et al., 2010) which provides yet another time frame for which a 
molecular profile needs to be determined. Perhaps more importantly, this may provide an earlier 
time frame in which superior animals can be identified and nutritional regimens can be modified 
to optimize production.  Much of the fattening that occurs during the finishing phase is a result of 
adipocyte hypertrophy as oppose to increased adipocyte number (Hausman et al., 2009). The 
populations of progenitor cells that undergo this hypertrophy is established during prenatal, early 
postnatal, and adolescence in beef cattle (Du et al., 2010). Therefore much of the dietary energy 
provided during the finishing process is used to reach a desired level of fatness with the ultimate 
goal of driving some of those nutrients to the IMF depot which has the lowest priority in terms of 
adipogenesis and nutrient partitioning compared to the SC and visceral AT depot (Du and 
Dodson 2011; Hausman et al., 2009).  This makes future approaches two-fold. Understand how 
these resident populations are established in various depots so that populations of progenitor 
127 
 
cells in low commercial value depots can be minimized thereby limiting their capacity to expand 
and the nutrient demand needed to support this expansion; and to understand how depot specific 
regulation coordinates and prioritizes AT depots for nutrient partitioning and adipogenic 
expansion.  
Future work should focus on relating gene expression data with performance data and 
histological analysis of target tissues to understand how molecular regulation governs tissue 
structure and function and gives rise to desirable phenotypes that support a profitable industry. 
The limited availability of resources for the current study may have hampered our ability to 
garner a comprehensive perspective of the GE profiles across the entire feeding period, but for 
the selected harvest dates we did observe some notable changes in expression between treatment 
groups and across DOF assignments. Future studies utilizing all of the sampling dates will 
augment the current data set and provide GE snapshots of cataloging the temporal changes in 
expression that give rise to specific beef cattle phenotypes. The ability to identify and monitor 
the expression of these regulatory factors has only recently been reported (many since the 
inception of this project) and has been met with variable success. Advances in sampling 
techniques, tissue and cell manipulation, and increase availability of genomic information will 
lead to more innovated approaches to monitor and modulate metabolic pathways via genetic 
selection or direct manipulation in order to improve the efficiency of beef cattle production. An 
improved understanding of how and when to administer BAA will maximize their efficacy and 
knowledge of their specific molecular targets will allow for advanced compounds that capitalize 
on their benefits and minimize the detrimental and/or unwanted side-effects of these metabolic 
modifiers.  Moreover, it will provide mechanistic perspective on how nutrient partitioning and 
128 
 
tissue priority is established and manipulated providing a valuable approach to efficiently 
manipulate carcass composition in beef cattle.  
 
 
 
 
 
  
129 
 
Chapter 4 Figures 
Figure 4.1 General structure of beta-adrenergic agonists including common aromatic 
substitutions1 
 
 1
Substitutions about the aromatic ring impart distinct chemical structure and function on 
the family of molecules; The R- group is generally a bulky chemical group such as a t-
butyl or isopropyl group; m- and p- designated the meta- and para- positions relative to 
-carbon of the phenolethanolamine molecule, respectively; From Smith D.J. 1998. 
J. Anim. Sci. 76:173-194. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
130 
 
 Figure 4.2 Chemical structure of Optaflexx? (Ractopamine HCl)1 
 
  
1Stars indicate Chiral Centers on the Ractopamine Molecule; Specific stereochemistry 
is not indicated on this figure; Optaflexx is a registered trademark of Elanco Animal 
Health (Greenfield, IN). 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
131 
 
Figure 4.3 Putative ?-adrenergic agonist mechanism of action via ?-adrenergic receptor1 
 
  
1?AR?????Adrenergic Receptor; PKA = Protein Kinase A; cAMP = Cyclic Adenosine 
Mono Phosphate; AC = Adenylate Cyclase; Gs = G-Stimulatory Protein; Binding of the 
BAA to the BAR results in activation of adenylate cyclase causing an increase in 
intracellular cAMP. This activates protein kinase A (PKA) which initiates a 
phosphorylation cascade resulting in modulation of specific cellular processes in adipose 
tissue and skeletal muscle including those noted in the diagram; Adapted from Hancock 
et al., 2006. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
132 
 
 
Table 4.1 Composition of diets fed to animals during the feeding trial period1 
 
Ingredient Percentage 
Corn 38.5 
Corn Gluten Pellets 17.5 
Cottonseed Hull Pellets 10 
Dried Distillers Grain 9.5 
Wheat Midds 6.5 
Soyhulls 6.5 
Cottonseed Hulls 5 
Molasses 2.5 
Limestone 1.25 
Bicarb 1 
Fat 1 
Salt 0.5 
Vitamins A,D,E 0.1 
Trace Minerals 0.1 
Rumensin 80 0.019 
 
1As Fed Basis. Dry Matter= 90%, CP=13.7%, NEgain00.78Mcal/Kg, NDF=32.2%, 
ADF= 15.7%; Adapted from Grubbs et al., 2008 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
133 
 
Table 4.2 High-capacity cDNA synthesis reaction components1 
 
 
 
 
 
 
 
 
 
1Individual Reaction components for complementary DNA synthesis from total RNA 
isolate. For each tissue a master-mix containing sufficient quantities of the individual 
reaction components was created and cDNA synthesis was conducted for each RNA 
sample; RT- Reverse Transcription; dNTP- deoxynucleoside triphosphates 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Component Volume/Reaction (?l) 
10x RT Buffer 2 
25x dNTP Mix (100mM) 0.8 
10X Random RT Primers 2 
MultiScribe Reverse Transcriptase 1 
Nuclease Free Water 4.2 
Total Reaction Volume 10 
134 
 
Table 4.3 cDNA synthesis reaction thermocycling conditions1 
 
  Step 1 Step 2 Step 3 Step 4 
Temperature (?C) 25?C 37?C 85?C 4?C 
Time 10min 120min 5min Hold3 
 
1Reaction conditions for the 4 step cDNA synthesis reaction; Temperatures were 
controlled in PCR thermocycler; upon completion reactions were held at 4?C until 
samples were stored for further use. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
135 
 
Table 4.4 Individual Reaction Components for Real-Time PCR Gene Expression 
Analysis1 
 
Component Volume 
TaqMan Gene Expression Master Mix 8ul 
Vic-Labeled 18S rRNA Probe2 1ul 
Custom TaqMAn Gene Expression Assay 1ul 
cDNA + H20 10ul 
Total Reaction Volume 20ul 
 
1Volumes shown are for individual Real-Time PCR reactions. For each gene a sufficient 
amount of master-mix with the appropriate amount of each component was generated to 
ensure consistency of the mix for all samples; 18S Vic Labeled 18S rRNA probe for Data 
Normalization in Multiplexed Reaction; cDNA = Complementary DNA. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
136 
 
Table 4.5 Real-Time PCR thermocycler reaction conditions1 
 
Step Description Time Temperature 
1 AMPerase UNG1 Activation 2 min 50?C 
2 Taq2 Activation/UNG Inactivation 10 min 95?C 
3 Polymerization 15 sec 95?C 
4 Primer Annealing/Detection3 1 min 60?C 
5 Repeat Steps 3-4 for 40 cycles NA NA 
   
  1UNG = AMPerase Uracil N Glycosylase- Prevents Carryover Contamination;  
 Taq = Thermostable Taq DNA polymerase; Fluorescence Detected at Step 4 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
137 
 
 
Table 4.6 Gene-Specific primer and TaqMan? probe sequences for the genes and tissues of interest1  
 
Target Gene Accession Number1 Forward Primer Reverse Primer MGB-Probe 
PPAR-? AF229356 TTGCCGGGAAGACCAACA CCATACACAGCGTCTCCATGTC ACAACCCCGCCTTTCT 
Poly-ubiquitin NM_174133 ACCTGGTCCTCCGTCTGA CCTCCAGGGTGATGGTCTTG CCGGTCAGGGTCTTCA 
UCP2 AF127029 GGACTCTGGAAAGGGACATCTC CCAGCTCAGCACAGTTGACA TCGCTCGCAATGCCAT 
FAS NM_00102669 CCTGGCCTTCGTGAGCAT AGCACGGCGTAGCCA CAGCAGCCATGCCCTT 
PGC-1?? AB10607 CGAGAATGAGGCTAGTCCTTCCT GCTTCTTAAGTAGAGACGGCTCTTC CTGACGGCACCCCTC 
PPAR-? Y12420 CCGCTGACCAAAGCAAAGG AGTTCATGTCATAGATAACAAACGGTGAT TTTCCCGTCAAGATCG 
Cpt1-b NM_001034349 GACTGGCAGCCCTCACT CTTGTTCTTGCCAGAGCTGAAG CCCACTCCACTCTTCC 
Pref-1/Dlk1 NM_174037.2 CGGAGGCAGCTGCGT GCAGAAGTTGCCCGAGAAG CCCCACGCTGTCTGC 
Leptin NM_173928 GCCTTCCAGAAATGTGGTCCAAATA CGGCCAGCAGGTGGA CCTCCGGGACCTTC 
GPAT NM_001012282 GGACTGACCTCTCTGGACTCT CGGAGCAGCAGCTCTTAGTG CCCGCCTGTGACTTC 
Ubiquitin E2 Ligase NM_001099372.1 AGTTCATGCATAGATA GCCTTCCAGAAATGTGGTCCA 
  
1Accession Number = NCBI Accession Number; MGB-Probe = FAM-labeled TaqMAN minor groove binding probe; CPT-1b = 
Carnitine Palmitoyl Transferase 1b; FAS = Fatty acid synthetase; GPAT = Glycerol phosphate acyl transferase; PPAR-? = 
Peroxisome Proliferator Activator Protein-?; PGC-1? = Peroxisome Proliferator Activator Protein Coactivator-1?; PPAR-g = 
Peroxisome Proliferator Activator Protein-???Pref-1 = Preadipocyte factor 1 (aka Dlk-1); UCP-2 = Uncoupling Protein 2.
138 
 
 
Animal Performance (from Grubbs et al., 2009) 
 
Table 4.7 Animal performance during the treatment period (final 35 DOF for each group)1 
 
 Treatment SEM P- Value 
Performance Measure Control RAC 
Trt ADG (kg/d) 1.1 1.24 0.04 0.02 
Trt Gain (kg) 38.6 43.7 1.5 0.02 
Trt DMI (kg/d) 329 322 5.5 0.4 
Trt DMFE  (feed:gain) 9.86 7.57 0.69 0.02 
 
1Least Square Means (+/-) SEM, Differences are significant at P<0.05; RAC = Ractopamine 
HCL 300mg/h/d; Trt ADG = average daily gain during treatment period; Trt Gain = Total gain 
during treatment period; Trt DMI = Total DMI during treatment period; Trt DMFE = Dry matter 
feed efficiency during treatment period; Modified from Grubbs et al., 2009 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139 
 
Figure 4.4 ?2-adrenergic receptor mRNA expression in rump adipose tissue from finishing 
heifers1-4 
 a-b For days on feed 79, 100, 121 and 142 bars lacking common superscript differ by (P < 0.05); 
Control and Ractopamine heifers did not differ in ?2-adrenergic receptor mRNA expression (P > 
0.05); 79 (79 d on feed), 100 (100 d on feed), 121 (121 d on feed), 142 (142 d on feed); Control 
(heifers not fed Ractopamine); Ractopamine (rRctopamine administered at 300 mg/hd/d 35 d 
prior to harvest; Modified From Grubbs et al., 2009. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
140 
 
Figure 4.5 ?2-adrenergic receptor mRNA expression in gastrocenimus muscle from finishing 
heifers1-4 
 
 
 
179 (79 d on feed), 100 (100 d on feed), 121 (121 d on feed), 142 (142 d on feed),  
Control = (heifers not fed Ractopamine); Ractopamine (Ractopamine administered at 300 
mg/hd/d 35 d prior to harvest; aFor days on feed 79, 100, 121 and 142 bars lacking common 
superscript differ by (P < 0.05); Control and Ractopamine heifers did not differ in ?2-adrenergic 
receptor mRNA expression (P > 0.05); Modified From Grubbs et al., 2009 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
141 
 
Gene Expression 
 
Figure 4.6 Relative PPAR gamma Expression in SM of Control vs. Ractopamine treated heifers1 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PPAR-g = 
Peroxisome Proliferator Activator Protein-gamma; Control= Control Heifers received no 
ractopamine for final 35 days on feed; RAC= Received ractopamine HCL (300mg/h/d) 
for final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
100
200
300
400
500
600
700
800
900
Re
lati
ve
 Ex
pre
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 (A
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ts)
 
Treatment Group 
Control
RAC
a a 
142 
 
Figure 4.7 Relative PPAR gamma expression in SM of control heifers harvested at 79 
and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PPAR-g = 
Peroxisome Proliferator Activator Protein-gamma; KD1= Control Heifers harvested at 
KD1 (79 Days on Feed); KD2= Control Heifers harvested at the final harvest date (142 
Days on Feed) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
200
400
600
800
1000
1200
Re
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pre
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 (A
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Harvest Date 
Control KD1
Control KD2
a 
b 
143 
 
 
 
Figure 4.8 Relative PPAR gamma Expression in SM of RAC heifers harvested at 79 and 
142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PPAR-g = 
Peroxisome Proliferator Activator Protein-gamma; RAC KD1= RAC Heifers harvested at 
KD1 (79 Days on Feed); RAC KD2= RAC Heifers harvested at KD2 (142 Days on Feed) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
100
200
300
400
500
600
700
800
900
1000
Re
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 Ex
pre
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on
 (A
rbi
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y Uni
ts)
 
Harvest Date 
RAC KD1
Rac KD2
a 
b 
144 
 
 
Figure 4.9 Relative PREF-1 expression in skeletal muscle tissue harvested from Control 
and Ractopamine treated finishing heifers1 
 
   
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Pref-1 = 
Preadipocyte factor 1/Dlk1; Control= Control Heifers received no ractopamine for final 
35 days on feed; RAC= Received ractopamine HCL (300mg/h/d) for final 35 days on 
feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Re
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Treatment Group 
Control
Rac
a 
b 
145 
 
Figure 4.10 Relative Pref-1expression in skeletal muscle tissue of finishing heifers 
harvested at 79 and 142 days1 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Pref-1 = 
Preadipocyte factor 1; DOF = Days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Re
lativ
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xp
ressi
on
 
Harvest Date 
79DOF
142DOF
a 
b 
146 
 
Figure 4.11 Relative Expression of PREF-1 in Longissimus dorsi and gastrocnemius 
muscle tissue harvested from finishing heifers1 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Gastroc = 
Gastrocnemius muscle; Pref-1 = Preadipocyte factor-1/Dlk-1. 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Re
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Tissue 
Longissimus Muscle
Gastroc Muscle
a 
b 
147 
 
Figure 4.12 Relative Expression of PREF-1 in skeletal muscle tissue harvested from 
Control and Ractopamine treated heifers at 79 and 142 days on feed1 
 
 
 
Least square means +/- SEM; a,bColumns not bearing a common superscript differ (P < 
0.05); 79 DOF = heifers Harvested at 79 days on feed; 142 DOF = Heifers harvested at 
142 days on feed; Control = Heifers not treated with Optaflexx; RAC= Heifers treated 
with Optaflexx at 300mg/hd/d for the final 35 days prior to harvest. 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
79DOF Control
79DOF RAC
142DOF Control
142DOF RAC
a 
b b b 
148 
 
Figure 4.13 Relative Expression of PREF-1 in gastrocnemius and longissimus muscle 
samples harvested from Control and Ractopamine treated heifers1 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Pref-1 = 
Preadipocyte factor 1; Control= Control Heifers received no ractopamine for final 35 
days on feed; RAC= Received ractopamine HCL (300mg/h/d) for final 35 days on feed; 
LM = Longissimus dorsi muscle; Gastroc = Gastrocnemius muscle. 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
Re
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Tissue and Treatment Group  
Control LM
Control Gastroc
RAC LM
Rac Gastroc
a 
b 
b 
b 
149 
 
Figure 4.14 Relative expression of glycerol-phosphate acyl transferase in skeletal muscle 
tissue collected from heifers in Control vs. Ractopamine treated finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); GPAT = 
glycerol-3-phosphate acyl transferase; Control = received no ractopamine for the final 35 
days on feed; RAC= received Optaflexx at 300mg/hd/d for the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
20
40
60
80
100
120
140
160
180
200
Re
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xp
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on
 (ar
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its)
 
Treatment Group 
Control
RAC
a 
a 
150 
 
Figure 4.15 Relative expression of glycerol phosphate acyl transferase in gastrocnemius 
vs. longissimus muscle collected from finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); GPAT = 
glycerol-3-phosphate acyl transferase. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
20
40
60
80
100
120
140
160
180
200
Re
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xp
ressi
on
 (ar
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its)
 
Tissue 
Gastroc Muscle
Longissimus Muscle
a 
a 
151 
 
Figure 4.16 Relative expression of glycerol-phosphate acyl transferase in skeletal muscle 
tissue collected from finishing heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); GPAT = 
glycerol-3-phosphate acyl transferase; DOF =  Days on feed 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
50
100
150
200
250
Re
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eE
xp
ressi
on
 (ar
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its)
 
Harvest Date 
79DOF
142DOFa 
b 
152 
 
Figure 4.17 Relative expression of CPT-1b in skeletal muscle tissue collected from 
control vs. ractopamine treated finishing heifers1 
 
   
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; CPT1-b = 
Carnitine palmitoyl acyl transferase 1b; Control = received no ractopamine for the final 
35 days on feed; RAC= received Optaflexx at 300mg/hd/d for the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
5
10
15
20
25
30
35
40
45
Re
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e E
xp
ressi
on
 (ar
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its)
 
Treatment Group 
Control
RAC
a 
a 
153 
 
Figure 4.18 Relative expression of CPT-1b in gastrocnemius and longissimus muscle 
collected from finishing heifers1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; CPT1-b = 
Carnitine palmitoyl acyl transferase 1b 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
5
10
15
20
25
30
35
40
45
Re
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xp
ressi
on
 (ar
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ary
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its)
 
Tissue 
Gastroc Muscle
Longissimus Muscle
a 
b 
154 
 
Figure 4.19 Relative expression of CPT1b in skeletal muscle tissue collected from 
finishing heifers harvested at 79 and 142 days on feed1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; CPT1-b = 
Carnitine palmitoyl acyl transferase 1b; DOF = Days on feed. 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
10
20
30
40
50
60
Re
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e E
xp
ressi
on
 (ar
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ary
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its)
 
Harvest Date 
79DOF
142DOF
a 
b 
155 
 
Figure 4.20 Relative expression of UCP2 in skeletal muscle tissue collected from control 
vs. ractopamine treated heifers1  
 
              
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; UCP2 = 
Uncoupling protein 2; Control = received no ractopamine for the final 35 days on feed; 
RAC= received Optaflexx at 300mg/hd/d for the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
3
Re
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xp
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on
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Treatment 
Control
Rac
a 
a 
156 
 
Figure 4.21 Relative expression Of UCP2 in gastrocnemius and longissimus muscle 
collected from finishing heifers1 
 
               
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); CPT1-b = 
Carnitine palmitoyl acyl transferase 1b.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
3
Re
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xp
ressi
on
 (ar
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its)
 
Tissue 
Gastroc Muscle
Longissimus Muscle
a 
a 
157 
 
Figure 4.22 Relative expression Of UCP2 in skeletal muscle tissue collected from 
finishing heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; CPT1-b = 
Carnitine palmitoyl acyl transferase 1b; DOF = Days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
3
Re
lati
ve 
Expr
essi
on
 (ar
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its)
 
Harvest Date 
79DOF
142DOF
a 
a 
158 
 
Figure 4.23 Relative expression of PPAR-? in skeletal muscle tissue collected from 
control vs. ractopamine treated finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; PPAR-a = 
peroxisome proliferator activator receptor alpha; Control = received no ractopamine for 
the final 35 days on feed; RAC= received Optaflexx at 300mg/hd/d for the final 35 days 
on feed. 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
10
20
30
40
50
60
Re
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e E
xp
ressi
on
 (ar
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its)
 
Treatment 
Control
Rac
a 
a 
159 
 
Figure 4.24 Relative expression of PPAR-? in gastrocnemius and longissimus muscle 
tissue collected from finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; PPAR-a = 
peroxisome proliferator activator receptor alpha. 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
10
20
30
40
50
60
Re
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xp
ressi
on
 (ar
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Tissue 
Gastroc Muscle
Longissimus Muscle
a
` 
a 
160 
 
Figure 4.25 Relative Expression of PPAR?? in skeletal muscle tissue collected from 
finishing heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05; PPAR-? = 
peroxisome proliferator activator receptor alpha. DOF = Days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-10
0
10
20
30
40
50
Re
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e E
xp
ressi
on
 (ar
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its)
 
Harvest Date 
79DOF
142DOF
a 
b 
161 
 
Figure 4.26 Relative expression of polyubiquitin in SM of control and ractopamine 
treated finishing heifers1 
   
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Control = 
received no ractopamine for the final 35 days on feed; RAC= received Optaflexx at 
300mg/hd/d for the final 35 days on feed. 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0.08
0.082
0.084
0.086
0.088
0.09
0.092
0.094
0.096
Re
lativ
e E
xp
ressi
on
 (ar
bir
rar
y u
nits)
 
Treatment Group 
Control
Rac
a a 
162 
 
Figure 4.27 Relative expression of polyubiquitin in skeletal muscle of finishing heifers 
harvested at 79 and 142 days1 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); 79DOF = 79 
days on feed; 142DOF = 142 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.02
0.04
0.06
0.08
0.1
0.12
Re
lativ
e E
xp
ressi
on
 
Harvest Date 
79DOF
142DOF
a 
b 
163 
 
Figure 4.28 Relative expression of polyubiquitin in gastrocnemius and longissimus dorsi 
muscle samples harvested from control and ractopamine treated heifers at 79 and 142 
days1 
 
 
 
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Control = 
received no ractopamine for the final 35 days on feed; RAC= received Optaflexx at 
300mg/hd/d for the final 35 days on feed. Harvest date 1 = 79 days on feed; Harvest date 
2 = 142 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Re
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Harvest Date and Treatment 
79DOF Control
79DOF Rac
142DOF Control
142DOF Rac
a 
a,b 
c 
b 
164 
 
Figure 4.29 Relative expression of ubiquitin E2 in skeletal muscle of control and 
ractopamine treated finishing heifers1 
   
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Control = 
received no ractopamine for the final 35 days on feed; RAC= received Optaflexx at 
300mg/hd/d for the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0.85
0.9
0.95
1
1.05
1.1
Re
lativ
e E
xp
ressi
on
 
Treatment Group 
Control
Rac
a 
a 
165 
 
Figure 4.30 Relative expression of ubiquitin E2 in skeletal muscle tissue of finishing 
heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Harvest date 
1 = 79 days on feed; Harvest date 2 = 142 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0.88
0.9
0.92
0.94
0.96
0.98
1
1.02
1.04
1.06
1.08
1.1
Re
lativ
e E
xp
ressi
on
 
Harvest Date 
79DOF
142DOF
a 
a 
166 
 
 
Figure 4.31 Relative expression of ubiquitin E2 in gastrocnemius and longissimus dorsi 
muscle samples from control and ractopamine treated heifers harvested at 79 and 142 
days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Control = 
received no ractopamine for the final 35 days on feed; RAC= received Optaflexx at 
300mg/hd/d for the final 35 days on feed. Harvest date 1 = 79 days on feed; Harvest date 
2 = 142 days on feed. 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
Re
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xp
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Harvest Date and Treatment 
79DOF Control
79DOF RAC
142DOF Control
142DOF RAC
a a 
a 
a 
167 
 
Figure 4.32 Relative expression of PSMD11 in skeletal muscle tissue collected from 
control vs. ractopamine treated finishing heifers1    
                           
   
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PSMD11 = 
proteasome (prosome, macropain) 26S subunit, non-ATPase 11; Control = received no 
ractopamine for the final 35 days on feed; RAC= received Optaflexx at 300mg/hd/d for 
the final 35 days on feed. 
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
5
10
15
20
25
30
35
40
45
Re
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xp
ressi
on
 (ar
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its)
 
Treatment Group 
Control
Rac
a 
a 
168 
 
Figure 4.33 Relative expression of PSDM11 in gastrocnemius and longissimus muscle 
collected from finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PSMD11 = 
proteasome (prosome, macropain) 26S subunit, non-ATPase 11. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
5
10
15
20
25
30
35
40
45
Re
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xp
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on
 (ar
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Tissue 
Gastroc Muscle
Longissimus Muscle
a 
b 
169 
 
Figure 4.34 Relative expression of PSDM11 in skeletal muscle tissue collected from 
finishing heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PSMD11 = 
proteasome (prosome, macropain) 26S subunit, non-ATPase 11; Harvest date 1 = 79 days 
on feed; Harvest date 2 = 142 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
5
10
15
20
25
30
35
40
45
Re
lativ
e E
xp
ressi
on
 (ar
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its)
 
Harvest Date 
79DOF
142DOF
a 
b 
170 
 
Figure 4.35 Relative expression of PPAR-gamma in adipose tissue of control vs. 
ractopamine treated finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PPAR-g= 
peroxisome proliferator activator receptor?gamma; Control = received no ractopamine 
for the final 35 days on feed; RAC= received Optaflexx at 300mg/hd/d for the final 35 
days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
2
4
6
8
10
12
14
16
Re
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e E
xp
ressi
on
 (ar
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its)
 
Treatment Group 
Control
Rac
a 
b 
171 
 
Figure 4.36 Relative expression of PPAR-gamma in adipose tissue of finishing heifers 
harvested at 79 and 142 days 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PPAR-g= 
peroxisome proliferator activator receptor?gamma; Harvest date 1 = 79 days on feed; 
Harvest date 2 = 142 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
0
2
4
6
8
10
12
14
Re
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e E
xp
ressi
on
 (ar
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ary
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its)
 
Harvest Date 
Harvest Date 1
harvest date 2
a 
a 
172 
 
Figure 4.37 Relative expression of Pref-1 in SC AT of finishing heifers at 79 and 142 
days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Pref-1 = 
preadipocytes factor 1; Harvest date 1 = 79 days on feed; Harvest date 2 = 142 days on 
feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Re
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its)
 
Harvest Datet 
Harvest Date 1
Harvest Date 2
a 
a 
173 
 
Figure 4.38 Average Relative Expression of Pref-1 in SC AT of control and 
ractopamine-treated finishing heifers1 
 
   
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Pref-1 = 
preadipocytes factor 1. Control = received no ractopamine for the final 35 days on feed; 
RAC= received Optaflexx at 300mg/hd/d for the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Re
lati
ve 
Expr
essi
on
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its)
 
Tissue 
Control
Rac
a 
a 
174 
 
Figure 4.39 Relative Expression of Pref-1 in SC AT harvested from control and 
ractopamine-treated finishing heifers at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); Control = 
received no ractopamine for the final 35 days on feed; RAC= received Optaflexx at 
300mg/hd/d for the final 35 days on feed. 79DOF = 79 days on feed; 142DOF = 142 days 
on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
Re
lati
ve 
Expr
essi
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 (ar
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Treatment and Harvest Date 
Control 79DOF
RAC 79DOF
Control 142DOF
RAC 142DOF
a 
a 
a a 
175 
 
Figure 4.40 Relative Expression of FABP4 in adipose tissue collected from control vs. 
Ractopamine treated heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); FABP4 = 
Fatty acid binding protein 4; Control = received no ractopamine for the final 35 days on 
feed; RAC= received Optaflexx at 300mg/hd/d for the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.005
0
0.005
0.01
0.015
0.02
Re
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on
 (ar
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its)
 
Treatment 
Control
Rac
a 
a 
176 
 
Figure 4.41 Relative expression of FABP4 in rump and deep subcutaneous adipose tissue 
collected from finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); FABP4 = 
fatty acid binding protein 4; Deep SC adipose tissue = subcutaneous adipose tissue 
harvested from the 13th rib directly proximal to the loin muscle. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 4.42 Relative expression of FABP4 in adipose tissue collected from finishing 
heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); FABP4 = 
fatty acid binding protein 4; Harvest date 1 = 79 days on feed; Harvest date 2 = 142 days 
on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 4.43 Relative Expression of UCP-2 in adipose tissue of control vs. ractopamine  
treated finishing heifers1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); PSMD11 = 
proteasome (prosome, macropain) 26S subunit, non-ATPase 11; Control = received no 
ractopamine for the final 35 days on feed; RAC= received Optaflexx at 300mg/hd/d for 
the final 35 days on feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 4.44Relative expression of UCP 2 in rump and deep deep subcutaneous adipose 
tissue of finishing heifers1  
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); UCP2 = 
Uncoupling protein 2; Deep SC adipose tissue = AT harvested from the 13th rib directly 
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Figure 4.45 Relative expression of UCP 2 in adipose tissue collected from finishing 
heifers harvested at 79 and 142 days1 
 
  
1LSM +/- SEM. Means not sharing a common superscript differ (P < 0.05); UCP2 = 
Uncoupling protein 2; Harvest date 1 = 79 days on feed; Harvest date 2 = 142 days on 
feed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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181 
 
Chapter 5. The Effects of Feed Restriction and Re-feeding on Gene Expression Patterns in 
the Loin Muscle and Subcutaneous Adipose Tissue of Forage Fed Beef 
 
Introduction 
 
Various factors contribute to the economic success of a beef finishing operation. Utley et 
al. (1975) summarized these factors and reported that selecting an efficient, economical finishing 
system is driven by: market specifications, the type of feed available, and cost per unit gain. 
More recently consumer preference and expectation have emerged as driving factors for beef 
industry success. Feed can represent upwards of 60% of production costs in cow/calf feeding 
systems and over 70% in finishing systems (Anderson et al., 2005). With these, constant input 
cost, the efficiency of animal growth and nutrient utilization is of major concern and a deciding 
factor in determining profitability. Management strategies including specialized nutrition 
regimens and breeding programs have emerged in order to make maximum use of available 
resources and amplify animal productivity and efficiency. These practices have improved the 
overall volume and efficiency of livestock production. With an estimated seventy percent 
increase in current production levels needed to sustain the world population by 2030 (FAO, 
2009); the industry will be required to make major advances in the face of dwindling land 
resources and increasing costs. Livestock genomics has begun to make notable contributions to 
our ability to identify, understand, and manipulate metabolic pathways to improve animal 
production (Bauman et al., 2011; Sellner et al., 2007; Dekkers, 2004).  
 
Forage-Finished Beef 
With proper pasture management, the ability to grow quality renewable forages year 
round is an advantage afforded to the beef industry in the south eastern US (SE), compared to 
less temperate climates in other regions of the country (Allen et al., 1996; McMillin et al., 1990). 
182 
 
Factors leading to increased adoption of a forage-based system have historically included 
increases in grain prices (Hoveland, 1975; Seideman et al., 1985), declining cattle prices, and 
increased concern about the environmental impact of beef production (Capper, 2011; Cook et al., 
1984). Currently, with rising cost and competition for various grains, the use of forage-based 
finishing systems is becoming an increasingly popular option in areas that can support such a 
system.  
The thought of returning to the forage finishing process seems ironic, considering the 
reasons for the widespread adoption of the feedlot process in the first place. Up until the middle 
of the last century cattle in the US were traditionally finished on pastures. This was the industry 
standard at the time and is still practiced in many countries around the world. Following World 
War II the United States witnessed a gastronomic revolution, which saw an increase in the per 
capita calorie intake (Unger and Scherer, 2010), and increased demand for beef and other high 
quality meat products  (Gaylean et al., 2011). Increased consumption and the need for improved 
production efficiency led to intensive research and development regarding feedlot and grain-
finishing processes. These practices have produced larger, higher quality cattle in a more 
efficient manner than previously experienced (McMurray, 2009; Ball and Cornett, 1996). 
Commercial feedlots have since become commonplace in the industry (MacDonald and 
McBride, 2009; Gaylean et al., 2011), and in the US consumers have become accustomed to the 
flavor and palatability (and cost), of grain-finished beef (Wood et al., 2003; Wood et al., 1999). 
Today, feedlots are larger, more highly specialized, and increasingly consolidated due to a rise in 
corporate ownership (Gaylean et al., 2011; MacDonald and McBride, 2009). Despite advances in 
feedlot production there is a ?grass-roots? movement (no pun intended) to return to more pastoral  
183 
 
cattle rearing methods due to several tangible and purported advantages of forage-based finishing 
systems (Allen et al., 1996; Wilkins, 2008).   
According to the USDA Agricultural Marketing Service (AMS, 2007), in order to qualify 
as grass-fed beef (GFB), cattle must have consumed grass and forage for their entire lifetime 
with the exception of milk consumed prior to weaning. Diets must not include grain or grain-
byproducts of any sort (AMS, 2007). The notion that cattle can be reared on forages is by no 
means a novel concept, but with the cost of feed continuing to rise and profit margins decreasing, 
commercial pasture-based systems have become an attractive and viable option for beef 
producers in some geographic locations, and some consumers are willing are to pay a premium 
for GFB products (Gaylean, 2011; Cox et al., 2006).  
Generally speaking cattle and other ruminants reared on forage-based systems are lighter, 
smaller, and leaner, and less tender compared to grain-finished animals (Neurnberg et al., 2005; 
Borton et al., 2005; McClure et al., 2000). Forage-finished animals also have slower growth rates 
compared to grain fed animals (Neurnberg et al., 2005; Mandell, 1998). Brown et al. (2005) 
showed that forage-fed cattle have smaller rib eye area (REA), which is an indicator of lean 
muscle growth. This trend is generally extended to most cattle on pasture vs. forage-based 
systems. Neel et al. (2007) found that in cattle fed to a similar age, the REA in feedlot cattle was 
significantly greater than that of forage-fed cattle. Supplementing animals on a pasture-based 
system with concentrates, such as soy-hull pellets or corn, can produce animals with greater REA 
and improve production efficiency compared to pasture alone (Baublits et al., 2004; Kerth et al., 
2006). This practice disqualifies animals from being considered GFB (AMS, 2007). Smaller, 
leaner carcasses can lead to reduced dressing percentage, and less acceptable lean, fat, and 
quality grades (Faucitano et al., 2008; Berthiaume et al., 2006). These lower scores can 
184 
 
jeopardize the already volatile profitability of a beef cattle operation (Berthiaume et al., 2006; 
Bidner et al., 1986). Based on the estimated decreases in production per animal, Capper et al., 
(2011) suggested that forage-finishing systems alone are not currently capable of meeting 
domestic and international demands for beef.  
The phenotypic differences observed in grass vs. grain-fed beef are the manifestations of 
differences in available energy in these feedstuffs and the amount of food consumed in these 
systems. There are several possible explanations for the ?underperformance? of GFB vs. grain-
fed animals in terms of carcass size and production efficiency. From a physiological standpoint 
energy metabolism and partitioning mechanisms are responsible for directing the utilization of 
feed energy and for producing the resulting carcass phenotypes. Depending on the plane of 
nutrition, all animals will undergo shifts in metabolic partitioning to accommodate maintenance 
1st and then subsequent growth, meat production, lactation etc. The increased available energy in 
concentrates compared to forage supports more rapid growth and increased fattening in beef 
cattle (Muir et al., 1998; Fontenot et al., 1995; Byers, 1982). This has been substantiated with 
molecular evidence that profiles the induction of various regulatory factors involved in skeletal 
muscle (SM) and adipose tissue (AT) development in rapidly growing animals on a high plane of 
nutrition compared to restricted animals (Schmidt et al., 2011; Graugnard et al., 2010; Graugnard 
et al., 2009).  Transcription factors and other regulatory players that control the accretion and 
composition of body tissues orchestrate these metabolic shifts (Graugnard et al., 2009). The 
exact mechanisms and identity of these regulatory factors particularly in GFB cattle are yet 
unknown.  
 
GFB and Adipose Tissue 
185 
 
Regardless of the finishing system, beef is a nutrient dense food packed with a variety of 
high quality nutrients. While the economic impact of carcass size and quality are obvious, forage 
finishing can affect the composition of beef carcasses in a manner that alters the nutrient profile 
and may affect consumer health (Neurnberg et al., 2005; Daley et al., 2010; Alfaia et al., 2009; 
Desmet et al., 2004). Different finishing strategies i.e. grain, forage, or different combinations 
thereof, consistently produce distinct carcass phenotypes in beef cattle. This is true particularly 
with respect to adipose tissue depots (Roberts et al., 2009; Hausman et al., 2009; Alfaia et al., 
2009; Faucitano et al., 2008; Kerth et al., 2006; Nuernberg et al., 2005; Realini et al., 2004), 
which has a direct impact on production efficiency as well as consumer preference and health 
(Kouba and Mourot, 2011; Scollan et al., 2001; Wood, 1999).  
Grass fed beef is leaner than grain-finished beef (Alfaia, et al., 2009; Leheska, et al., 
2008; Realini, et al., 2004), and grass feeding results in beef products with different fatty acid 
profiles, which affects organoleptic qualities and consumer acceptability (Daley et al., 2010, 
Faucitano et al., 2008; Kerth et al., 2006; Cox et al., 2006; French et la., 2000). GFB reportedly 
has increased proportions of conjugated linoleic acids (Noviandi et al., 2012; French et al., 
2000), which have been purported to be beneficial to human health. Higher concentrations of 
anti-oxidant compounds have also been observed in GFB compared to grain-fed animals. While 
potentially beneficial to consumer health, changes in FA composition and antioxidant content 
can impart an ?off? flavor and/or color to GFB that may be undesirable to most consumers 
(Daley et al., 2010; Wood et al., 2003). These distinctions are part of the consumer appeal for 
GFB for those that desire it but also represent concerns for less reluctant producers or consumers.  
This distinct carcass composition in GFB is a function of nutrient content and availability 
in the forage. Changes in age or quality of forage result in differences in nutrient availability to 
186 
 
the animal (Rayburn et al., 2005; Drouillard and Kuhl, 1999; Preston, 1994), and ultimately 
affect carcass composition. Thus, proper management of forage for grazing is an important 
consideration for producing GFB (Funston et al., 2011; Rayburn et al., 2005). Additionally, it is 
also important to note that while GFB may have a higher proportion of beneficial FA, the 
absolute amounts due to the differences in animal size and total volume of AT depots between 
the two systems may dilute the beneficial effects of GFB. As such, it remains to be seen how 
these and other ?improved? characteristics of GFB can translate into improved consumer health 
compared to beef from grain-fed animals. In order to improve upon this beneficial profile, 
specific metabolic pathways can be targeted to augment the proportions of favorable FA. 
Various factors including age, genetics, and sex, influence the volume and composition 
of SM tissues and AT depots in beef cattle and other species (Hausman et al., 2009; Desmet et 
al., 2004; Utrera and Van Vleck, 2004; Owens et al., 1993). While these components are 
objective or predetermined, other production aspects, such as management strategies and 
nutrition are controllable factors and have a marked impact on carcass characteristics (Dalet et 
al., 2010; Owens et al., 1993; Crouse et al., 1984). There are quantitative and qualitative 
differences in AT volume and composition in grain-fed vs. GFB that occur in a depot-specific 
fashion. In particular, marbling is lower in GFB, a critical factor that must be addressed because 
IMF directly impacts meat quality scores and consumer acceptance of beef (Wood et al., 1999). 
Different AT depots (e.g. IMF, SC, Intermuscular Fat, etc.) are metabolically distinct entities and 
require individual attention with respect to their specific regulatory paradigm (Bergen and 
Burnett, 2011; Hausman et al., 2009; Rosen et al., 2000). Regulatory networks governing the 
processes of adipogenesis and AT metabolism have been described in various model systems 
(Poulous et al., 2010; Bergen and Mersmann, 2005). Putative players such as PPAR-? and 
187 
 
CEBP/? (Sierebaek et al., 2011; Roesen et al., 2002; Christy et al., 1989;) have been identified 
and substantiated, while the identity and role novel factors are constantly being investigated 
(Romao et al., 2011; Stegar and Lezar, 2011; Wang et al., 2011). The ability to independently 
manipulate individual AT depots in a manner that improves carcass quality and production 
efficiency is a priority goal for beef producers and researchers and would be a major benefit to 
the GFB industry due to concerns with AT accretion and carcass quality.   
Variability in the types and quality of forages in various seasons and regions of the 
country must be considered because it can affect the widespread adoption of forage-finishing 
systems (Preston, 2004; Drouillard and Kuhl, 1999). Different varieties of forages, geography, 
and climate affect the ability of different forages to thrive in various areas of the country 
(Preston, 2004; Drouillard and Kuhl, 1999). This threatens the ability of a commercial forage-
feeding operation to produce carcasses of consistently high value due to the inherent differences 
in plant composition, nutrient availability, geography/climate, and management practices that 
can affect animal growth and development (Drouillard and Kuhl, 1999). Understanding the 
molecular components that regulate nutrient-gene interactions in economically relevant tissues 
can lead to strategies that can minimize fluctuations in carcass quality and increase consistency 
despite the inherent differences in forage composition and quality in different areas of the 
country. 
Along with health awareness, consumers are becoming increasingly environmentally 
conscious making the perceived sustainability of beef production as important a factor as the 
quality of the product itself (Capper, 2011; Gaylean et al., 2011; Harper and Makatouni, 2002). 
While there is data to the contrary (Capper and Caddy, 2010), some of today?s consumers view 
more pastoral agricultural practices as more ?natural? and ?environmentally friendly (Capper et 
188 
 
al., 2009),? which has been a driving force for the grass-fed beef revolution. The alleged 
renewability of a forage-based system lends itself to a long-term sustainability given proper land 
and animal management practices.  In order to live up to these ?greener? expectations; the 
production efficiency (FE, growth rate, REA etc.) must be improved in forage-based finishing 
systems (Capper, 2011; Capper et al., 2008). The advent of genomic technologies in beef cattle 
will aid in this progress (Hill, 2009; Sellner et al., 2007; Dekkers, 2004).   
The wealth of evidence supporting disparities in growth rate, mature size, and to a less 
conclusive extent the nutrient profiles and production sustainability of grass-fed versus grain-fed 
beef have yet to be conclusively explained on a biological level. Of particular interest to our 
laboratory are the molecular distinctions that give rise to these divergent phenotypes and how 
they can be manipulated to deliver a consistently high-quality product in an efficient manner. 
These regulatory mechanisms have been scarcely investigated in beef cattle. Understanding these 
molecular distinctions will allow for specific interventions and manipulations to mitigate the 
undesirable aspects associated with grass-fed beef while capitalizing on the advantages of this 
sustainable finishing system. Livestock production in the post-genomic era will benefit from 
molecular platforms that will advance our understanding of how feeding systems and nutrient 
availability in these systems effects phenotypic outcome and the production efficiency associated 
with delivering a quality beef product. The application of these and other genomic technologies 
to GFB production can lead to improved consumer acceptance and demand, better production 
efficiency, and larger profit margins in the GFB industry.  
 
Compensatory Growth  
 
Another strategy that has been utilized to maximize production efficiency in beef cattle is 
the use strategic feed restriction/re-feeding regimens to capitalize on physiological phenomenon 
189 
 
known as compensatory gain. Compensatory gain is observed in animals and is characterized by 
a period of accelerated growth following a period of suppressed growth as a result of nutrient 
restriction (Gerrard and Grant, 2002; Wilson and Osbourne, 1960). As a result of this occurrence, 
animals on a lower plane of nutrition show a retarded growth trajectory but upon re-alimentation 
eventually match that of their non-restricted counterparts (Gerrard and Grant, 2002).  From a 
production standpoint, this practice can be advantageous because if animals can be fed on a 
restricted level and eventually reach the same level of performance (meat, milk wool, etc.) as 
non-restricted animals, this will reduce the cost to produce a marketable product and have a 
positive impact profit margins. Evidence for compensatory growth has been reported in various 
animals including pigs, cattle, chickens, and other species (Austad 2010; Zhan et al., 2007; 
Lebret et al., 2007; Lin et al., 2004; Therkildsen et al., 2003; Chiba et al., 1999; Zubair and 
Leeson, 1996 Sainz et al., 1995 Mersmann et al., 1987).  
The concept that caloric restriction can be beneficial to metabolic efficiency in animals 
has been investigated in various species ranging from roundworms (C. elegans,) to yeast (S. 
cerevisiae), rodents, and larger mammals (Lin et al., 2004; Austad, 2010; Higami et al., 2000). It 
has been observed that caloric restriction can prolong lifespan and improve metabolic efficiency 
in these various species (Ungarvi et al., 2008; Kritchevsky, 2002; Merry, 2002). Prolonging 
lifespan is usually not the objective of livestock production, but the effects on metabolic 
efficiency and nutrient partitioning are relevant to the comprehensive understanding of the 
molecular regulation of production efficiency. In a livestock production setting caloric restriction 
is generally not conducive to generating product, but the benefits of strategic caloric restriction 
followed by re-alimentation may increase profit margins and reduce environmental impact in the 
beef industry (Funston et al., 2011). It may also allow producers to tailor management strategies 
190 
 
to seasonal forage growth patterns and fluctuations in feed prices or availability, to make their 
overall operation more efficient. The effect of a properly managed restriction and re-feeding 
regimen can be so pronounced that animals that have been feed restricted prior to finishing can 
often attract a premium price in the feedlot due to the expected improved efficiency of gain 
relative to well-fed animals (McCurdy et al., 2010; NRC, 1996; Sainz et al., 1995). As such, this 
warrants further investigation to understand the molecular mechanisms responsible for this 
response.     
Compensatory growth in livestock has been studied for years in livestock and other 
species (Fox et al., 1972; Osbourne and Mendel, 1916) but our understanding of the mechanistic 
basis of this phenomenon remains vague. Additionally, how the mechanisms of compensatory 
gain impact growth, carcass composition, and energy partitioning is still poorly understood 
(McCurdy et al., 2010; Sainz and Bently, 1997; Sainz et al., 1995). In terms of putative 
knowledge, it has been documented that during the re-alimentation period, animals undergoing 
compensatory growth are more efficient compared to their counterparts fed a more suitable and 
consistent plane of nutrition and that this improved efficiency is transient as the effect is 
diminished once the animals are re-alimented for extended periods (Sainz et al., 1995; Kabbali et 
al., 1992; Abdallah et al., 1988). 
Adipose tissue and SM tissue protein, serve as repositories for cellular energy and 
building blocks, respectively (Havel, 2000). These are dynamic metabolic tissues that are tightly 
regulated in order to meet the energetic and metabolic demands of the organism based on the 
availability of energy and nutrients. In the case of nutritional challenges (in this case, 
restriction/re-feeding) these tissues adapt and remodel in an attempt to accommodate the 
metabolic demands of the organism and achieve relative homeostasis. The degree and duration of 
191 
 
caloric restriction dictates the metabolic response to this challenge and this response is 
orchestrated by molecular regulatory factors. Patterns of SM protein metabolism are important in 
terms of the lean tissue accretion in beef cattle production. Adipose tissue metabolism during 
feed restriction and re-feeding is also important due to the economic ramifications of AT 
accretion in specific depots. Because of the plasticity of these tissues, their central roles in 
energy and protein metabolism, as well as their economic value in terms of beef carcasses, AT 
and SM warrant special focus with respect to their contribution their metabolic response to feed 
restriction and repletion.  
The involvement of protein metabolism during restriction and re-feeding have been 
documented in rodents, pigs (Whang et al., 2003; Therkildsen et al., 2002; Hornick et al., 2000; 
Jones et al., 1990; Millward et al., 1975). Whang et al. (2003) reported that pigs fed a protein-
deficient diet exhibited compensatory growth once re-alimented and during the compensatory 
period the crude protein requirements are higher than that of non-restricted pigs. These authors 
argue that increased efficiency of protein utilization is responsible for the compensatory gain 
following restriction observed in pigs. This evidence substantiates the dynamics of protein 
turnover as a likely contributor to the compensatory response. The role of protein turnover, the 
interaction of various factors including increased feed intake, altered composition of gain, and 
energy partitioning during the compensatory response are vaguely understood and but it is well 
known that these processes are controlled by molecular and cellular alterations.  
Endocrine and blood metabolite changes observed during the restriction and re-feeding 
periods have been studied in a variety of cattle species and production settings (Hersom et al., 
2004; Hornick et al., 1998; Yamabayamba et al., 1996; Hayden et al., 1993; Hammond et al., 
1990). The GH-IGF axis has often been implicated in the compensatory response in beef cattle 
192 
 
(Hayden et al., 1993). The somatotropic axis, which includes GH, Insulin-like Growth Factors, 
and their associated proteins, is responsible for growth and lactation in cattle and swine (Lucy, 
2008; Renaville et al., 2002) and other species. GH secretion induces an anabolic paradigm in 
which protein accretion is enhanced and AT accretion is decreased. During fasting (or feed 
restriction) GH secretion spares protein breakdown and mobilizes energy from AT depots. This 
has been substantiated in several species (Lucy, 2008; Thirkilsden et al., 2004; Norrelund et al., 
2001; Norrelund et al., 2000; Jones et al., 1990) and supports the observed increased body 
protein gains and increased efficiency of nutrient use in compensating cattle.  
Yamabayamba et al. (1996) conducted a study in beef heifers fed ad libitum or restricted 
for 95 days followed by re-feeding. They measured several blood metabolites as well as resting 
metabolic rate before during and after restriction and concluded that the lower metabolic rate 
along with activation of the IGF-GH axis was responsible for the enhanced growth rates and 
efficiency observed in restricted cattle. While these and other metabolites have been identified 
the molecular signaling pathways responsible for, or responsive to these changes remain unclear.  
Moderate caloric restriction results in a metabolic paradigm which attempts to conserve 
SM tissue protein and mobilizes AT energy by releasing FA. During re-alimentation, protein 
accretion continues transiently before protein synthesis decreases and AT accretion commences. 
The general pattern has been seen in cattle in studies that demonstrate that following feed 
restriction, the empty body weight of cattle is composed of increased proportion of protein and 
water compared to non-restricted cattle and that upon re-feeding the proportion of fat increases 
with declining proportions of protein and water (Hornick et al., 1998; Wright and Russell, 1991). 
Hornick (1998) conducted an experiment using Belgian blue bulls subjected to a low growth 
restriction period followed by a fattening re-alimentation period. They showed that the 
193 
 
compensatory effect lasted for a maximum of 2 months and that it resulted in increased FE in 
compensating animals. Additionally, compensating animals showed lower intramuscular fat and 
peripheral fat was higher compared to non-restricted animals.  An interesting observation by the 
authors is that although the carcass quality grades suffered from increased peripheral AT, the fat 
content of the meat was actually lower than well-fed animals. These data show differences in 
peripheral and IMF metabolism that result in markedly different carcasses however the 
molecular components responsible have not been conclusively identified. 
Mitochondrial efficiency and liver size have also been investigated in feed restricted and 
compensating animals (Connor et al, 2009; Pamplona and Barja, 2006; Merry, 2002). Connor et 
al. (2009) reported that during the re-alimentation period, feed-restricted cattle showed increased 
expression of genes involved in cellular metabolism, cholesterol synthesis, oxidative 
phosphorylation, glycolysis, and gluconeogenesis. They also reported reduced hepatic size, and 
based on these data concluded that liver size and mitochondrial function might be responsible for 
the compensatory response in these animals.  
Understanding the ideal timing of compensatory process is an important economic 
consideration because the costs and rate of gain can vary depending on production phase, and 
with the use of different production inputs (Sainz et al., 1995; Coleman et al., 1993; Wright and 
Russel, 1991). Much of this is affected by, or coincides with molecular events that occur as a 
normal part of growth and development (Du et al., 2010) as well as those induced by 
environmental factors. For example, studies have shown that age of the animal affects the degree 
of the compensatory response and can also have an impact on composition of gain during this 
period (Boddicker et al., 2011; Choasap et al., 2011; Coleman et al., 1993; Wright and Russel, 
1991). In order to implement and maximize the benefits of this practice in SE beef cattle 
194 
 
production, the optimal initiation and duration of the restriction period, particularly in a forage-
based system, must be determined.  An improved understanding of the molecular adaptations 
that are responsible for the accelerated growth and nutrient partitioning during the restriction and 
re-alimentation periods will provide critical pieces in the understanding of metabolic efficiency 
and provide the basis for management strategies for maximizing the economic and production 
benefits of this practice without compromising animal health and/or performance.  
While the exact mechanisms for compensatory gain have not been elucidated, it is clear 
that metabolic shifts are responsible for the observed effects of restriction and re-feeding 
(Hornick et al., 2000; Hornick et al., 1998; Christensen et al., 1997). Energy conservation 
pathways are likely to be employed under periods of restriction and improved efficiency in the 
use of available nutrients may carryover for a transient time during the re-alimentation period 
leading to rapid compensatory growth. Mitochondrial function is also a likely player and this 
information must be identified. The identity of these mechanisms and pathways were the subject 
of the current inquiry because they may play a critical role in our understanding of the 
compensatory growth phenomenon and improve our ability to manipulate it towards more 
efficient and profitable beef production. 
 
Rationale and Objective: 
The initial thinking upon undertaking this project was to evaluate the effects of a forage-
based finishing system using nutrient rich perennial ryegrass (Lolium perenne) pastures 
compared to dry lots on animal performance and gene expression patterns in finishing steers. 
Feeding forage is a common practice in the SE and when properly managed, can produce beef of 
desirable quality in an economically efficient and environmentally sustainable manner. It has 
been documented that molecular changes occur throughout the course of the feeding period that 
195 
 
result in changes to animal phenotype and  meat quality (Graugnard et al., 2010; Du et al., 2010; 
Dayton and White, 2009; Hausman et al., 2008; Johnson and Chung, 2007). In addition, there is 
substantial evidence that caloric restriction followed by re-alimentation is capable of inducing a 
compensatory gain effect in cattle and other species, a phenomenon that may be useful in 
improving the efficiency of production in beef cattle. Unpublished data have shown a marked 
effect on growth trajectory when cattle subjected to ryegrass pastures after being on dormant 
pastures. The purpose of this project was to determine the effects of feed restriction and re-
feeding in a pasture system, accomplished using differing periods of forage grazing (nutrient 
availability), on the expression of regulatory genes involved in lipid, protein, and energy 
metabolism in grazing steers.  
 
Materials and Methods 
 
Animals:  
All procedures were conducted in accordance to the Auburn University Animal Care and 
Use Committee guidelines (IACUC Approval Number: 2008-1490). Animals were housed at the 
Auburn University E.V. Smith Beef Cattle Research Center. Animals were maintained on 
dormant and/or growing pastures (per experimental design) for the different time periods during 
the experiment. 
 
Feeding Trial: 
Animals were divided into one of four feeding groups to receive perennial Rye grass 
(Lolium perenne), or dormant pasture for different durations during the trial. Figure 5.1 shows 
the grazing assignments for each treatment group during the feeding period. The first group 
(Group 126; n=12) received ryegrass for the full 126 days of the experiment. The second group 
196 
 
(Group 84; n=12) was on dormant pasture for the initial 42 days and then grazed the ryegrass 
pasture for the final 84 days. Group 42 cattle (n=12) were on dormant pasture for half of the trial 
and then received ryegrass for the final half. Group 0 cattle (n=12) were on dormant pasture for 
the entire duration of the experiment and did not receive Ryegrass. All Animals were weighed 
every 21 days and biopsy samples were collected on day 42, 84, and 126 of the trial.  
 
Skeletal Muscle and Adipose Tissue Biopsies 
For each of the treatment groups, half (n=6) of the animals were subjected to an 
excisional biopsy to collect subcutaneous adipose tissue (SC) and Longissimus dorsi muscle 
tissue (LM) in order to obtain samples for gene expression analysis. This procedure was 
conducted in the field during the collection of fecal samples and weight and height data. While 
the animal was restrained in a cattle chute, a 12 cm x 12 cm area centered over the loin area just 
caudal to the 13th rib was clipped and scrubbed for aseptic surgery. Lidocaine hydrochloride 2% 
(15 mL SQ) was infused with a sterile 20ga. x 1 ? inch needle along the cranial and dorsal edges 
of the surgically prepared area to achieve local anesthesia. A 2cm vertical skin incision was 
made and approximately 1 gram of subcutaneous adipose tissue was surgically excised. The 
incision was extended ventromedially into the longissimus dorsi muscle. A 1-gram sample of the 
dorso-lateral aspect of the Longissimus dorsi muscle was removed by sharp dissection. The skin 
incision was closed with #3 braided coated nylon suture (POLYWEB, Webster Veterinary 
Supply, Sterling, MA) in a cruciate pattern. Skeletal muscle and adipose tissue samples were 
labeled and immediately snap frozen, in liquid nitrogen, until they could be taken back to the lab 
and stored in a freezer at minus 80 ?C. Cattle were then sprayed with fly repellant and monitored 
for complications post procedure. Each sample was taken from the same approximate location at 
the 42, 84, and 126d collection points. In addition samples were taken at slaughter from the loin 
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muscle, gastrocnemius muscle and from the subcutaneous back fat depot. A 0d sample to provide 
a gene expression baseline for each animal was scheduled to be collected, but was not due to 
logistical complications. For the current experiment only biopsy samples collected at 84 and 
126d were analyzed. 
 
RNA Isolation 
Total RNA was extracted from tissue samples using TRIzol reagent (Invitrogen, 
Carlsbad, CA). Approximately 0.5g of tissue was removed from the minus 80 ?C freezer and 
placed in a portable liquid nitrogen container. The frozen tissue was then crushed using a metal 
mortar, and the crushed tissue was placed in 5ml of TRIzol. The sample was then homogenized 
using a polytron? tissue homogenizer and then the extraction carried out as per the 
manufacturers protocol (Invitrogen, Carlsbad, CA). The crude RNA extract was subjected to 
deoxyribonuclease (DNAse) treatment using Turbo DNA free kits (Kit No. AM1907M; Ambion 
Inc., Austin, TX) to remove residual genomic DNA that could interfere with downstream 
procedures. A 20ul aliquot of the crude RNA isolate was placed in a nuclease free micro-
centrifuge tube and 2ul of Turbo DNAse (2U/?l) and 2ul of 10X DNAse reaction buffer were 
added to each sample. The tubes were then placed in an incubator at 37 ?C and the DNAse 
reaction was allowed to continue for 30 minutes. After the 30 minute period the 2ul of the re-
suspended DNAase inactivation reagent was gently mixed into the reaction and allowed to sit for 
5 minutes before being centrifuged at 10,000 x g. The supernatant containing the RNA was then 
collected and placed into a new, labeled, nuclease-free tube. The total RNA isolate was then 
subjected to RNA purification using RNeasy mini elute columns (Kit 74102; Qiagen, Vencia, 
CA). RNA concentration was determined using a Nanodrop ND-1000 spectrophotometer 
(Thermo Fisher Scientific, Willmington, DE).  
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Complimentary DNA synthesis 
Complimentary DNA (cDNA) was synthesized from the purified RNA isolate using high 
capacity cDNA Reverse Transcription kits (Kit # 4368814, Applied Biosystems, Foster City, 
CA). One microgram of DNAse treated total RNA was subjected to cDNA synthesis according 
to the manufacturer?s protocol. The resulting cDNA was then stored in the freezer until it was 
used for quantitative Real-Time PCR (qRT-PCR) to determine the expression of specific target 
genes. 
Quantitative Real-Time PCR 
  Quantitative Real-Time PCR was used to determine the expression (mRNA abundance of 
a selected panel of target genes. Custom TaqMan gene expression assays (Applied Biosystems, 
Foster City, CA) were designed using available gene bank information for the genes of interest in 
this study. The assays in this category are indicated by an asterisk in table 1.   Gene expression 
assays were conducted using an ABI 7500 Real-Time Thermocycler (Applied Biosystems, Foster 
City, CA). As described in the previous chapters, 18s ribosomal RNA was used as an 
endogenous control and relative mRNA abundance was determined using the ABI SDS software 
v2.0.3 (Applied Biosystems Foster City, CA).   
 
Real-Time PCR Reactions 
For each 20?l-multiplexed reaction the reaction components and PCR cycling conditions 
were as indicated in table 2 and table 3, respectively. For each reaction 1ul (50ng) of cDNA 
product was diluted to 8ul in a 2ml nuclease-free micro-centrifuge tube and placed on ice.  A 
sufficient amount of master mix containing enough TaqMan gene expression master mix (Part 
No. 4369016; Applied Biosystems Foster City, CA), 18S probe, and the custom gene expression 
assay was prepared for the tissue/gene of interest and then placed on ice. For each sample 12ul of 
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master mix was added to the tube containing the 8ul cDNA solution. The reactants were gently 
mixed and then the 20ul reaction was pipetted into individual tubes in 0.2ml MicroAmp? 
Optical 8-Tube Strips (Part No. 4316567; Applied Biosystems, Foster City, CA) that were 
resting in a chilled PCR cooler. The strips were then capped with MicroAmp? Optical 8-Cap 
Strip (Part No. 4323032 Applied Biosystems, Foster City CA) and centrifuged gently to remove 
air bubbles and ensure all components of the reaction were thoroughly mixed at the bottom of the 
tube. For each tissue sample, the multiplexed assay was run in duplicate and this process was 
repeated for each gene/tissue combination. 
 
Statistical Analysis 
Statistical analysis was done using the PROC GLM procedure of SAS (SAS Inst. Inc., 
Cary, NC). The GLM model statement included the main effects of biopsy date and treatment 
group as well as the interaction effect of biopsy date and treatment group. Treatment means were 
compared using the Least Square Means statement in SAS. Differences in least square means 
were considered significant at P< 0.05 and tendencies were considered at P < 0.10. The Proc 
CORR procedure of SAS was used to determine correlation coefficients between performance 
traits and average relative expression for each gene. Correlations were considered significant at P 
< 0.05 and tendencies were considered at P < 0.10. 
 
Results and Discussion 
 
For the current experiment, gene expression (relative mRNA abundance) and animal 
performance data were analyzed. Adipose tissue and skeletal muscle biopsy samples collected at 
84d and 126d were used for GE analysis. Weights were collected every 21d.  Total weight and 
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average daily gain were analyzed to correspond to the biopsy dates. Overall gain/ADG for the 
126 day trial was also determined and related to gene expression data. 
 
Animal Performance 
 
Both overall gain and ADG for the duration of the trial showed significant differences 
between treatment groups (Figure 5.2). For the full 126 day trial, Group 126 had the highest total 
gain and ADG of any group (Figure 5.2). These Group 126 animals were allowed to graze 
growing ryegrass for the entire feeding period and gained an average of 205kg. Group 84 
animals were heavier (P < 0.05) compared to Group 0 animals, which only gained around 68kg 
in 126d (Figure 5.2). The group 0 animals were relegated to the lowest plane of nutrition and 
were not allowed to graze growing rye grass at any point during the 126d trial. This impact of 
this low energy regimen is evidenced by their lack of substantial weight gain compared to other 
groups after 126d (Figure 5.2). Group 42 and 84 animals reported similar weight gains (P > 0.05) 
that were higher (P < 0.05) than group 0 but lower (P < 0.05) compared to Group 126 animals 
(Figure 5.2). As expected, similar patterns were observed for average daily gain. Also expected 
was the decreasing trend in weight total gain/ADG as days on ryegrass decreased. 
 
Compensatory Effect  
The differing periods of access to growing ryegrass served to alter the plane of nutrition 
of the finishing steers in the current study. During the trial period group 84 and 42 animals were 
allowed to graze growing ryegrass after being subjected to a dry lot and supplemented with hay 
for 42 and 84d, respectively. This was intended to impart caloric restriction on these animals for 
varying times in the finishing period. Upon re-alimentation (being released on ryegrass pasture) 
there were noticeable changes in growth trajectory indicating a potential compensatory effect in 
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these animals. From 63 to 84d, the group 84 animals showed similar total and average daily 
gains to group 126 (Figure 5.5; P > 0.05) which were higher compared to group 42 and group 0 
animals which had not grazed ryegrass (P < 0.05). In contrast, group 42 animals were placed on 
ryegrass at 84d and recorded weight gains that were higher (P < 0.05) than all other groups 
between 84 and 105d despite groups 84 and 126 also consuming ryegrass during this period 
(Figure 5.8). These results indicate that a period of feed restriction followed by re-alimentation 
did indeed provoke a compensatory response in grass-fed cattle under these conditions. While 
there was an observable compensatory effect with immediate impact on growth trajectory upon 
re-alimentation on the ryegrass pasture, this effect was not substantial enough to accomplish the 
same total weight as animals receiving ryegrass for the entire 126d (Group 126; Figure 5.2). 
 While there were significant differences for overall gain and ADG for the length of the 
trial, there was substantial variation within and between treatment groups with respect to weight 
gain during the treatment phases. Group 126 appeared to level off with respect ADG and thus 
total gain during the final phase of the treatment period gaining an average of ADG of 0.4lkgs/d 
and averaging 13.3kgs total gain during the final 42d of the trial period (Figure 5.8, 5.10). 
Despite continuously grazing on the ryegrass, this group reported similar gains (P > 0.05) to the 
other treatment groups during this period (Figure 5.10).  This was particularly interesting with 
respect to group 0 which only grazed the dry lot during the trial. One possible explanation for the 
tapering off of growth towards the end of the experiment may be the result of improper pasture 
management to sustain high levels of gain as animals grew and began to consume more. This 
would lead to a wash out of the high plane of nutrition and decrease overall growth during the 
later periods of the trial as evidenced by ADG and total gain data.  Nonetheless, at this time point 
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there were no differences in gain between the treatment groups regardless of diet (Figure 5.10; P 
> 0.05).  
When attempting to rear cattle on pasture, an important consideration is the carrying 
capacity and quality of the pasture. It is possible that the levels of growth under the SE climatic 
conditions during the trial period were not sufficient to support the consistent growth of these 
maturing steers during the latter phase of the trial thereby relegating the animals to a relatively 
restricted caloric regimen by default. The lack of differences in the late phases is likely due to the 
substantial individual animal variation with respect to gain during this treatment phase (Figure 
5.7, and 5.12) which can possible be attributed to variation in individual animal feed efficiency. 
That is to say that some animals still performed well during this period while others recorded 
declining weight gains compared to previous periods (Figure 5.12)   At 84d groups 126 and 84 
showed similar weight gain (around 2.3kg, P > 0.05) which was higher than groups 42 and 0 (P 
< 0.05) which showed similar gains to each other (Figure 5.5; P > 0.05). Additionally, we noted 
that total Gain for the 126d trial was highly correlated with gain determined at 84d (R= 0.71; P = 
0.0004).  
 
Gene Expression Analysis 
 The performance of the animals in the current study was used to compare to expression 
of key metabolic genes that are responsible for differences production efficiency in finishing 
animals. While these factors have been investigated in other settings, the effect of a forage-based 
restriction/re-feeding paradigm under SE finishing conditions on the selected GE profile has not 
been investigated. Composition of gain was not determined, but this would have been very 
valuable in order to determine the net effect of the balance of metabolic networks determine 
carcass phenotype.  
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Serial Biopsy of Loin Muscle Tissue 
Excisional biopsies of longissimus dorsi (LM) were collected from steers at three points 
during the feeding trial. The original intention for this experiment was to conduct a longitudinal 
grazing study in order to collect and analyze economically relevant SM and AT samples at 
important developmental time points to follow gene expression patterns. The use of skeletal 
muscle samples to monitor the expression of adipogenic genes involved in the development of 
the marbling depot has been previously established (Barnes et al., 2012; Graugnard et al., 2010; 
Graugnard et al., 2009; Wang et al., 2010; Lee et al., 2007). Graugnard et al., (2009) contend that 
alterations in gene expression networks are responsible for driving adipogenesis, lipid filling, and 
intracellular energy metabolism in the IMF depot. This provides an opportunity to evaluate long-
term effects of nutrition and other management practices on adipogenic gene expression in SM 
of beef cattle as the IMF and other AT depots develop.  In terms of sample collection strategy, 
the efficacy of the serial biopsy approach in understanding temporal and developmental aspects 
of adipogenesis in SM has also been documented in previous studies (Barnes, 2012; Graugnard 
et al., 2010; Wang et al., 2009; Lee et al., 2007; Lehnert et al., 2006). In the current study biopsy 
samples were collected at 84 and 126d for gene expression profiling.  As mentioned before a 0d 
sample to provide a gene expression baseline for each animal was scheduled to be collected, but 
was not due to logistical complications. Additionally, based on the experimental design we only 
looked at the final 2 biopsy dates due to the fact that it was at this point where we truly had 4 
treatment groups. Prior to this animals had received either ryegrass or dormant pasture with no 
combinations of the two (Group 126 on ryegrass for 41 d; Groups 84, 42, and 0 for 41d) on 
dormant pasture.  Therefore it was hypothesized that these time points would be most 
informative in terms of developmental windows in response to dietary regimens.  
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Effect of Biopsy Date on Relative Gene Expression in Skeletal Muscle Biopsies 
Energy and protein metabolism are dynamic processes that change throughout the course 
of growth and development. In addition, the process of adipose tissue deposition in cattle and 
other species is a developmental process that follows a temporal pattern and is affected by 
genetic, environmental, nutritional, and other factors (Du et al., 2010; Hausman et al., 2009). 
Many studies regarding the developmental process have been conducted in tissues from fetal and 
early postnatal cattle (Du et al., 2010a; Du et al., 2010b). To garner a perspective of this 
developmental time course in finishing cattle we collected biopsy samples at time points during 
the finishing process to understand how the expression of target genes might change with time 
and treatment and how these relate to animal performance.  
In skeletal muscle, PPAR-???the putative master regulator of adipogenesis, showed 
increased expression in SM at biopsy date 126 compared to 84 (Figure 5.14; P < 0.05). This is 
consistent with results from Lee et al. (2007) who observed marked up-regulation of these 
PPAR-? and CEBP/? in the latter fattening stage of finishing Hanwoo steers compared to earlier 
time points. It should be noted that their study lasted much longer and they collected biopsy 
samples at 12 and 27 months. The current study lasted only 126 days. Unlike the Lee study we 
did not observe a significant increase in CEBP-? expression in the later biopsy date (Figure 5.16; 
P > 0.05). In a study profiling gene expression patterns during IMF in Wagyu x Hereford and 
Piedmontese x Hereford cattle, Wang et al., (2009) determined that there were noticeable 
differences in the expression of adipogenic genes in a time-frame specific manner. They 
collected a series of 6 biopsy samples from 2 to 25 months and noticed that PPAR gamma 
expression was elevated at later time points compared to the earlier in animals that had the higher 
genetic propensity to have high IMF.  Although we used animals from a similar genetic 
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background, the Wang et al., (2009) study highlights a temporal PPAR-? gene expression pattern 
similar to that observed in the current study. These and other data support the notion that the 
marbling depot experiences a lag in maturation that is controlled by transcriptional regulators of 
adipose tissue metabolism.   
PGC-1? expression was higher at 126d vs. 84d (Figure 5.20; P < 0.05). Increased PGC-
1? expression at the latter biopsy date may indicate increased mitochondrial biogenesis/function 
as the animal?s age or in response to changes in diet by the 126d time point. In a study Connor et 
al., (2009) fed 8-month old steers ad libitum (n=6) or restricted them to 60-70% of the intake of 
the control animals (n=6). The authors collected liver biopsies at -14, +1, and +14d relative to re-
feeding. They determined that during re-alimentation that the restricted steers showed improved 
feed efficiency relative to their control counterparts. They also reported an increase in the 
expression of genes encoding mitochondrial complex proteins indicating a potential role for 
increased mitochondria in improving feed efficiency. While our study utilized specifically SM 
and AT tissues and not liver, the findings of Connor et al., (2009) are informative in terms of the 
integration of mechanisms responsible for improved feed efficiency experienced during 
compensatory gain. In addition to PGC-1??we looked at UCP-2 expression. Uncoupling proteins 
are capable of dissipating the proton gradient used to drive the synthesis of ATP in them 
mitochondria. UCP-2 expression in skeletal muscle was not affected by biopsy date (Figure 5.15; 
P > 0.05). Previous studies have reported a relationship between UCP expression and feed 
efficiency (Erlanson-Albertsson, 2003). While uncoupling proteins are involved in the 
dissipation of energy, the oxidation of FA serves as a major source of ATP for cellular energy 
purposes in ruminants and other species. CPT-1b expression is indicative of increase FA 
oxidation as this gene represents a major regulatory point governing the entrance of FA into the 
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mitochondria, the site of ?-oxidation in the cell. CPT-1b expression was higher in SM biopsy 
samples collected at the 126d compared to d84 (Figure 5.13 P < 0.05).  
Proteasome Macropain Subunit 11 expression was about four times higher at d126 
compared to day 84 (Figure 5.18; P < 0.05) indicating higher rates of turnover at this time point. 
Increased protein turnover later in the feeding phase may be indicative of decreased efficiency. 
Protein turnover is an energetically expensive process given the ATP investment required to 
synthesize and breakdown proteins and efficiency of production can be increased by decreasing 
the rate of protein turnover (Bergen, 2008; Bergen and Merkel, 1991). Rate of weight gain 
decreased later in current trial (Figure 5.10 and Figure 5.11) and this could be due to increased 
protein turnover to supply energy for basic metabolic needs.  
 
Effect of Treatment Groups on Relative Gene Expression in Skeletal Muscle Biopsies 
In addition to the temporal aspect of growth and development, plane of nutrition and 
energy balance can impact the metabolic disposition and efficiency of growing animals. We 
sought to understand the effects of treatment group (duration of ryegrass vs. dry lot exposure) on 
the expression of regulatory genes involved in energy and protein metabolism. Despite observing 
significant changes in animal performance with respect to treatment groups, there were limited 
effects of treatment on the expression of genes in SM in these cattle.  
UCP-2 expression was highest in treatment Group 126 (Figure 5.23 P < 0.05) compared 
to Groups 84, 42, and 0 which all showed similar expression (P > 0.05). FABP4 expression in 
SM showed substantial variation within treatment groups and there were no significant 
differences between treatments (Figure 5.24; P > 0.05). This could be indicative of differential 
marbling potential within animal groups. FABP4 is a putative marker of late adipocyte 
differentiation and would be expected to increase as the depot develops and adipocyte 
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metabolism and lipid storage ensues. Treatment group 42 showed increased expression of 
FABP4 relative to the other groups (P < 0.05). Pref-1 expression was higher in treatment group 
42 compared to group 126 (P < 0.05), but was not different compared to group 84 and 0 (P > 
0.05). PPAR-? expression in SM was not affected by treatment (P > 0.05). CEBP-? expression 
was lower in group 42 compared to the group 126 (P < 0.05), but did not differ from group 84 
and group 0 (P > 0.05). Groups 126, 84, and 0 all showed similar expression to each other (P > 
0.05).   
Pref-1 expression was higher at the second biopsy date compared to the d84 (Figure 5.19; 
P < 0.05). This is counter-intuitive given the putative school of thought on marbling 
development. It is generally accepted that the marbling depot is the late maturing therefore, as a 
negative regulator of adipogenesis, Pref-1 expression would be expected to decrease as the 
finishing phase progressed and the IMF depot expands. This has been demonstrated in a recent 
study by Key et al., (2013) in which cattle were subjected to grain or forage based diets for 
differing numbers of days on feed. The authors determined that Pref-1 mRNA in subcutaneous 
AT was decreased in response to DOF and grain finishing. The authors did not however measure 
Pref-1 expression in the IMF depot.  It could be possible that the duration of the trial period was 
not long enough to truly monitor the Pref-1 profile over an adequate developmental window. 
Because Pref-1 is involved in muscle and adipose development which begins prenatally and 
continues throughout early postnatal and adult life, it would also be helpful to analyze earlier 
developmental time points to determine the overall effect of Pref-1 expression early and late in 
IMF development which has yet to be established.  
PGC-1? drives mitochondrial biogenesis and as such is a major factor in energy 
metabolism. It has been shown that mitochondrial function is related to metabolic efficiency in 
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cattle and other species. Mitochondria are responsible for 90% of intracellular oxygen 
consumption and serve as the primary site for ATP synthesis. PGC-1? expression has been 
shown to be higher in elevated in low RFI (more efficient) animals compared to high RFI 
animals indicating that it may play a role in determining metabolic efficiency (Kelly et al., 2010). 
In the current study PGC-1? expression was highest in group 42 (Figure 5.27; P < 0.05) 
compared to the other groups which had similar levels of expression (P >0.05). Animal variation 
with respect to weight gain and performance could have contributed to the lack of differences in 
the current study. More robust treatments and animal numbers that generate larger performance 
disparities may be necessary to define the role of PGC-1??in metabolic efficiency of forage-
finished steers.  
As mentioned, the efficacy of the biopsy approach in assessing skeletal muscle gene 
expression has been documented in previous studies (Barnes, 2012; Graugnard et al., 2010; 
Wang et al., 2009; Lee et al., 2007; Lehnert et al., 2006). In the current study we observed 
fluctuations in gene expression that corresponded to the some of the variation we observed in 
animal performance. Larger animal numbers would likely help to mediate the amounts of 
variation we observed in performance and gene expression. It would be useful to correlate our 
expression data with specific parameters associated with the genes of interest such as marbling 
for adipogenic gene, skeletal muscle fiber types for oxidative and mitochondrial genes, and loin 
eye area for genes involved in protein metabolism. These carcass data were not made available 
for the current study. 
 
Effect of Biopsy Date on Relative Expression of Genes in AT Biopsies 
Adipose tissue is an important repository of cellular energy and also plays an active role 
in energy homeostasis, adaptations of physiological status and plane of nutrition. The selected 
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genes in AT for study here have been established as important regulators and indicators of AT 
accretion and fat metabolism. Understanding of these genes and the pathways they regulate will 
be critical to the understanding and ability to manipulate AT depots to the economic and health 
advantage of producers and consumers, respectively.  
In AT Leptin expression decreased from biopsy date 84 to 126 (P<0.05). Leptin is an 
indicator of adiposity in various animals ranging from rodents to humans (Jequier 2006; Benoit 
et al., 2004). Serum leptin levels increase linearly with adiposity in sheep and cattle (Delavaud et 
al., 2007; Delavaud et al., 2002), and would be expected to increase as time on feed increased in 
finishing cattle as AT accretion occurs. The effects of feeding level on leptin expression can be 
long term, mid-term and short-term (Chilliard and Bonnet, 2005). Long term effects of feeding 
level are as a result of changes in adiposity in response to nutrition and development. Days on 
feed at a given plane of nutrition can also affect leptin levels as can size and frequency of meals. 
As evidenced by the decrease in weight gain towards the end of the feeding trial (Figure 5.10 and 
Figure 5.11), feed intake may not have been adequate to support substantial weight gain, much 
less fattening at this point in the trial. In addition to governing energy homeostasis, leptin serves 
an adaptive role for periods of under nutrition or caloric restriction. When feed is restricted, 
leptin concentrations decrease and the physiological effect of this is to stimulate re-feeding. In 
terms of metabolic adaptations this results in increased glucocorticoid secretion, decreased 
energy expenditure and decreased protein synthesis.  
A similar pattern was observed for GPAT which was higher at 84 compared to 126d 
(Figure 5.28; P < 0.05). GPAT is an indicator of triglyceride synthesis and increased levels 
would be expected to correspond with increased fat deposition in AT. In ruminant animals, 
GPAT expression is highest in adipose tissue compared to non-ruminants where the liver is a 
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primary site of triglyceride synthesis and shows high levels of expression (Roy et al., 2006). 
GPAT expression has been shown to be highly correlated with IMF deposition in Korean steers 
(r = 0.71; P < 0.001) however studies investigating GPAT in subcutaneous AT are lacking. In the 
current study, molecular adaptations to plateauing nutrient availability and weight gains may 
have given rise to this expression pattern. Earlier in the trial when feed may have been 
substantial enough to support growth (84d) and fattening GPAT expression was higher versus 
later (126d) when animals were larger and required more nutrients for fattening and GPAT 
expression was lower. Biochemical assessments of tissue and circulating triglycerides could have 
been conducted to determine if GPAT expression influenced these parameters as well as carcass 
composition.  
 There were no differences in expressions of or ZFP423 or FABP4 across biopsy dates 
(Figure 5.29 and Figure 5.30, respectively; P > 0.05). These are indicators of adipogenesis and 
their steady expression across biopsy dates may be responsible for, or indicative of, the lack of 
substantial AT accumulation across treatment groups throughout the trial. Key et al., 2013 
showed that transcription factors involved in adipogenesis and backfat thickness increased with 
days on feed and grain feeding in subcutaneous AT of finishing heifers. In the current study 
carcass data to support or refute these conclusions was not available.  
UCP2 expression was also higher in the d84 compared to d126 (Figure 5.31 P < 0.05). 
One of the effects of reduced leptin in the face of caloric restriction is to reduce energy 
expenditure via direct and indirect mechanisms. While one must be careful not to over speculate, 
it is possible that the decreased leptin levels at 126d corresponded to decreased mitochondrial 
energy oxidation however biochemical assessment of mitochondrial function is necessary to 
support this conclusion.  Regardless, increased UCP2 expression later in the trial may correspond 
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to decreased metabolic efficiency and thus decreased production efficiency as cattle grew and 
feed resources became scarcer.  
 
Effect of Treatment on Relative Expression of Genes in AT Biopsies 
Leptin expression was not impacted by treatment group other than Group 0 which 
showed lower expression compared to groups 126, 84, and 42 (Figure 5.37; P < 0.05).  Group 0 
did not graze ryegrass at all in the current experiment and thus were subjected to the lowest plane 
of nutrition. Animals in this group would be expected to have relatively less fat cover due to their 
relative feed restriction and this may be responsible for the lower leptin expression in this group. 
Groups 126, 84, and 42 did not differ (Figure 5.37 P > 0.05) in relative leptin expression. In 
addition, leptin levels are known to decrease with feed restriction and this may have contributed 
to lower leptin expression in Group 0 steers.   
GPAT expression was similar across all treatment groups (Figure 5.36; P > 0.05). As 
mentioned before, GPAT is involved in to formation of glycerolipids and has been shown to be 
upregulated by nutritional and other factors. Up-regulation of this gene results in increased TAG 
synthesis. The relatively constant level of GPAT expression in subcutaneous adipose tissue 
across treatments is interesting given the differences in energy intakes expected in treatment 
diets. Variation in animal performance and lack of substantial forage growth throughout the trial 
may have led to the lack of significant differences. Individual animal carcass data would be 
useful to determine if GPAT levels can be related to backfat thickness or other adipose tissue 
carcass traits as has been previously described for IMF (Jeong et al., 2012).  
 UCP2 expression was higher in group 42 compared each of the other groups (Figure 
5.35; P < 0.05) which all showed similar expression to each other (P > 0.05). Uncoupling 
proteins dissipate the proton-gradient in the mitochondrial respiration chain and energy is lost in 
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the form of heat instead of being used for ATP production. UCP2 expression can therefore be 
associated with metabolic efficiency because energy lost as heat, even in the necessity of 
thermoregulation, cannot be used for productive purposes. Increased expression of UCP2 in 
group 42 did not affect weight gain for the duration of the trial as group 42 had similar gains to 
group 84 (Figure 5.2; P > 0.05), and higher total gain than group 0 (P < 0.05).  
Zfp423 is a novel metabolic regulator that has been shown to promote adipogenesis 
through its influence on preadipocyte determination (Gupta et al., 2010). In the current study 
Zfp423 expression was higher in group 42 AT biopsies compared to group 126 and 84 (Figure 5. 
23; P < 0.05). Group 0 expression did not differ from any of the other groups (P > 0.05). 
Increased Zfp423 expression is expected to indicate increased adipogenic metabolism and as 
such should correspond to increased backfat thickness in cattle with high expression. The exact 
regulatory effect of Zfp423 in vivo remains unclear. Gupta et al., (2010) showed that ectopic 
expression of Zfp423 in non-adipogenic NIH-3T3 L1 fibroblasts induces PPAR-? expression in 
undifferentiated cells. In the current study there was no discernible relationship between Zfp423 
and PPAR-?? While this mechanism has been studied in cell lines, little information exists 
regarding Zfp423 expression or involvement in adipogenesis in beef cattle particularly in 
response to differing planes of nutrition. In this regard more detailed studies are warranted to 
understanding the timing and significance of this contribution.  
 
Correlations between Animal Performance and Gene Expression 
 When taken alone, performance data yielded relatively expected information as animals 
responded based on the plane of nutrition they were exposed to, and gene expression values 
varied between and within treatments in some cases. When performance and gene expression 
were taken together, a few interesting observations emerged.  
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Total ADG tended to be correlated with FABP4 expression at 84d (P = 0.08). A similar 
trend was observed with CEBP/? expression (P = 0.09). The expression of these two genes, both 
involved in AT differentiation and metabolism, were also highly correlated with each other (P < 
0.0001) at 84d. CEBP/??expression was also correlated with PGC1??expression at 84d in LM 
samples (P = 0.03). The true significance of these correlations remains to be elucidated but it is 
interesting to note the transcriptomic picture that emerged and how this transcriptomic pattern 
could be related to observed animal performance. 
Another interesting, but expected correlation was the expression of UBE2 and PSDM11 
which was highly correlated at 126d (0.71; P = 0.01). These genes are involved in protein 
turnover pathways and would be expected to be auto-correlated based on their function in these 
pathways.   
It may be prudent to look directly at indices of fat accumulation including back-fat 
thickness and marbling scores, as well as biochemical and histological assessments of IMF, as it 
would be interesting to relate these parameters with gene expression data. These are the types of 
associations that are likely to make practical sense of biological data as they can be readily 
compared or observed in terms of a measurable production parameter of economic importance. 
Not only that, stakeholders are likely to respond more favorably to genomics based platforms 
when they can be directly related to production parameters that impact their bottom line.   
 
Summary and Conclusions 
The objective of the current study was to determine the effects of a feed restriction and 
re-feeding paradigm in a pasture-based system on the expression of regulatory genes involved in 
lipid, protein, and energy metabolism in grazing steers subjected to differing levels of forage 
growth. We sought to determine the efficacy of a targeted transcriptomic approach in elucidating 
214 
 
the molecular determinants of energy and protein metabolism, and how these components are 
affected and employed during a pasture based restriction/re-feeding regimen. The results from 
this study indicate that there. Earlier sampling may be informative in terms of monitoring and 
relating developmental trajectory with gene expression profiles.  
Unfortunately forage based research of this nature is dependent on adequate conditions to 
maximize forage growth and generate robust performance differences in treatment groups. This 
was not the case during the trial period as Alabama experienced formidable drought conditions 
resulting in retarded forage growth (especially toward the end of the trial) which may have 
confounded the design and results of the experiment. There were limited differences in 
expression across treatment groups particularly within the treatment phases. Again, the lack of 
differential performance amongst the treatment groups may have hampered the robustness of this 
expression profiling approach. The differences in expression observed in biopsy date indicate 
that metabolic paradigms appear to shift as growth and development occur, however the 
application of these specific paradigms may be limited to the conditions experienced during this 
pasture/feeding trial because of the lack of substantial forage growth that would likely not be 
experienced under more ideal production conditions.   
215 
 
Chapter 5 Figures 
 
Figure 5.1 Grazing Assignments and Treatment Phases for Steers for the 126d Feeding Trial1  
 
Group Number 
  
Feeding Trial Phase and Assignments 
0-41 Days 42-83 Days 83-126 Days 
Group 126    
Group 84    
Group 42    
Group 0    
 
Ryegrass =   Dormant Pasture =  
1Ryegrass = Growing Perennial Ryegrass Pasture (Lolium perenne); Dormant Pasture = Dry lot 
supplemented with hay; Group 126, 84, 42, 0  = 126, 84, 42, and 0 days grazing ryegrass, 
respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
216 
 
Table 5.1 Reaction components for cDNA synthesis from total RNA from skeletal 
muscle and adipose tissue biopsies from grazing steers1 
 
 
 
 
 
 
 
 
 
 
 
1Individual Reaction components for complementary DNA synthesis from total RNA 
isolate. For each tissue a master mix containing sufficient quantities of the individual 
reaction components was created and cDNA synthesis was conducted for each RNA 
sample; RT = Reverse Transcription; dNTP = deoxynucleoside triphosphates. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Component Volume/Reaction (?l) 
10x RT Buffer 2 
25x dNTP Mix (100mM) 0.8 
10X Random RT Primers 2 
MultiScribe Reverse Transcriptase 1 
Nuclease Free Water 4.2 
Total Reaction Volume 10 
217 
 
Table 5.2 Thermocycling conditions for cDNA synthesis from total RNA extracted from 
skeletal muscle and adipose tissue biopsies from grazing steers.1 
  
 
 
 
 
 
1Reaction conditions for the 4-step synthesis of cDNA from RNA; upon completion, 
reactions were held at 4?C until samples were removed. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Step 1 Step 2 Step 3 Step 4 
Temperature (?C) 25? 37? 85? 4? 
Time 10min 120min 5min Hold 
218 
 
 
Table 5.3 Reaction components for gene expression analysis using quantitative real-time 
PCR.1 
 
 
 
 
 
 
 
 
 
 
118S Vic Labeled 18S ribosomal RNA probe for data normalization in multiplexed 
quantitative Real-Time PCR reaction; PCR = Polymerase Chain Reaction; cDNA = 
Complementary DNA. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Component Volume 
TaqMan Gene Expression Master Mix 8?l 
Vic-Labeled 18S rRNA Probe1 1?l 
Custom TaqMAn Gene Expression Assay 1?l 
cDNA2 + H20 10?l 
Total Reaction Volume 20?l 
219 
 
Table 5.4 Thermocycling conditions for quantitative Real-Time PCR analysis1 
 
Step Description Time Temperature 
1 AMPerase UNG Activation 2 min 50?C 
2 Taq Activation/UNG Inactivation 10 min 95?C 
3 Polymerization 15 sec 95?C 
4 Primer Annealing/Detection 1 min 60?C 
5 Repeat Steps 3-4 for 40 cycles NA NA 
 
1UNG = AMPerase Uracil N Glycosylase- Prevents Carryover Contamination;  
Taq = Thermostable Taq DNA Polymerase. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
220 
 
Animal Performance 
 
Figure 5.2 Effect of treatment group on total weight gain in forage-fed steers for 126d 
trial period 
 
  
a,b,c Columns represent simple mean comparisons (+/- SEM) of average total weight gain 
in each treatment group for the duration of the 126d trial. Columns not sharing a common 
superscript differ (P > 0.05); Group 126, 84, 42, 0 = 126, 84, 42, and 0 days grazing 
ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
50
100
150
200
250
To
tal Wei
gh
t Gai
n (
kg
) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
b b 
c 
221 
 
 
Figure 5.3 Effect of treatment group on average daily gain in forage-fed steers for 126d 
trial period 
 
  
a,b,c Columns represent simple mean comparisons of average daily weight gain (+/- SEM) 
in each treatment group for the duration of the 126d trial. Group 126, 84, 42, 0 = 126, 84, 
42, and 0 days grazing ryegrass, respectively; Columns not sharing a common superscript 
differ (P > 0.05). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Av
erag
e D
ail
y Gai
n (
kg/d
 ) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
b 
b 
c 
222 
 
 
Figure 5.4 Effect of treatment on average daily gain of individual forage-fed steers for 
126d trial period 
 
  
1Average daily weight gain for individual animals in each treatment group from 0 to 126d 
of the trial; Group 126, 84, 42, 0  = 126, 84, 42, and 0 days grazing ryegrass, 
respectively; Average daily gain = kg of weight gained per day for duration of the 126d 
trial. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
Av
erag
e D
ail
y Gai
n (
kg)
 
Individual Animal 
Group 126
Group 84
Group 42
Group 0
223 
 
Figure 5.5 Effect of treatment group on total weight gain in forage-fed Steers from 63d 
to 84d of the trial period  
 
  
a,b Columns represent simple mean comparisons (+/- SEM) of average total weight gain in 
each treatment group from 63 to 84d of the trial. Columns not sharing a common 
superscript differ (P > 0.05); Group 126, 84, 42, 0 = 126, 84, 42, and 0 days grazing 
ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
10
20
30
40
50
60
To
tal Wei
gh
t Gai
n (
kg)
 
Treamtent Group 
Group 126 Group 84 Group 42 Group 0
a a 
b b 
224 
 
Figure 5.6 Effect of treatment group on average daily gain in forage-fed steers from 63d 
to 84d of the trial period 
 
  
a,b Columns represent simple mean comparisons (+/- SEM) of average daily weight gain 
in each treatment group from 63d to 84d of the trial. Columns not sharing a common 
superscript differ (P > 0.05); Group 126, 84, 42, 0 = 126, 84, 42, and 0 days grazing 
ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
3
Av
erag
e D
ail
y Gai
n (
kg
 ) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a a 
b 
b 
225 
 
Figure 5.7 Effect of treatment on average daily gain of individual forage-fed steers from 
63d to 84d of the trial period1 
 
  
1Average daily weight gain for individual animals in each treatment group from 63d to 
84d of the trial; Average daily gain = kg gained per day from 63 to 84d; Group 126, 84, 
42, 0  = 126, 84, 42, and 0 days grazing ryegrass, respectively. 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
Av
erag
e D
ail
y Gai
n (
kg/d
) 
Individual Animal 
Group 126
Group 84
Group 42
Group 0
226 
 
Figure 5.8 Effect of treatment group on total weight gain in forage-fed steers from 84 to 
105 of the trial period1 
 
  
a,b Columns represent simple mean comparisons (+/- SEM ) of average total weight gain 
in each treatment group from 84d to 105d of the trial. Columns not sharing a common 
superscript differ (P > 0.05). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
10
20
30
40
50
60
70
80
To
tal Wei
gh
t Gai
n (
kgs
) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a a 
a 
b 
227 
 
Figure 5.9 Effect of treatment group on average daily gain in forage-fed steers from 84d 
to 105d of the trial period 
 
  
a,b Columns represent simple mean comparisons (+/- SEM ) of average daily weight gain 
in each treatment group from 84d to 105d of the trial. Columns not sharing a common 
superscript differ (P > 0.05). Group 126, 84, 42, 0 = 126, 84, 42, and 0 days grazing 
ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
3
3.5
Av
erag
e D
ail
y Gai
n (
kg)
 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a a 
a 
b 
228 
 
Figure 5.10 Effect of treatment group on total gain in forage-fed steers from 105 to 126d 
of the trial period 
 
  
a Columns represent simple mean comparisons (+/- SEM) of average total weight gain in 
each treatment group from 105d to 126d of the trial. Columns not sharing a common 
superscript differ (P > 0.05). Group 126, 84, 42, 0 = 126, 84, 42, and 0 days grazing 
ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-20
-10
0
10
20
30
40
50
60
To
tal Wei
gh
t Gai
n (
kg
 ) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
a 
a 
229 
 
Figure 5.11 Effect of treatment group on average daily gain in forage-fed steers from 105 
to 126d of the trial period 
 
  
a Columns represent simple mean comparisons (+/- SEM) of average daily weight gain in 
each treatment group from 105d to 126d of the trial. Columns not sharing a common 
superscript differ (P > 0.05). Group 126, 84, 42, 0 = 126, 84, 42, and 0 days grazing 
ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Av
erag
e D
ail
y Gai
n (
kg/d
) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
a 
a 
230 
 
Figure 5.12 Effect of treatment on average daily gain of individual forage-fed steers from 
105 to 126d of the trial period1 
 
  
1Average daily weight gain for individual animals in each treatment group from 63d to 
84d of the trial; Average daily gain = kg gained per day from 105-126d of trial; Group 
126, 84, 42, 0  = 126, 84, 42, and 0 days grazing ryegrass, respectively. 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Av
erag
e D
ail
y Gai
n  
(kg
) 
Individual Animal 
Group 126
Group 84
Group 42
Group 0
231 
 
Gene Expression 
 
Figure 5.13 Effect of biopsy date on relative expression of CPT-1b in steer skeletal 
muscle biopsies1 
 
 
 
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
CPT-1b = Carnitine Palmitoyl Transferase-1b. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126da 
b 
232 
 
Figure 5.14 Effect of biopsy date on relative expression of PPAR-? in steer skeletal muscle 
biopsies1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
PPAR-? = Peroxisome Proliferator Activator Receptor-gamma. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126da 
b 
233 
 
Figure 5.15 Effect of biopsy date on relative expression of UCP2 in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
UCP-2 = Uncoupling Protein-2. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
a 
234 
 
Figure 5.16 Effect of biopsy date on relative expression of CEBP/? in steer skeletal 
muscle biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
CEBP/a = CAAT Enhancer Binding Protein/alpha. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
a 
235 
 
Figure 5.17 Effect of biopsy date on relative expression of FABP4 in steer skeletal 
muscle biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
FABP4 = Fatty Acid Binding Protein 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
a 
236 
 
Figure 5.18 Effect of biopsy date on relative expression of PSDM11 in steer skeletal 
muscle biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
PSDM11 = Proteasome Macropain Subunit 11. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
b 
237 
 
Figure 5.19 Effect of biopsy date on relative expression of Pref-1 in steer skeletal muscle 
biopsies 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
Pref-1 = Preadipocyte Factor 1; Dlk1. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126da 
b 
238 
 
Figure 5.20 Effect of biopsy date on relative expression of PGC-1? in steer skeletal 
muscle biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d = skeletal muscle biopsies taken at 84d and 126d;  
PGC-1? = Peroxisome Proliferator Activator Protein Coactivator-1?. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.05
0.1
0.15
0.2
0.25
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126da 
b 
239 
 
Figure 5.21 Effect of treatment on relative expression of CEBP/? in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
CEBP/? = CAAT Enhancer Binding Protein/alpha. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a,b 
b 
a,b 
240 
 
Figure 5.22 Effect of treatment on relative expression of CPT-1b in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
CPT1-b = Carnitine Palmitoyl Transferase 1-b. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
a 
a 
241 
 
 
Figure 5.23 Effect of treatment on relative expression of UCP-2 in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
UCP-2 = Uncoupling Protein-2. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
b 
b 
b 
242 
 
Figure 5.24 Effect of treatment on relative expression of FABP4 in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
FABP4 = Fatty Acid Binding Protein 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a a 
b 
a 
243 
 
Figure 5.25 Effect of treatment on relative expression of Pref-1 in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
Pref-1 = Preadipocyte Factor 1; Dlk-1. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a,b 
b 
a,b 
244 
 
Figure 5.26 Effect of treatment on relative expression of PPAR-? in steer skeletal muscle 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
PPAR-?? = Peroxisome Proliferator Activator Protein- gamma. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
a 
a 
245 
 
Figure 5.27 Effect of treatment on relative expression of PGC-1? in steer skeletal muscle 
biopsies for the 126d trial period1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
PGC-1? = Peroxisome Proliferator Activator Protein-1?? 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
b 
a 
246 
 
Adipose Tissue Biopsies 
 
Figure 5.28 Effect of biopsy date on relative expression of GPAT in steer adipose tissue 
biopsies collected at 84 and 126d1  
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d =Adipose Tissue biopsies taken at 84d and 126d; GPAT = Glycerol 
Phosphate Acyl Transferase. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.1
0.2
0.3
0.4
0.5
0.6
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
b 
247 
 
Figure 5.29 Effect of biopsy date on relative expression of ZFP423 in steer adipose 
tissue biopsies collected at 84 and 126d1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d =Adipose Tissue biopsies taken at 84d and 126d. ZFP423 = Zinc 
Finger protein 423. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
a 
248 
 
Figure 5.30 Effect of biopsy date on relative expression of FABP4 in steer adipose tissue 
biopsies collected at 84 and 126d1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d =Adipose Tissue biopsies taken at 84d and 126d; FABP4 = Fatty Acid 
Binding Protein 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
a 
249 
 
Figure 5.31 Effect of biopsy date on relative expression of UCP2 in steer adipose tissue 
biopsies collected at 84 and 126d1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d =Adipose Tissue biopsies taken at 84d and 126d; UCP-2 = 
Uncoupling Protein 2. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
b 
250 
 
Figure 5.32 Effect of biopsy date on relative expression of Leptin in steer adipose tissue 
biopsies1  
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); 84 and 126d =Adipose Tissue biopsies taken at 84d and 126d; LSM not sharing a 
common superscript differ (P > 0.05). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Biopsy Date 
84d
126d
a 
b 
251 
 
Figure 5.33 Effect of treatment on relative expression of ZFP423 in steer adipose tissue 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
ZFP423 = Zinc Finger Protein 423. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.5
1
1.5
2
2.5
3
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
b 
a,b 
252 
 
Figure 5.34 Effect of treatment on relative expression of FABP4 in steer adipose tissue 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
FABP4 = Fatty Acid Binding Protein 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a a a 
253 
 
Figure 5.35 Effect of treatment on relative expression of UCP-2 in steer adipose tissue 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
UCP-2 = Uncoupling Protein 2. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a 
b 
a 
254 
 
Figure 5.36 Effect of treatment on relative expression of GPAT in steer adipose tissue 
biopsies1 
 
  
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively; 
1GPAT = Glycerol Phosphate Acyl Transferase. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a 
a a 
a 
255 
 
Figure 5.37 Effect of treatment group on relative expression of Leptin in steer adipose 
tissue biopsies1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1Least Square Means +/- SEM; Columns not bearing a common superscript differ (P < 
0.05); Group 126, 84, 42, and 0 = 126, 84, 42, and 0 days grazing ryegrass, respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Re
lativ
e E
xp
ressi
on
 (a.u
.) 
Treatment Group 
Group 126 Group 84 Group 42 Group 0
a a 
a 
b 
256 
 
Chapter 6. Summary of Experiments 
Rising feed costs represent a constantly looming threat to profitability of beef cattle 
operations worldwide. These pressures come in the midst of a burgeoning global population with 
increasing demand for beef and other meat products. To compound the issue, environmental 
concerns have led to anxieties over the long-term sustainability of intense livestock production in 
general and the beef cattle industry in particular. These factors create the following dilemma for 
the industry: How to produce more product in the face of decreasing profit margins, waning 
resources, and increased regulatory pressures.   
Terms like ?efficiency? and ?sustainability? can be vague and context dependent, and 
there are individual aspects (i.e. metabolic efficiency, production efficiency, economic 
efficiency, ecological efficiency, etc.) that cooperate to determine the overall efficiency and 
environmental impact of  beef production.  A longstanding goal of animal research has been to 
understand the specific factors that are responsible for variations in feed efficiency and nutrient 
partitioning because of their impact on production costs and sustainability. These objectives are 
complex but livestock operations in the post-genomic era are in a position to make use of 
existing and emerging molecular technologies to identify and consistently produce more efficient 
animals with the specific carcass characteristics that appeal to consumers.  
Metabolic processes that have been hypothesized (and many cases substantiated) to effect 
performance efficiency include protein turnover, mitochondrial function, and AT tissue and lipid 
metabolism. Furthermore, research regarding metabolic regulation has identified several 
governing transcription factors that up or down regulate pathways involved in these pathways 
and can affect nutrient partitioning. While energetics and thermodynamics determine biological 
efficiency of individual nutrient use, the important consideration with respect to performance 
257 
 
efficiency is the effective use of feed resources to deliver a valuable phenotype. The ability to 
independently manipulate the metabolic disposition of individual tissues in favor of efficient 
nutrient use and an economically valuable phenotype is the ultimate objective of animal 
researchers in the post-genomic era. In this respect, three studies were conducted in order to 
determine molecular events associated with metabolic efficiency in specific SM and AT depots 
in finishing beef cattle.  
 
Study 1: Relationship between Residual Feed Intake and the Transcriptomic Signature of 
Performance Efficiency in Finishing Beef Cattle 
 
Residual feed intake is a feed efficiency parameter. High-cost and labor associated with 
determining RFI are limitations that have prevented wide-scale adoption as a FE parameter. The 
factors that contribute RFI variation within a contemporary group of animals are likely to be due 
metabolic processes that are governed by molecular-regulatory factors. If these particular 
regulatory pathways can be ascertained and articulated they can subsequently be targeted to 
identify more efficient animals. In addition, the comprehensive understanding of the pathway 
dynamics will allow for effective targeting using genetic and molecular tools in order to achieve 
a more desirable RFI phenotype.  The results from our study indicate that RFI appears to be 
related to genes involved in protein turnover and to a lesser extent, other metabolic genes; 
however genes involved in AT metabolism did not appear to be related. It should be noted that 
the animals used in this study were the initial progeny of a breeding program designed to select 
for RFI, and these animals were only moderately segregated with respect to this parameter. A 
future study using subsequent generations that have been divergently selected (High vs. Low 
RFI) will be prudent to complete to aid our understanding of these relationships. 
 
258 
 
Study 2: Effect of Days on Feed and Beta-Agonist Administration on the Expression of 
Regulatory Genes in Skeletal Muscle and Adipose Tissue of Finishing Heifers 
 
It is well known that production efficiency is one of the primary factors that contribute to 
the success and profitability of a beef cattle operation. Wasteful accumulation of AT in the SC 
depot is an economically inefficient process that has a negative impact on profit margins. 
Metabolic modifiers including beta-adrenergic agonists have been utilized to improve feed 
efficiency and repartition nutrients towards economically valuable tissues and away from AT. 
These compounds are not without controversy with respect to their effect on meat quality and the 
specific mechanism of action of these compounds is not completely understood. Identification of 
specific molecular targets of these compounds and optimal timing of administration with respect 
to the finishing period can maximize the effectiveness of these compounds and/or lead to 
strategies to mitigate undesirable effects. Our objective was to determine  the effect of days on 
feed and Ractopamine administration on regulatory gene expression in skeletal muscle and 
adipose tissue of finishing beef heifers. We observed temporal changes in gene expression 
between days on feed but no discernable effects of Ractopamine on molecular regulation of 
metabolic pathways with respect to the genes we examined.  
 
Study 3: The Effects of Feed Restriction and Re-feeding on Gene Expression Patterns in 
the Loin Muscle and Subcutaneous Adipose Tissue of Forage Fed Beef 
 
Temperate climates and year-round forage growth provide the basis for niche forage-
based finishing systems in the SE. Forage based systems eliminate the costs and labor associated 
with purchasing and transporting concentrates and other feeds. This system is dependent on 
proper land and animal management, and adequate forage growth in order to support a profitable 
and sustainable operation. In our final study we sought to determine the effect of a forage-based 
finishing system on gene expression patterns in SM and AT of finishing steers subjected to 
259 
 
differing levels of forage growth (nutrient availability) and to determine how these patterns 
reflected phenotype and animal performance. In terms of animal performance, treatment group 
did affect total and ADG for the duration of the trial. Animals on the highest plane of nutrition 
gained the most weight and those on the lowest gained the least weight. Likewise, the 
intermediate plane of nutrition supported intermediate levels of animal growth.  The results from 
this study were variable and equivocal with respect to the gene expression analysis. Some genes 
showed differences with respect to treatment group. In terms of performance there was some 
variation within treatment group which may have led to the variation in GE patterns. As a result 
of normally variable rainfall and agronomic conditions, which are naturally occurring 
phenomena, raising cattle on grass in the SE is a potentially rewarding niche market that is not 
without its drawbacks. The genes selected in this study represent pathways that are putatively 
recognized as important in regulating key metabolic processes involved in SM and AT 
metabolism. These pathways contribute to the efficiency and quality of meat production and as 
such are of the utmost economic interest in beef cattle production. It appears that the 
transcriptomic changes were not robust enough for these current data to be very impactful in 
terms of identifying bona fide biomarkers. That is not to say that the genes selected are not 
critical in these processes, however under the conditions experienced over the course of the trial, 
the targeted gene expression based performance evaluation tools could not be verified as 
consistently effective for phenotypic evaluation in the current study. 
 
Implications and Conclusions 
The preceding research was conducted to determine if targeted transcriptomic profiling 
could be used to monitor the progression and metabolic activity within specific SM and AT 
260 
 
depots in finishing beef cattle. The results with respect to the overarching objectives were 
variable and equivocal, but taken en masse provides the informative basis for further research. 
Depot specific AT metabolism is likely to remain of intense research interest due to the 
economic and biomedical implications of AT accretion in agricultural species, and humans, 
respectively. In future related studies, advanced histology and imaging tools such as laser-
capture micro-dissection can also be utilized in conjunction with molecular and genomic 
techniques to isolate cell types and gain a more comprehensive understanding of their specific 
metabolism and contributions in AT and SM depots. It may also be prudent to determine IMF 
adipocyte number in order to determine the relative contribution of these cells to gene expression 
especially when attempting to compare expression with larger AT depots such as SC. The advent 
of gene silencing techniques is also certain to be a major contributor to our understanding of 
specific genes and pathways to economically valuable phenotypes.  
While the current studies involved very limited snap shots of what are inherently 
dynamic processes, a more comprehensive approach must be taken to integrate animal 
performance with functional genomics (transcriptomic, proteomic, metabolomics) data in order 
to gain a more panoptic understanding of the molecular regulation of economically relevant 
traits. Genome wide association studies are becoming in vogue in terms of understanding the 
interconnection of genomic regions with metabolic pathways at the organismal level. Systems 
biology approaches have proven valuable in studying how physiological state, nutrition, etc. can 
affect tissue function and development in a depot-specific fashion. Next generation sequencing 
technologies are generating genomic data at unprecedented rates at a much cheaper cost which 
will aid in wide scale adoption in the industry.  In addition to these high-throughput approaches, 
a reductionist perspective will be critical to understanding how molecular machinery governs 
261 
 
cell/tissue-specific alterations, leads to functional changes, and contributes to the desirable 
phenotypes in beef cattle. Therefore microarray, GWAS, and other high-throughput techniques 
must be used in conjunction with targeted transcriptomic approaches to identify and validate 
putative and novel pathways and their contributing regulatory factors.   
The current work did not elucidate any specific regulatory targets but did indicate that 
there was some association of specific genes with various efficiency and performance 
parameters. Moreover, these studies demonstrated the ability to target and monitor the expression 
of specific regulatory genes in beef cattle indicative of the advancing state and availability of 
genomic information and technological platforms for use in beef cattle and other livestock 
species. This will undoubtedly foster future hypothesis driven research in this field that is likely 
to validate the transcriptomic approach as a valuable asset in improving production efficiency 
through targeted regulation of molecular processes in a tissue- and depot- specific manner. Also 
it should be emphasized that while the expression of individual candidate genes was 
characterized in the current project, these genes are representative of putative and novel 
metabolic regulatory junctions, so it is important not to over speculate based on individual genes. 
The influence of other cellular modifications and participation of micro-RNAs and other novel 
regulatory factors is also like to compound the issue.  Instead this information should be used to 
glean into the overall mechanisms and pathways that can be targeted with management strategies 
and pharmacological agents such as BAA to improve animal performance. Ultimate therapeutic 
approaches may or may not include these individual targets specifically but are likely to involve 
these and other relevant metabolic pathways. These advances will result from a combination of 
other molecular and biochemical data as well as performance data to understand more clearly 
262 
 
how biological processes contribute to producing a valuable beef cattle phenotype in an efficient 
manner.  
The genomic revolution has brought with it a wealth of useful knowledge in livestock 
production but has also produced more questions than answers in many cases. Perhaps victims of 
the technologies success, researchers are generating information at rates that exceed our current 
capacity to understand and/or make use of. On one hand the resulting information bottleneck is 
fertile ground for future hypothesis driven research. Further such logjams can appear to be 
meritless with no tangible practical advantage in the eyes of producers and industry leaders. This 
dampens the incentive for industry and shareholder investment in, and hampers the application 
these technologies. Hopefully, the advent of dedicated genomic platforms will break through this 
impasse and expedite progress given the wealth of information that has yet to be discovered in 
this burgeoning field.  The success of this is however dependent on the willingness for industry 
stakeholders, academic institutions, and funding agencies to cooperate to collect and share data, 
refine technologies, and implement techniques in a practical manner suitable for large scale beef 
cattle production.  
As a whole, this body of work provides the basis for future studies and has established 
several findings. First off, transcriptomic/gene expression profiling in beef cattle determined by 
quantitative RT-PCR of skeletal muscle and adipose tissue samples can be an effective approach 
in conducting a performance/phenotypic evaluation of individual cattle particularly in grazing 
systems in the SE. This will increase our understanding of mechanisms at the genomic/molecular 
regulation level that regulate production efficiency, and such results can be used to design 
improved beef cattle feeding strategies for production systems in the SE. Finally these 
experimental methods can be used in conjunction with more elaborate approaches to identify and 
263 
 
manipulate molecular distinctions in a manner that improves production efficiency and carcass 
value by augmenting specific tissues independently of less valuable ones. These findings are 
aimed at improving production efficiency through molecular mechanisms which dictate animal 
performance and phenotype. These molecular subtleties have the potential to orchestrate large-
scale phenotypic changes and reshape our approach to genetic selection and performance 
evaluation. Ultimately this will improve the viability and sustainability of the beef cattle industry 
across production systems. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
264 
 
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