MOLECULAR GENETICS OF RESIDUAL FEED INTAKE AND MITOCHONDRIAL FUNCTION IN CATTLE
By Nadiatur Akmar Zulkifli
A thesis submitted to the University of Adelaide in fulfilment of the requirement of the degree of Doctor of Philosophy
The University of Adelaide School of Animal and Veterinary Sciences July 2016
TABLE OF CONTENTS
Table of Contents ii
Index of Figures vii
Index of Tables ix
Index of Appendices xii
Abstract xiii
Declaration xvi
Acknowledgements xvii
Dedication xix
Chapter 1 : Literature Review 1
1.1 Introduction 2
1.2 Cattle 3
1.2.1 Jersey cattle 3 1.2.2 Limousin cattle 4 1.2.3 Jersey x Limousin cattle 6 1.2.4 Genetics of cattle 6
1.3 Residual feed intake 7
1.3.1 Residual feed intake as a genetic trait 10 1.3.2 Measurement of feed intake 11 1.3.2.1 Centralised testing facility 12 1.3.2.2 On-farm testing 12 1.3.3.3 Measurement on pasture 12 1.3.3 Genetic correlations for measures of body composition with residual feed intake 13 1.3.4 Quantitative trait loci in residual feed intake 14
1.4 Mechanisms affecting residual feed intake 17
1.4.1 Energy metabolism 17 1.4.2 Mitochondrial function 19 1.4.3 Cellular respiration process 21
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1.4.3.1 Glycolyis 21 1.4.3.2 Krebs cycle 24 1.4.3.3 Electron transport chain 26 1.4.3.3.1 Complex I 27 1.4.3.3.2 Complex II 28 1.4.3.3.3 Complex III 28 1.4.3.3.4 Complex IV 29 1.4.3.3.5 Complex V 29 1.4.3.4 Oxidative phosphorylation 31 1.4.3.5 Reactive oxygen species 32 1.4.4 Other known energy pathways affecting residual feed intake 34 1.4.4.1 Uncoupling proteins 34 1.4.4.2 Adenine monophosphate activated protein kinase (AMPK) 34
1.5 Relationship between mitochondrial function and residual
feed intake in animals 35
1.5.1 Poultry 35 1.5.2 Pigs 36 1.5.3 Cattle 37
1.6 Summary 38
Chapter 2 : Materials and Methods 40
2.1 QTL mapping of residual feed intake in cattle 41
2.1.1 Cattle QTL mapping experimental design 41 2.1.2 Single Nucleotide Polymorphisms (SNP) experiments 43 2.1.2.1 Selection of candidate genes for SNP detection 43 2.1.2.2 Genomic DNA purification 43 2.1.2.3 DNA concentration 44 2.1.2.4 Primer design 44 2.1.2.5 Polymerase Chain Reaction (PCR)
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condition optimisation 44 2.1.2.6 Gel electrophoresis 46 2.1.2.7 PCR purification 46 2.1.2.8 DNA sequencing 47
2.2 Genotyping 49
2.2.1 High Resolution Melt (HRM) 49 2.2.2 Genotyping analysis 51 2.2.3 Pathway analysis 53
2.3 Mitochondrial enzyme experiments 53
2.3.1 Selection lines 53 2.3.2 Sample collection: Liver 54 2.3.3 Mitochondrial preparation from frozen liver samples 54 2.3.4 Bradford Assay 55 2.3.5 Oxidative phosphorylation enzyme complexes assays 55 2.3.5.1 Complex I Activity 55 2.3.5.2 Complex III Activity 56 2.3.5.3 Complex IV Activity 56 2.3.5.4 Protein carbonyl assay 56 2.3.5.5 Analysis for Biochemical Assay 57
Chapter 3 : Candidate Genes for RFI : Identification and
DNA Variants 58
3.1 Introduction 59
3.2 Materials and Methods 60
3.2.1 DNA samples 60 3.2.2 Primer design 61 3.2.3 Polymerase Chain Reaction (PCR) 62 3.2.4 Sequencing 63
3.3 Results 63
3.3.1 Candidate Genes 63 3.3.1.1 Catalase 64
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3.3.1.2 Aldolase B 66 3.3.1.3 Adenylate Kinase 1 67 3.3.1.4 Superoxide Dismutase 1 68 3.3.1.5 Succinyl Co A Synthetase 69 3.3.1.6 Superoxide Dismutase 2 71 3.3.1.7 Ghrelin 71 3.3.1.8 NADH Dehydrogenase (Ubiquinone) I Beta Subcomplex, 5, 16kDa 72 3.3.1.9 NADH Dehydrogenase (Ubiquinone) I Alpha Subcomplex, 8, 19kDa 73 3.3.1.10 Hydroxyacyl-CoA Dehydrogenase-β Subunit 73 3.3.2 Sequencing variants 74
3.4 Discussion 80
3.5 Summary 83
Chapter 4 : Candidate Gene Associations 85
4.1 Introduction 86
4.2 Methods 88
4.2.1 Genotyping 88 4.2.1.1 High Resolution Melt (HRM) 89 4.2.1.2 Data analysis 90
4.3 Results 90
4.3.1 High Resolution Melt (HRM) 90 4.3.2 SNP association studies 96 4.3.3 Haplotype effects 105 4.4 Discussion 108
4.5 Summary 114
Chapter 5 : Candidate Gene Pathways and Epistatic Effects 116
5.1 Introduction 117
5.2 Methods 117
5.3 Results 118
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5.3.1 Pathway analysis 118 5.3.2 Epistatic effects 122 5.3.2.1 Candidate gene epistatic interactions 122 5.3.2.2 Additional epistatic interactions on RFI and fat traits 130 5.4 Discussion 131
5.5 Summary 134
Chapter 6 : Mitochondrial enzyme assays 136
6.1 Introduction 137
6.2 Materials and Methods 139
6.2.1 Liver samples 140 6.2.2 Analysis for biochemical assays 141
6.3 Results 141
6.3.1 Bradford assay 141 6.3.2 Complex I enzyme assay 142 6.3.3 Complex III enzyme assay 144 6.3.4 Complex IV enzyme assay 146 6.3.5 Protein carbonyl assay 148
6.4 Discussion 150
6.5 Summary 155
Chapter 7 : General discussion 156
7.1 Introduction 157
7.2 Residual feed intake and mitochondrial function 160
7.3 Residual feed intake candidate genes 163
7.4 Residual feed intake and body composition 166
7.5 Conclusion 168
Appendices 169
References 195
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INDEX OF FIGURES
Figure 1.1 Relationship between feed intake and the expected weight gain 9
Figure 1.2 Contribution of biological mechanisms to variation in residual feed intake as determined from experiments on divergently selected cattle 19
Figure 1.3 Mitochondria structure 20
Figure 1.4 The reactions of glycolysis convert glucose to pyruvate 23
Figure 1.5 Reactions of the Krebs cycle 26
Figure 1.6 Electron transport chain 30
Figure 2.1 Davies cattle QTL mapping backcross design 42
Figure 3.1 Mechanism of oxidative cellular damage 65
Figure 3.2 Fructose utilisation in the liver showing its interrelationship with glucose and fatty acid metabolism 67
Figure 3.3 Regulation of energy homeostasis by the AMPK system 68
Figure 3.4 The disproportionation of superoxide is a two-step oxidation-reduction reaction that involves the cycling of the copper atom in SOD1 from Cu2+ to Cu+ and back to Cu+2 69
Figure 3.5 Krebs cycle and methylmalonate metabolism 70
Figure 4.1 Melt curve of SOD2SNP3, a transversion SNP with two genotypes, GC and GG 92
Figure 4.2 Melt profile analysis of SOD2SNP3, a transversion SNP with two genotypes, GC and GG 92
Figure 4.3 Melt curve of HADSNP7, a transversion SNP with three genotypes, AT, TT and AA 93
Figure 4.4 Melt profile analysis of HADSNP7, a transversion SNP with three genotypes, AT, TT and AA 93
Figure 4.5 Melt curve of HADSNP2, a transition SNP with three genotypes, AA, GG and AG 94
Figure 4.6 Melt profile analysis of HADSNP2, a transition SNP with three genotypes, AA, GG and AG 94
Figure 6.1 Standard curve at 595 nm for low RFI samples 142
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Figure 6.2 Correlation between mid-parent RFI EBV and Complex I activity 144
Figure 6.3 Correlation between mid-parent RFI EBV and Complex III activity 146
Figure 6.4 Correlation between mid-parent RFI EBV and Complex IV activity 148
Figure 6.5 Correlation between mid-parent RFI EBV and ROS activity 149
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INDEX OF TABLES
Table 1.1 Heritability estimates for net feed efficiency in different species 11
Table 1.2 Genetic correlation for measures of body composition with residual feed intake 14
Table 1.3 NFE QTL identified in the Davies Jersey x Limousin herd 16
Table 2.1 PCR reagents concentration 45
Table 2.2 Sequencing reagents 47
Table 2.3 High resolution melt reagents concentration 50
Table 3.1 F1 mapping sires and their parents identification number 61
Table 3.2 Primer sets for neighbouring exons 62
Table 3.3 Primer sets for exon > 500bp 62
Table 3.4 Selected candidate genes and their function 64
Table 3.5 DNA variants identified in the candidate genes 75
Table 3.6 Summary of DNA variants found in candidate genes 78
Table 3.7 Synonymous and non-synonymous SNPs 78
Table 4.1 List of genotyped SNPs 89
Table 4.2 Summary data of the 14 genotyped SNPs 91
Table 4.3 Number of progeny with each genotype and allele frequencies 95
Table 4.4 Residual feed intake related traits 96
Table 4.5 Specific fat depot traits for association studies 97
Table 4.6 Traits affected by SNPs without and with MSTN F94L genotype in the model 98
Table 4.7 Number of SNPs affecting each trait with and without MSTN F94L genotype in the model 100
Table 4.8 Fat traits affected by SNPs without and with MSTN F94L genotype in the model 101
Table 4.9 Number of SNPs affecting fat depot trait with and without MSTN F94L genotype in the model 102
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Table 4.10 SNP effects on daily feed intake (DFI) and residual feed intake (RFI) with and without MSTN F94L genotype in the model 103
Table 4.11 Additional SNPs analysed for effects on RFI with and without MSTN F94L genotype in the model 104
Table 4.12 Additional SNPs with significant effects on residual feed intake with and without MSTN F94L genotype in the model 105
Table 4.13 SNP interactions within genes without MSTN F94L genotype in the model 107
Table 4.14 SNP interactions within genes with MSTN F94L genotype in the model 107
Table 5.1 Protein state interactions 120
Table 5.2 Gene expression interactions 121
Table 5.3 Tests of significance for SNP interactions between candidate genes affecting RFI related traits 125
Table 5.4 Tests of significant for SNP interactions between candidate genes affecting specific fat depot traits 128
Table 5.5 SNP interactions between genes for RFI and DFI with and without the MSTN F94L genotype in the model 129
Table 5.6 Additional SNPs interactions with selected fat traits without MSTN F94L genotype in the model 130
Table 5.7 Additional SNPs interactions with selected fat traits with MSTN F94L genotype in the model 130
Table 6.1 Complex I enzyme activity of high and low residual feed intake animals 142
Table 6.2 Regression analysis for RFI related traits and Complex I enzyme activity with correlation and test of significant slope 143
Table 6.3 Complex III enzyme activity of high and low residual feed intake animals 144
Table 6.4 Regression analysis for RFI related traits and Complex III enzyme activity with correlation and test of significant slope 145
Table 6.5 Complex IV enzyme activity of high and low residual feed intake animals 146
Table 6.6 Regression analysis for RFI related traits and Complex IV enzyme activity with correlation and test of significant slope 147
Table 6.7 Protein carbonyl content activity of high and low residual feed intake animals 148
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Table 6.8 Regression analysis for RFI related traits and ROS concentration with correlation and test of significant slope 150
Table 7.1 Residual feed intake QTL in the Jersey x Limousin herd 164
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INDEX OF APPENDICES
Appendix A.1.1 Jersey x Limousin RFI Phenotyping 170
Appendix A.1.2 Angus Trangie RFI Selection Lines 173
Appendix A.2 Purification Kit Protocol (UltraClean® PCR Clean-Up Kit, Mo Bio Laboratories Inc) 174
Appendix A.3 Purification of sequencing products 175
Appendix A.4 Ice-cold lysis buffer 175
Appendix A.5 Isolation buffer (pH 7) 176
Appendix A.6 Coomassie Blue solution 176
Appendix A.7 Complex I activity reagent mix 176
Appendix A.8 Complex III activity reagent mix 177
Appendix A.9 Complex IV activity reagent mix 177
Appendix A.10 Protein Carbonyl assay 178
Appendix B List of primers designed for PCR 179
Appendix C.1 Haplotype SNP genotype frequencies 183
Appendix C.2 List of additional SNPs 184
Appendix D.1 Epistatic SNP genotype frequencies 186
Appendix D.2 Result of pathway analysis: no interactions 187
Appendix D.3 Result of pathway analysis: catalyze two conversions connected via a common molecule 189
Appendix D.4 SNP interactions between candidate genes without MSTN F94L genotype in the model 191
Appendix E List of high and low residual feed intake animals according to mid-parents RFI EBV 194
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ABSTRACT
Residual feed intake (RFI) is a measure of net feed efficiency, an economically important trait in livestock. RFI is affected by many factors including genetics and diet although residual feed intake is usually independent of diet as it is calculated for a group of animals on a standard feed test. The RFI of an animal depends on the ability of the animal to consume less feed than expected based on their weight gain and weight maintained during the feed testing period. Those that eat less than expected have a negative RFI and are deemed more efficient. Recent work has implicated mitochondrial function as being involved in the feed efficiency of livestock including cattle, sheep, pigs and poultry.
The objectives of this study were to identify genes involved in mitochondrial function that may affect net feed efficiency in cattle and to examine the enzyme activities in the mitochondria of high and low residual feed intake animals. Several quantitative trait loci (QTL) affecting feed efficiency were previously mapped in Jersey x Limousin double backcross progeny in three sire families. Based on the QTL mapping results, ten candidate genes related to mitochondrial function and energy metabolism were identified: aldolase B (ALDOB), adenylate kinase 1 (AK1), catalase (CAT), ghrelin
(GHRL), hydroxyacyl CoA dehydrogenase beta subunit (HADHB), NADH dehydrogenase alpha subcomplex 8, 19kDa (NDUFA8), NADH dehydrogenase beta subcomplex 5, 16kDa (NDUFB5), superoxide dismutase 1, soluble (SOD1), superoxide dismutase 2 (SOD2), and succinyl Co-A synthetase (SUCLG1).
All ten genes were sequenced in the three Jersey x Limousin sire families in order to locate DNA variants in the genes for association studies. A total of 58 DNA variants were discovered, which included six insertion/deletions (in/dels) and 52 single
xiii nucleotide polymorphisms (SNPs). Of the 52 SNPs, 34 SNPs were located in introns,
9 in exons and 9 in the untranslated regions (UTR).
Fourteen SNPs were selected for genotyping in the 366 progeny from the three sire families. Genotyping results were analysed to observe the effect of the SNPs with 27
RFI related traits and specific fat depot traits, including residual feed intake and daily feed intake. The F94L myostatin (MSTN) genotype was included in some of the models as this variant was known to be segregating in the progeny and has a major effect on body composition.
Only 4 SNPs in the candidate genes were associated with residual feed intake, 3 of which were in the HADHB gene. The haplotype of HADHB from these 3 SNPs explained 8.5% of the variation in RFI. The other SNP was in the SOD1 gene, which had a p-value <0.001 for residual feed intake and explained another 3% of the variation in this trait. The gene with a significant haplotype effect was NDUFB5, which had a significant effect on residual feed intake (p = 0.005) and explained 5% of the variation.
To examine potential epistatic effects, interactions between the genes were also analysed. The analysis of the SNP interactions between genes revealed that residual feed intake was affected by 10 SNP interactions. A SNP in the NADH dehydrogenase alpha subcomplex 8, 19kDa gene (ND5SNP5’) had the most interactions. Four SNP interactions (between NDUFB5 and SOD1, between NDUFA8 and SUCLG1, between
ALDOB and NDUFB5, and between HADHB and SOD1) explained 21% of the RFI variation. Thus, the results indicate that DNA variants in genes involved in mitochondrial function and energy metabolism can influence RFI.
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Mitochondria from animals differing in residual feed intake were measured for function by assaying major mitochondrial electron transport complexes and the reactive oxygen species. The liver mitochondrial assays included Complex I, III and IV enzyme activities as well as the protein carbonyl assay to measure the reactive oxygen species. The results indicated that Complex I and Complex III enzyme activities and the ROS concentration were significantly different between the high and low residual feed intake groups. The mitochondria from low RFI animals had higher Complex I and
Complex III enzyme activity and more ROS.
The outcomes of this study contribute to the knowledge of net feed efficiency at the molecular level. The results indicate the mitochondria may indeed play a role in residual feed intake in animals and that genes involved in energy metabolism may have variants that affect efficiency. The role of mitochondria was shown both at the genetic and biochemical level. The results imply that residual feed intake is not entirely a function of body composition and/or appetite.
The DNA variants discovered in the candidate genes could potentially be used as genetic markers for selection although their size of effect was not large. Future studies would need to be conducted using different populations of cattle to verify the effects of the DNA variants identified herein on feed efficiency. It is also important to repeat the mitochondrial assays in different cattle populations that has not been selected for net feed efficiency or body composition in order to validate the relationship between residual feed intake, mitochondrial function and cellular efficiency.
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DECLARATION
I declare that this thesis is a record of original work and contains no material that has been accepted for the award of any other degree or diploma in any university or other tertiary institution to Nadiatur Akmar Zulkifli. To the best of my knowledge and belief, this thesis contains no material previously published or written by any other person, except where due reference is made in the text.
I give consent to this copy of my thesis, when deposited in the University Library, being made available for loan and photocopying, subject to the provisions of the
Copyright Act 1968.
I also give permission for the digital version of my thesis to be made available on the web, via the University’s digital research repository, the Library catalogue, the
Australasian Digital Theses Program (ADTP) and also through web search engines, unless permission has been granted by the University to restrict access for a period of time.
Nadiatur Akmar Zulkifli
July, 2016
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ACKNOWLEDGEMENT
When I first accepted the job as a tutor at the National University of Malaysia (UKM), I had no idea what was I getting myself into. Next thing I know, I was awarded a scholarship to further my studies in Animal Breeding and Genetics. Without the scholarship from the National University of Malaysia and the Ministry of Higher
Education, I would not be where I am. Thank you.
My sincere gratitude goes to Professor Cynthia Bottema, the best supervisor one can ever wished for. Thank you for having faith in me, for being really patient with all my antics, for answering the same question over and over again, for guiding me through all the tedious lab work, and for being a great mentor and role model. To Professor
Wayne Pitchford, I extend my gratitude for helping out with the analysis and providing ideas for me to ponder, as well as sharing lessons of life. To Dr Brian Siebert, for assisting me with the biochemical assays experiments, I am truly indebted.
My bumpy PhD journey has been made easier with the help of my brilliant labmates.
Irida Novianti, who is more like a sister to me, and who was there through thick and thin. Rugang Tian, the person to turned to when things do not work out in the lab. Lei-
Yao Chang, someone to discuss with about basically everything. Andrew Egarr, who patiently help us improve our English. David Lines, the problem solver when it comes to technical issues. I am sincerely thankful for all your help and I hope our paths will cross again someday.
Adapting to the new life and environment in Australia within a short time was possible with the help of these lovely individuals. Audrey Stratton, Jane Copeland, Mary-Rose and Sally Polkinghorne from the International Student Services, for making us feel welcomed. Rebecca Athorn, Dessy Kusbandi, Liem Mahalaya, Tzu-Liu, Tai Yuan
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Chan, Ruidong, Babu and Renu, the best neighbours ever. My special thanks go to
Lesley Menzel, for the priceless friendship and all the pleasant memories. To the
Mitochondria Group; Cons (ETC1), Liza (ETC3), Janet (ETC4) and Birte (ETC5), thank you for making me complete by transferring positive and happy vibes during the most critical time in my life.
To my parents, the one who brought me to the world, who raised me to be who I am, and loved me unconditionally no matter what. No words could describe my appreciation and love for them. To my brother, the person I could turn to in literally any situation and for solving all the mess I got myself into. Thank you.
And to my support system; Farah, Nida, Soffa, Izfa, Marlia, Salmah, and Liza. I would still be crumpled if it was not for your care and support. Thank you for picking me up and help me stand tall.
Above all, I thank God for His mercy and blessings.
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DEDICATION
For the past, present and future.
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Chapter 1 Literature Review
1
1.1 Introduction
Australia’s stable economy is a result of various industries contributing to the wealth of the country. Amongst the industries involved are manufacturing, information technology, telecommunications, mining and agriculture. Although the demands for modern and newer industries are higher, the agricultural industry is still essential to the Australian economy and this includes the production of beef and dairy cattle, sheep, poultry and pigs.
The beef industry in Australia is recognized as one of the best in the world and the third largest exporter following Brazil and India. Australia exported 74% of its total beef and veal production to 86 countries in 2014-2015. During the same period, the value of total beef export was $9.04 billion. In addition, the Australian live cattle exports were valued at $1.4 billion. The beef industry alone accounts for 58% of all farms with agricultural activity (MLA Report, 2015).
The beef production industry is also significant for domestically consumption. The domestic expenditure on beef was approximately $7.8 billion in 2014-2015, and the estimated amount of beef consumed per person in Australia is 28.6 kg annually
(MLA Report, 2015).
For the beef industry, the overall aim of cattle breeding is to improve profitability. For instance, cattle are commonly selected on weight because increasing the growth rate of the cattle will increase economic returns for beef producers with minimal additional input (Archer et al., 1999).
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1.2 Cattle
Cattle are mammals of the order Artiodactyla, wherein the weight of the whole body is supported by the tip of the third and fourth toes. These even-toed animals can be narrowed down to the family Bovidae (Lenstra and Bradley, 1999). Apart from the noticeable hooves, Bovidae are specialised herbivores, which have four chambered stomachs, called rumens, that allow the digestion of the cellulose from the plants.
The mechanism behind this digestive system is the presence of micro-organisms in the rumen that degrade the food particles to smaller sizes and ferment the plant products (Priolo et al., 2001). Animals with this complex digestive system are known as ruminants.
1.2.1 Jersey cattle
Dairy cattle are bred for the production of milk in large quantities. One of the most widely used dairy breed is the Jersey cattle. Jersey cattle, which originated from
Channel Island of Jersey, are well known for their small size, ranging from 400-500 kg (Gowen, 1933). Due to the small size and lower body weight, their maintenance requirement is reduced.
Jersey cattle can be found all over the world due to their ability to adapt to hot weather (Oklahoma State University, 1997). In Australia, Jersey cattle are distributed across the country, primarily in Victoria. Their main diet is grass and
Jerseys are known to be good grazers. Naturally, cattle of this breed are calm and docile.
Unlike the Holstein cattle which can easily be distinguished by the white and black coat, Jerseys are mostly brown in colour, ranging from light tan to almost black.
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The butterfat content in milk in both Holstein and Jersey cattle varies, where
Jerseys have a higher milk fat percentage of 4.80% as compared to 3.69% in
Holstein (NDHIA Annual Report, 2015).
In relation to the small body size, the calving process of the Jerseys is much easier compared to other breeds (American Jersey Cattle Association, 2015). The injuries related to calving are reduced in Jerseys and this has led to the crossbreeding of
Jerseys with other dairy breeds and even some beef breeds.
1.2.2 Limousin cattle
Beef cattle are bred mainly to obtain meat for consumption. Limousin cattle, which originated from the Limousin region of France, were once used as draft animals though. Over the years, Limousin cattle have been acknowledged for their highly muscled properties and are now bred all over the world as beef cattle.
The Limousin cattle are naturally horned and the coat colour ranges from light wheat to darker golden-red (Oklahoma State University, 1997). The male Limousin can weigh from 1000 to 1100 kg and the weight of the females ranges from 650 to
700 kg. Although the Limousin have a large frame, they are known to have lower birth weights which make calving easier than other well muscled breeds.
Apart from the ability of Limousin to produce lean and tender meat, studies have shown that Limousin have a high feed conversion efficiency (Crowley et al., 2010). It is reported that Limousin convert feed significantly faster compared to the British breeds. Due to the efficiency of feed conversion as well as improved yield, Limousin are favoured for crossbreeding with other breeds such as Angus, Hereford and
Shorthorn.
4
The Limousin are also associated with the myostatin gene, a gene that plays an important role in regulating development and tissue homeostasis (McPherron et al.,
1997). Also known as the growth developmental factor 8 (GDF8) gene, the myostatin gene is a member of the TGF-β superfamily of secreted growth and differentiation factors utilised throughout the body (McPherron et al., 1997; Schuelke et al., 2004; Mosher et al., 2007). Myostatin acts as a negative regulator of muscle growth and the myostatin gene appears to be highly conserved across species
(McPherron et al., 1997). Thus, it is not unexpected that the mutations in the myostatin gene have been shown to cause increased muscling in a variety of species including mice (McPherron et al., 1997; Szabó et al., 1998), humans
(Schuelke et al., 2004), dogs (Mosher et al., 2007), sheep (Clop et al., 2006), and cattle (Grobet et al., 1997).
As myostatin negatively regulates muscle development, the disruption of the myostatin gene in mice causes a large increase in skeletal muscle resulting from a combination of muscle cell hyperplasia and hypertrophy (McPherron et al., 1997;
Szabó et al., 1998). Similarly, mutations in the gene result in muscle hypertrophy or over-growth in cattle, including the so-called double muscling phenotype observed when the gene is knocked out in cattle (McPherron and Lee, 1997).
Mutations in the myostatin gene can occur at different sequence sites in different species and yet give the same effect on muscling. In whippet dogs, a mutation discovered in the gene resulted in a double muscled phenotype in individuals carrying two copies of a two base-pair deletion in the third exon of myostatin gene, which leads to a premature stop codon at amino acid 313 (Mosher et al., 2007).
Human cases of myostatin gene mutations have also been found. A young boy of an athletic mother was born with double muscles due to a transition of G to A at
5 nucleotide g.IVS1+5 which was present at both alleles of the boy. Further analysis indicated that the possibility of mis-splicing of the myostatin precursor mRNA in the boy was likely (Schuelke et al., 2004).
The muscular hypertrophy in the well known Belgian Blue cattle is caused by an 11 base-pair deletion in the coding sequence for the bioactive carboxyl-terminal domain of the protein (Grobet et al., 1997). Additionally, the Limousin cattle have an amino acid substitution of F94L in the signal peptide which moderately increases muscling in the breed (Sellick et al., 2007; Esmailizadeh et al., 2008).
1.2.3 Jersey x Limousin cattle
Jersey cattle, which are small in size, when bred to the highly muscled Limousin produce a cross-breed which is intermediate in type. Subsequent mating of these hybrids allows genes to segregate leading to large variation for gene mapping. This variation includes traits related with carcass composition, body size, weight, growth rate, retail beef yield, degree of muscularity, marbling, meat tenderness, fat content and fat distribution (Novianti, 2009). These two breeds are relevant to the commercial dairy and beef production in Australia and New Zealand (Morris et al.,
2009).
1.2.4 Genetics of cattle
For generations, breeders and farmers have used phenotypic selection as a method to improve traits of economic interest, i.e. to better meat quality, to increase milk production, to produce disease-resistant species, etc. In recent years, breeders
6 have begun to include new traits for selection, including fertility and carcass traits, as well as feed intake traits (Archer et al., 1999).
1.3 Residual feed intake
The major cost in almost any animal production industry is feed. In pork production, feed represents between 60 and 70% of the total cost in modern capital-intensive system (Patience et al., 2015). Correspondingly, in beef production, 70-75% of the total feed cost is used by mature cattle to maintain total body mass (Ferrell and
Jenkins, 1985). The remainder of the energy is used for production including growth, pregnancy and lactation (Bermudez et al., 1990).
Energy is released when the carbon-containing compounds in the feed such as fat, carbohydrate and protein are oxidized and is required for processes such as the growth of muscle, bones and fat, and biochemical processes associated with maintenance (Patience et al., 2012). Maintenance energy is the feed energy required to sustain an animal’s body tissue without any changes in gain or loss of body mass after allowing for different energy densities of body components (Ferrell and Jenkins, 1985; Evan, 2001). The ratio of body weight to feed intake at zero body weight change can be defined as the maintenance efficiency (Archer et al.,
1999). Animals of different weights have different requirements for maintenance
(Koch et al., 1963). Faster growing and larger cattle tend to require a greater amount of feed. Cattle with a lower intake for weight gained are much preferred as their cost of beef production is less and they are more profitable. Hence, reducing the maintenance energy of animals will make the herd more efficient.
The term residual feed intake (RFI) was introduced by Koch (1963) in the context to selection of animals with lower requirements for the maintenance of body weight.
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Residual feed intake is the difference between an animal’s actual feed intake and its expected feed requirements for maintenance and growth (Sherman et al., 2008;
Smith et al., 2010). It is also referred to also referred to as net feed intake (NFI) and is phenotypically independent of body and weight gain (Koch et al., 1963). RFI or
NFI is the preferred method to measure feed conversion efficiency since it considers the daily weight gain and adjusts for the metabolic weight of the individual
(Barendse et al., 2007). RFI is of importance because it conceptually captures variation in activity, protein turnover, digestibility and heat increment of fermentation
(Herd et al., 2004) and as such it is often referred to a measure of metabolic efficiency (Pryce et al., 2014).
RFI acts as a measure of net feed efficiency (NFE). The net feed efficiency of an animal depends on the ability of the animal to consume less feed per kg weight gain than expected (Herd and Arthur, 2009). This can be achieved by improving the utilization of nutrients and energy from the feed for maintenance and growth. Net feed efficiency is opposite in sign but equal in magnitude to residual feed intake
(that is, NFE = -RFI). As described by Pitchford (2004), feed efficiency is increased by decreasing feed intake.
Animals that eat less than expected will have a negative RFI, which means the net feed efficiency is better than expected (Figure 1.1). For example, an animal that weighs 600 kg is expected to have a daily feed intake of approximately 12 kg. If the animal eats more than expected, it is considered inefficient. On the other hand, if the animal eats less than 12 kg, the animal is said to have a high net feed efficiency.
Greater net feed efficiency has been targeted as a means of improving profitability of beef industry. Likewise, in pork production, low residual feed intake pigs which eat less than expected are more efficient than their high residual feed intake
8 counterparts and selection of low residual feed intake could improve the efficiency of swine operations (Grubbs et al., 2013).
2 0 Low NFE 1 18 Feed 6 1 14 Intake Expected 12 intake 0 High NFE 8 (kg/day) 30 40 50 60 70 80 90 0 0 0 0 0 0 0
Figure 1.1 Relationship between feed intake and the expected weight gain (Source: Pitchford, W.S)
A 5% improvement in net feed efficiency could have an economic impact four times greater than a 5% improvement in average daily gain (Li et al., 2002). As a result of awareness that there is potential to reduce feed costs by measuring RFI, substantial progress had been made in the poultry, pig, cattle and sheep industries to address net feed efficiency as a trait. This is due to the increasing cost of feed and the fact that accurate feed measurements are now more easily quantified. Archer et al.,
(1999) have shown that selection for net feed efficiency provides an opportunity to significantly reduce feed costs in beef cattle breeding programs. Related studies have come to the same conclusion (Herd et al., 1997; Archer et al., 1998; Archer et al., 1999; Arthur et al., 2001; and Arthur et al., 2004).
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Similarly, in the dairy cattle industry, the cost of feeding accounts for 40-60% of production and the ability to select for low residual feed intake animals could be very beneficial. Even small reductions in feed intake per animal would improve producer profitability (Connor, 2015). Thus, screening tools for feed efficiency in growing heifers and lactating cows has been introduced and has shown to be promising. The tests performed include the evaluation of relationship between nuclear magnetic resonance profiles of lactating Holstein cows with RFI and other production traits
(Maher et al., 2013). Alternatively, using informative SNP markers that are strongly linked to RFI measures can be used to identify genetically superior animals for low metabolic efficiency (Connor, 2015).
1.3.1 Residual feed intake as a genetic trait
Recent studies have provided evidence showing that net feed efficiency in beef cattle, pigs, poultry and mice is moderately heritable (Table 1.1) (Pitchford, 2004).
Heritability estimates for net feed efficiency in beef cattle range from 0.14 (± 0.12) to
0.41 (± 0.07) (Archer et al., 1997). If the genetic variation in residual feed intake is known, it is possible to improve net feed efficiency by genetic selection using RFI as a measure.
Genetic selection to reduce RFI has been shown to result in progeny that eat less without sacrificing growth performance (Herd et al., 1997; Richardson et al., 1998).
However, accurately measuring RFI involves automated measurements of individual feed intake and weight, which is difficult and expensive to do over the extended time period required (Barendse et al., 2007)
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Table 1.1 Heritability estimates for net feed efficiency in different species (adapted from Pitchford, 2004)
Species Physiological No. of Heritability Reference state animals Beef cattle Growing 1324 0.28±0.11 Koch et al (1963)
Beef cattle Growing males 534 0.14±0.12 Fan et al (1997)
Beef cattle Growing 966 0.41 ± 0.07 Archer et al (1997)
Beef cattle Growing 540 0.16 ± 0.08 Herd and Bishop (2000)
Beef cattle Weaner bulls 792 0.32 ± 0.04 Arthur et al (2001 b)
Beef cattle Yearling bulls 397 0.25 ± 0.10 Arthur et al (2001 b)
Dual purpose Growing 235 0.27 ± 0.23 Brelin and Brannang (1982) cattle
Dairy cattle Growing males 650 0.08 ± 0.05 to Jensen et al (1992), 0.36 ± 0.17 Jakobsen et al (2000)
Dairy cattle Growing females 417 0.22 ± 0.11 Korver et al (1991)
Dairy cattle Lactating heifers 360 0.19 ± 0.12 van Arendonk et al (1991)
Dairy cattle Lactating cows 247 0.16 Ngwerume and Mao (1992)
Dairy cattle Lactating cows 353 0 Svendsen et al (1993)
Dairy cattle Lactating cows 204 0.05 Veerkamp et al (1995)
Pigs Growing boars 7562 0.30, 0.33, 0.38 Mrode and Kennedy (1993)
Pigs Growing boars 3188 0.18 Von Felde et al (1996)
Poultry Laying hens 704 0.42 to 0.62 Luiting and Urff (1991a)
Poultry Laying hens Realised 0.12, 0.21, 0.28 Bordas et al (1992)
Poultry Laying hens - 0.27 Tixier-Boichard et al (1995)
Poultry Cockerels - 0.33 Tixier-Boichard et al (1995)
Mice Growing 1000 0.27 ± 0.06 Archer et al (1998)
Mice Growing 500 0.24 ± 0.08 Archer et al (1998)
Mice Growing Realised 0.16, 0.23, 0.27 Hastings et al (1997)
Mice Growing Realised 0.28 ± 0.003 Nielsen et al (1997a)
Mice Growing Realised 0.27 ± 0.02, 0.26 Hughes et al (1998) ± 0.03
1.3.2 Measurement of feed intake
Currently, there are three options available for the measurement of RFI: centralized testing facility, on-farm testing and on pasture testing (Archer et al., 1999).
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1.3.2.1 Centralized testing facility
In centralized testing facilities, feed intake is measured electronically. An electronic ear tag is attached to each animal, which registers a unique number when it enters the feeding box. The feed intake and body weight for that particular animal are automatically downloaded and processed by the system. The difference between the weight of the feed bin before and after eating is calculated to obtain the total feed intake. The advantage of this system includes a high level of control over the testing period and relatively few errors (Archer et al., 1999). However, the cost per animal is over AUD$500. Therefore, usually only a subset of animals from a given herd is tested.
1.3.2.2 On-farm testing
The on-farm testing system involves establishing on-farm facilities for the measurement of RFI. Feed intake can be measured by keeping records of the feed provided to animals in individual pens. At the same time, accurate measures of weight must be recorded. The advantage of on-farm testing is that more animals can be tested for residual feed intake and the total costs are lower than central testing (Archer et al., 1999). However, this system is difficult for farmers to operate for large numbers of animals. Therefore, farmers need a low-cost automated system, which is currently unavailable.
1.3.2.3 Measurement on pasture
The technique of measurement of feed intake on pasture is based on providing markers, such as chromium oxide, to animals at a constant, known dosage with intra-ruminal controlled-release devices to dispense the marker (Archer et al.,
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1999). The marker is then measured in the faeces. However, this technology has limited accuracy and is not adequate for comparing residual feed intake between individual animals (Herd et al., 1996).
1.3.3 Genetic correlations for measures of body composition with residual feed intake
A compilation of the genetic correlations for measures of body composition with residual feed intake was detailed in a paper by Herd and Arthur (2009) (Table 1.2).
The animals measured ranged from bulls and heifers to older feedlot steers. Arthur et al., (2001) reported that the subcutaneous fat depth and rump fat depth had a positive correlation with residual feed intake with +0.17 and +0.06, respectively.
Fatness was found to have a correlation in yearling bulls as well (r=0.16) (Schenkel et al., 2004). In a study by Robinson and Oddy (2004), three body composition traits, namely the 12th -13th rib fat depth, P8 rump fat depth and intramuscular fat were discovered to have correlations with RFI. Backfat thickness score and marbling fat score in young feedlot steers were also found to be correlated with RFI
(Nkrumah et al., 2007). A positive correlation between fat traits and RFI indicates that the inefficient animals are expected to have higher fat levels.
Thus, these findings suggest that body composition traits, particularly traits related to fat, are genetically correlated with residual feed intake. This is supported by recent results of three generations of Angus heifers selected for residual feed intake, which provided evidence of fat deposition differences in the high and low RFI selection lines wherein the high RFI line animals retained more energy as fat (Lines et al., 2014). In particular, there were large differences in the deposition of subcutaneous fat between the high and low RFI animals (Lines et al., 2014).
Selection for RFI following three generations was observed to have larger
13 divergence in fatness as compared to the progeny selected for one generation of
RFI (Richardson and Herd, 2004).
Table 1.2 Genetic correlations for measures of body composition with residual feed intake Reference Animals Body composition traits Correlations Arthur et al., 2001 Bulls and heifers Subcutaneous fat depth 0.17 Rump fat depth 0.06
Schenkel et al., 2004 Yearling bulls Fatness trait 0.16
Nkrumah et al., 2007 Young feedlot Backfat thickness score 0.35 steers Marbling fat score 0.32
Robinson & Oddy, Older feedlot steers 12th – 13th rib fat depth 0.48 2004 Rump fat depth 0.72 Intramuscular fat 0.22
1.3.4 Quantitative trait loci for residual feed intake
In addition to phenotypic selection, another option to select cattle for improved residual feed intake is to use DNA markers. Any identifiable segment of DNA in the genome which shows variation for a trait between animals can be used as a marker for the particular trait (Simm et al., 1998). Markers can be defined as genetic variants or polymorphisms that can be detected and used in genetic linkage analysis (Liu and Cordes, 2004). To use this marker-assisted selection approach, it is crucial to detect the genes that carry mutations affecting residual feed intake or at least DNA markers linked with these mutations.
With the advancement of DNA biotechnologies, scientists can study genetic material at the gene level and identify regions of the genome that cause variation in traits. It is now possible to dissect the genetic variability of complex traits underlying
14 quantitative trait loci (QTL). A QTL is a region of DNA that is associated with a particular phenotypic trait. It implies that in that region, there is a gene or genes that control the trait. QTL detection is the first step towards the identification of the genes involved and determining the causal mutations (Boichard et al., 2003). When QTL mapping is combined with other analyses (e.g. gene expression microarrays and gene knockouts), it is possible to identify specific gene function and networks causing genetic effects (Zeng, 2005). Currently, strategies involving comparative positional and functional candidate gene mapping are being used to identify the molecular background of QTL affecting important production traits in various farm animals.
Previous studies using two groups of cattle, the Trangie Angus RFI selection line cattle and the University of Adelaide Davies Jersey x Limousin gene mapping cattle, identified ten (10) chromosomal regions or QTL which have significant or nearly significant effects on residual feed intake (Fenton, 2004). Four of the chromosomal regions found to affect residual feed intake in the Jersey x Limousin experiment clearly affected residual feed intake in the Trangie Angus selection line cattle (Table
1.3). These regions were on cattle chromosomes 1, 6, 8, and 20 (Fenton, 2004).
There was also suggestive evidence that the remaining chromosomal region, on cattle chromosome 11, also affects residual feed intake in the Trangie Angus selection lines (Naik, 2007). A few other studies in different cattle breeds have also demonstrated QTL for RFI. In Angus, Charolais and hybrid bulls, QTL for RFI were discovered on BTA 1, 7, 18 and 19 (Sherman et al., 2009). A GWAS study using various beef and dairy cattle breeds indicated that significant SNPs for RFI were located on BTA 3, 5, 7 and 8 (Bolormaa et al., 2011).
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Table 1.3 NFE QTL identified in the Davies Jersey x Limousin mapping herd
Cattle NFE NFE NFE QTL DFI LOD Mid-wt ADG chromosome LOD size* position score LOD score LOD score (cM) score BTA 1 3.2 98 95 4.7 3.0 3.9 BTA 8 2.2 65 67 1.6 NA NA BTA 20 2.0 57 49 NA NA 1.7 BTA 9 1.9 50 14 2.2 1.8 3.4 BTA 6 1.7 46 61 2.2 2.0 NA BTA 16 1.6 50 27 2.1 2.2 NA BTA 17 1.6 46 13 1.5 NA 1.5 BTA 11 1.5 51 74 NA NA 2.5 BTA 7 1.5 46 39 NA NA 2.9 BTA 5 1.5 45 31 2.3 2.3 NA
*Additive size of effect in percentage phenotypic standard deviation (1.55 kg/day in cattle) in family where QTL had greatest effect, assuming no interaction between the QTL effect and sex or breed. NA: not available (Naik, 2007).
By using information from the chromosome regions, it is possible to select candidate genes for residual feed intake. Candidate genes are genes that are likely to control a particular phenotypic trait based on their known function. By sequencing the candidate genes, one may detect DNA variants, which include single nucleotide polymorphisms (SNPs) plus insertions and deletions (in/dels) (Kim and Misra,
2007). SNPs are polymorphisms caused by point mutation that give rise to different alleles containing alternative bases at a given nucleotide position within a locus. The use of SNPs in molecular marker development are important since they are the most abundant type of polymorphism in any organism and are adaptable to automated genotyping (Liu and Cordes, 2004). SNPs with significant effects on residual feed intake could act as DNA markers for the selection of net feed efficiency provided they are causative or in linkage disequilibrium with the causative variant.
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1.4 Mechanisms affecting residual feed intake
1.4.1 Energy metabolism
There are many physiological mechanisms contributing to variation in residual feed intake (Richardson and Herd, 2004). The variation in residual feed intake may be influenced by five major physiological components, namely the intake of feed, digestion of feed, metabolism, physical activity and thermoregulation (Herd and
Arthur, 2009).
The difference in feed intake is associated with the amount of energy expended to digest the feed. Higher amount of energy will be used to digest feed if the feed intake is higher and this is partly due to the increase size of the digestive organs. It is reported that the key factors in determining the energy cost of eating in cattle are the rate of ingestion and duration of the meal (Adam et al., 1984). In Angus steers, a study has reported that high RFI steers have greater decline in average daily feeding session times and spend more time eating than the low RFI animals
(Richardson, 2003). Robinson and Oddy (2004) reported that the high RFI feedlot steers were associated with longer time feeding per day, more eating sessions per day and faster eating rate (g/min).
Residual feed intake may be also influenced by digestion, such that as the feed intake increases, the digestion of feed decreases (Andersen et al., 1959). This is supported by Richardson and Herd (2004), wherein less RFI was associated with greater digestibility. It has been estimated from the studies on net feed efficiency that differences in digestion contribute 10% to the variation in residual feed intake
(Richardson and Herd, 2004).
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Body composition was found to contribute 5% of the variation in residual feed intake
(Figure 1.2). Lean tissue deposition explains more variation in feed intake and feeding patterns and was determined to account for 2% of the variation in residual feed intake. The heat increment of feeding pattern and general locomotive activity were both estimated to contribute 9% and 10%, respectively, to the variation in residual feed intake.
Thirty seven percent (37%) of the variation in residual feed intake was estimated to be from protein turnover, tissue metabolism and stress (Richardson and Herd,
2004). Studies conducted using plasma protein between both high and low RFI cattle showed an increased level of plasma protein in the less efficient cattle, which might reflect the differences in the rate of protein turnover (Richardson and Herd,
2004). It was also reported that the more efficient steers possess a more efficient mechanism for protein deposition or a lower rate of protein degradation compared to less efficient steers. Interestingly though, the concentration of blood aspartate amino transferase which is a marker of liver function indicates higher levels of protein catabolism in the liver of less efficient steers (Richardson and Herd, 2004). It should be noted that energy metabolism underlies the majority of these mechanisms and this is the focus herein.
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Body composition (5%) Feeding patterns (2%) Other (27%)
Protein
turnover, tissue metabolism & stress (37%)
Activity (10%)
Digestibility Heat increment (10%) of fermentation (9%)
Figure 1.2 Contributions of biological mechanisms to variation in residual feed intake as determined from experiments on divergently selected cattle (Richardson and Herd, 2004)
1.4.2 Mitochondrial function
It is well known that genetics and diet have profound influence on mitochondrial function (Bottje et al., 2006). Mitochondria have no fixed size and the shapes vary from spherical to rod-like with dimension of 3 µm (length) and ~1 µm (diameter)
(Scheffler, 2008). On occasion, they appear to form a network. The number of mitochondria per cell varies significantly from cell to cell as well as between organisms (Scheffler, 2008).
The mitochondria are membrane-bound organelles consisting of an outer membrane and inner membrane (Scheffler, 2008) (Figure 1.3). The space between the two membranes is known as the intermembrane space and inside the inner membrane is the matrix . The inner mitochondrial membrane is the site of the
19 electron transport chain and is where the process of oxidative phosphorylation occurs that facilitates ATP synthesis (Hatefi, 1985).
Mitochondria are the site of energy production in the cell and produce the majority of cellular ATP (Kolath et al., 2006). ATP or adenosine triphosphate provides energy by the breakage of the phosphoanhydride bonds to generate ADP (adenosine diphosphate) and inorganic phosphate ion (Nelson and Cox, 2008). Mitochondria produce approximately 90% of cellular energy and are found in large numbers in metabolically active cells such as liver, kidney, muscle and brain cells (Herd and
Arthur, 2009).
The production of ATP can be observed in several metabolic pathways in the cellular respiration process, starting from the glycolysis pathway. While the glycolysis metabolism does not occur in the mitochondria, the initial ATP is produced during glycolysis in the cytosol before entering mitochondria for Krebs cycle and electron transport chain.
Figure 1.3 Mitochondria structure
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1.4.3 Cellular respiration process
The cellular respiration process is a cumulative function of three metabolic stages, two of which occur in the mitochondria. The first stage is glycolysis, the degradation process of glucose into two molecules of pyruvate which takes place in the cytosol.
This is then followed by the Krebs cycle in the mitochondria matrix, wherein the pyruvate is decomposed into carbon dioxide. The final stage, the electron transport chain accepts electrons from the two other stages to form water (Campbell et al.,
1999).
1.4.3.1 Glycolysis
The glycolysis pathway occurs in the cytosol and is the first stage in the cellular respiration process. The word glycolysis carries the meaning of splitting of sugar, which is the main purpose of this pathway (Campbell et al., 1999).
Glucose is a six carbon sugar with high potential energy. The complete oxidation of glucose to carbon dioxide and water proceeds with a standard free-energy change of -2,840 kJ/mol. Glucose also supplies a huge array of metabolic intermediates for various other biosynthetic reactions (Nelson and Cox, 2008).
During glycolysis, a glucose molecule is oxidized to two molecules of pyruvate, with energy conserved as ATP and NADH. The breakdown process of the six-carbon glucose occurs in 10 steps involving 10 glycolytic enzymes (Figure 1.4). The first five steps are called the energy-investment phase or the preparation phase. The remaining five steps are grouped in the energy-payoff phase (Nelson and Cox,
2008).
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The process is initiated when glucose enters the cell and is phosphorylated by the enzyme hexokinase, which transfers a phosphate group from ATP to the sugar. This is followed by the rearrangement of the glucose 6-phosphate to be converted to its isomer, fructose 6-phosphate with the aid of the enzyme phosphoglucoisomerase.
The next step involves the enzyme phosphofructokinase that transfers a phosphate group from ATP to sugar. With two phosphate groups on opposite ends of the sugar, the sugar is prepared to be split into half by the enzyme aldolase. Aldolase cleaves the sugar molecule into two different three-carbon sugars, namely the glyceraldehyde phosphate and dihydroxyacetone phosphate. With the presence of the two isomers, the isomerase enzyme then catalyses the reversible conversion between the two sugars since only glyceraldehyde phosphate will be used in the next step (Campbell et al., 1999).
The energy-payoff phase begins with the enzyme triose phosphate dehydrogenase catalysing two sequential reactions. First, the sugar is oxidised by the transfer of electrons and H+ to NAD+, forming NADH. The enzyme then uses the energy released to attach a phosphate group to the oxidised substrate (Campbell et al.,
1999).
The phosphate group added in the previous step is transferred to ADP using the phosphoglycerokinase enzyme. For each glucose molecule that begins glycolysis, two molecules of ATP will be produced at this step. However, two ATPs have been used in the earlier stage to split the sugar. The end product of this step is two molecules of 3-phosphoglycerate. This is then followed by the relocation of the remaining phosphate group by the enzyme phosphoglyceromutase (Campbell et al.,
1999).
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The enzyme enolase then forms a double bond in the substrate by extracting a water molecule to form phosphoenolpyruvate. The last reaction of glycolysis produces more ATP by transferring the phosphate group to ADP with the pyruvate kinase enzyme. The end result is two molecules of pyruvate (Campbell et al., 1999).
Figure 1.4 The reactions of glycolysis convert glucose to pyruvate (Nature Education, 2010)
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1.4.3.2. Krebs cycle
Following glycolysis, two molecules of pyruvate will be produced. In the event that oxygen molecule is present, the pyruvate will then enter the mitochondria wherein the enzymes in Krebs cycle will complete the oxidation of the organic fuel (Campbell et al., 1999). The Krebs cycle, also known as the tricarboxylic acid (TCA) cycle or citric acid cycle, is the process that oxidizes the acetyl groups into carbon dioxide and water, producing the high energy compound ATP (Nelson and Cox, 2008).
The pyruvate molecules from glycolysis must be converted to the acetyl groups before entering the Krebs cycle. The oxidation of pyruvate to acetyl CoA and carbon dioxide is performed by the pyruvate dehydrogenase (PDH) complex, a cluster of enzymes which catalyses three reactions. First, the pyruvate’s carboxyl group is removed and given off as CO2. Second, the remaining two-carbon fragment is oxidised to form acetate. Third, Coenzyme A, a sulfur-containing compound, is attached to the acetate by an unstable bond that makes the acetyl group very reactive (Campbell et al., 1999).
The eight steps in the Krebs cycle are catalysed by specific enzymes in the mitochondrial membrane (Figure 1.5). The reaction is initiated by the condensation of acetyl-CoA with oxaloacetate to form citrate, catalysed by the enzyme citrate synthase. Next is the formation of isocitrate from citrate, using the enzyme aconitase. Aconitase contains an iron-sulfur centre which acts both in the binding site and in the catalytic addition or removal of H2O (Nelson and Cox, 2008).
This is followed by oxidation of isocitrate to α-ketoglutarate and carbon dioxide by the enzyme isocitrate dehydrogenase. There are two different forms of isocitrate dehydrogenase, one requiring NAD+ as electron acceptor and the other requiring
24
NADP+. The reactions of both NAD+ and NADP+ are similar, wherein they will be reduced to NADH and NADPH (Campbell et al., 1999; Nelson and Cox, 2008). The
α-ketoglutarate is then converted to succinyl-CoA and carbon dioxide by the action of α-ketoglutarate dehydrogenase complex, where the NAD+ serves as the electron acceptor and CoA as the carrier of the succinyl group (Nelson and Cox, 2008).
The next step involves the conversion of succinyl-CoA to succinate, catalysed by the enzyme succinyl-CoA synthetase. Succinyl-CoA has a thioester bond and the energy released in the breakage of this bond is used to drive the synthesis of a phosphoanhydride in ATP. Succinate is then oxidised to fumarate by succinate dehydrogenase. The electrons are passed from succinate through the FAD and iron-sulfur centres before entering the chain of electron transfer in the mitochondrial inner membrane.
Subsequently, the hydration of fumarate to L-malate is then catalysed by fumarase.
In the last reaction of the Krebs cycle, the enzyme L-malate dehydrogenase catalyses the oxidation of L-malate to oxaloacetate and produces another molecule of NADH (Nelson and Cox, 2008).
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Figure 1.5 Reactions of the Krebs cycle (Berg et al., 2012)
1.4.3.3 Electron transport chain
The electron transport chain is a sequence of electron carrier molecules that shuttle electrons during the redox reactions that release energy used to produce ATP
(Hatefi, 1985). Most components of the chain are proteins, which exist in multiprotein complexes numbered I through V. During electron transport along the chain, electron carriers alternate between reduced and oxidized states as they accept and donate electrons (Figure 1.6).
The electron transport chain is composed of more than 80 polypeptide components that are grouped together into four enzymatic complexes. The polypeptides that
26 constitute complex I (NADH: ubiquinone oxidoreductase), III (ubiquinol cytochrome c reductase), and IV (cytochrome c oxidase) are coded for by both nuclear and mitochondrial DNA. In contrast, complex II (succinate: ubiquinone oxidoreductase) is coded exclusively by the nuclear genome (Heales and Bolanos, 2002).
1.4.3.3.1 Complex I
Complex I is also known as the NADH-ubiquinone oxireductase. Complex I reaction requires a complex with 45 subunits (>900kDa) in mammalian mitochondria
(Scheffler, 2008). In addition, Complex I is composed of an FMN-containing flavoprotein and at least six iron-sulfur centres. Under the high-resolution electron microscopy, Complex I can be described as L-shaped, with one arm of the L in the membrane and the other extending to the matrix (Nelson and Cox, 2008). The overall reaction catalysed by Complex I can be described as follows:
+ + + NADH + Q + 5H → NAD + QH2 +4H
Q and QH2 refer to the oxidized and reduced form of ubiquinone, respectively
(Scheffler, 2008). Complex I catalyses the electron transfer from NADH to ubiquinone homologs, ferricyanide and NAD (Hatefi, 1985). This reaction is accompanied by the net transfer of four protons from the matrix side to the intermembrane space.
The reduction of Q is coupled to vectorial proton translocation and is inhibited by rotenone A, piericidin A, barbiturates, and Demerol in which the electron flow from the Fe-S centres of Complex I to ubiquinone will be inhibited and therefore, the overall process of oxidative phosphorylation is blocked (Hatefi, 1985; Nelson and
Cox, 2008).
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1.4.3.3.2 Complex II
Complex II or the succinate: ubiquinone oxidoreductase is made of only four peptides (140 kDa), making it the simplest complex in the electron transport chain
(Scheffler, 2008). It is also smaller than Complex I and contains five prosthetic groups of two types (Nelson and Cox, 2008). The reaction catalysed by Complex II is
Succinate + Q → Fumarate +QH2
Complex II plays an important role in the Krebs cycle in which the two largest peptides constitute the peripheral portion of the complex and function as the enzyme succinate dehydrogenase. Electrons from the oxidation of succinate to fumarate are channelled through this complex to ubiquinone (Hatefi et al., 1976;
Hatefi et al., 1985; Hatefi, 1985).
The succinate formed from succinyl-CoA is oxidised to fumarate by the flavoprotein succinate dehydrogenase. Electrons are transferred from succinate through the
FAD and iron-sulfur centres before entering the chain of electron carriers in the mitochondrial inner membrane (Nelson and Cox, 2008). Thus, Complex II links the
Krebs cycle to the electron transport chain (Scheffler, 2008).
1.4.3.3.3 Complex III
Complex III or ubiquinone-cytochrome c oxidoreductase is also known as cytochrome c reductase and as the bc1 complex after the two cytochromes found within it. Complex III contains two b-type and one c-type cytochromes and an iron- sulfur protein (Hatefi, 1985).
28
The reaction of Complex III is similar with Complex I, in which the oxidation of one of the substrates (QH2) and the transfer of electrons to the mobile carriers (Cyt C) is coupled to the transfer of protons across the inner mitochondria (Leung and Hinkle,
1975; Scheffler, 2008).
The Complex III reaction, also known as the Q cycle, accommodates the switch between the two-electron carriers (ubiquinone) and the one electron carriers
(cytochromes b and c) and explains the stoichiometry of four protons translocated per pair of electrons passing through Complex III to cytochrome c. Cytochrome c is a soluble protein of the intermembrane space. After its single heme accepts an electron from Complex III, cytochrome c moves to Complex IV to donate the electron to a binuclear copper centre (Nelson and Cox, 2008).
The reaction catalysed by Complex III, also known as the Q cycle, is
3+ + 2+ + QH2 +2 cyt c + 2 H → Q + 2 cyt c + 4 H
1.4.3.3.4 Complex IV
Complex IV is commonly referred to as cytochrome c oxidase. It is a large enzyme with 13 subunits (160 kDa) of the inner mitochondrial membrane (Nelson and Cox,
2008). The overall reaction catalysed is
2+ + + 3+ + 4 cyt c + 4 H (s)in + 4 H (p)in + O2 → 4 cyt c + 4H (p)out + 2 H2O
The reaction involves the molecular oxygen as the terminal electron acceptor, the mobile carrier cytochrome c will be re-oxidised, and four protons are transferred to the intermembrane space (Scheffler, 2008). In the Complex IV reaction, the first process is initiated when four electrons are donated on the intermembrane space
29 side, while four protons are taken up on the matrix side which results in a transfer of four positive charges across the membrane. The second process is an average of one proton being pumped through the enzyme for each electron transferred to oxygen (Branden et al., 2006).
This four-electron reduction of oxygen involves redox centres that carry only one electron at a time, and it must occur without the release of incompletely reduced intermediates such as hydrogen peroxide or hydroxyl free radicals that would damage cellular components (Nelson and Cox, 2008). The intermediates remain tightly bound to the complex until completely converted to water (Nelson and Cox,
2008).
1.4.3.3.5 Complex V
The Complex V, also known as the ATP synthase is responsible for ATP synthesis from ADP and inorganic phosphate (Pi) at the expense of protonic energy derived from the operation of the respiratory Complexes I, III and IV (Hatefi, 1985).
Furthermore, Complex V is capable of ATP hydrolysis linked to proton translocation from the matrix to the cytosolic side of the mitochondrial inner membrane (Hatefi,
1985).
Figure 1.6 Electron transport chain
30
In summary, the electron transport chain is initiated when Complex I and II receive electrons from the oxidation of NADH and succinate, respectively. These electrons will be delivered to a lipid electron carrier, coenzyme Q. Subsequently, Complex III oxidizes the reduced form of coenzyme Q and which in turn reduces cytochrome c.
Cytochrome c is a protein electron carrier that is mobile within the inner membrane.
Finally, Complex IV couples the oxidation of cytochrome c to the reduction of oxygen to water. The energy released creates a proton gradient across the inner membrane, with protons being pumped into the intermembrane space. Protons then re-enter the membrane through a specific channel in Complex V (Mathews and Van
Holde, 1996).
1.4.3.4 Oxidative phosphorylation
The cellular respiration process comprises of glycolysis, Krebs cycle, the electron transport chain and oxidative phosphorylation. Except for glycolysis, the other processes occur in the inner membrane of the mitochondria. The similarities between these metabolic stages are that they involve transfer of electrons and generate ATP. Oxidative phosphorylation is the process that produces ATP using energy derived from the redox reactions of an electron transport chain. Oxidative phosphorylation accounts for almost 90% of the ATP generated by respiration
(Campbell et al., 1999).
Energy enters an organism as food and exits the organism as heat and faecal matter. Energy is released from food as it is combusted to carbon dioxide and water. This is accomplished by enzymatically controlled mitochondrial oxidative phosphorylation in which a portion of the energy content of food is converted to ATP
(Lowell and Shulman, 2005).
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Oxidative phosphorylation produces most of the ATP made in aerobic cells.
Complete oxidation of a molecule of glucose to carbon dioxide yields 30 or 32 ATP.
Aerobic oxidative pathways that result in electron transfer to oxygen are accompanied by oxidative phosphorylation, and therefore, account for the vast majority of the ATP produced in catabolism (Nelson and Cox, 2008). Thus, the production of ATP via oxidative phosphorylation is essential to fulfil the demand of the cells for energy.
1.4.3.5 Reactive oxygen species
Mitochondrial inefficiency may occur as a result of electron leakage from the respiratory chain. Rather than being completely reduced to water, 2% to 4% of oxygen consumed by mitochondria may be incompletely reduced to reactive oxygen species due to univalent reduction of oxygen by electrons (Boveris and Chance,
.- 1973). The major reactive oxygen species (ROS) are the superoxide radical (O2 ),
. hydrogen peroxide (H2O2), and the hydroxyl radical (OH ) (Scheffler, 2008).
Reactive oxygen species are produced in the mitochondria during oxidative phosphorylation and they are very destructive towards biomolecules. Excessive reactive oxygen species can cause oxidative damage to nucleic acids, proteins and lipids, as well as the organelles such as mitochondria (Nelson and Cox, 2008). The constituents of the mitochondrial membrane are particularly vulnerable to oxidative damage by oxygen free radicals which are generated continuously by the mitochondrial respiratory chain.
It is hypothesized that peroxidation of membrane lipids components to be a major cause of oxygen free radical attack and results in generalized impairment of the membrane function (Paradies et al., 2002). The mitochondrial membrane is made of
32 phospholipid components rich in unsaturated fatty acids that are particularly susceptible to oxygen radical attack because of the presence of double bonds that can undergo peroxidation through a chain of oxidative reactions. For example, cardiolipin is a particularly vunerable mitochondrial phospholipid species with interesting chemical and structural characteristics including being highly acidic and having a head group (glycerol) that is esterified to two phophatidyl glyceride backbone fragments rather than one (Petrosillo et al., 2009). Thus, due to their membrane composition, mitochondria are sensitive to lipid peroxidation (Paradies et al., 2002) as well as being the target of oxygen free radical attack (Petrosillo et al.,
2009).
Upon oxidative attack, a range of different types of damage can occur to the biomolecules including the oxidation of the deoxyribose moiety in DNA resulting in strand breaks and the oxidation of amino acids side chains forming carbonyl groups, which targets the protein for degradation. Reactive oxygen species are controlled by the enzymes catalase and superoxide dismutase which convert superoxides to water and oxygen. The superoxide dismutase catalyses the reaction:
. - + 2 O2 + 2H → H2O2 + O2
The reaction of catalase is:
2H2O2 → 2H2O + O2
33
1.4.4 Other known energy pathways affecting residual feed intake
1.4.4.1 Uncoupling proteins
Proton leakage occurs during the movement of protons across the mitochondrial inner membrane, and happens spontaneously or is mediated by uncoupling proteins
(UCPs) (Krauss et al., 2005). A small number of uncoupling proteins have been identified and they are believed to be related to the regulation of energy expenditure and heat production. The uncoupling proteins form a channel in the inner mitochondrial membrane that allows protons to re-enter the mitochondrial matrix without passing through the ATP synthase complex (Nelson and Cox, 1998). UCP1 is expressed in brown adipose tissue, UCP3 is expressed in muscles and UCP2 occurs in all other tissues (Jezek et al., 1998). UCP2 and UCP3 may have roles in influencing maintenance energy requirement (Fleury et al., 1997).
Uncoupling proteins are also involved in controlling superoxide production, as well as the production of reactive oxygen species (Echtay et al., 2002). Increases in reactive oxygen species production will induce the anti-oxidant metabolic pathways involving catalase, superoxide dismutase, glutathione peroxidise, and glutathione S transferase (Echtay et al., 2002). It may be possible that low residual feed intake animals will show less oxidative stress in comparison to high residual feed intake animals because there is less proton leakage and ROS production.
1.4.4.2 Adenine monophosphate activated protein kinase (AMPK)
The adenine monophosphate activated protein kinase (AMPK) is considered to be a cellular energy sensor that contributes to the regulation of energy balance and caloric intake. AMPK regulates the expression of genes involved in lipogenesis and
34 mitochondrial biogenesis. AMPK is distributed in most organs, including liver, skeletal muscle, heart, hypothalamus, and even in adipose cells.
AMPK is activated in response to low ATP levels and high AMP levels, which results in an increased AMP to ATP ratio (Corton et al., 1994). Food intake is regulated through glucose concentration via ATP status and the AMPK axis. Hence, if an animal can use less energy to maintain higher ATP and glucose concentrations, it should have a lower feed intake. There is also evidence that the regulation of hormones involved in appetite control affects AMPK activity and food intake (Lee,
2005).
1.5 Relationship between mitochondrial function and residual feed intake in animals
1.5.1 Poultry
Bottje et al. (2002) measured the differences in mitochondrial function and biochemistry in male broilers of high and low feed efficiency. The broilers were chosen from the same genetic line and were fed with the same diet. The findings showed that mitochondria obtained from broilers of low feed efficiency exhibit greater uncoupling of the electron transport chain. This is apparently due to site- specific defects in electron transport resulting in higher amounts of reactive oxygen species compared with high feed efficiency mitochondria (Bottje et al., 2006).
Bottje et al. (2006) also observed that higher amounts of reactive oxygen species production in the mitochondria of low feed efficiency chickens were likely responsible for higher protein carbonyl levels. This indicates higher protein oxidation in comparison with mitochondria and tissues from high feed efficiency animals.
35
Higher protein damage in the mitochondria may have contributed to lower activity of the electron transport chain complexes (Bottje et al., 2006).
Another study conducted by Bottje et al. (2002) using male broilers also revealed that there was a greater amount of electron leakage in mitochondria from low feed efficiency in comparison to mitochondria from high feed efficiency chickens.
Electron leakage increased the inhibition of electron transport in Complex I and
Complex III in the low feed efficiency animals but not in high feed efficiency animals.
The results of this study also indicated that lower respiratory chain coupling in low feed efficiency chicken mitochondria may be due to lower activities of Complexes I and II (Bottje et al., 2002).
Iqbal et al. (2004) concluded that protein carbonyl levels were higher in the low feed efficiency broilers which indicated enhanced protein oxidation in the high feed intake mitochondria. It was also discovered that the activities of all respiratory chain complexes (I, II, III, IV) were higher in the breast muscle mitochondria of high feed efficiency broilers (Iqbal et al., 2004).
1.5.2 Pigs
Research on the relationship of residual feed intake and mitochondria is also evident in pigs. Consistent with findings from other species, the differences between the high and low feed intake groups in pigs were observed in various experiments.
Grubbs et al. (2013) found that less efficient animals have greater levels of oxidative stress, and hence, investigated the protein profile of mitochondria of pigs genetically selected for high and low residual feed intake. Similar to the results of Naik (2007), the heat shock proteins, HSP 60 and HSP 70, were found to be increased in the low
36 residual feed intake line. It was also discovered that the endoplasmic reticulum oxidase-1 α (ERO1α) decreased in the longissimus muscle of the mitochondria from the low feed intake pig line (Grubbs et al., 2013).
A study on the reactive oxygen species (ROS) was conducted by the same research group with the hypothesis that greater efficiency would be linked to less
ROS production from the mitochondria. Results indicated that there was less ROS production in mitochondria from the white portion of the semitendinosus muscle in the low feed intake pig line when both NADH and FADH2 energy substrates were used (Grubbs et al., 2015). Furthermore, mitochondria from the red portion of the semitendinosus in the low feed intake line had less ROS production when succinate was used as an energy substrate. However, no differences were seen in the liver mitochondria of both high and low feed intake lines (Grubbs et al., 2015).
1.5.3 Cattle
Extensive work has been done in cattle to observe the connection between mitochondrial function and residual feed intake. Most of the studies came to a conclusion that there are differences in the mitochondria between high and low feed intake cattle.
Kolath et al. (2006) reported that calves with low residual feed intake had higher respiratory-chain coupling in muscle mitochondria compared to high residual feed intake calves. It was also reported that mitochondria from low feed intake group had more rapid uptake of oxygen then the calves from the high feed intake group (Kolath et al., 2006).
37
Similar results was observed in a study by Lancaster et al. (2007), wherein the low feed intake calves was found to have higher respiratory chain coupling in liver mitochondria as compared to the high feed intake calves. Research by Kerley’s laboratory discovered that the ratio of mitochondrial Complex I to III was 1.3-fold higher in calves with low feed intake than calves with high feed intake (Carstens and
Kerley, 2009).
Naik (2007) emphasized variation in proteins related to feed intake in the Angus cattle selection lines. The results from the proteomics study indicated that the levels of stress related proteins such as albumin, catalase, heat-shock proteins 70kDA and
60kDA were elevated in the low feed intake animals. On another note, the author discovered that the level of oxidative phosphorylation subunits was lower in the high feed intake animals than the low feed intake animals (Naik, 2007).
1.6 Summary
With all the evidence provided, it is clear that the amount of feed consumed by cattle may be controlled by many mechanisms at the molecular level. However, previous studies suggest that residual feed intake in livestock might be regulated by particular genes affecting energy metabolism and particularly, the efficiency of mitochondrial function. Mitochondria are involved of many biochemical pathways but all involve energy production for the cell. Thus, it is hypothesised that there are genes related to mitochondrial function affecting residual feed intake in cattle and variants in these genes have significant effect on residual feed intake. In addition, animals with higher residual feed intake are hypothesised to have less efficient mitochondria as compared to the lower residual feed intake animals.
38
Therefore, the main objective of this study was to identify candidate genes related to mitochondrial function and energy metabolism which might affect residual feed intake in cattle by screening the genes for DNA variants and analysing the relationships between the variants and residual feed intake. In addition, mitochondrial function was examined between high and low residual feed intake cattle to determine if there are differences at the biochemical level which may explain the feed efficiency variation.
39
Chapter 2 Materials and Methods
40
2.1 QTL mapping of residual feed intake in cattle
Quantitative trait loci (QTL) mapping experiments were done previously using the
Davies gene mapping herd of Jersey x Limousin cattle. Two stages of QTL mapping experiment were carried out. The first stage was the whole genome mapping of QTL for residual feed intake using microsatellite markers, completed by Fenton (2004).
The second stage herein was fine mapping these identified QTL using SNP markers as described below. In addition to the Davies gene mapping herd, cattle from the
Trangie Angus residual feed intake (RFI) selection line families were used in this study for the mitochondrial assays.
2.1.1 Cattle QTL mapping experimental design
The aim of the Davies Cattle Gene Mapping Project was to identify DNA markers for important traits in cattle. It involved a double back-cross design using two Bos taurus breeds, the Jersey dairy breed of small frame and the Limousin beef breed of moderate to large frame. By using the two extreme breeds, the trait variation in the progeny was maximized and this included carcass composition, body size and meat tenderness (Morris et al., 2009).
In 1993, the first phase of this study was conducted by mating 280 purebred Jersey and Limousin to produce first cross progeny. The first cross progeny, Jersey x
Limousin F1, were born in 1994 and 1995. In the second phase, three Jersey x
Limousin F1 sires were mated to pure Jersey and Limousin dams in Australia and
New Zealand to produce double backcross animals, namely Limousin backcross progeny and Jersey backcross progeny (Figure 2.1). In order to generate the
Australian backcross progeny, 210 purebred Limousin and Jersey dams were used.
41
Limousin X Jersey (L) (J)
backcross backcross
F1
Jersey x Limousin
F2 F2
XL XJ
Figure 2.1 Davies cattle QTL mapping backcross design
There were 161 Limousin cross progeny and 205 Jersey cross progeny born in
Australia over three breeding seasons – 1997, 1998 and 1999. For approximately
28 months from birth, the calves were fed on pasture but were finished on grain for at least six months as part of an intensive feed efficiency trial. QTL for residual feed intake were previously mapped in the backcross progeny using genotype data for
150 microsatellite markers approximately 20 cM apart and covering all the autosomes (Fenton, 2004; Naik, 2007). Phenotypic data from the progeny included live measures, such as average daily gain, daily feed intake, and weights, as well as carcass measures such as hot standard carcass weight, organ weights, bone weights, fat weights, marbling scores and fat depths from MSA grading, ossification, eye muscle area, etc. The carcass data were used to calculate meat to bone ratio,
42 meat %, fat to bone ratio, bone % and fat %. The feed intake and live weights were used to calculate residual feed intake (as detailed in Appendix A.1.1).
2.1.2 Single Nucleotide Polymorphism (SNP) experiments
2.1.2.1 Selection of candidate genes for SNP detection
A fine mapping approach was performed to refine and confirm the RFI QTL location using the preliminary QTL analysis results (Fenton 2004). Herein, candidate genes involved in energy metabolism and mitochondrial function that may affect residual feed intake in cattle were identified based on human-cattle comparative genome mapping and the limited BTAU 3 bovine genome sequence available from the
Ensembl database (http://www.ensembl.org/index.html). The candidate genes were sequenced using the genomic DNA from the F1 sires to identify single nucleotide polymorphism (SNP) variants. Specific SNPs were then selected for genotyping in the Davies Jersey-Limousin backcross progeny depending upon their location in the gene, disequilibrium with other SNPs in the gene and whether they may be potential functional variant.
2.1.2.2 Genomic DNA purification
Cattle blood was collected from the jugular vein using an 18 gauge needle (Terumo) in ACD-solution A vacutainers (Becton-Dickinson). Genomic DNA from blood was extracted by phenol/chloroform extraction method and stored at -20°C for long term storage.
43
2.1.2.3 DNA concentration
Concentration of the genomic DNA was originally measured using the Shimadzu
UV-160 spectrophotometer (Shimadzu Corp.). Measurement of optical density (OD) was performed at 260 nm and 280 nm. The DNA samples were also measured using NanoDrop ND-1000 spectrophotometer (Thermo Scientific) to re-evaluate of samples quality. This was performed by pipetting 1 µl of DNA sample on the pedestal and determining the optical density at 260 nm and 280 nm.
2.1.2.4 Primer Design
For primer design, the bovine DNA sequences from the Ensemble database
(www.ensembl.org) were used as well as the human DNA sequences for comparison. Both the bovine and human DNA sequences were then aligned together to determine the similarity using ClustalW (www.ebi.ac.uk). Upon confirmation, primers were designed for the 5’UTR, 3’UTR and all the exons of the candidate genes selected using the Prime3 software. In order to avoid hairpin structures and primer dimer formation, Oligo7 software (Molecular Biology Insights
Inc.) were used. Oligonucleotides were synthesized by GeneWorks (Australia) and
Proligo (Australia).
2.1.2.5 Polymerase Chain Reaction (PCR) condition optimisation
Primer sets were diluted to 2.5 µM using PCR-quality water in 1.5 ml tubes. All primer sets were tested for optimal conditions using a Palm Cycler (Corbett
Research) using trial DNA to ensure only a single expected size product was generated. Two 25 µl reactions were prepared for each set of primers to produce a
44 final volume of 50 µl of PCR product (Table 2.1). Amplification was carried out in the
96-well PCR plates.
Table 2.1 PCR reagent concentration
PCR COMPONENT Volume Final concentration 10x PCR Buffer 2.5 µl 1x
Magnesium chloride (25 mM) varies 1.5 - 3.0 mM dNTP (1.25 mM each of dATP, dCTP, dGTP, and dTTP) 2.5 µl 0.5 mM each
Forward primer (2.5 µM) 1 µl 0.1 µM
Reverse primer (2.5 µM) 1 µl 0.1 µM
Template DNA (100 ng) 1 µl 4 ng
Taq Polymerase (0.5 U) 1 µl 0.02 U
PCR water to a final volume of 25 µl
The amplification conditions of the reactions were:
Step 1– 1 cycle
95°C for 10 minutes (initial denaturation and enzyme activation)
Step 2 – 35 cycles
95°C for 45 seconds (denaturation)
60°C for 45 seconds (annealing step)
72°C for 45 seconds (primer extension)
Step 3 – 1 cycle
72°C for 10 minutes (final primer extension)
45
The concentrations of magnesium chloride tested for optimisation were 1.5 mM, 2 mM, 2.5 mM and 3 mM. Each set of primers was also tested at different temperatures. To achieve the optimal conditions for each set of primers, 3 touch- down programs were used with the annealing temperature of 70°C, 60°C and 50°C.
The touch-down program with annealing temperature 60°C was used as default program. Apart from temperature and magnesium chloride, the types of DNA polymerase enzymes present are also crucial. Both KapaTaq (Kapa Biosystems) and AmpliTaq Gold (Perkin Elmer Applied Biosystem) were used with the latter as the default enzyme.
2.1.2.6 Gel Electrophoresis
Following PCR optimisation, a 2% agarose gel was prepared for gel electrophoresis to ensure the PCR product was of the correct size. The gels were made by adding agarose powder (Sigma) to 1x TAE buffer and heated in the microwave for two minutes prior to pouring. Electrophoresis was conducted in a gel tank (Biorad) at
110 volts for 40 minutes. Loading dye (Blue/Orange 6x, Promega) was used to track the electrophoresis. To compare the size of the bands, pGEM® DNA Markers
(Promega) (51 bp-2645 bp) was used. After 40 minutes, the gels were stained with ethidium bromide (0.5 µg/ml) for 15-20 minutes. Using a Gel-Documentation 1000 system (Biorad), the gels were photographed under UV illumination (312 nm) and images were captured using Quantity One 1.0.1 software (Biorad).
2.1.2.7 PCR Purification
If satisfactory results were obtained based on the electrophoresis gel image, the
PCR products were prepared for sequencing by PCR purification process. The two
25 µl PCR reactions for each set of primers were combined in one tube for a total
46 volume of 50 µl. The PCR purification was performed using the UltraClean® PCR
Clean-Up Kit (Mo Bio Laboratories Inc.) according to the manufacturer’s protocol
(Appendix A.2). Gel electrophoresis was performed to ensure the quality of the purified products. In addition, the DNA concentration of the purified products was measured using a spectrophotometer (NanoDrop 1000).
2.1.2.8 DNA Sequencing
Cycle sequencing was done after obtaining ample amount of DNA from the PCR amplification. A 10 µl reaction was performed in a thin-walled 0.5 ml PCR tube
(Eppendorf) (Table 2.2) using the BigDye Terminator Kit following the manufacturer’s protocol (PE Applied Biosystems). Each reaction was sequenced for one direction only, preferably the forward direction which was more convenient for examining the sequencing results. DNA variants found in the amplicons were confirmed by sequencing the PCR products in the opposite direction.
Table 2.2 Sequencing reagents
Component Volume
BigDye Terminator 2 µl (PE Applied Biosystem)
DNA 3 - 5 µl
Glycogen 1 µl
Primer 1 µl
Purified water to 10 µl total volume
Sequencing was performed using a Palm Cycler (Corbett Research). Conditions for the sequencing were as follows:
47
Step 1 – 1 cycle
95°C for 5 seconds (initial denaturation and enzyme activation)
Step 2 – 24 cycles
96°C for 30 seconds (denaturation)
50°C for 15 seconds (annealing step)
60°C for 4 seconds (primer extension)
Step 3 – 1 cycle
40°C for 5 seconds (final primer extension)
Cycle sequencing products were precipitated by alcohol reagents as described in
Appendix A.3. The reactions were air-dried and were sent to the Institute of Medical and Veterinary Science (IMVS, Adelaide, Australia) for electrophoresis on an ABI
377 (Applied Biosystems) DNA sequencing machine.
The sequencing results received from IMVS were analysed using SEQUENCHER
4.7 software (Gene Codes Corporation) to identify any sequence variants.
Sequence variants could either be single nucleotide polymorphisms (SNPs), which are more common, or insertion/deletions (in/dels).
If a sequence variant was discovered in one or all the sires, the variant was validated by sequencing the PCR product of the sires in the opposite direction.
Should both directions of the sequencing results demonstrate the same variant, it is most certain that the sequence variant is real. However, PCR products from the grandparents were also sequenced to confirm Mendelian inheritance. A total of 14
SNPs out of 58 DNA variants discovered were selected for genotyping.
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2.2 Genotyping
2.2.1 High Resolution Melt
The high resolution melt (HRM) technique is similar to PCR amplification but with the presence of a specialised double stranded DNA binding dye. This dye is added with the other reagents for PCR and is highly fluorescent when bound to the double stranded DNA. If the dye is incorporated into the double stranded PCR products and the PCR products are slowly heated, then the fluorescence of the dye decreases as the double stranded DNA PCR product denatures or “melts”. The sensitivity of HRM is very high such that a single base change between identical nucleotide sequences of the PCR products can be detected as the fluorescence melt curves will differ.
Fourteen SNPs were selected for HRM based on their location, disequilibrium relative to other SNPs and their potential to be functional variant. DNA samples of a total of 366 progeny with RFI phenotypes were genotyped using HRM. The DNA from the sire progeny (sire 361, 368 and 398 families) was divided into four 96 well plates. DNA samples of the progeny were stored in four 96-well plates in the -20°C freezer and thawed and centrifuged prior to usage. The DNA concentration was verified using the NanoDrop 1000 spectrophotometer (Thermo Scientific).
The binding dye used was the SsoFast™ EvaGreen® Supermix (Bio-Rad
Laboratories), which was light sensitive and need to be handled with care. Primers for HRM were designed to cover a short region of the targeted DNA (100-150 bp) with the SNP located in the middle. The forward and reverse primers were designed using the Primer3 and Oligo7 Software (Molecular Biology Insights Inc.). Primers were synthesized by GeneWorks (Australia) and the PCR conditions were optimised as above. High Resolution Melt (HRM) was performed using Roto-Gene 6000
49
(Corbett Research) in a 100-well ring using a master mix and the primers (Table
2.3). Prior to HRM, the DNA samples were transferred from a 96-well plate to the
100-well ring by a CAS 1200 PCR Setup Robot (Corbett Research).
Table 2.3 High resolution melt reagents concentration
COMPONENT Volume Final concentration KapaTaq Buffer A 2.5 µl 1x dNTP (1.25 mM each of dATP, dCTP, dGTP, and dTTP) 2.5 µl 0.5 mM each
Forward primer (2.5 µM) 1 µl 0.1 µM
Reverse primer (2.5 µM) 1 µl 0.1 µM
Kapa Taq Polymerase (0.5 U) 1 µl 0.02 U
PCR water to a final volume of 20 µl
The CAS 1200 PCR Setup Robot (Corbett Research) was programmed to aspirate
15 µl of the master mix solution. The master mix was then dispensed in each well in the 100-well ring. Usage of robotic system reduced the amount of tips and increased accuracy. Five µl of DNA template from the 96-well plate was then aspirated and dispensed in the allocated position in the 100-well disc. With the aid of the robotic system, the risk of cross contamination between DNA samples could be minimized and was time-saving.
Once all the reagents were in the wells, a Rotor-Disc Heat Sealing Film (Corbett
Research) was laid on the 100-well disc to secure the contents of the disc. The disc with the sealing film was fixed to a Rotor-Disc 100 Loading Block (Corbett
Research) before inserting the block into the Rotor-Disc Heat Sealer (Corbett
Research).
50
Following sealing, the 100-well disc was inserted into a Rotor-Disc 100 Rotor
(Corbett Research) and secured with a Rotor-Disc 100 Locking Ring (Corbett
Research) before placing the disc in the Rotor-Gene 6000 (Corbett Research). The high resolution melting process was performed with a melting temperature ranging from 65°C to 95°C. During optimisation, the melting point for each sample was recorded. The cycles for each sample vary from 30 to 40 cycles, depending on the optimisation outcome.
Results obtained from HRM were first viewed using the Rotor-Gene 6000 Series
Software (Corbett Research). Vital information such as the completion of PCR amplification and the melting temperature were screened with this software.
Provided that satisfactory results were obtained, results were then transferred to the
ScreenClust Software in which all the samples were clustered according to genotype. A cluster plot was also generated for convenient viewing.
The results were double-checked manually to ensure there were no genotypes mis- assigned. This was done by checking the genotypes of the sire against each progeny and verifying the shape of curves reflected for the assigned genotype. The genotypes were recorded in a spread sheet for further analysis. DNA from animals of known genotypes based on sequencing was used on each plate to serve as positive controls.
2.2.2 Genotyping analysis
Genotyping results were analysed using Genstat 11th Edition Software (VSN
International). The model used was an unbalanced analysis of variance ANOVA with fixed effects. The fixed effects used were:
51
Cohort (sex and year interaction: 96H, 96S, 97H, 97S, 98H, 98S)
Breed (XJ = ¾ Jersey and ¼ Limousin; XL = ¾ Limousin and ¼ Jersey)
Sire (361, 368 or 398)
Two models were used for each SNP, one with the myostatin F94L genotype (CC,
CA or AA) included as fixed effect and the other without including the myostatin
F94L genotype.
The first model fitted cohort, breed, sire, myostatin F94L genotype and the SNP genotype of the candidate gene:
Yijkm = µ + αi + βj + γk + θl + (xijklm- x) + λm + εijklm
Where:
Yijkm is there response variable (phenotypic trait)
µ is the overall mean
th αi is the effect of i cohort
th βj is the effect of j breed
th γk is the effect of k sire
th θl is the effect the l myostatin F94L genotype
th λm is the effect of the m SNP genotype
εijklm is the residual effect
Interactions between each SNP genotype were examined by fitting the other SNP in the model and its interaction with the other SNP.
Yijklmn = µ + αi + βj + γk + θl + η (xijklm- x) + λm + δn + (λδ) mn
Where:
th δn is the effects on n SNP B
th th (λδ) mn is the interaction between m SNP A genotype and n SNP B
52
The analysis was performed using 27 feed intake and fat depot related traits of the
Davies mapping herd.
2.2.3 Pathway analysis
Potential interactions between the candidate genes were identified using the
Pathway Commons software (www.pathwaycommons.org) to determine if they share the same biochemical pathway, gene network or have common regulators.
The analysis was performed for all the candidate genes in pairwise combinations.
2.3 Mitochondrial experiments
2.3.1 Selection lines
Samples used for mitochondrial enzyme experiments were obtained from the Angus
Elite Progeny Testing progeny derived from the Trangie post-weaning RFI cattle selection lines (as detailed in Appendix A.1.2). A total of 208 Angus steers with a large-divergence in mid-parent residual feed intake estimated breeding values (RFI
EBVs) were selected. The steers were progeny of 26 sires with numbers of progeny ranging from 1 to 21. At the age of 13 to 16 months, the steers were sent to a feedlot. The steers were allocated into three pens based on mid-parent RFI EBV
(low RFI EBV = -0.85 to -0.52, n = 68; medium RFI EBV = -0.29 to 0.14, n = 72, high RFI EBV = 0.16 to 0.98, n = 68). Steers were fed with grain for 250 days, adjusted weekly for under or over feeding. Two sub-sets of animals were selected for this experiment. The first set of cattle were born in July 2003, weaned in March
2004 and slaughtered in Queensland in 2006. For the second sub-set, the animals were born in August 2004, weaned in March 2005 and slaughtered in New South
Wales in 2006. All the steers were slaughtered at the age of 21 to 24 months.
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2.3.2 Sample collection: Liver
Approximately four grams of liver was collected from the caudal lobe 30 minutes after the animal was killed. Samples were placed in 50 ml flat bottom containers and were snap frozen in liquid nitrogen. Samples were stored in a -80°C freezer for future use.
2.3.3 Mitochondrial preparation from frozen liver samples
The entire procedure for mitochondrial preparation was done at 4°C to avoid enzyme degradation. Liver samples were stored in -80°C and thawed at room temperature. Four grams of each sample were dissected using a sharp knife on a clean cutting board as soon as the samples were thawed sufficiently. Samples were chopped into fine pieces and transferred into a 50 ml Falcon tubes. Five ml of ice- cold lysis buffer solution (Appendix A.4) was added to the tube prior to a 15 minute incubation period. The samples were then homogenised with Ultra-Turrex S25-8G homogeniser (IKA Labortechnik) at 12,000 rpm for 30 seconds. Samples were homogenised twice for 30 seconds and placed on ice between each homogenisation. Right after homogenisation, samples were centrifuged using
Beckham J2-HS refrigerated centrifuge with JA-20 rotor at 2500 g for 10 minutes at
4°C. The supernatant produced by centrifugation was filtered through two layers of nylon cloth into a clean tube. The filtered supernatant was centrifuged once more at
14000 g for 30 minutes at 4°C to obtained brown coloured mitochondrial pellet. This was followed by washing the mitochondrial pellet twice in isolation buffer (Appendix
A.5) to remove any impurities. The resulting pellet was then resuspended in 0.4 ml isolation buffer and stored at -20°C for subsequent biochemical analysis.
54
2.3.4 Bradford Assay
The Bradford assay was performed to determine the protein concentration of the samples (Kruger, 1994). The Coomassie Blue Dye solution (Appendix A.6) was prepared in the lab prior to the experiment and was stored at 4°C. The protein standard was Bovine Serum Albumin (BSA) (0.5 mg/ml) (Sigma Laboratories).
For the standard curve, four 1.5 ml tubes were prepared with 5 µl, 10 µl, 15 µl, and
20 µl of 0.5 mg/ml BSA (Sigma Laboratories) with 95 µl, 90 µl, 85 µl and 80 µl of
0.15 M NaCl, respectively. For each of the tubes, 1000 µl of Coomassie Blue Dye was added. Two 1.5 ml tubes containing 100 µl of NaCl and 1000 µl of Coomassie
Blue Dye were included in the experiments as blank controls.
A Shimadzu UV-160 spectrophotometer (Shimadzu Corp.) was used to measure the protein concentration of the BSA in the control tubes at 595 nm. The experiment was then repeated by using the extracted mitochondria instead of the BSA in order to obtain the protein concentration based on the standard curves.
2.3.5 Oxidative phosphorylation enzyme complex assays
2.3.5.1 Complex I Activity
The assay for complex I was adapted from Kirby et al. (2007) with some modification. Complex I is a rotenone-sensitive NADH: ubiquinone oxidoreductase.
The oxidation of NADH in Complex I activity was measured by following the decrease in absorbance at 340 nm with 425 nm as the reference wavelength.
In a 1 ml cuvette, 305 µl of reagent mix (Appendix A.7) was added and water was used to make up the final volume of 1 ml. Cuvettes were equilibrated at 30°C for seven minutes before recording the absorbance change for one minute to ensure
55 the baseline was stable. A total of 40 µg of mitochondria was added and the NADH: ubiquinone oxidoreductase activity was measured for four minutes. Rotenone (2 µl of 2 µg/ml rotenone/ml) was then added and the activity was measured for an additional three minutes.
2.3.5.2 Complex III Activity
Complex III ubiquinol-cytochrome c reductase activity was measured by following the increase in absorbance due to the reduction of cytochrome c at 550 nm, with
580 nm as the reference wavelength (Kirby et al., 2007). The sample was added to
241 µl of reagent mix in a cuvette (Appendix A.8) and water was added to make up a final volume of 1 ml. Cuvettes were equilibrated at 30°C for seven minutes. The baseline was then recorded for one minute. To start the reaction, 20 µg of mitochondria was added together with cytochrome c and was measured for three minutes.
2.3.5.3 Complex IV Activity
The protocol for mitochondria Complex IV ferrocytochrome c: oxidoreductase activity was adapted from Kirby et al. (2007). A total of 100 µl buffer (Appendix A.9) and 15 µl n-dodecyl-β-D-maltoside were added to a 1 ml cuvette and made to a volume of 1 ml with water. Cuvettes were equilibrated at 30°C for seven minutes.
Reduced cytochrome c was added and the rate was recorded. The reaction started by adding 40 µg of mitochondrial extract and the decrease in absorbance at 550 nm for two minutes was recorded.
2.3.5.4 Protein carbonyl assay
The protein carbonyl assay was performed using a Protein Carbonyl Assay Kit
(Cayman Chemical Company) according to manufacturer’s protocol. This assay was
56 designed to monitor reactive oxygen species (ROS) activity by calculating protein carbonyl, which is by far the most general indicator commonly used.
A total of 100 µl (1-10 µg) of mitochondria sample was prepared in separate tubes and the reaction conducted by following the manufacturer’s protocol (Appendix
A.10). A final volume of 220 µl of mixture was transferred to a 96-well plate. Control tubes were also included in this experiment. A Benchmark Plus Microplate reader
(Biorad) was used to measure the absorbance at 360-385 nm.
The calculation of protein carbonyl was as the following equation:
Protein Carbonyl (nmol/ml) = [(CA)/ (*0.011 µM-1)](500 µl/200 µl)
*The actual extinction coefficient for dinitrophenylhydrazine at 370 nm is 22,000
M-1cm-1(0.022 µM-1cm-1). This value was adjusted for the path length of the solution in the well.
2.3.5.5 Analysis for Biochemical Assays
Results for the biochemical assays were analysed using the T-test with two-tail distribution and two sample unequal variance to determine the statistical differences between the samples. Additionally, a regression analysis was performed to determine the strength of the relationship between the enzyme assays with residual feed intake related traits. Less phenotypic data were available for the Trangie progeny as fewer traits were measured. From the available data, the traits selected for the regression analysis were mid-parent EBV for residual feed intake (RFI), hot standard carcass weight (HSCW), ribfat (RibFt), intramuscular fat (IMF), eye muscle area (EMA), seam fat (SF) and ossification (OSS).
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Chapter 3
Candidate Genes for RFI: Identification & DNA Variants
58
3.1 Introduction
Residual feed intake (RFI) is a measure of feed efficiency which is an economically important trait in livestock and depends on the ability of the animal to consume less feed than expected based on their weight gained and weight maintained during feed testing. This may occur by improving the utilisation of nutrients and energy from the feed for maintenance and growth. Recent work has implicated mitochondrial function as being important in residual feed intake in cattle (Naik, 2007). It also is well known that genetics and diet have profound influence on mitochondrial function
(Bottje et al., 2002).
Mitochondria are the site of energy production in the cell and produce the majority of the cellular ATP (Kolath et al., 2006). The electron transport chain in the mitochondria is a sequence of electron carrier molecules that shuttle electrons during the redox reactions to release energy used to make ATP. Most components of the chain are proteins, which exist in multi-protein complexes numbered I through
V. During electron transport along the chain, electron carriers alternate between reduced and oxidized states as they accept and donate electrons. However, mitochondrial inefficiency may occur as a result of electron leakage from the chain.
As a consequence, 2% to 4% of oxygen consumed by mitochondria may be incompletely reduced to reactive oxygen species (ROS) rather than being completely reduced to water due to the univalent reduction of oxygen by the electrons (Boveris and Chance, 1973).
These reactive oxygen species produced in the mitochondria are very destructive.
Reactive oxygen species can cause oxidative damages to nucleic acids, lipids and proteins, as well as damaging organelles such as the mitochondria itself (Nelson and Cox, 2008). Thus, the reactive oxygen species themselves can cause the
59 mitochondria to function less efficiently and produce even more ROS.
Studies in poultry have shown that mitochondria obtained from chickens of low feed efficiency (high RFI) exhibit greater uncoupling of the electron transport chain
(Bottje et al., 2006). It was also observed that there is a higher level of reactive oxygen species production in the mitochondria of the low feed efficiency chickens.
Similar studies in chickens revealed that there is a greater electron leakage in mitochondria from low feed efficiency chickens in comparison to mitochondria from high feed efficiency chickens (Bottje et al., 2002). In addition, mitochondrial proteomics studies by Naik (2007) suggest an association between RFI and mitochondria from cattle liver and muscle (Naik, 2007). Findings from these studies provide evidence of a link between RFI and mitochondrial function.
Hence, the objectives of this study were to identify genes involved in mitochondrial function, energy production and the regulation of reactive oxygen species, which may affect feed intake in cattle. Several QTL affecting residual feed intake have been mapped in Jersey x Limousin backcross progeny (Fenton, 2004; Naik, 2007).
Herein candidate genes within these QTL that are involved in mitochondrial function or the reactive oxygen species regulation were selected and screened for DNA variants that might be used as DNA markers for selecting highly efficient animals.
3.2 Materials and Methods
3.2.1 DNA samples
DNA samples used were from the Davies Gene Mapping Herd cattle. Three F1 sires
(361, 368 and 398) were sequenced to detect variants. Confirmation of the variants was performed by sequencing the parents of the sires (Table 3.1).
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Table 3.1 F1 mapping sires and their parent identification numbers
F1 Sire Parents 361 77, 65 368 75, 266 398 78, 209
3.2.2 Primer design
The sequence of the candidate genes were obtained from the bovine genome databases: Ensemble (www.ensemble.org) and Biomanager (www.angis.org).
Primers were designed for the exons, promoters and untranslated regions of the genes using Prime3 software and tested against OLIGO 4.04 software to avoid hairpin structures and primer dimer formation and to minimize the GC content. As detailed in Chapter 2, the primers were then optimised for PCR.
A total of 77 pairs of primers were designed for 10 candidate genes (Appendix B).
The forward and reverse primers were designed to flank the exons of the genes.
The length from the first base pair of the forward primer to the last base pair of the reverse primer was designed to be at around 700 – 800 base pairs. In some cases where the exons and the distance between adjacent exons were short, only one set of primers were used to cover the region. For example, the gene NDUFB5 (ND5) has eight exons. Exons 3, 4, 5 and 6 plus the surrounding region consisted of 480 base pairs, due to the short length of the exons as well as the close location of each exon. There were six similar cases (Table 3.2).
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Table 3.2 Primer sets for neighbouring exons
Candidate genes Combined exons Primers HADHB 2 and 3 HADex2.3 F/R
HADHB 11 and 12 HADex11.12 F/R AK1 2 and 3 AK1ex2.3 F/R
NDUFB5 3,4,5 and 6 ND5ex3.4.5.6 F/R SOD2 5,6 and 7 SOD2ex5.6.7 F/R
SOD2 8 and 9 SOD2ex8.9 F/R GHRL 2 and 3 GHRex2.3 F/R
Longer exons were amplified as two products and were covered by two different sets of overlapping primers (Weckx et al., 2005; Wu and Monroe, 2006). In total, three exons of 3 different genes were sequenced as two products (Table 3.3) :
Table 3.3 Primer sets for exons >500bp
Candidate genes Exon Base pair Primers CAT 13 699 CATex13.1 F/R CATex13.2 F/R HADHB 16 773 HADex16.1F/R HADex16.2F/R NDUFB5 8 671 ND5ex8.1F/R ND5ex8.2F/R
3.2.3 Polymerase Chain Reaction (PCR)
Once the appropriate conditions were obtained for each pair of primers, the genomic DNA from the three Davies F1 sires were amplified. This was followed by gel electrophoresis for product size confirmation as described in Chapter 2. The amplified PCR products were then purified for sequencing using PCR purification kit to remove excessive primers and dNTPs.
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3.2.4 Sequencing
After obtaining the purified PCR products, the DNA was sequenced using BigDye terminator cycle sequencing kit. The sequencing reactions were conducted using
BigDye terminator ready reaction mix, 25 pmol of primer and 30-100 ng purified
PCR products.
3.3 Results
3.3.1 Candidate Genes
Previous studies by Fenton (2004) using the Davies gene mapping herd identified five QTLs with significant effects on RFI in cattle on:
i. Chromosome 1 (BTA1), 95 cM ii. Chromosome 6 (BTA6), 61 cM iii. Chromosome 8 (BTA8), 67 cM iv. Chromosome 11 (BTA11), 74 cM v. Chromosome 20 (BTA20), 49 cM.
The candidate genes selected for this study was based on the RFI QTL discovered and their effects of mitochondria function, energy metabolism or reactive oxygen species. Ten candidate genes were selected, seven of which were in the known RFI
QTLs (Table 3.4). The remaining three were chosen for to their pivotal involvement in mitochondrial function and the results from the previous proteomics studies on the same animals (namely, ghrelin, superoxide dismutase 2 and catalase (Naik,
2007).
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Table 3.4 Selected candidate genes and their function
Candidate genes BTA Function position NADH Dehydrogenase Beta BTA 1 Involved in electron transport chain Subcomplex5, 16kDa (NDUFB5)
Superoxide dismutase 1, soluble BTA 1 Involved in binding copper and zinc (SOD1) ions and destroying free superoxide radicals
Aldolase B (ALDOB) BTA 8 Involved in fructose metabolism
Superoxide dismutase 2 (SOD2) BTA 9 Involved in destroying radicals
Adenylate kinase (AK1) BTA 11 Involved in maintaining cellular energetic economy
NADH Dehydrogenase Alpha BTA 11 Involved in electron transport chain Subcomplex 8, 19kDa (NDUFA8)
Hydroxyacyl-co- A BTA 11 Involved in synthesising mitochondrial Dehydrogenase β Subunit trifunctional protein (HADHB)
Succinyl Co-A synthetase BTA 11 Involved in generating high energy (SUCLG1) phosphate
Catalase (CAT) BTA 15 Involved in reactive oxygen species (ROS) metabolic pathway
Ghrelin (GHRL) BTA 22 Involved in growth regulation, has an appetite-stimulating effect
3.3.1.1 Catalase
Oxidative stress is a consequence of an imbalance between the production of ROS and a biological system’s ability to repair the resulting damage. Hence, cells are protected against oxidative stress by an interacting network of enzymes including catalase (Morόn and Cortázar, 2012). Catalases are enzymes defining peroxisomes, where they remove hydrogen peroxide generated by peroxisomal oxidation (Scheffler, 2008). Catalase is believed to have crucial effects in the ROS metabolic pathway by converting the superoxides to water and oxygen (Figure 3.1).
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Although the catalase (CAT) gene was not located in a highly significant QTL, it was selected based on function and previous mitochondrial proteomics studies (Naik,
2007). The catalase gene is located on bovine chromosome 15 (64,689,389 -
64,724,651 Mb).
According to Naik (2007) in his study on mitochondrial protein expression, catalase was the only protein that was differentially expressed in both muscle and liver tissues between high and low residual feed intake cattle. Despite the tissue differences, catalase was consistently lower in low RFI animals in comparison to the high RFI animals (Naik, 2007). Under high oxidative stress, the level of catalase is increased (Suttorp et al., 1986). Thus, the high RFI animals are likely to be under oxidative stress as catalase is elevated. This leads to the hypothesis that mitochondrial energy production in the low RFI animals is very effective and oxidative stress is limited.
Figure 3.1 Mechanisms of oxidative cellular damage. Free radicals are reduced into water with the cooperation of the three main antioxidant enzymes: SOD, catalase, and GSHPx. The generation of hydroxyl radicals from hydroperoxide produces the development of oxidative cell injury: DNA damage; carboxylation of proteins; and lipid peroxidation, including lipids of mitochondrial membranes. By these pathways, oxidative damage leads to cellular death. (García-Fernández et al, 2008)
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3.3.1.2 Aldolase B
The aldolase B gene (95,972,320 – 95,987,722 Mb) is located in an RFI QTL on bovine chromosome 8 (Fenton, 2004). Aldolase B is an isoenzyme of fructose 1,6- bisphosphate aldolase (aldolase A), which cleaves fructose 1-phosphate to form D- glyceraldehyde and dihydroxyacetone phosphate (DHAP) (Figure 3.2). D- glyceraldehyde is then phosphorylated in an ATP-dependent reaction to give the glycolytic intermediate glyceraldehyde-3-phosphate. These two products are utilised in glycolysis pathway. Aldolase B, thus, controls fructose metabolism and glycolysis
(Cox, 1988; Lolis et al., 1990).
Aldolase B is only expressed in tissues where these pathways are active; it is expressed at high levels in the liver but also can be found in the kidney and small intestine (Burgess and Penhoet, 1985; Martin et al., 2003). The aldolase B activity was found to fluctuate according to dietary status in rat, in which the enzyme activity rapidly declines during fasting. When the rats are re-fed with carbohydrate rich diet, the enzyme activity is restored (Munnich et al., 1985). Lower amounts of aldolase B were discovered in low residual feed intake animals in a proteomics study using cattle (Naik, 2007). This is expected because in low residual feed intake or fasting animals, fructose will not be converted by aldolase B to glyceraldehyde and dihydroxyacetone phosphate and hence, the aldolase B level would be minimal
(Marshall et al., 1991; Naik, 2007).
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Figure 3.2 Fructose utilisation in the liver showing its interrelationship with glucose and fatty acid metabolism (Mayes, 1993)
3.3.1.3 Adenylate Kinase 1
Adenylate kinase 1 (102,121,749 – 102,126,949) is one of the most prominent members of the adenylate kinase family and is highly expressed in well- differentiated tissues with a high energy demand like brain, heart and skeletal muscle (Khoo and Russel, 1972; Bruin et al., 2004). The function of adenylate kinase 1 is to maintain the cellular energy and enable skeletal muscle to perform at the lowest metabolic cost (Figure 3.3). Low expression of adenylate kinase 1 causes a high ATP turnover rate and larger amounts of ATP are consumed per muscle contraction, thereby increasing maintenance energy requirements (Janssen et al., 2000). In skeletal muscle of AK1-deficient mice, the AMPK phosphorylation is lower. Activation of AMPK is a signal, sensitive to energy demands, which is involved in the regulation of glucose uptake and fatty acid oxidation in contracting
67 muscle (Hancock et al., 2006; Panayiotaou et al., 2014). An excess AMP signalling induced by leptin and fructose convey to the brain false “low energy” signals forcing it to increase food consumption (Dzeja and Terzic, 2009; Zhang et al., 2014).
Previous research implicated the AMPK pathway in residual feed intake in cattle
(Lee, 2005). The gene for this protein is located on BTA 11 within the RFI QTL
(Naik, 2007).
Figure 3.3 Regulation of energy homeostasis by the AMPK system (Hardie, 2007)
3.3.1.4 Superoxide Dismutase 1
The SOD1 gene (3,113,949 – 3,122,613) is located on human chromosome 21 and contains five exons (Bannister et al., 1991). In cattle, SOD1 is located on BTA 1 with five exons as well (Naik, 2007). Confirmation of the gene was made by comparing both genes using ClustalW (www.ebi.ac.uk) and a high degree of homology between the genes in the two species was found. The superoxide dismutase is present in the cytosol, nucleus and the intermembrane space of mitochondria
(Juarez et al., 2008).
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SOD1 protein is a metalloenzyme that contains 153 amino acids with copper (Cu) and zinc (Zn) binding sites (Levanon et al., 1985). Copper acts as the catalyst for superoxide disproportionation while zinc contributes in proper protein folding (Leitch et al., 2009). The high stability of SOD1 is conferred by the quaternary structure of the protein as well as the binding site of Cu and Zn, two metal irons with catalytic roles positioned in the active site channel (Trumbull and Beckman, 2009; Perry et al., 2010; Franco et al., 2013) (Figure 3.4). SOD1 is believed to be the main defence mechanism against superoxide radicals (Leitch et al., 2009) as SOD1 converts superoxide into hydrogen peroxide and molecular oxygen (Juarez et al., 2008). The bovine SOD1 gene is located in the RFI QTL on BTA1 (Fenton, 2004).
Figure 3.4 The disproportionation of superoxide is a two-step oxidation- reduction reaction that involves the cycling of the copper atom in SOD1 from Cu2+ to Cu+ and back to Cu+2 (Franco et al., 2013)
3.3.1.5 Succinyl CoA Synthetase Alpha Subunit
Succinyl-CoA synthetase is an enzyme located in the mitochondrial matrix, composed of an α-subunit encoded by SUCLG1 and a β-subunit encoded by either
SUCLA2 or SUCLG2 (Rouzier et al., 2010). The SUCLG1 subunit is ubiquitously expressed in heart, liver, kidney and brain (Rivera et al., 2010). It is involved in
69 mitochondrial DNA replication as well as being the only enzyme that generates high-energy phosphate at the substrate level during the Krebs cycle (Lowenstein,
1969). The Krebs cycle depends on succinyl-CoA synthetase which converts succinyl-CoA to succinate and produces guanosine tri-phosphate (GTP) (Figure
3.5). Altered expression of succinyl-CoA synthetase affects the overall production of reducing agents, like NADH and FADH2, which are involved in the electron transfer during oxidative phosphorylation. The SUCLG1 gene is located on BTA 11
(52,305,755 – 52,340,599) within the RFI QTL (Fenton, 2004).
Figure 3.5 Krebs cycle or TCA cycle and methylmalonate metabolism (Ostergaard et al., 2007)
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3.3.1.6 Superoxide Dismutase 2
Superoxide dismutase 2 (97,399,159 – 97,404,522) is one of the three members in the superoxide dismutase family. The SOD2 gene is composed of five exons and four introns in species such as human, rat, mouse and bovine (Zelko et al., 2002).
Similar to SOD1, this gene encodes the manganase-SOD2 enzyme which transforms products of the mitochondrial electron transport chain into hydrogen peroxide and diatomic oxygen. Higher concentrations of SOD2 increase protection against oxidative stress (Epperly et al., 2002). Previous studies indicated that polymorphisms in this gene might affect functions of the mitochondria. A non- synonymous SNP that changes valine to alanine is believed to affect the cellular location and mitochondrial transport of manganese-SOD2 (Rosenblum et al., 1996).
Studies have also shown that animals with reduced level of oxidative stress such as animals fed a calorie restriction diet have extended life spans, while animals with high levels of oxidative stress such as mice lacking SOD have shortened life spans
(Qiu et al., 2010). SOD2 was chosen as a candidate gene based on the results of the previous proteomics study (Naik, 2007) and its function as a major anti-oxidant enzyme.
3.3.1.7 Ghrelin
Ghrelin is a peptide hormone produced in the lining of the stomach with 28 amino acids in humans and rats, and 27 amino acids in bovine and ovine (Nelson and Cox,
2008; Delporte, 2013). Ghrelin was originally recognised as the stimulus for growth hormone and subsequently shown to be a powerful appetite stimulant that works on a shorter time scale than leptin and insulin (Nelson and Cox, 2008). Ghrelin is known to be involved in meal initiation as it is a fast acting hormone (Klok et al.,
2007). Studies in rats have shown that ghrelin helps in increasing food intake,
71 weight gain and adiposity (Wren et al., 2001). Extensive studies of ghrelin in mice suggest that it has a role in choosing between fat or carbohydrates to be used for energy balance maintenance (Wortley et al., 2004). In cattle, ghrelin is synthesised predominantly in the abomasal and ruminal tissue of the stomach (Hayashida et al.,
2001). Studies by Sherman et al. (2008) indicated an A/G SNP (G375A) in intron 3 of ghrelin (GHRL) has a minor association with feed efficiency trait (P<0.10) in beef cattle. Ghrelin which is located on bovine chromosome 22 (54,945,535 –
54,950,005) was selected as one of the candidate genes due to the importance of its previous association with feed efficiency and known role in feed intake.
3.3.1.8 NADH Dehydrogenase (Ubiquinone I) Beta Subcomplex, 5, 16kDa
NADH Dehydrogenase (Ubiquinone I) Beta Subcomplex, 5, 16kDa or NDUFB5 gene is located on the bovine chromosome 1 (89,657,067 – 89,671,591) within a
RFI QTL (Fenton, 2004). Similar to NDUFA8, it is a subunit of the complex I protein which is involved in the electron transport chain (Millour et al., 2006). The immediate electron acceptor for the enzyme is believed to be ubiquinone. In mammals, complex I is composed of 45 different subunits. The NDUFB5 gene has been associated with diseases such as Huntington’s disease and Parkinson’s disease
(Keeney et al., 2006). A study by Sparks et al. (2005) hypothesized that high fat diet may affect expression of genes involved in mitochondrial function and biogenesis. In the experiment, 10 insulin sensitive male rats were fed with isoenergetic high fat diet for three days and an oligonucleotide microarray analysis was performed. Results revealed six genes involved in oxidative phosphorylation decreased and this includes NDUFB5 (Sparks et al., 2005).
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3.3.1.9 NADH Dehydrogenase (Ubiquinone) I Alpha Subcomplex, 8, 19kDa
NADH Dehydrogenase (Ubiquinone) I Alpha Subcomplex, 8, 19kDa, also known as
NDUFA8, is a protein coding gene. It is one of the subunit of complex I protein and is the largest and most complicated (Distelmaier et al., 2009). The protein contains
NADH dehydrogenase and oxireductase activity which is crucial in transferring electrons to the respiratory chain. During electron transfer, ubiquinone is believed to be the acceptor of the electron. Mutations in the genes encoding structural subunits of complex I can result in mitochondrial disorders involving neurodegeneration
(Distelmaier et al., 2009, Szklarczyk et al., 2011). It was observed that human cDNA sequence show 86.2% identity with the bovine sequence. A study using multiple tissue blots revealed that the highest NDUFA8 mRNA expression is in tissues with high metabolic energy demands such as the human heart, skeletal muscle and fetal heart (Triepels et al., 1998). The NDUFA8 gene is located on bovine chromosome
11 (93,011,815 – 93,029,730) in a RFI QTL (Fenton, 2004).
3.3.1.10 Hydroxyacyl-CoA Dehydrogenase-β Subunit
The mitochondrial trifunctional protein is made of four hydroacyl-CoA dehydrogenase-α (HADHA) and four hydroacyl-CoA dehydrogenase-β (HADHB) subunits (Das et al., 2006). The three activities in mitochondrial trifunctional protein are the enoyl-CoA hydratase, 3-hydroxyacyl-CoA dehydrogenase and 3-ketoacyl-
CoA thiolase, with the latter encoded by the HADHB gene (Das et al., 2006, Park et al., 2009). The long-chain fatty acids cannot be oxidized if there are defects in this protein complex. In particular, the lack of energy is important for highly energetic organs such as the heart and skeletal muscle (Spiekerkoetter, 2010). In humans, the deficiency of mitochondrial trifunctional protein is an autosomal recessive disorder and can cause diseases such as severe infantile cardiomyopathy to mild
73 chronic progressive polyneuropathy (Park et al., 2009). This gene has 16 exons and is located on bovine chromosome 11 (75,320,761 – 75,351,105) in a RFI QTL
(Fenton, 2004).
3.3.2 Sequencing variants
The 10 selected candidate genes were amplified by PCR to produce a sufficient amount of DNA for sequencing. DNA sequencing was performed to detect DNA variants in the Davies F1 sires. DNA variants are not common in the coding regions of genes. Thus, in order to ensure that DNA markers were found for each gene, the intronic sequences flanking the exons and the 5’ and 3’ regions flanking the genes were also partially sequenced.
In total, 54347 bases were sequenced in 10 genes. A total of 58 DNA variants were found across the 10 genes including six insertion/deletions (in/del) in introns and 52 single nucleotide polymorphisms (SNPs). Of the 52 SNPs, 34 were in introns, nine were in exons, and nine were in the untranslated regions (Table 3.5).
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Table 3.5 DNA variants identified in the candidate genes
SNP Variants Region Sequence context
AK1ex1 AG Intron 1 ggggtgtgctttggggagacatgc[a/g]gggacggtttggaggtagctctgg AK1ex5 11bp in/del Intron 4 gaagtgtgagcaggggagagg[-]ggtttgagcaagcttccgcaggctg ALDex1 5bp in/del Intron 1 ggagagggtaaaagttagaaaaaaaaaaaaaa[aaaaa]caaccaacaagcca ALDex3 AG Exon 3 tcctcttcactgtggacagctcc[a/g]tcagccagagcatcgggggtgatc ALDex6 CT Intron 6 agctgttacatgtgaagtattcta[c/t]ggttagaatcattatgtagtgttg ALDex8 GA Exon 8 tggcaaggctgaaaacaagaag[g/a]caacccaggaggcctttatgaagcgg CATex8 TC Exon 8 gtttggctcacggcgactatcc[t/c]cttattccagttggtaaattggtct CATex10 CT Intron10 ttcagtcatgtccgactctgtg[c/t]gaccccatagacggcagcccaccagg CATex11 1bp in/del Intron11 acttgagttaagcaaacttaaaaaaaaaa[a]tgagtatgaatccccaatcaa CATex12 TC Intron11 tctattttacccatggggtata[t/c]gctaactgtaaattcaggcagtgtt CATex13 1bp in/del Intron 13 atatgtaacttgaaatgtctcaaga[t]ttcttaatctgaatatcatgttat CATex13 GA 3’UTR ctggccctgcaccacgttgcc[g/a]tccgcttgtgaagcagagcatggt CATex13 AG 3’UTR tgtgggtgaatgaaggttaa[a/g]gcttaacaatcatttaaaagaaacatg HADex1 AG 5’UTR gagacctaaggtgagaaggg[a/g]cgtgcgctcggctaccgcctgcccttt HADex2 TG Intron 1 cacaggtccttatgagaattaatgat[t/g]agattagtggtttgcatg HADex2 AG Intron 1 tagtaacccatgggtgagatatcagag[a/g]tcatagcctacagtttaaact HADex2 TC Exon 2 tctttttcagatttcagaatga[t/c]ttccctcttgacttacatttaaaaaa HADex2 1 bp in/del Intron 2 gtaagtttcatttgttctttgtttt[t/-]aagattagacttgaatgtgtaag HADex4 AT Intron 3 atattatcactaggtataatctctat[a/t]tttgccttaaattttatttgg
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SNP Variants Region Sequence context HADex4 GA Exon 4 aaacccaacataaggaatattgtggtggt[g/a]gatggtgttcgcactccat HADex4 TC Intron 4 taagtatgaaatgattctgttacttgtta[t/c]tcctttttttcttttttct HADex5 GA Intron 4 atagcaaagtcacactgttacttt[g/a]gaatgatgttaagtgcttttatat HADex5 GA Intron 5 aaaacacttagtagattttataa[g/a]attcatatagtcttcattttagccg HADex6 AG Intron 5 tttatagattatattccaaattattg[a/g]tgattctttctatttaacttct HADex7 TA Intron 6 taccatacagtgaagtactgc[t/a]cacagcattgctcacagtgaagtactc HADex7 TC Intron 7 catttacatttttttatttttga[t/c]ggtagttctccacctttttcttgcc HADex8 AG Intron 7 ccagaggtctgttagtttatgc[a/g]aaagatgctcatgtgcatatgacc HADex8 GA Exon 8 ggccaatgtgacgtggtcgtggc[g/a]ggtggtgtagagttgatggtct HADex10 AT Intron 9 atttctctgtagaagatatataa[a/t]gcttttgagcttagagttaaaaat HADex14 AT Intron 13 aagttagacaaactcatgcataa[a/t]agaaagccttgagggattttac HADex14 CA Intron 14 ttctatccctggtgagggaatt[c/a]agatcctgtatgccacacattgtgg HADex14 CT Intron 14 atcctgtatgccacacattgtgggac[c/t]cccccccccccaccaaaacct HADex15 AG Intron 15 gagggctggggagtacaacttc[a/g]gtcctgagagtactttgaatgaaa HADex16 CT 3’ UTR caatactttgcaattaagcctttc[c/t]ggtgttctgagctttccaagaat ND8ex1 AG Intron 1 gcaggaggtgagactcgtccgga[a/g]aaggcaagcggggcacagggctgca ND5 5’ CT 5’UTR acgccttcagagcggctgagactctt[c/t]tgcagaaaacagccaaactgg ND5ex8.2 AG 3’UTR aatggaagttagagaaacct[a/g]tgttgaatatcaggaaatatgcaattt SUCex3 CT Intron 2 aaattccatggacagaggagcctggtg[c/t]gctatagtccatggggttgca SUCex3 TC Intron 2 gagttggacacaacttagtgac[t/c]gactgaacaacaacaaacgacagat SUCex3 CT Intron 2 ttaacatatttgtttttcttttc[c/t]aagttgctgaagattgtctttaggc SUCex3 AC Exon 3 aggaaccactccagggaaaggagg[a/c]aagacgcacctgggcttaccagt
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SNP Variants Region Sequence context SUCex3 GA Exon 3 accagtctttaacactgtgaa[g/a]gaggtaattaatggtacctgagtttg SUCex4 GA Exon 4 tcaagcacaaactgcgacagg[g/a]aaagaccaggctggtcgggccaaac SUCex4 AG Intron 4 gtgagtgtacaaaacgaaac[a/g]agcccgattttagcctctgaagcctaa SUCex6 1bp in/del Intron 6 aagctgttatgatcttgagttta[t]ttgcttatacatgaagctggctgt SUCex7 TC Intron 6 cataggcaaaataagaaacc[t/c]gttgaaacaatattttctgacttaa SUCex7 TC Intron 6 aaacctgttgaaacaatattt[t/c]ctgacttaattttcagagggtgttga SUCex7 TG Intron 7 tggccactcagggaatgtaggactct[t/g]tctgggttgattctcctcccc SUCex7 CG Intron 7 ccatttcccttgatgcctgttctt[c/g]gcttctctaattaatcctccct SUCex7 TG Intron 7 tgcctgttcttcgcttctctaat[t/g]aatcctccctccaggcatctcctt SUCex8 GA Intron 8 gagcagtggcctgaatgctcc[g/a]aggttatgggccttaaagtggagg SOD1ex3 AC Intron 2 cacacagtagttag[a/c]atttgtctctactccctgccctctgttgctgtt SOD1ex4 GA Intron 3 cctaatccctctgctttttc[g/a]tgttaggcatgttggagacctgggca SOD2ex3 AG Intron 3 gcatctttctcatagcag[a/g]gggtacgacagagctgtaactttcagagg SOD2ex3 CG Intron 3 ggagaaccccaaggttggtttaaa[c/g]tggggcaaccttggcttcatttt SOD2 3’ CA 3’ UTR ggcaacgtttgaaaacgttaagtg[c/a]tttgtatgatttagccttttga SOD2 3’ CT 3’UTR cgttaagtgctttgtatgatttagc[c/t]ttttgattgaacattttc SOD2 3’ GT 3’UTR gccttttgattgaacattttcttc[g/t]gagagctaaactagctaggaggt
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Table 3.6 Summary of DNA variants found in candidate genes
Candidate Gene In/del UTR Intron Exon Total AK1 1 1 2 ALDOB 1 1 2 4 CAT 2 2 2 1 7 HADHB 1 2 15 3 21 GHRL 0 NDUFA8 1 1 NDUFB5 2 2 SUCLG1 1 10 3 14 SOD1 2 2 SOD2 3 2 5
Table 3.7 Synonymous and non-synonymous SNPs
Exonic SNPs Potential functional DNA variants ALDex3 c.208A>G ALDex8 c.967G>A CATex8 c.921T>C HADex2 c.5T>C HADex4 c.174G>A HADex8 c.489G>A SUCex3.1 c.276A>C SUCex3.2 c.315G>A SUCex4 c.488G>A
The AK1 gene had two DNA variants (Table 3.6). One SNP was found in intron 1,
86 base pair from exon 1. In intron 4, an 11 base pair in/del in intron 4, 140 bp from exon 5, was discovered. The low number of DNA variants in this gene might be due to the small size of the gene and the number of base pairs sequenced.
As for ALDOB, a 5 base pair in/del was detected in intron 1 and one intronic SNP was found in intron 6 (Table 3.6). Interestingly, two non-synonymous SNPs were
78 identified in two exons of ALDOB. In exon 3, a change of an A nucleotide to G resulted in an alteration of isoleucine72 to valine (Isoleucine72>Valine; c.208A>G). A substitution of a G nucleotide to A changed of alanine325 to threonine
(Alanine325>Threonine; c.967G>A) was found in exon 8 (Table 3.7).
In the catalase (CAT) gene, two intronic SNPs were discovered in intron 10 and 11, two 1 bp in/del were found in intron 11 and intron 13, and two SNPs were found in the 3’UTR (Table 3.6). One synonymous SNP was detected in exon 8. The SNP changed a T nucleotide to C but was silent and did not change the amino acid proline307 (Proline307>Proline; c.921T>C).
The largest gene sequenced was HADHB with 16 exons in total. This gene had 21
DNA variants which was not unexpected for a gene of this size. Out of the 21 DNA variants, 15 were SNPs found in the introns and two SNPs were discovered in the
UTR regions, one in the 5’UTR and one in the 3’UTR (Table 3.6). One 1 bp in/del was identified in intron 2. Exon 2 of this gene has a change of amino acid from isoleucine2 to threonine (Isoleucine2>Threonine; c.5T>C). Two other exonic SNPs were silent with no changes of amino acids in exon 4 (Valine58>Valine; c.174G>A) and 8 (Alanine163>Alanine; c.489G>A).
One of the smallest genes sequenced was NDUFA8 with only four exons. Only one
SNP was found in intron 1 (Table 3.6). There were no DNA variants found in the other small gene sequenced, ghrelin, including the previously observed SNP in intron 3 previously observed to be associated with net feed efficiency (Sherman et al., 2008). Given that ghrelin only has 5 exons and is highly conserved, the lack of
DNA variants is not surprising. The SOD1 gene had only two SNPs in introns 2 and
3 (Table 3.6). Again, with only five exons in this gene, a small number of DNA variants was anticipated.
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NDUFB5 is a gene of eight exons. No DNA variants were identified amongst the exons. However, exon 2 of this gene was not sequenced due to the incomplete genomic sequence in Ensembl. At the time sequencing was done, there was a section of 942 unknown bases immediately after exon 2 which made it impossible to design primers for the exon. There were only two SNPs identified, both in the untranslated regions, one each in the 3’UTR and 5’UTR (Table 3.6).
The SUCLG1 gene had a total of 13 SNPs and one in/del (Table 3.6). In exon 3, two
SNPs were discovered. One SNP has a base change of A to C (c.276A>C) but did not change the amino acid (Glycine92>Glycine). The second SNP in exon 3 also did not change the amino acid lysine105 (Lysine105>Lysine; c.315G>A). In exon 4, one
SNP has been found that changed glycine163 to glutamate (Glycine163>Glutamate; c.488G>A). A total of 10 intronic SNPs were found throughout the gene. One 1 bp in/del was also found in intron 6.
With 10 exons, a reasonably large number of DNA variants in the gene was expected in SOD2. However, only five SNPs were detected, two in intron 3, and three in the 3’ untranslated region (Table 3.6).
3.4 Discussion
The candidate gene approach has been successful in human and animal genetics
(Rothschild and Soller, 1997). The basis of that candidate gene approach is that a major component of quantitative genetic variation is caused by functional mutations in putative causative genes (Zhu and Zhao, 2007). The initial step in this approach is to choose a suitable gene that is believed to have a relevant role in the particular trait. This is done by selecting candidate genes based on knowledge of the gene’s biological function and includes structural genes or genes involved in the regulation
80 of metabolic pathways (Kwon and Goate, 2000; Pflieger et al., 2001; Zhu and Zhao
2007).
Three candidate genes were selected, albeit not being within the RFI QTL (namely
CAT, SOD2 and GHRL), but based on other evidence of being involved with net feed efficiency in cattle. The role of catalase gene (CAT) in converting superoxides to water and oxygen is acknowledged to be crucial from a range of studies conducted on the effect of CAT variants in malnourished elderly patients (Fabre et al., 2008) to hypertension between ethnic groups (Zhou et al., 2005). Results from such studies suggested there are polymorphisms in the promoter region of this gene that affect function of catalase in humans. However, herein no polymorphism or variants were detected in the promoter region.
The ghrelin gene has been studied extensively due to its role in meal initiation (Klok et al., 2007) and deciding the intake of fat or carbohydrates for maintaining energy balance (Wortley et al., 2004). A DNA variant, G375A, in intron 3 was found to have minor association with feed efficiency in beef cattle (Sherman et al., 2008). In dairy cattle, research conducted to determine the significance of this particular SNP in milk related traits but no significant effects were found (Kowalewska-Luczak et al.,
2011). Unfortunately, no DNA variants were detected in ghrelin in the Jersey-
Limousin backcross herein. The most likely explanation is that the breeds used by
Sherman et al. (2008) were of Continental x British extraction but do not necessarily have the same genetic variants as the Jersey x Limousin backcross progeny used herein. It is not uncommon for different breeds to have different polymorphisms (Li et al. 2002). Alternatively, the polymorphism may not just be present in the six sires sequenced herein.
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The most frequent type of DNA variant expected to be seen in the DNA sequence is the single nucleotide polymorphism (SNP) (Ramensky et al., 2002). In human populations, SNPs occur at a frequency of <1% (Wang et al., 1998) but information gained from the SNPs detected can be used to understand the genetics of phenotypic variation (Ramensky et al., 2002).
SNPs can be of synonymous and non-synonymous. Synonymous SNP or silent
SNP do not change the amino acid sequence and consequently, are less likely to affect the protein function (Kimchi-Sarfaty et al., 2007). However, Hunt et al. (2009) pointed out that synonymous SNP are capable of affecting messenger RNA splicing, stability, structure and protein folding. Both synonymous and non- synonymous are probably equal in association with a trait (Chen et al., 2010). Thus, the five synonymous SNP discovered in this study may or may not affect the protein structure and further study is needed.
Non-synonymous SNP are of two types, the nonsense mutations which result in stop codons and the missense mutations in which the amino acid is substituted with another amino acid. The substitution can be conservative, semi-conservative or non-conservative depending upon the similarity of the amino acids. Four non- synonymous SNPs were detected in this study; two of which were in exon 3 and exon 8 of the ALDOB gene with a substitution of Isoleucine72>Valine and
Alanine325>Threonine, respectively. A substitution of Isoleucine2>Threonine was discovered in exon 2 of the HADHB gene. Out of three exonic SNPs found in the
SUCLG1 gene, one SNP was a non-synonymous SNP with a substitution of
Glycine163>Glutamate in exon 4. All four of these SNPs are conservative substitutions.
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The exonic SNPs in this study were compared to the SNPs in the Ensembl database (http://www.ensembl.org) and seven SNPs have been reported in that database. The two non-synonymous SNPs in ALDOB gene, ALDex3
(Isoleucine72>Valine; c.208A>G) and ALDex8 (Alanine325>Threonine; c.967G>A) was reported as rs42312271 and rs132867205. A synonymous SNP in CATex8
(Proline307>Proline; c.921T>C) was identified as rs20918250. Out of three exonic
SNPs in HADHB gene, two were synonymous, HADex4 (Valine58>Valine; c.174G>A) and HADex8 (Alanine163>Alanine; c.489G>A) was reported as rs134611580 and rs132649181. In addition, the non-synonymous SNP HADex2
(Isoleucine2>Threonine; c.5T>C) was reported as rs134753728. A synonymous
SNP in SUCLG1 gene, SUCex3.1 (Glycine92>Glycine; c.276A>C) was identified as rs43680222. The remaining two SNPs in this gene, SUCex3.2 (Lysine105>Lysine; c.315G>A) and SUCex4 (Glycine163>Glutamate; c.488G>A) have not been reported in the Ensemble database.
Estimation of occurrence of SNPs in human population is one SNP per 1000 bp for
SNPs with high allele frequencies (Taillon-Miller et al., 1998). Rare variant SNPs with minor allele frequencies of <1% are estimated to occur every 200-300 bases
(Kruglyak & Nickerson, 2001). Across the 10 candidate genes, 54347 base pair were sequenced in order to locate DNA variants. In this study, a total of 58 DNA variants were discovered, which is approximately one variant per 937 base pairs.
Thus, the number of SNPs discovered in this study was similar to previous estimates.
3.5 Summary
In summary, the objective of this study was to identify candidate genes related to mitochondrial function that might affect residual feed intake and discover variants
83 within those genes that might be used to associate those genes with RFI. A total of
10 candidate genes were identified (ALDOB, AK1, CAT, HADHB, GHRL, NDUFA8,
NDUFB5, SOD1, SOD2 and SUCLG1) and a total of 58 DNA variants found including 52 SNPS that can be potentially used for genotyping. These genes are of importance in the pathways occurring in the mitochondria and in energy metabolism. Note that these genes are nuclear genes and not to be confused with the mitochondrial genes.
Future studies in identifying more candidate genes affecting residual feed intake should consider sequencing more genes related to mitochondrial function and energy metabolism. Karisa et al. (2013), in their study using Angus and Charolais beef steers, discovered additional candidate genes that might affect residual feed intake. Amongst the genes of interest which might be associated are Myosin-X
(MYO10), Cytochrome P450 subfamily 2B (CYP2B4), Aldehyde oxidase (AOX1), and Ubiquitin-like modifier activating enzyme 5 (UBA5).
84
Chapter 4
Candidate Gene Associations
85
4.1 Introduction
Genotyping is the method of determining the DNA variants of an individual in order to establish which alleles are inherited from the parents and is usually the first step in determining if the variants are associated with phenotypes (Gut, 2001). Among the many techniques used for genotyping are the restriction enzyme fragment analysis, pyrosequencing, microarray hybridisation, allele-specific PCR amplification and primer extension (Norambuena et al., 2009).
It is agreed that no one technique is perfect for genotyping. However, there are a few elements that should be taken into consideration in choosing the best genotyping method. According to Kwok (2001), it is necessary for the ideal genotyping method to have the following characteristics. First, the assay must be easily and quickly developed from sequence information. Second, the cost to develop the assay must be low and this includes the marker-specific reagents and time spent for optimisation. Third, the reaction must be robust, in which reliable results are expected even with low quality DNA samples. Fourth, the assay must be easily automated and require minimal manual operation. Fifth, the data obtained must be simple to analyse. Sixth, the method should be capable of performing a few hundred to millions assays per day. Finally, the last characteristic is the total assay cost per genotype must be low. Thus, the selection of high resolution melting (HRM) as the genotyping method herein was based on HRM meeting most of these criteria.
High resolution melting is a means of genotyping that is widely employed due to the rapid detection of variants and affordability. The simple yet brilliant design of HRM is based on the presence of a specialised double stranded DNA (dsDNA) binding dye
(Taylor, 2009). When bound to double stranded DNA, the dye is highly fluorescent but will fluoresce poorly in the unbound state. Polymerase chain reaction (PCR)
86 amplification, which is the first step of HRM, can be monitored by the fluorescence of the dye (Vossen et al., 2009).
Following PCR amplification, the amplified product is gradually denatured by slowly increasing the temperature, releasing the dye which results in a decrease in the fluorescence. The temperature is increased in small increments in order to produce a characteristic melting profile. This profile is dependent on the length and sequence of the amplicon. HRM is sensitive enough to allow the detection of a single base change between identical nucleotide sequences (Taylor, 2009).
Melting of double stranded DNA is influenced by several factors including the length, GC content and heterozygosity of the amplicon (Ririe et al., 1997). Not only will the two homozygous forms of a variant differ in their melt profiles or curves, the homozygous sequence variants usually differ in the melting temperature (Tm) of the duplex from that of the heterozygote (Taylor, 2009). The Tm is the temperature at which 50% of DNA sample is double-stranded DNA (dsDNA) and 50% single stranded DNA (ssDNA). In the event of an exchange between G:C and T:A base pairs, the changes in Tm will be relatively large, approximately 0.8-1.4°C (Liew et al., 2004). However, if the bases swap strands but the base pair does not change, the change in Tm is smaller becoming undetectable if there is also nearest neighbour symmetry (Liew et al,. 2004).
HRM has made it possible to monitor changes at the nucleotide level. The exchange of the nucleotides are categorized into transitions and transversions.
Transition changes occur between the 2-ring purines (A↔G) or the 1-ring pyrimidines (C↔T), while the transversions involved changes between purines and pyrimidines [(A↔T), (A↔C), (G↔C) and (G↔T)]. The likelihood of transversions occurring is double as compared to transitions. However, transitions are generated
87 at higher frequencies in mammals. This is because the rate of transition mutation is elevated at CpG dinucleotides by ~30 fold relative to the average rate of mutation in great apes and ~15 fold in other mammals (Siepel and Haussler, 2004; Hwang and
Green, 2004; Hodgkinson and Eyre-Walker , 2011; Keightley et al., 2011).
With this knowledge in mind, it was possible to genotype the DNA variants in the candidate genes and then examine the effects of the DNA variants on phenotypes related to residual feed intake by using HRM to genotype the variants. Thus, the objectives were to genotype the selected DNA variants in the progeny of the Jersey x Limousin herd cattle using high resolution melting and to analyse the genotypes with regards to related feed intake related phenotypes using an unbalanced ANOVA model.
4.2 Methods
4.2.1 Genotyping
Fourteen SNPs out of the 52 discovered SNPs (Chapter 3) were selected for genotyping (Table 4.1) via high resolution melting (HRM) using a Rotor-Gene 6000 thermocycler (Corbett Research) (as described in Chapter 2) using 366 progeny, 3 sire and 6 grandparent samples. The reaction mix for genotyping (section 2.1.1) was prepared in a 96-well plate. In some circumstances where the genotyping was repeated for individual samples, 0.2 ml PCR tubes were used.
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Table 4.1 List of genotyped SNPs
SNP Region F1 sire 361 368 398 AK1SNP1 Intron 1 AG AG AG ALDSNP3 Exon 3 AG AG AG ALDSNP8 Exon 8 GA GA GA CATSNP8 Exon 8 TT TC TC CATSNP12 Intron 11 TT TC TC HADSNP2 Intron 1 AG AA GG HADSNP4 Intron 4 TC TT CC HADSNP7 Intron 7 TA AA TT ND5SNP5’ 5’UTR CT CC CT ND5SNP8.2 3’UTR AG GG AG ND8SNP1 Intron 1 AA GG AG SOD1SNP3 Intron 2 CC AA AC SOD2SNP3 Intron 3 CG GG GG SUCSNP4 Exon 4 GG GA GG
4.2.1.1 High Resolution Melting (HRM)
The Rotor-Gene 6000 thermocycler (Corbett Research) is capable of amplifying the
PCR products internally which means the process was continuous without having to transfer the samples between machines. Number of cycles varied from 30 cycles up to 40 cycles, depending on the amount of products generated. Optimisation of HRM for each SNP was performed by adjusting the melting temperature ranging between
65°C to 95°C. The resulting melt curves were screened with the Rotor-Gene 6000
Series Software (Corbett Research).
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4.2.1.2 Data analysis
The genotypes were analysed for association with residual feed intake traits using the GenStat 11th Edition Software. Analysis was performed using unbalanced
ANOVA with cohort, breed and sire as the fixed effects in the first model and the second model also included the MSTN F94L genotype. The MSTN F94L genotype is a major gene affecting body composition traits in Limousin cattle, including muscle weight, retail yield and fat depth (Sellick et al., 2007). As there are evidence that residual feed intake is related to body composition (Basarab et al., 2003; Lines et al., 2014), models with and without the MSTN F94L genotype were used in the analysis.
4.3 Results
4.3.1 High Resolution Melting (HRM)
The sires were sequenced to detect SNPs and all SNPs discovered were confimed by sequencing the grandparents.The selection of SNPs to be genotyped was based on several factors. At least one informative SNP from each gene was selected. The exception was the ghrelin (GHRL) gene as no DNA variants (SNPs or in/del) were found in the gene. For genes with more than one SNP, exonic SNPs were preferentially selected. In the event where only intronic SNPs were available, the
SNPs with the best sequencing data were selected. Where possible, 2-3 SNPs that were not in linkage disequilibrium were selected for each gene (ALDOB, HADHB and CAT).
While it was preferable that SNPs selected were exonic, there was an exception for the HADHB gene which had 3 exonic SNPs. None of the exonic SNPs could be selected. This was due to the location of the SNPs; either they were too close to
90 another variant or there were poly T sequences nearby. For NDUFB5, two SNPs were genotyped based on their location in the 5’UTR and 3’UTR. Out of the 14
SNPs genotyped, three of the SNPs were transversions and 11 were transitions
(Table 4.2, see Appendix C.1 for allele frequencies).
Table 4.2 Summary data of the 14 genotyped SNPs
SNP Variant type # genotypes # genotypes in sires in progeny AK1SNP1 Transition 1 3 ALDSNP3 Transition 1 3 ALDSNP8 Transition 1 3 ND5SNP5’ Transition 2 2 SUCSNP4 Transition 2 2 CATSNP8 Transition 2 3 CATSNP12 Transition 2 3 SOD2SNP3 Transversion 2 2 HADSNP7 Transversion 3 3 SOD1SNP3 Transversion 3 3 HADSNP2 Transition 3 3 HADSNP4 Transition 3 3 ND5SNP8.2 Transition 3 3 ND8SNP1 Transition 3 3 *1 = heterozygote, *2 = one heterozygote, one homozygote, *3 = one heterozygote, two homozygotes
AK1SNP1, ALDSNP3 and ALDSNP8 had only one heterozygote genotype in the three sires, A/G, A/G and A/G respectively. However, when the progeny of the three
SNPs were genotyped, all three genotypes (A/G, G/G, A/A) were present. All three
SNPs were transition SNPs.
Two SNPs, ND5SNP5’ and SUCSNP4, were transition SNPs and had two genotypes in the three sires. One of the homozygous genotypes was also missing in
91 the progeny, as ND5SNP5’ only had the C/T and C/C genotypes present in the progeny and SUCSNP4 only had the G/G and A/G genotypes present. Three transversion SNPs were amongst the 14 SNPs genotyped. SOD2SNP3 was a transversion SNP with only two genotypes, G/G and G/C, in the sires and progeny
(Figure 4.1 and Figure 4.2).
GG
GC
Figure 4.1 Melt curve of SOD2SNP3, a transversion SNP with two genotypes, GC and GG
GG
GC
Figure 4.2 Melt profile analysis of SOD2SNP3, a transversion SNP with two genotypes, GC and GG
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HADSNP7 and SOD1SNP3 were transversion SNPs with three genotypes in the sires and progeny. HADSNP7 was a transversion of A↔T with three genotypes, AT,
AA and TT. The melt curve of the homozygous AA and TT for HADSNP7 was hard to distinguish as the melting point was very similar (Figure 4.3). Therefore, two different formats were used for visualing the results, curve and profile (Figure 4.3 and Figure 4.4).
AA AT
TT
Figure 4.3 Melt curve of HADSNP7, a transversion SNP with three genotypes, AT, TT and AA
AA
TT AT
Figure 4.4 Melt profile analysis of HADSNP7, a transversion SNP with three genotypes, AT, TT and AA.
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In contrast, the melt curve of the transition SNPs were more distinct (Figures 4.5 and 4.6). HADSNP2, HADSNP4, ND5SNP8.2 and ND8SNP1 were SNPs found to be transition SNPs with three genotypes.
AG AA
GG
Figure 4.5 Melt curve of HADSNP2, a transition SNP with three genotypes, AA, GG and AG
AA GG
AG
Figure 4.6 Melt profile analysis of HADSNP2, a transition SNP with three genotypes, AA, GG and AG
Both CATSNP8 and CATSNP12 had two genotypes in the sires (T/T and C/T).
When the progeny were genotyped though, all three types of genotypes were discovered for both SNPs (T/T, C/T and C/C).
94
A total of 366 progeny were genotyped for each SNP using HRM and the genotypes assigned after manual scrutiny and pedigree analysis checks (see Chapter 2). The allele frequencies were then determined (Table 4.3, Appendix C.1). However, for three SNPs (CATSNP8, CATSNP12, and HADSNP2), the number of progeny successfully genotyped was less than 366. In total, 96 samples did not amplify well even after repeating the experiments, which included 30 samples for CATSNP8, three samples for CATSNP12 and 63 samples for HADSNP2. This is probably due to a combination of the quality of DNA and sub-optimal HRM conditions or primers.
The minor allele frequency for the SNPs genotyped ranged from 0.10 to 0.49 (Table
4.3). Three SNPs, namely ND5SNP5’, SOD2SNP3 and SUCSNP4, had only one homozygous genotype while the remaining 11 SNPs had both homozygous genotypes. Two SNPs, CATSNP8 and HADSNP2, had less than 5 animals with one homozygous genotype.
Table 4.3 Number of progeny with each genotype and allele frequencies SNP Genotype 11 12 22 p q AK1SNP1 AG 111 154 101 0.51 0.49 ALDSNP3 AG 104 188 74 0.54 0.46 ALDSNP8 GA 101 186 79 0.53 0.47 CATSNP8 TC 204 127 5 0.73 0.27 CATSNP12 TC 104 205 54 0.56 0.44 HADSNP2 AG 153 146 4 0.62 0.38 HADSNP4 TC 152 172 42 0.65 0.35 HADSNP7 TA 94 164 108 0.48 0.52 ND5SNP5' CT 192 174 0 0.76 0.24 ND5SNP8.2 AG 78 210 78 0.50 0.50 ND8SNP1 AG 106 198 62 0.56 0.44 SOD1SNP3 AC 66 214 86 0.47 0.53 SOD2SNP3 CG 0 129 237 0.18 0.82 SUCSNP4 GA 0 75 291 0.10 0.90 1 = first alphabetic allele, 2 = second alphabetic allele
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4.3.2 SNP association studies
Analysis of the genotypes was performed using the GenStat 11th Edition Software.
An unbalanced ANOVA model was used with cohort, breed and sire as the fixed effects against residual feed intake (RFI) and 16 other traits potentially related to residual feed intake with and without the F94L myostatin genotype in the model
(Table 4.4).
Table 4.4 Residual feed intake related traits
Trait Abbreviation Mean SD Bone as percent of carcass weight (estimated %) bone% 17.76 1.79 Daily feed intake (kg) dfi 13.21 2.33 Eye muscle area (cm2) ema 80.7 17.0 Trimmable fat (estimated %) fat % 13.6 2.7 Fat to bone ratio fattobone 0.77 0.19 Heart weight as percent of carcass weight (%) heart% 0.55 0.09 Heart weight (kg) heartwt 1.80 0.25 Hot standard carcass weight (kg) hscw 33.5 61.7 Kidney weight as percent of carcass weight (%) kidney% 0.32 0.05 Kidney weight (kg) kidneywt 1.05 0.17 Liver weight as percent of carcass weight (%) liver% 1.80 0.32 Liver weight (kg) liverwt 5.89 0.96 Meat as percent of carcass weight (estimated %) meat% 68.62 2.99 Meat to bone ratio meattobone 3.91 0.53 Ossification score ossms 225.4 47.12 Residual feed intake (kg/day) rfi 0.01 1.57 Average daily gain (kg) adg 1.09 0.46
Once there was evidence that residual feed intake may be correlated with body composition in cattle, specifically subcutaneous fatness (Lines et al., 2014), an additional 10 fat traits related to specific fat depots were analysed (Table 4.5). The
96 reasoning was that if a gene affects both RFI and fat deposition, the gene is may be affecting RFI through the same mechanisms affecting fat deposition (eg increased appetite) rather than through energy metabolism or mitochondrial function. On a related point, the strongest genetic correlation between RFI and fat was reported as
0.72 by Robinson and Oddy (2004). This implies that despite the findings of Lines et al. (2014), half of the genetic variation in RFI should be independent of fat depth.
Therefore, fat and non-fat related variants affecting RFI are of interest.
Table 4.5 Specific fat depot traits for association studies
Trait Abbreviation Mean SD Intramuscular fat imf 5.2 1.71 Kidney fat (kg) kidneyfat 12.5 3.86 AusMeat marbling score marbam 1.53 0.79 MSA marbling score mbms 1.73 0.68 USDA marbling score mbusms 381.4 0.79 Melting point (⁰C) meltpt 37.4 3.09 Omental fat weight (kg) omenfat 12.0 4.14 Fat depth at P8 rump site (mm) p8fat 12.3 5.23 Rib fat depth (mm) rftms 9.7 3.64 Intermuscular seam fat area (mm2) seam fat 308 160.1
The analyses involved multiple testing and it is recognised that accepting significance as p < 0.05 means that at a false discovery rate of one in twenty, there are likely to be some false associations detect. Nevertheless in general, far more significant associations were found at p < 0.05 than would be expected for the number of SNPs and traits tested. Also, for the purposes of comparing with the literature, all associations that had a p-value of < 0.1 were noted.
The initial analysis was carried out using 17 RFI related traits and all genotyped
SNPs showed significant effects for at least one trait except for ALDSNP3 and
97
SUCSNP4 which did not significantly affect any trait (p > 0.1, Table 4.6). It should be noted that the progeny only had two of the three possible genotypes for
SUCSNP4. On the other extreme, HADSNP2, HADSNP4 and HADSNP7 had significant or nearly significant effects on five traits for each of the SNPs. HADSNP2 was a rare allele with only five animals in one of the homozygous classes.
Interestingly, the significant traits that all three SNPs affected in common were residual feed intake (RFI) and daily feed intake (DFI).
Table 4.6 Traits affected by SNPs without and with MSTN F94L genotype in the model SNP SNP effect SNP effect with MSTN AK1SNP1 meattobone (0.073) meattobone (0.039) ALDSNP8 ossms (0.007) ossms (0.007) CATSNP8 heart% (0.085) heart% (0.085) CATSNP8 kidney% (0.094) kidney% (0.090) CATSNP8 liver% (0.002) liver% (0.002) CATSNP8 slope (0.019) slope (0.019) CATSNP12 kidney% (0.061) kidney% (0.058) CATSNP12 liver% (0.041) liver% (0.040) HADSNP2 dfi (0.018) dfi (0.019) HADSNP2 ema (0.067) HADSNP2 meat% (0.068) meat% (0.014) HADSNP2 meattobone (0.035) meattobone (0.017) HADSNP2 rfi (0.017) rfi (0.017) HADSNP4 bone% (0.099) HADSNP4 dfi (0.015) dfi (0.016) HADSNP4 kidneywt (0.061) kidneywt (0.062) HADSNP4 meattobone (0.029) meattobone (0.013) HADSNP4 rfi (0.009) rfi (0.009) HADSNP7 bone% (0.087) HADSNP7 dfi (0.025) dfi (0.026) HADSNP7 fat% (0.064)
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HADSNP7 fattobone (0.030) fattobone (0.022) HADSNP7 rfi (0.077) rfi (0.078) ND5SNP8.2 ema (0.069) ema (0.040) ND5SNP8.2 heart% (0.004) heart% (0.003) ND5SNP8.2 hscw (0.026) hscw (0.025) ND5SNP8.2 kidneywt (0.098) kidneywt (0.099) ND5SNP5’ ema (0.023) ema (0.013) ND5SNP5’ heart% (0.093) heart% (0.092) ND5SNP5’ hscw (0.016) hscw (0.015) ND5SNP5’ liverwt (0.068) liverwt (0.069) ND8SNP1 bone% (0.013) bone% (0.011) ND8SNP1 kidneywt (0.019) kidneywt (0.019) ND8SNP1 meattobone (0.026) meattobone (0.012) SOD1SNP3 dfi (0.035) dfi (0.036) SOD1SNP3 meat% (0.069) meat% (0.069) SOD1SNP3 meattobone (0.086) meattobone (0.049) SOD1SNP3 rfi (<0.001) rfi (<0.001) SOD2SNP3 ema (0.088) ema (0.062) SOD2SNP3 heartwt (0.031) heartwt (0.032)
P-values (< 0.1) in brackets. MSTN = myostatin F94L genotype was included in the model. See Table 4.4 for trait abbreviations.
All 17 traits were associated with at least one SNP either in the presence or absence of the F94L myostatin genotype in the model (Table 4.7). The trait that was affected by the most SNPs was the meat to bone ratio as five SNPs were significantly associated with this trait (AK1SNP1, HADSNP2, HADSNP4, ND8SNP1 and SOD1SNP3). This was true for the trait with and without myostatin in the model.
For fat percentage (fat%), none of the SNPs had a significant effect in the absence of F94L myostatin genotype in the model. However, when F94L myostatin genotype was added in the model, the HADSNP7 was almost significant.
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Table 4.7 Number of SNPs affecting each trait with and without MSTN F94L genotype in the model
Traits Without MSTN With MSTN bone % 1 3 dfi 4 4 ema 3 4 fat % 0 1 fat to bone 1 1 heart % 3 3 heart wt 1 1 hscw 2 2 kidney % 2 2 kidney wt 3 3 liver % 2 2 liver wt 1 1 meat % 2 2 meat to bone 5 5 ossms 1 1 rfi 4 4 slope 1 1
* Number of SNPs with P values < 0.1. See Table 4.4 for the trait abbreviations.
Ten additional specific fat depot traits were analysed using the same model as the
RFI related traits. Of the 14 SNPs genotyped, four SNPs had no association with any of the fat traits (CATSNP8, ALDSNP3, ALDSNP8 and SODSNP3). The SNP with the most associations with the fat traits was ND5SNP8.2 with a total of six effects which includes all three marbling scores as well as the kidney fat, intermuscular fat and intramuscular fat (Table 4.8).
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Table 4.8 Fat traits affected by SNPs without and with MSTN F94L genotype in the model SNP SNP effect SNP effect with MSTN AK1SNP1 marbam (0.038) marbam (0.036) AK1SNP1 mbms (0.030) mbms (0.029) AK1SNP1 mbusms (0.055) mbusms (0.053) ALDSNP8 p8fat (0.098) CATSNP8 imf (0.095) CATSNP12 omenfat (0.047) omenfat (0.048) HADSNP2 rtfms (0.021) rtfms (0.031) HADSNP4 kidneyfat (0.084) kidneyfat (0.084) HADSNP4 rtfms (0.021) rtfms (0.031) HADSNP4 meltpt (0.090) meltpt (0.089) HADSNP7 marbam (0.071) marbam (0.066) ND5SNP8.2 kidneyfat (0.002) kidneyfat (0.002) ND5SNP8.2 marbam (<0.001) marbam (<0.001) ND5SNP8.2 mbms (0.050) mbms (0.047) ND5SNP8.2 mbusms (0.010) mbusms (0.010) ND5SNP8.2 imf (0.031) imf (0.028) ND5SNP8.2 seamfat (0.074) seamfat (0.075) ND5SNP5’ marbam (0.011) marbam (0.011) ND5SNP5’ imf (0.062) imf (0.058) ND5SNP5’ kidneyfat (0.005) kidneyfat (0.005) ND5SNP5’ seamfat (0.045) seamfat (0.045) ND8SNP1 marbam (0.023) marbam (0.021) ND8SNP1 rtfms (0.027) rtfms (0.025) SUCSNP4 mbms (0.077) mbms (0.075) SUCSNP4 mbusms (0.048) mbusms (0.048) SUCSNP4 omefat (0.099) SOD1SNP3 marbam(0.016) marbam (0.014)
P-values (< 0.1) in brackets. MSTN = myostatin F94L genotype was included in the model. See Table 4.5 for trait abbreviations.
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Of the 10 fat traits analysed, the AusMeat marbling score had the most SNP associations (Table 4.9). Interestingly, ND5SNP8.5 had a p-value of <0.001 for the association with the AusMeat marbling score. The p8 fat depth was the only trait with no association with any of the SNPs in the absence of the F94L myostatin genotype.
Table 4.9 Number of SNPs affecting fat depot trait with and without MSTN F94L genotype in the model*
Traits Without MSTN With MSTN imf 2 3 kidneyfat 3 3 marbam 6 6 mbms 3 3 mbusms 3 3 meltpt 1 1 omenfat 2 1 p8fat 0 1 rtfms 3 3 seam fat 2 2
*Number of SNPs with P values <0.1. See Table 4.4 for the trait abbreviations.
Of all the traits analysed, the most interesting traits were daily feed intake (dfi) and residual feed intake (rfi) as RFI is a measure of net feed efficiency and DFI is directly relevant. The outcome expected from these traits was that the majority of the SNPs would a show significant effect since these SNPs were in candidate genes within known QTL. However, only four SNPs were associated with residual feed intake, three of which were the HADHB SNPs (HADSNP2, HADSNP4 and
HADSNP7). The SNPs only explained 0.8%, 1.0% and 0.4%, respectively, of the variation in RFI, though. It should be noted that HADSNP2 was a rare allele so the
102 size of effect is less accurately estimated. The size of the effect for the SNP in the
SOD1 gene (SOD1SNP3) was 3.0% with a p value < 0.001 (Table 4.10).
Table 4.10 SNP effects on daily feed intake (DFI) and residual feed intake (RFI) with and without MSTN F94L genotype in the model. Trait SNP Without MSTN With MSTN DFI HADSNP2 0.018 0.019 HADSNP4 0.015 0.016 HADSNP7 0.025 0.026 SOD1SNP3 0.035 0.036
RFI HADSNP2 0.017 0.017 HADSNP4 0.009 0.009 HADSNP7 0.077 0.077 SOD1SNP3 <0.001 <0.001
*P values < 0.1.
In the proteomics study by Naik (2007), a number of genes were discovered that may affect residual feed intake in cattle. SNPs from these genes were included in an Illumina SNP chip and used to genotype animals from the Trangie Angus residual feed intake selection lines. These studies revealed 31 SNPs which may be associated with residual feed intake (Appendix C.2). To verify that these candidate genes are associated with residual feed intake, these SNPs were analysed to determine their effect on RFI with and without the presence of the F94L myostatin genotype in the Davies Jersey x Limousin progeny (Table 4.11). Of the 31 SNPs that were analysed, seven SNPs had significant or nearly significant effects on RFI in both models (Table 4.12). None of the SNPs were highly significant but the 2 of the most significant SNPs in the sucrose-isomaltase (SI) and phosphatidylinositol-
4,5-bisphosphate 3-kinase (PIK3CA) genes explained 5.8% and 1.9%, respectively, of the variation in RFI.
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Table 4.11 Additional SNPs analysed for effects on RFI with and without F94L MSTN genotype in the model* SNP P-value P-value without MSTN with MSTN mho031 0.288 0.318 ampk1 0.065 0.085 igf1snp2 0.055 0.077 rdhe2e13 0.377 0.465 atp2b432 0.145 0.141 bcdo2snp3 0.657 0.663 Il2 0.073 0.067 umps2 0.248 0.187 ppargcia10 0.224 0.228 tek16 0.253 0.269 ghr5 0.182 0.195 tek2 0.480 0.544 ass1 0.287 0.346 bcmo14 0.329 0.349 map1b 0.098 0.126 umps1 0.420 0.454 si_3 0.019 0.020 ahsg2 0.369 0.397 pi3k_2 0.170 0.169 hadha_1 0.053 0.056 capn1snp530 0.406 0.396 elong_3 0.128 0.124 foll1 0.752 0.668 pi3k_1 0.013 0.015 si_1 0.126 0.126 ahsg1 0.675 0.754 fhsr2 0.580 0.583 fhsr1 0.215 0.267 ghr1 0.191 0.248 ghr_2 0.378 0.371 foll2 0.578 0.567
*See Appendix C.2 for gene and SNP abbreviations.
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Table 4.12 Additional SNPs with significant effects on residual feed intake*
Gene name Location SNP P-value P-value Size of without with effect MSTN MSTN (%) 5-AMP-activated BTA 20 ampk1 0.065 0.085 1.5 protein kinase, catalytic 33,688,291- alpha-1 chain (PRKAA1) 33,716,677
Insulin-Like Growth BTA 5 igf1snp2 0.055 0.077 5.0 Factor I 66,532,877- 66,604,734
Interleukin 2 (IL2) BTA 17 Il2 0.073 0.067 2.1 35,618,100- 35,622,852
Microtubule-associated BTA 20 map1b 0.098 0.126 2.0 protein-1B (MAP1B) 9,330,175- 9,419,040
Sucrase-Isomaltase BTA 1 si_3 0.019 0.020 5.8 (Alpha-Glucosidase) (SI) 103,088,853- 103,204,728
Hydroxyacyl-CoA BTA 11 hadha_1 0.053 0.056 3.7 Dehydrogenase/3- 73,246,485- Ketoacyl-CoA 73,288,803 Thiolase/Enoyl-CoA (HADHA)
Phosphatidylinositol- BTA 1 pi3k_1 0.013 0.015 2.0 4,5-Bisphosphate 3- 88,504,290- Kinase, Catalytic 88,533,061 Subunit Alpha (PIK3CA) *P values < 0.1. Model with and without MSTN F94L genotype.
4.3.3 Haplotype effects
For the original candidate genes, there were 14 SNPs genotyped across nine genes. More than one SNP were genotyped in four of the genes, namely ALDOB
(ALDSNP3 and ALDSNP8), CAT (CATSNP8 and CATSNP12), HADHB (HADSNP2,
HADSNP4 and HADSNP7), and NDUFB5 (ND5SNP5’ and ND5SNP8.2). The interactions between these SNPs were analysed in order to determine the haplotype
105 effects of the SNPs in the same gene. The haplotypes may be more variable and hence, more informative than the individual SNPs. However, some SNP genotype combinations were rare (Appendix C.1).
The haplotype effects within the CAT gene had a significant effect on five residual feed intake related traits without the F94L myostatin genotype in the model (Table
4.13). Interestingly, among the traits affected by the SNPs were heart weight, kidney weight and liver weight, all of which are organs with high energy demand.
When the F94L myostatin genotype was added in the model, the meat-to-bone ratio was also nearly significant (Table 4.14).
The haplotype effects between the two SNPs in the ALDOB gene, ALDSNP3 and
ALDSNP8, had significant or nearly significant effects on five traits in the absence of the F94L myostatin genotype (Table 4.13). However, in the presence of myostatin genotype in the model, there was no longer a significant effect on the meat to bone ratio (P > 0.1, Table 4.14). Of the 10 fat traits analysed, omental fat was the only trait that had siginificant haplotype effects. The interactions between the SNPs of the ALDOB gene explained 4.5% of the variation in omental fat.
There were three traits with significant effects of the NDUFB5 SNP haplotype including trimmable fat (fat%) with and without myostatin in the model. The haplotype effects within the NDUFB5 gene was the only one with a significant effect on residual feed intake (p=0.005) and daily feed intake (p=0.027) without the myostatin genotype in the model (Table 4.13). When the myostatin genotype was included, both residual feed intake and daily feed intake were still significant
(p=0023 and p=0.004, respectively) (Table 4.14). The NDUFB5 SNP haplotype explained 4.6% of the variation in RFI. The NDUFB5 haplotype did not significantly affect any of the fat traits.
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Table 4.13 Haplotype effects without MSTN F94L genotype in the model*
dfi ema fat% heart% heartwt kidney% kidneywt liverwt meat% meattobone rfi adg imf omenfat marbam CATSNP8xCATSNP12 0.048 0.007 0.006 0.042 0.017 0.038 ALDSNP3xALDSNP8 0.016 0.008 0.062 0.099 0.070 0.088 HADSNP2xHADSNP7 0.050 HADSNP2xHADSNP4 0.092 0.074 ND5SNP5’xND5SNP8.2 0.027 0.052 0.005
*P-values less than 0.1 indicated. See Tables 4.4-4.5 for trait abbreviations.
Table 4.14 Haplotype effects with MSTN F94L genotype in the model*
dfi ema fat% heart% heartwt kidney% kidneywt liverwt meat% meattobone rfi adg imf omenfat marbam CATSNP8xCATSNP12 0.046 0.006 0.004 0.043 0.057 0.071 ALDSNP3xALDSNP8 0.046 0.012 0.056 0.088 0.049 ND5SNP5’xND5SNP8.2 0.023 0.050 0.004 HADSNP2xHADSNP4 0.092 0.083 HADSNP2xHADSNP7 0.049 0.077
*P-values less than 0.1 indicated. See Tables 4.4-4.5 for trait abbreviation.
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The HADHB gene was one of the largest candidate genes and had the most SNPs although HADSNP2 was a rare allele. Three SNPs were genotyped for this gene, and the haplotype explained of 8.5% of the RFI variation. Animals with the
HADSNP2/HADSNP4 AA/CC genotype combination ate 4 kg/day less that the animals with the GG/TT genotype combinations. However, as the HADSNP2 was a rare allele, there was a large standard error of 1.2 kg/day. The interaction between
HADSNP2 with HADSNP4 and between HADSNP2 with HADSNP7 had nearly significant effects on meat percentage and kidney percentage, respectively, in the absence and presence of the F94L myostatin genotype. Like NDUFB5, the HADHB haplotype did not affect any of the fat depot traits significantly.
4.4 Discussion
Genotyping of the SNPs discovered in the candidate genes was performed using the high resolution melt (HRM) technique. At least one SNP was selected for genotyping from each of the 10 candidate genes except for ghrelin which did not have any DNA variants. For those genes with multiple SNPs, more than one SNP was selected for genotyping the genes if the variants were not in linkage disequilibrium.
Despite some unsuccessful samples, the performance of HRM was outstanding.
Low quality and quantity of DNA are said to influence error rates in genotyping. A low number of target DNA molecules in an extract results from either extreme dilution of the DNA or from degradation, which leaves only a few intact molecules
(Pompanon et al., 2005). It is also said that human error contributes to the unsuccessfulness of genotyping (Pompanon et al., 2005). Although in this study a robotic system was used, there were a few steps that were manual which might contribute to errors in genotyping. Therefore, the genotypes were double checked.
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The melting curves from the HRM were convincing. It was observed that the melt curves of transversion SNP homozygotes was harder to distinguish as compared to transition SNP due to the similarities of the melting point. This was the case for three SNPs out of the 14 that were genotyped. Transversion SNPs (that is, the changes between purine and between pyrimidine bases) are known to occur less often than transition SNPs which is fortuitous for most genotyping methods.
However, transversions did not preclude the HRM genotyping of these SNPs as the melt profiles allowed accurate allele assignment.
There were three SNPs (AK1SNP1, ALDSNP3 and ALDSNP8) that were heterozygous in all three sires. The SNPs were confirmed by sequencing the grandparents and then selected for genotyping. When the SNPs were genotyped in the progeny, all three genotypes were present as many of the dams were heterozygous.
Generally, it is expected that all three genotypes would exist in a large sample size such as the 366 progeny. However, this was not the case for three SNPs:
ND5SNP5, SUCSNP4 and SOD2SNP3. The initial screening of the genotypes in the sires revealed that only two genotypes were present, one being a homozygote and the other a heterozygote. It was anticipated that a third genotype would appear amongst the progeny samples genotypes but that was not the case for all three
SNPs presumably because the minor allele frequency was very low.
The SNP were analysed for association with RFI using an unbalanced ANOVA with and without the presence of the F94L myostatin genotype in the model. The growth factor differentiation factor 8 (GDF8) gene, commonly known as myostatin (MSTN), is located at 0 cM on BTA2 (Sellick et al., 2007). Variants found in the gene has been revealed to be functionally associated with increased muscle mass and
109 carcass yield in cattle (McPherron and Lee, 1997). In the Davies Jersey x Limousin progeny, a SNP was discovered in the myostatin gene (g.433C>A) at amino acid position 94 of the myostatin protein which causes a substitution of phenylalanine to leucine (F94L) (Sellick et al., 2006; Sellick et al., 2007). The substitution of that particular amino acid appears to lead to moderate phenotypic effects on muscling in
Limousin and Limousin-cross cattle and the specific SNP is the Limousin functional
DNA variant which increases muscle mass in this breed. The use of the F94L myostatin genotyped in a study by Novianti (2009) demonstrated that there were interactions between the myostatin with other genes of interest for increased muscling and retail beef yield. Interestingly, results obtained from the analysis herein showed that there were few differences in the p-values or size of effect when the F94L myostatin genotype was added in the model. A direct effect of the F94L myostatin genotype has never been observed on residual feed intake in the Jersey x Limousin backcross population previously. The results herein confirm that myostatin has little influence on residual feed intake traits analysed.
The genotyping data were analysed using ANOVA for 17 RFI related traits to examined the individual effects of each SNP against the traits of interest. This analysis could lead to multiple testing problems given the number of traits.
Correcting for high false discovery rates (FDR) is conventionally done using the
Bonferroni method. The Bonferonni method or correction is a multiple comparison correction used when several dependent or independent statistical tests are being performed simultaneously. In order to avoid a lot of spurious positives, the alpha value needs to be lowered to account for the number of comparisons being performed (Weisstein, 2004). The Bonferroni procedure yields a strong control of family-wise error rate at alpha, but it could be too conservative when many tests are independent (Yang et al., 2005). Although the importance of the false discovery rate is acknowledged in multiple testing, this study did not test thousands of SNPs like
110 most GWAS studies, therefore the correction for FDR was not as necessary. The false discovery rate in this study was expected to be around 5% and was considered to be a reasonable threshold given that all significant SNPs would be confirmed in another population prior to use in any selection program. Also it would prevent any truly linked SNP from being overlooked.
Initially, the traits selected for analysis were traits related to RFI and body composition. The relationship between RFI and body composition has been shown in studies by Richardson et al. (2001), Basarab et al. (2003) and Richardson and
Herd (2004). Even though these traits are not proxy traits, it was of interest to determine if the SNPs affect these traits and thus, provide a biological explanation.
The traits of most interest were the residual feed intake and daily feed intake. The expected outcome was most of the SNPs would have significant effects on one or both traits. However, only four SNPs from two genes, HADHB and SOD1, were significant and the size of effects were small (~5% in total). Ten additional fat depot traits were included in the analysis based on evidence that body composition, specifically subcutaneous fat depots, may play a role in feed intake of cattle (Lines et al., 2014). ND5SNP8.2 of the NDUFB5 gene was the SNP with the most significant effects on the fat traits, including intramuscular fat (as measured by
IMF% and 3 marbling scores), intermuscular fat (seam fat) and internal fat (kidney fat).
The gene product of HADHB is the mitochondrial trifunctional protein. A total of
10,624 base pairs of this gene were sequenced across 16 exons. The variants discovered in this gene were the highest as compared to the other candidate genes and included one in/del, two SNPs in the UTRs, 15 intronic SNPs and three exonic
SNPs. Although one of the exonic SNP had an amino acid change from isoleucine to threonine (Isoleucine2>Threonine; c.5T>C), this SNP could not be genotyped due
111 to the occurrence of other variants nearby which precluded consistent high resolution melt curves.
The HADHB gene, as the name suggests, encodes the beta subunit of the mitochondrial trifunctional protein which catalyses the last three steps of mitochondrial beta-oxidation of long-chain fatty acids. Long-chain fatty acid oxidation defects mainly affect highly energetic organs such as the heart, liver and muscle. With defects of the long-chain fatty acid oxidation, long-chain fatty acids from the diet or from endogenous lipolysis cannot be oxidized, and the provision of energy from fat is not sufficient to cover the demands (Spiekerkoetter, 2010). Thus, it was not unexpected that the SNPs in this gene have a significant effect on the residual feed intake and daily feed intake, as both traits are closely related to the highly energetic organs. In future studies, the gene should be considered for genotyping additional SNPs in the exons to determine their impact on residual feed intake and the SNPs identified herein should be verified in other cattle populations.
The other SNP which had an effect on the residual feed intake and daily feed intake was in the SOD1 gene. SOD1 gene is located on chromosome 1 in cattle and has five exons. A total of 3773 base pairs were sequenced and only two intronic SNPs were discovered. It is believed that SOD1 is responsible for the main defence mechanism against superoxide radicals (Leitch et al., 2009) as SOD1 coverts superoxide into hydrogen peroxide and molecular oxygen (Juarez et al., 2008).
Thus, SOD1 may affect mitochondrial efficiency by preventing ROS damage.
It is not obvious that the intronic SNPs in HADHB and SOD1 would affect function.
They are more likely to linked to the causative variant and thus, showing an effect.
However, intronic SNPs should not be discounted as being functional as there is
112 growing evidence that DNA variants in introns may affect mRNA stability (Chorley, et al., 2008).
One of the SNPs which had the most significant effects on the additional fat depot traits was ND5SNP8.2 of the NDUFB5 gene. The NDUFB5 gene encodes a subunit of the Complex I protein which is involved in the electron transport chain (Millour et al., 2006). A study has revealed that the expression of this gene decreased when male rats are fed with high fat diet (Sparks et al., 2005). In relation to the results of this study in which the NDUFB5 gene was associated with the fat traits, the study by
Spark et al. (2005) is of interest as it suggests a direct relationship between the gene expression of electron transport proteins and fat metabolism.
As for the additional SNPs that were used for genotyping analysis, seven out of the
31 SNPs were significant or nearly significant (<0.1), namely SNPs in PRKAA1,
IGF1, IL2, MAP1B, SI, HADHA and PIK3CA genes. All of these genes are located on the BTA QTL chromosomes (BTA 1, 6, 8, 11 and 20), except for the IL2 gene located on chromosome 17. The functions of these genes are related to mitochondrial function and energy metabolism with the exception of IL2 and
MAP1B. These genes have no obvious relationship to residual feed intake and therefore, these genes were examined further in a pathway analysis (Chapter 5).
Analysis of single SNP interaction may lead to over-estimated SNP effects and it is likely that the SNPs effects of this study are over-estimated given the relatively low number of animals in each of the genotypic class. However, it was not possible to quantify the over-estimation. An alternative is to analyse multiple SNPs, particularly for whole genome selection (Zhang et al., 2010). Another analysis that could be considered is by incorporating the SNP genotypes in the QTL analysis, with the
113 expectation that if the SNP had a major effect, the QTL effects after fitting the SNP genotype should disappear or greatly reduced.
4.5 Summary
The main objective was to genotype the DNA variants using high resolution melt and this was accomplished wherein 14 selected SNPs were successfully genotyped. HRM proved to be the appropriate method for genotyping that was both low cost and highly accurate. Although there were some samples not successfully genotyped, HRM is capable of genotyping large numbers of samples. Future work should take into consideration the quality of DNA samples to be used though as this appears to have a major role in ensuring the reliability of amplification.
Analysis of the genotyping results with the RFI related traits was performed using an unbalanced ANOVA model. A total of 17 RFI related traits were chosen and analysis was done with the absence and presence of the F94L myostatin genotype.
The importance of the F94L myostatin genotype on traits in this population was observed as SNPs were not always significant when the myostatin genotype was included in the model as was also demonstrated in another study by Novianti,
(2009). However, in general, the F94L myostatin genotype did not affect the results, indicating that body composition may not influence residual feed intake greatly this cattle population.
Due to time and financial constraints, genotyping could not be performed on all the
SNPs discovered. Only 14 SNPs were selected with at least one SNP for each candidate genes. The exception was for ghrelin which had no SNPs. Although genotyping of the SNPs in the exons would probably enhance the understanding of the specific gene by examining the association studies, five of the mentioned SNPs
114 could not be genotyped because of sequence constraints. Future studies should consider genotyping more DNA variants in the exons in order to observe the effect of changes in the nucleotides and amino acids in residualfeed intake.
The 4 SNPs identified herein that affect RFI could potentially be utilised in cattle breeding programs to select for RFI. However, these SNPs would need to be verified in other cattle populations, such as in the Trangie selection line. Given that their size of effect does not appear to be large, this may not be feasible.
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Chapter 5 Candidate Gene Pathways and Epistatic Effects
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5.1 Introduction
The success of the candidate gene approach in humans and animals is due to the ability to select candidate genes that are believed to have a relevant role in a particular trait based on knowledge of the gene’s biological function and preferably, the location of the gene within a known QTL region (Rothschild and Soller, 1997;
Kwon and Goate, 2000; Pflieger et al., 2001; Zhu and Zhao 2007). The candidate gene approach has been ubiquitously applied for gene-disease research, trait association studies, biomarkers and drug targets in many organisms from animals to plants (Tabor et al., 2002).
The candidate gene approach would ideally lead to identification of DNA variants, most commonly the single nucleotide polymorphism (SNP). One aspect of the candidate gene approach that is of interest is the assessment of SNP-SNP interactions (Hartwig, 2013). Typically, the SNP-SNP interaction involves two SNPs and although relying on only two loci, the analysis can be significant if a true interaction exists. Thus, the objective of this study was to identify any SNP interactions between different genes on RFI related traits and thus, determine potential epistatic effects.
5.2 Methods
Prior to the SNP interaction analysis for epistatic effects, a pathway analysis was conducted to determine if there are any known direct interactions between the candidate genes using the Pathway Commons analysis software (Cerami et al.,
2011) (www.pathwaycommons.org, most recently accessed March, 2016) in addition to examining the more general relationships between the genes using DAVID analysis software (Huang, et al. 2009) (https://david.ncifcrf.gov/, most recently
117 access March 2016). The fourteen SNPs identified from this study (Chapters 3-4) and the seven SNPs from Naik’s study (2007) shown to affect RFI herein (Chapter
4) were included in the analysis.
All SNPs were analysed using the GenStat 11th Edition (VSN International) software. The unbalanced ANOVA model used breed, cohort and sire as the fixed effects and the pairwise SNP interactions were fitted in addition to the SNP main effects (Chapter 2). The F94L myostatin genotype was also included in the model because myostatin is a major gene affecting body composition in Limousin and body composition is known to affect RFI in cattle (Lines et al., 2014). The SNPs were analysed for the 17 RFI related traits as in Chapter 4 (Table 4.4). As described in Chapter 4, 10 additional specific fat depot traits were also analysed to account for body composition affecting RFI (Table 4.5). The 14 SNPs analysed individually
(Chapter 4) were tested for their interactions. In addition, the seven SNPs from
Naik’s study (2007) shown to affect RFI herein (Chapter 4) were analysed for their epistatic effects but only for residual feed intake.
5.3 Results
5.3.1 Pathway analysis
It is of interest to determine if specific biological pathways and/or gene networks differ between animals that have a low versus high residual feed intake. This is important in terms of understanding the biology underpinning net feed efficiency but it also impacts on breeding program strategies utilising molecular markers for selection of RFI. The 10 genes identified as candidates for RFI herein share known relationships through mitochondrial function and energy metabolism because this
118 was the basis of their selection (Chapter 3) and these relationships were confirmed by using DAVID to determine the most common pathways. However, the seven genes from Naik’s study shown to affect RFI herein were selected on different criteria, namely proteomics and location within RFI QTL. Of the seven genes, 5 are also related to mitochondrial function and energy metabolism as verified by using
DAVID. However, the relationship of two genes, IL2 and MAPIB, with RFI was not obvious. The IL2 genes is a growth factor for T cells and natural killer cells. The
MAP1B gene encodes the microtubule-associated subunit 1B protein which is involved in the assembly of microtubules.The DAVID analysis indicated that these genes are not related to energy metabolism although MAP1B gene is affected by nutrition.
However, in terms of understanding which genes are most likely to have epistatic interactions, knowledge of such general relationships are not entirely useful. Thus, an additional pathway analysis was conducted to determine if the candidate genes are likely to directly interact using Pathways Common (Cerami et al., 2011). Each pair of the 17 genes was analysed and for 75 of the combinations, there were no direct interactions between the genes or their products (Appendix D.2). For another
43 combinations, their protein products only shared a common molecule in their biochemical reactions (Appendix D.3). Of greater interest were those genes whose products either changed the state of another gene product, genes that control the expression of another gene or genes whose expression is controlled by a common mechanism.
There were seven gene combinations wherein the gene products changed the state of the other product (Table 5.1). SOD2 interacts directly with both CAT and SOD1,
IGF1 interacts directly with GHRL and PIK3CA. IL2 also interacts with PIK3CA and
SI interacts with GHRL. Lastly, AK1 interacts with PRKAA1 (AMPK).
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Table 5.1 Protein state interactions
Genes Interaction* Other genes involved CAT x SOD2 SOD2 changes the state of CAT INS SOD1 x SOD2 AOC2 changes the state of SOD1 AOC2, AOC3, INS, POR AOC3 changes the state of SOD1 INS changes the state of SOD1 POR changes the state of SOD1 SOD2 changes the state of SOD1 PRKAA1 x AK1 AK1 changes the state of PRKAA1 IGF1 x GHRL IGF1 changes the state of GHRL IGF1 x IL2 IGF1 changes the state of MAPK3 IL2 changes the state of MAPK3 IGF1 changes the state of MAPK1 IL2 changes the state of MAPK1 IGF1 changes the state of PIK3CD IL2 changes the state of PIK3CD IGF1 changes the state of AKT1 IL2 changes the state of AKT1 IGF1 x SI IGF1 changes the state of GHRL SI changes the state of GHRL IGF1 x PIK3CA IGF1 changes the state of PIK3CA VEGFA changes the state of PIK3CA IGF1 changes the state of VEGFA ESR1 changes the state of IGF1 ESR1 changes the state of PIK3CA IL2 x PIK3CA IL2 changes the state of PIK3CA IFNG changes the state of PIK3CA SI x GHRL SI changes the state of GHRL *Yellow highlight indicates direct interactions.
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No genes were found to control the expression of another gene. However, the gene expression of 10 gene pairs was controlled by a common mechanism (Table 5.2).
The control included 6 microRNA species in common (MIMAT), 4 transcription factors in common (GATA1, HNF4A, HNF1A, VSX1), and 1 cytokine (IL2).
Table 5.2 Gene expression interactions
Genes Interaction ALDOB + HADHB MIMAT00000422 controls expression of HADHB & ALDOB ALDOB + SOD1 MIMAT0000732 controls expression of ALDOB & SOD1 SOD1 + SOD2* MIMAT0000730 control expression of SOD1 & SOD2 PRKAA1 + ALDOB GATA1 controls expression of ALDOB & PRKAA1 IGF1 + SOD1 ESR1 controls expression of SOD1 & IGF1 IL2 + HADHA MI0002468 controls the expression of IL2 & HADHA MAP1B + SOD1 HNF4A controls expression of SOD1 & MAP1B MAP1B + SI VSX1 controls expression of MAP1B & SI SI + ALDOB HNF1A controls the expression of ALDOB & SI HADHA + ALDOB MIMAT0000422 controls expression of HADHA & ALDOB MIMAT0003283 controls expression of HADHA & ALDOB
*Other genes involved include AOC2, AOC3, INS, and POR.
Based on these results, one could predict that these genes that directly share pathways or gene networks may have epistatic interactions. The results also suggest that IL2 and MAP1B may have unknown functions as IL2 interacts with unexpected proteins (e.g PIK3CA). The gene expression of MAP1B is unexpectedly controlled by transcription factors associated with intestinal organ development and hepatic gene expression (e.g HNF4A) as well as factors associated with eye development and opsin gene expression (e.g VSX1) in common with genes involved in ROS inhibition (e.g SOD1) and energy metabolism (e.g SI).
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5.3.2 Epistatic effects
Having predicted from the pathway analysis that many of the genes are likely to share the same biological pathways or gene networks directly, the genes were analysed for epistatic effects. The SNP interactions between genes were analysed using an unbalanced ANOVA with and without the presence of the myostatin F94L genotype in the model to observe the epistatic effects of the genes. In general, like the individual SNP analysis (Chapter 4), the presence of the myostatin F94L genotype in the model had little effect on the results but the results were still recorded for completeness (Appendix D.4). Also for completeness, the specific fat depot traits were analysed in addition to the RFI related traits and all results with p- values less than 0.10 were recorded.
All of the genotyped candidate gene SNPs were analysed for epistatic interactions even if there was more than one SNP in the gene. It should be noted that not all of the nine possible gene combinations were balanced for each pair of genes as there were cases where particular combinations of genotypes were missing or less than five animals had a specific genotype combination (Appendix D.1). This was not surprising as several of the SNPs had a very low minor allele frequency (Chapter 4).
5.3.2.1 Candidate gene epistatic interactions
Most of the SNPs were involved in at least one interaction affecting the RFI related traits when the myostatin F94L genotype was in the model (Table 5.3). As a consequence, all of the genes had epistatic effects. Most SNPs were involved in multiple interactions and there were 4 SNP combinations that affected more than 5 traits (namely, AK1SNP1 and ALDSNP3, AK1SNP1 and ALDSNP8, ALDSNP3 and
SUCSNP4, HADSNP2 and SUCSNP4). Most of these interactions involved the
122 internal organ traits. The internal organ traits were included in the study because internal organ size has been reported to be correlated with RFI (Knott et al., 2003;
Souffrant and Metges, 2003; Książek and Łapo, 2004). However, none of these combinations also affected RFI. Nevertheless, several of these interactions
(AK1SNP1 and ALDSNP3, AK1SNP1 and ALDSNP8) were the highly significant observed for kidney and heart weight (p = 0.002, p < 0.001, p = 0.008, and p =
0.005 respectively). The interaction between HADSNP2 and SUCSNP4 were highly significant for kidney weight and liver weight (p = 0.003 and p = 0.006, respectively).
Other body composition traits were also affected by this interaction, including meat- to-bone ratio, eye muscle area and hot standard carcass weight (p = 0.004, p =
0.017 and p = 0.015, respectively). The results suggest that in selecting candidate genes for mitochondria function and energy metabolism, visceral organs and carcass traits may be affected. However, individually, the only SNPs with significant effects on body composition was HADSNP2 which was a rare variant (Chapter 4).
In addition to the RFI related traits, the fat depot traits were analysed for epistatic effects (Table 5.4). Again, it was of interest to determine if body composition, specifically fatness, was affected by the same epistatic interactions as RFI. There were 35 interactions that affect specific fat depots with the myostatin F94L genotype in the model. Most SNP combinations only affected one or two traits although the interaction between CATSNP12 and SUCSNP4 was significant were 3 traits
(namely, marble score, omental fat and P8 fat depth). The interaction between
CATSNP12 and SUCSNP4 that affected omental fat was highly significant (p =
0.004). The interactions between HADSNP2 and SOD1SNP3 and between
ND8SNP1 and SOD1SNP3 were highly significant for rib fat (p = 0.002 and p =
0.005, respectively). None of the significant SNP combinations, however, also had epistatic effects on RFI. The SNPs that affected the specific fat depots individually did not have significant interactions also affecting fatness. In particular, the SNPs in
123 the NDUF5B gene that had very significant effects individually on the fat depot traits, especially the intramuscular traits (Chapter 4), did not have any significant interactions affecting fat.
124
Table 5.3 Tests of significance for SNP interactions between candidate genes affecting RFI related traits*
125
Table 5.3 Tests of significance for SNP interactions between candidate genes affecting RFI related traits* (continued)
126
Table 5.3 Tests of significance for SNP between candidate genes affecting RFI related traits* (continued)
*Model including myostatin F94L genotype. P-values < 0.10 reported for comparative purposes. Trait abbreviations in Table 4.4
127
Table 5.4 Tests of significande for SNP interactions between candidate genes affecting specific fat depot traits*
*Model with myostatin F94L genotype. P-values < 0.10 reported for comparative purposes. Trait abbreviations in Table 4.5
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The traits of most interest were residual feed intake and daily feed intake. For residual feed intake, 10 SNP interactions between the genes had significant or nearly significant effects (Table 5.5). ND5SNP5’ was the SNP with the most interactions for residual feed intake in both models with and without the F94L myostatin genotype. Six interactions between SNPs had significant or nearly significant effects on daily feed intake.
Table 5.5 SNP interactions between genes for RFI and DFI with and without the F94L myostatin genotype in the model*
rfi without with dfi without with ALDSNP8*HADSNP7 0.087 0.087 AK1SNP1*SUCSNP4 0.094 0.082 ALDSNP8*ND5SNP5' 0.023 0.022 ALDSNP3*SOD2SNP3 0.006 0.007 ALDSNP8*ND5SNP8.2 0.092 0.098 ALDSNP8*ND5SNP5' 0.013 0.009 CATSNP12*SUCSNP4 0.088 0.094 ALDSNP8*SOD2SNP3 0.029 0.038 HADSNP2*ND5SNP5' 0.069 0.078 HADSNP2*ND5SNP5' 0.096 0.034 HADSNP2*ND5SNP8.2 0.099 0.091 HADSNP2*ND8SNP1 0.063 0.092 HADSNP4*ND5SNP5' 0.085 0.076 HADSNP7*SOD1SNP3 0.056 0.029 ND5SNP5'*SOD1SNP3 0.039 0.006 ND8SNP1*SUCSNP4 0.049 0.028
*P-values less than 0.1 indicated for comparative purposes.
Four interactions were highly siginificant for RFI, namely the ALDSNP8 x
ND5SNP5’, HADSNP7 x SOD1SNP3, ND5SNP5’ x SOD1SNP3, and ND8SNP1 x
SUCSNP4, which explained 3.1%, 9.2%, 7.8%, and 1.4%, respectively, of the RFI variation. In total, this explains more than 20% of the variation of RFI. Interestingly, of these SNPs, only HADSNP7 and SOD1SNP3 affected RFI individually and these effects were much smaller (0.2% and 3.0%, respectively) than the epistatic interaction (9.2%).
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5.3.2.2 Additional epistatic interactions on RFI and fat traits
Based on the SNP association studies from previous study (Naik, 2007), seven
SNPs were discovered to have significant effects on RFI herein (Chapter 4). These seven genes were also analysed for epistatic effects but only on RFI and for a selected sub-set of the most relevant fat depot traits for which phenotypes were available (namely marbling score, rib fat, omental fat and intramuscular fat) (Tables
5.6 and 5.7).
Table 5.6 Additonal SNPs interactions with selected fat traits without MSTN F94L genotype in the model
rfi marbam rtfms omenfat imf ALDSNP3xSI3 0.063 CATSNP8xPI3K1 0.029 CATSNP12xAMPK1 0.041 SOD1SNP3xMAP1B 0.025 SOD2SNP3xHADHA1 0.030 SOD2SNP3xIGF1SNP2 0.074
Table 5.7 Additional SNPs interaction with selected fat traits with MSTN F94L genotype in the model rfi marbam rtfms omenfat imf ALDSNP3xSI3 0.064 CATSNP8xHADHA1 0.074 CATSNP8xPI3K1 0.028 CATSNP8xSI3 0.084 CATSNP12xAMPK1 0.089 SOD1SNP3xMAP1B 0.051 SOD2SNP3xHADHA1 0.059 SOD2SNP3xIGF1SNP2 0.070 SOD2SNP3xIL2 0.080
*P-values less than 0.1 indicated for comparative purposes. See Table 4.5 for abbreviations.
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Only one of the interactions between ALDSNP3 and SI3 was nearly significant for
RFI. However, one of the other SNP interactions between SOD1SNP3 and MAP1B affecting intramuscular fat was of interest because SOD1 and MAP1B showed a potential relationship in the pathway analysis in that their gene expression is mutually controlled by HNF4A (Table 5.6). When MSTN F94L genotype was added in the model, more interactions which were nearly significant were discovered
(Table 5.7), and the interaction between SOD1 and MAP1B remained significant.
5.4 Discussion
The SNP interaction analysis between genes was performed to determine if there were epistatic effects affecting the RFI related traits. The analysis was done using the unbalanced ANOVA with the absence and presence of the F94L myostatin genotype in the model in order to account for this major quantitative trait nucleotide
(QTN), which affects body composition in Limousin cattle, as it may also affect the residual feed intake related traits.However, this was not the case as the results were nearly identical with or without the F94L myostatin genotype in the model.
The false discovery rate (FDR) for multiple testing should be taken into consideration when dealing with very large number of samples, especially in GWAS studies. Apart from the conventional Bonferroni method for correction, the newer methods include procedures by Benjamini and Hochberg (1995), and Storey (2002).
Both approaches give strong control of FDR but weak control of family-wise error rate (Yang et al., 2005). However, in this study, a 5% false discovery rate was expected and consider reasonable given that there was a limited number of SNPs and traits analysed.
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It was hypothesized that all the SNPs identified in the candidate genes would have significant effects on residual feed intake and/or daily feed intake and that interactions between the genes would be prevalent as the genes tested are related to mitochondrial function and energy metabolic pathways. Indeed, residual feed intake and daily feed intake were affected by interactions between genes. The SNP interaction analysis between genes demonstrated 10 significant or nearly significant interactions for residual feed intake. Interestingly, the ND5SNP5’ SNP of the
NDUFB5 gene had the most interactions with SNPs from other genes in the analysis of residual feed intake (ND5SNP5’*ALDSNP8, ND5SNP5’*HADSNP2,
ND5SNP5’*HADSNP4, ND5SNP5*SOD1SNP3). The most significant interaction was between ND5SNP5 and SOD1SNP3 for which the p value was 0.006.
NDUFB5 is a subunit of the complex I protein in the electron transport chain and as such, is involved in oxidative phosphorylation. In a study involving feeding rats different levels of fat, the expression of 6 genes involved in mitochondrial function decreased when the animals were feed a high fat diet, including the NDUFB5 gene
(Sparks et al., 2005). This suggests that variants in the NDUFB5 gene might affect the metabolism of the pathways in which the gene is involved, and thus, affect residual feed intake.
For daily feed intake, six interactions were observed between the SNPs of different genes. The most significant interaction was between ALDSNP3 and SOD2SNP3, in which the p-values for both models without and with myostatin F94L genotype were p=0.006 and p=0.007, respectively. It was also noted that another interaction between these two genes was also of interest, ALDSNP8 and SOD2SNP3 (p=0.029 and p=0.038 without and with the MSTN 94L genotype in the model, respectively).
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The question is how these two genes are related as ALDOB is involved in the glycolysis pathway and SOD2 transforms ROS products from the electron transport chain. These two genes are part of the cellular respiration process and both are located in the mitochondria. Nevertheless, no direct relationship was discovered in the Pathway Commons analysis between the ALDOB gene with the SOD2 gene.
However, there is a microRNA (hsa-miR-378a-3p) that controls the expression of both the ALDOB and SOD1 genes. Although both SOD proteins are similar and are responsible for destroying radicals, they differ based on their location in the cell.
SOD2 is found in the mitochondria and SOD1 is found in the cytosol. However, both are crucial for protecting mitochondria (Juarez et al., 2008). Further studies should be conducted at the gene expression level to investigate the relationship of these genes.
Considering that all the genes selected for genotyping are believed to be important in the mitochondrial function, energy metabolism and residual feed intake, it was expected that interactions could occur between genes in the same pathways.
Specifically, five of the genes are known to be involved in the mitochondrial electron transport chain: CAT, SOD1, SOD2, NDUFA8 and NDUFB5. CAT and the SOD genes play an important role in the reactive oxygen species (ROS) pathway by converting superoxides to water and oxygen. Three interactions were discovered between the CAT gene with the SOD genes but none were found to affect residual feed intake.
On the whole, the SNP with the most interactions with other SNPs was SUCSNP4 in the SUCLG1 gene. SUCLG1 is part of the Krebs cycle as the gene encodes succinyl-CoA synthetase which converts succinyl-CoA to succinate and GTP is produced during the process. Altered expression of succinyl-CoA synthetase affects the overall production of reducing agents like NADH and FADH2, which are involved
133 in the electron transfer (Lowenstein, 1969). It is possible that the interactions between the SUCLG1 gene with other genes might be related to the fact that most of these genes are directly or indirectly associated with the production of ATP and presumably are related by the substrate and product concentrations which control their activities.
Adding the fat depot traits in analysis contributed to a clearer picture of how the traits are affecting the SNPs. The greatest number of interactions affected P8 fat with 15 interactions. Interestingly, the genes with the most interactions with this trait were the HADHB and NDUFB5. This is not unexpected since HADHB gene is associated with the long chain fatty acid oxidation. As for the NDUFB5 gene, there was evidence that the expression of this gene decreases with high fat diet (Sparks et al., 2005). Although not all fat traits were affected by epistatic effects, it was worth examing these possible association and interaction between fat traits, RFI related traits and mitochondrial function.
5.5 Summary
In conclusion, the SNP interaction analysis was conducted for SNPs within the same genes to observe potential epistatic effects. All the traits used in the analysis were observed to be significant or nearly significant affected by SNP interactions.
However, only 10 epistatic SNP interactions were discovered for residual feed intake. However, the combined size of effect was quite large (21%). Utilising these markers in a breeding programs though will be hampered by the inability to incorporate epistatic effects into the breeding strategies.
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It can be concluded that these candidate genes related to mitochondrial function and energy metabolism are worth further investigation based on their interactions and/or direct effects on residual feed intake.
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Chapter 6 Mitochondrial Assays
136
6.1 Introduction
Mitochondria are the sites of energy production in the form of cellular ATP (Kolath et al., 2006). Known for its unique double membrane structure, the inner mitochondrial membrane is the site of the electron transport chain in which a series of electrons are transferred between protein complexes in order to produce ATP. The enzymatic complexes in the chain are the Complex I (NADH: ubiquinone oxidoreductase),
Complex II (succinate: ubiquinone oxidoreductase), Complex III (ubiquinol cyctochrome c reductase), Complex IV (cytochrome c oxidase) and Complex V
(ATP synthase).
In the event that the rate of electron entry into the respiratory chain and the rate of electron transfer through the chain are mismatched, the production of superoxides increases at Complex I and Complex III (Nelson and Cox, 2008). Superoxide ion
- (O2), hydrogen peroxide (H2O2), and hydroxide radical (OH ) are the most common forms of these reactive oxygen species (ROS) (Seifried et al., 2006). ROS can cause oxidative damage to all types of molecules and hence, organelles, but the damage is normally prevented by the superoxide dismutase and catalase enzymes that metabolise the ROS (Nelson and Cox, 2008). Although ROS are natural by- products of various normal mitochondrial and cellular activities, an excessive amount of these compounds can damage the mitochondria in a range of pathologies involving cellular proteins and lipids (Seifried et al., 2006; Murphy,
2009). This damage results in less efficient mitochondria. Thus, calculating the level of ROS in the cell is an indicator of the efficiency of the electron transport in the mitochondria.
Studies have been conducted in high and low residual feed intake broilers and a link between chicken breast muscle mitochondria function with residual feed intake was
137 indicated (Bottje et al., 2002). It was found that the mitochondria from the low RFI broilers had less electron leakage in the mitochondria compared to the high RFI broilers. The authors suggested that this leads to improved respiratory coupling in the low RFI broilers, increasing their efficiency. Using the same lines of broilers, mitochondria from the low RFI broilers were also observed to an increased Complex
I, II, III and IV activities (Bottje et al., 2004). In addition, mitochondria from the low
RFI broilers had a lower level of reactive oxygen species (ROS) as measured by the amount of protein carbonyl. The authors postulated that the higher level of ROS in the high RFI broilers causes mitochondrial damage and reduces the activity of the
Complex I-IV enzymes.
A relationship between mitochondria function and feed intake has been also observed in cattle. In a study using muscle mitochondria from Angus steers (Kolath et al., 2006), the high and low RFI groups had no difference in the mitochondria function, however, the rate of mitochondrial respiration was greater in the low RFI steers indicating greater efficiency. In contrast to the result in chickens though
(Bottje et al.2002), the electron leakage was similar for both high and low residual feed intake groups in the Angus cattle (Kolath et al., 2006). Also in contrast to the results in broilers, Kolath et al. (2006) observed the ROS was increased in the low
RFI cattle mitochondria.
The relationship between mitochondria function and feed intake was also seen in a proteomics study and mitochondria enzyme activity assays of high and low RFI cattle from the Trangie Angus selection lines (described in Chapter 2). The low RFI cattle had reduced levels of stress related proteins such as albumin, catalase and heat shock protein 70kDa and 60kDa in the proteomics studies as compared to the high feed efficiency group. The same group also had higher levels of Complex I and
Complex III subunits (Naik, 2007).
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Naik (2007) demonstrated that liver mitochondrial Complex I enzyme activity was lower in the low RFI animals although there was no difference in the muscle mitochondria. In addition, there was no difference in Complex II or Complex IV enzyme activities in the liver and muscle mitochondria from the high and low RFI groups.
Based on these previous studies, it would appear that mitochondrial function may differ between animals selected for high and low RFI. Thus, the objectives of this study were to verify the role of mitochondrial function in RFI by examining the liver mitochondrial biochemical activity of the 3 electron transfer complexes, namely
Complex I, III and IV, and to determine the level of ROS present in the mitochondria using a sensitive protein carbonyl assay in samples from high and low residual feed intake cattle in order to observe any differences between the two groups. It was hypothesized that the mitochondria enzyme activities would be higher in the low RFI animals as the high net feed efficiency animals would have less respiratory uncoupling. In addition, it was hypothesized that the level of ROS would be decreased in the mitochondria of the low RFI animals implying less mitochondrial damage and more efficient mitochondrial function.
6.2 Methods
Samples used in this study were taken from the livers of 20 high and 20 low feed intake Angus Trangie selection line cattle (Chapter 2). Liver samples were sliced after slaughter to fit in a 10 ml tube and frozen immediately. Samples were stored at
-80°C until analysed. Purification of mitochondria from the liver was as described on chapter 2. The Bradford assay was performed prior to enzyme assay experiment in order to determine the amount of protein in each sample.
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6.2.1 Liver samples
The liver samples collected were from two Angus lines of Trangie cattle selected for high or low RFI. The selection was based on the mid-parent RFI EBV of the animals
(Appendix E). The top 20 animals were in the high residual feed intake group and the bottom 20 animals were in the low residual feed intake group.
Complex I activity (i.e. the rotenone-sensitive NADH: ubiquinone oxidoreductase activity) is determined based on the oxidation of NADH. The Complex I oxidation of
NADH was measured by following the decrease in absorbance at 340 nm with 425 nm as the reference wavelength. The assay was done in four steps using a spectrophotometer. First, the reagent mix in the cuvette was equilibrated for seven minutes at 30°C. Second, to ensure the baseline is stable, the absorbance change was recorded for one minute. Third, 40 µg of mitochondria was added and the activity of Complex I activity was measured for three minutes. Fourth, rotenone was added and was measured for three minutes.
In step 2 which was to verify a stable baseline, a constant absorbance was expected throughout the 60 seconds allocated time. Most of the baseline absorbance observed were constant except for 12 samples with slight changes of
±0.001 and was still accepted as constant absorbance. During the third and fourth step where mitochondria sample and rotenone were added, the decrease in absorbance was observed.
Similar to Complex I activity, the first step in calculating the activity of Complex III was to equilibrate the reagent mix at 30°C for seven minutes prior to verifying a constant baseline. Next, the mitochondria sample and cytochrome c were added
140 and the activity was recorded for three minutes. Ascorbic acid was then added to the mixture and absorbance change was recorded for one minute.
For complex IV, the reduced cytochrome c was added into the reagent mix and the rate of absorbance was recorded. A decrease in absorbance at 550 nm was expected after the mitochondria sample was added.
6.2.2 Analysis for biochemical assays
The biochemical assays were performed as described in Chapter 2. Results for the biochemical assays were analysed using the T-test with a two-tail distribution and two sample unequal variance to determine the statistical differences between the samples. Regression analysis was performed to determine the strength of the relationship between the enzyme assays with the residual feed intake related traits.
6.3 Results
6.3.1 Bradford assay
Four grams of liver sample were required for mitochondria purification (Section
2.3.3). A duplicate was prepared for each sample for each experiment. Upon completing the purification, the protein concentrations of the samples were determined using the Bradford assay. The concept of this assay is that the binding of Coomassie blue dye to the sample protein is compared to a protein standard, usually bovine serum albumin (BSA). A standard curve was constructed by plotting absorbance 595 nm versus protein concentrations (Figure 6.1). Samples were then prepared for each assay at a standard recommended concentration.
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Absorbance at 595 nm 0.5
0.4
0.3
0.2 Axis Title Axis Absorbance at 595 nm 0.1
0 0 0.2 0.4 0.6 Axis Title
Figure 6.1 Standard curve at 595 nm for low RFI samples
6.3.2 Complex I enzyme assay .
The Complex I activity was calculated for both high and low residual feed intake animals using 6.8 mM-1 as the extinction coefficient (Table 6.1). The average activity for Complex I in the high residual feed intake animals was 24.2 ± 0.87 nmol/min/mg.
Two sample, #108 and #86, did not show any activity after repeated measurements and could not be included in the analysis. The Complex I enzyme activity for low residual feed intake animals was successfully measured for all 20 samples. The activity had an average of 27.4 ± 0.67 nmol/min/mg. Thus, the mitochondria from the low RFI (high efficiency) animals had 13% greater Complex I activity than high
RFI animals.
Table 6.1 Complex I enzyme activity of high and low residual feed intake animals
Complex I Maximum Minimum Mean±SD (nmol/min/mg) (nmol/min/mg) (nmol/min/mg) High RFI (n=18) 49.3 10.2 24.2±0.87 Low RFI (n=20) 36.6 13.6 27.4±0.67
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A T-test was conducted to determine if there was a significant difference between the activities of Complex I for high and low residual feed intake animals. There was nearly significant difference between the high and low residual feed intake groups for this enzyme activity (p=0.077).
A regression analysis was performed to determine the relationship of the mid-parent
RFI EBV with Complex I enzyme activity. The correlation between RFI and complex
I enzyme activity was significant (F=0.032), but they were not highly correlated
(r=0.35) (Table 6.2). Because the samples were selected from a subset of animals, correlations are likely to be over-estimated. The distribution of the mid-parent RFI
EBV for the high and low residual feed intake animals was clearly dissimilar as expected (Figure 6.3, Appendix E).
Table 6.2 Regression analysis for RFI related traits and Complex I enzyme activity with correlation and test of significant slope.
Traits r F prob rfi 0.35 0.032 ribfat 0.14 0.395 imf 0.03 0.791 ema 0.31 0.059 hscw 0.12 0.457 seamfat 0.08 0.625 oss 0.30 0.069
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4
3.5
3
2.5
2
1.5
1 y = -0.3771x + 2.5318 Complex I (10 x x nmol/min/mg) (10 I Complex 0.5 R² = 0.122 0 -1 -0.5 0 0.5 1 1.5 RFI EBV
Figure 6.2 Correlation between mid-parent RFI EBV and Complex I activity.
The correlation with another 6 traits was also examined to observed their relationship with Complex I activity (Table 6.2). The trait selection was limited because only standard carcass measures were taken. However, the traits related to body composition and age were selected as these have been shown to affect RFI in this cattle population. Of these traits, only eye muscle area was nearly significant
(F=0.059), but again the correlation was not high (r=0.31).
6.3.3 Complex III enzyme assay
The Complex III activity was measured by following the reduction of cytochrome c at
550 nm with 580 nm as the reference wavelength.. The Complex III activity was calculated for both high and low residual feed intake animals using 18.7 mM-1 as the extinction coefficient (Table 6.3).
Table 6.3 Complex III enzyme activity of high and low RFI animals
Complex III Maximum Minimum Mean±SD (nmol/min/mg) (nmol/min/mg) (nmol/min/mg) High RFI (n=20) 11.45 3.74 6.53 ±0.2 Low RFI (n=20) 19.40 3.51 9.64±0.5
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All 20 samples of the high residual feed intake animals were successfully assayed for the Complex III enzyme activity. The average for the Complex III enzyme activity in the high residual feed intake group was 6.53 ± 0.2 nmol/min/mg. The Complex III enzyme activity for low residual feed intake animals was also successfully measured for all 20 samples. The activity averaged of 9.64 ± 0.5 nmol/min/mg.
Therefore, the low RFI animals had 48% greater Complex III activity compared to the high RFI animals.
In order to determine if there were significant differences in the activities between the high and low residual feed intake animals, a T-test was conducted. The T-test for the Complex III assay demonstrated that there was significant difference between the two groups of animals (p=0.011).
The correlation between the Complex III enzyme activity and RFI plus the body composition traits was also determined. The relationship between Complex III and
RFI was highly significant (F=0.009), but the relationship was not strong (r=0.40)
(Table 6.4, Figure 6.3). The other significant correlation was between Complex III enzyme activity and rib fat (F=0.054), but again the relationship was not very strong
(r=0.31) (Table 6.4).
Table 6.4 Regression analysis for RFI related traits and Complex III enzyme activity with correlation and test of significant slope.
Traits R F prob rfi 0.40 0.009 ribfat 0.31 0.054 imf 0.03 0.874 ema 0.24 0.136 hscw 0.03 0.996 seamfat 0.07 0.660 oss 0.09 0.569
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25.00
20.00 y = -2.1661x + 8.2275 R² = 0.1644 15.00
10.00
5.00 Complex III (nmol/min/mg)III Complex
0.00 -1 -0.5 0 0.5 1 1.5 RFI EBV
Figure 6.3 Correlation between mid-parent RFI EBV and Complex III activity.
6.3.4 Complex IV enzyme assay
Complex IV activity was measured by following the oxidation of cytochrome c. The
Complex IV activity was calculated for both high and low residual feed intake animals using 18.7 mM-1 as the extinction coefficient (Table 6.5).
Table 6.5 Complex IV enzyme activity of high and low residual feed intake animals
Complex IV Maximum Minimum Mean±SD (nmol/min/mg) (nmol/min/mg) (nmol/min/mg)
High RFI (n=20) 72.5 11.7 34.7± 0.17 Low RFI (n=19) 77.1 15.2 39.6± 0.19
All 20 samples of the high feed intake animals were successfully assayed for the
Complex IV enzyme activity. The average for Complex IV enzyme activity in high feed intake animals was 34.7 ± 0.17 nmol/min/mg. For the low residual feed intake
146 group, one sample (#8) could not be measured despite repeated attempts and was excluded from this analysis. The activity for Complex IV low residual feed intake averaged average of 39.6 ± 0.19 nmol/min/mg. The low RFI group had 14% greater
Complex IV activity than high RFI, but this was not significant based on the T-test
(p=0.399).
A regression analysis was performed with the Complex IV enzyme activity and RFI plus the body composition traits. None of the traits were significantly correlated with
Complex IV enzyme activity, and this included RFI (Figure 6.4). Only seam fat was nearly significant (F=0.080) and the relationship was not strong (r=0.28) (Table 6.6).
Table 6.6 Regression analysis for RFI related traits and Complex IV enzyme activity with correlation and test of significant slope.
Traits R F prob rfi 0.11 0.491 ribfat 0.11 0.496 imf 0.20 0.221 ema 0.12 0.461 hscw 0.11 0.476 seamfat 0.28 0.080 oss 0.22 0.172
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9.00
8.00 y = -0.2758x + 3.7259 R² = 0.0129 7.00 6.00 5.00 4.00 3.00 2.00
Complex IV (10 x nmol/min/mg) x (10 IV Complex 1.00 0.00 -1 -0.5 0 0.5 1 1.5 RFI EBV
Figure 6.4 Correlation between mid-parent RFI EBV and Complex IV enzyme activity.
6.3.5 Protein carbonyl assay
Protein carbonyl was measured as a proxy for ROS concentration. The protein carbonyl content was calculated using the following equation:
Protein carbonyl (nmol/ml) = [(CA)/ (0.011 µM-1)] (250µl/100µl), with 0.011 being the adjusted extinction coefficient adjusted for the path length of the solution in the cuvette of 10 mm. The amount of protein carbonyl for high and low RFI samples was measured and calculated using this equation (Table 6.7).
Table 6.7 Protein carbonyl content activity of high and low residual feed intake animals
Protein carbonyl Maximum Minimum Mean±SD (pc/mg protein) (pc/mg protein) (pc/mg protein)
High RFI (n=20) 15.6 1.6 5.9± 0.3 Low RFI (n=20) 27.7 3.1 9.7± 0.6
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All 20 samples of the high residual feed intake animals were successfully assayed for the protein carbonyl activity. The average for protein carbonyl content in the high residual feed intake group was 5.9 ± 0.3 pc/mg protein. For the low feed intake group, the protein carbonyl assays were successful for all 20 samples. The average for the low feed intake was 9.7± 0.6 pc/mg protein. The low RFI group had 64% greater protein carbonyl content than high RFI group , which was significantly different based on the T-test (p=0.018).
A regression analysis of the protein carbonyl content and RFI and the body composition traits was also performed. The protein carbonyl concentration and RFI were highly significant (F=0.007) and the relationship was moderate (r=0.42) (Table
6.8, Figure 6.5). The protein carbonyl concentration was not significant for any of the body composition traits although the correlation with rib fat was strong (r=0.69).
3
y = -0.2945x + 0.8013 2.5 R² = 0.1757
2
1.5
1
ROS (10 x pc/mg protein) x pc/mg (10 ROS 0.5
0 -1 -0.5 0 0.5 1 1.5 RFI EBV
Figure 6.5 Correlation between mid-parent RFI EBV and ROS activity
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Table 6.8 Regression analysis for RFI related traits and ROS concentration with correlation and test of significant slope
Traits R F prob rfi 0.42 0.007 ribfat 0.69 0.177 imf 0.03 0.846 ema 0.13 0.416 hscw 0.03 0.963 sf 0.22 0.168 oss 0.26 0.107
6.4 Discussion
Authors of many studies have come to the conclusion that mitochondrial function has a role in net feed efficiency (i.e residual feed intake, RFI) (Bottje et al. 2004;
Kolath et al., 2006, Sharifabadi et al., 2012). It was observed previously that there were more Complex I and Complex III subunits but lower Complex I activity in the
Angus Trangie low RFI more efficient selection line (Naik, 2007). The results for the liver Complex I enzyme activity herein did not verify this observation using a different set of animals of Angus Trangie selection line animals. Complex I and
Complex III enzyme activities were higher in the low RFI animals as hypothesized and as previously observed in chickens, fish and sheep (Iqbal et al., 2004; Eya et al., 2012; Sharifabadi et al., 2012).
It is not clear why there is a discrepancy between the previous results for Complex I activity and the results herein. As there were more Complex I and Complex III subunits in the low RFI animals (Naik, 2007), one might expect higher enzyme activities but more subunits does not necessarily equate to greater activity. Also not all the subunits were elevated (Naik, 2007). It is known that both genetics and diet
150 affect mitochondrial function (Bottje et al., 2006). However, this does not provide an adequate explanation for the discrepancy as the genetic background and the diet were the same for the animals in the two studies.
Another aspect that should be taken into consideration is the type of tissue being used in the experiments. In addition to using liver samples, Naik (2007) determined the enzyme activity of Complex I using muscle samples and found no significant differences between the high and low residual feed intake groups. This suggests that other factors, such a tissue type, affect Complex I activity and must be considered when comparing studies. However, it does not provide an explanation for the observations herein as the source of the sample was liver in both cases. The most likely explanation is that a relative low number of samples were available for analysis in each study (n=20) and reinforces that replication of such studies is required before acceptance of the results.
The studies in broilers by Iqbal et al. (2004; 2005) demonstrated that the Complex I,
II, III and IV activities in the muscle and liver mitochondria of low RFI chickens were significantly greater than those from the high RFI chickens, and the biggest difference was in the Complex IV activity (Iqbal et al., 2004; Iqbal et al., 2005).
Unlike the results in chickens, the Complex IV enzyme activity measured herein, although elevated in the low RFI animals, did not differ significantly between the low and high RFI cattle.
Interestingly, the activity of Complex I and Complex III in muscle mitochondria of low
RFI trout also consistently exhibited increased activity. The liver mitochondrial samples, however, showed only increased Complex I activity (Eya et al., 2011), again emphasing potential tissue differences. On the other hand, the liver mitochondria from catfish had elevated activity for all the Complex I, II, III and IV in
151 the low RFI fish compared to the high RFI fish (Eya et al., 2012).Thus, there appears to be species differences as well.
In a study using fat-tailed Ghezel lambs, the activities of Complexes I, II, III, IV and
V were greater in the low RFI group (that is, the high efficiency group) than the high
RFI group (Sharifabadi et al., 2012). Thus, the results in cattle, broilers, fish and sheep for the mitochondrial electron transport complex enzyme assays all suggest that the activities of these enzymes are elevated in the low RFI, more efficient animals as hypothesized.
It is puzzling that the ROS, as measured by protein carbonyl concentration, was significantly higher in low RFI cattle liver mitochondria herein. This is contrary to the hypothesis and to the previous observations in studies on chicken muscle mitochondria (Iqbal et al., 2005; Bottje et al., 2006) and pig muscle mitochondria
(Grubbs et al., 2013), wherein the mitochondria from the low RFI animals had lower
ROS levels.
In contrast to the results herein, lower levels of protein carbonyl were found in the livers of low RFI broilers than the high RFI broilers (Iqbal et al., 2005). Protein carbonyls, which are the indicators of oxidative protein damage, have been found to be lower in a variety of tissues obtained from low RFI broilers (Iqbal et al., 2005) including breast muscle mitochondria (Iqbal et al., 2003; 2004), duodenal mitochondria (Ojano-Dirain et al., 2004), lymphocytes (Lassiter et al., 2004) and cardiac muscle (Tinsley et al., 2004). According to Bottje et al. (2004), decreased
ROS production is presumably responsible for decreased proton oxidation in the muscle mitochondria of the low RFI broilers.
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Similar results were observed for pig muscle mitochondria where less hydrogen peroxide was found in the low RFI animals. On the other hand, there was no significant difference in the hydrogen peroxide levels of pig liver mitochondria between high and low RFI animals (Grubbs et al., 2013), again suggesting tissue differences in mitochondrial function.
Moreover, the only other similar mitochondrial study in cattle, Kolath et al. (2006) also found that ROS was elevated in the low RFI animals. Lastly, it should be noted that the only protein that was consistently lower in the proteomics study of muscle and liver mitochondria from the Angus Trangie low RFI animals (Naik, 2007) was catalase. Catalase is the major enzyme that decomposes hydrogen peroxide to water and oxygen, thereby protecting the cell from oxidative damage (Zámocký and
Koller, 1999). Hence, it is not surprising the ROS would be elevated in the low RFI animals if the amount of catalase is substantially lower.
Other authors have suggested that ROS levels should be higher in high RFI, less efficient animals, as higher ROS levels would result more mitochondrial damage and hence, less efficient mitochondrial function (Bottje et al., 2006; Grubbs et al.,
2013). However, it could be also argued that increased Complex I and Complex III enzyme activities in the mitochondria of low RFI animals should result in more ROS production.
Evidence from previous studies have demonstrated that electron leaks occur mainly within Complex I and Complex III of the electron transport chain (Turrens and
Boveris, 1980; Nohl et al., 1996; Herrero and Barja, 1998; Bottje et al., 2004).
However, Kwong and Sohal (1998) did demonstrate that the sites of hydrogen peroxide production are tissue dependent so this may be related to the type of tissue. This was evident in broilers with pulmonary hypertension syndrome, wherein
153 increased ROS production was associated with Complex I and Complex III enzyme activities only in the heart, skeletal muscle and lung mitochondria (Iqbal et al., 2001;
Tang el at., 2002; Bottje et al., 2004).
The discrepancy between the chicken and pig studies described above and the cattle results may reflect species differences, tissue differences and/or even dietary differences. ROS is a normal product of oxygen metabolism and results from a variety of intracellular mechanisms related mainly to the NADPH oxidase (NOX) complexes (Murphy, 2009). These complexes are found in most cell membranes, but particularly the mitochondria, peroxisomes, and endoplasmic reticulum.
Therefore, the level of ROS is not entirely based on the efficiency of the mitochondria and other sources of ROS may be more significant in the low RFI animals relative to high RFI animals. Since all the studies only measured ROS within the mitochondria, the overall level of ROS within the cells is unknown and should be examined.
There was no evident relationship between the mitochondrial enzyme activities or
ROS with the body composition traits. Given that fatness has been linked to mitochondrial efficiency in the medical literature (Harper et al., 2008) and that the animals utilised herein differ in their body composition, this finding might be considered surprising. However, the human studies have involved obese individuals with fat distributions that are quite aberrant. The animals herein differ in their subcutaneous fat depth but that variation is not extreme. Therefore, any evidence for a relationship between the mitochondrial function and fatness would not necessarily be strong.
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6.5 Summary
To summarize, four assays reflecting the efficiency of mitochondria were successfully conducted using liver samples from animals selected for high and low residual feed intake. It was hypothesized that the low residual feed intake animals, which are more efficient, would have greater enzyme activities and would have less
ROS. Indeed, the complex activies were greater although only Complex I and III were significantly different in the low RFI cattle. The low RFI group also had a higher ROS concentration, as measured by protein carbonyl. Thus, the hypothesis that the more efficient RFI animals will have lower ROS is rejected.
Future studies should consider using additional samples for each assay to verify the results, particularly from animals that differ in RFI but not in body composition or diet which may confound the results. It would be also interesting to use different types of tissues such as using the muscle and heart tissues to compare the activities between tissues as this may explain some of the discrepancies with other studies.
Lastly, although not as directly involved in electron transport, conducting the assays for Complex II and Complex V should be taken into consideration as this would complete the whole electron transport chain.
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Chapter 7 General Discussion
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7.1 Introduction
Generally, the aim of beef production industry is to maximise profit where the primary income is beef production and expenses are feed costs (Archer et al.,
1999). The traditional method of selection to improve profitability genetically is through breeding programs in which animals are chosen based on the phenotypes of their parents or siblings. However, this is not always possible or accurate for traits with phenotypes that are difficult or expensive to measure. Thus, the use of genotypes in selection has been introduced to increase the accuracy of selection for such traits (Arthur et al., 2001). Although there has been a paradigm shift from marker assisted selection to the successful application of genome-wide high density
SNP chips for genomic prediction including for RFI EBV (Meuwissen et al., 2001), a better understanding of the genetic mechanisms affecting cattle selected for RFI is necessary.
Quantitative trait loci (QTL) studies have proved to be beneficial as they highlight the location of specific genes in the chromosomes that control traits of interest. One such trait of interest is residual feed intake (RFI) because it is a difficult and expensive trait to measure but potentially can be highly valuable to producers
(Archer et al., 1999). It has been estimated that the value from genetic improvement in RFI to the commercial sector of the southern Australian beef cattle industry would be AUS$162 million over 25 years based on an adoption rate of at least 2% per year for 16 years (Arthur et al., 2004). The question is how to best accomplish the aim of lowering the cost of feed and at the same time, maintain the weight and size of the animals to meet the desired market requirements. One approach is to include the selection of residual feed intake into cattle breeding programs.
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Improving profitability of beef production systems comes through increased efficiency. RFI is often the measure used to select animals for improved efficiency as they have lower feed intake requirements for the same maintenance of body weight and growth rate (Koch et al., 1963). RFI is calculated as the difference between an animal’s actual feed intake and its expected feed requirements for maintenance and growth (Sherman et al., 2008; Smith et al., 2010).
As beneficial as it might sound to select for RFI, measurement of feed intake is not easy. The best available method for measuring RFI is to use centralised testing facilities which are most accurate but expensive. On-farm testing is more affordable but difficult for farmers and the measurement on pasture has limited accuracy
(Archer et al., 1999). Technically, all these methods are equivalent but they are different types of measurements. Considering the drawbacks of the available methods to measure residual feed intake, a different approach is needed.
The use of genotypes to select for residual feed intake has the potential to overcome the phenotyping issues because fewer animals would need to be measured. Numerous studies have been conducted on residual feed intake, ranging from the length test for RFI and the physiological basis of RFI to the identification of candidate gene markers. Archer et al. (1997) found that the optimum length of test for feed efficiency in cattle is 70 days. Tests conducted using the centralised testing facilities which would normally take 120 days at a higher cost but the additional days do not greatly improve the accuracy of the measurement. Decreasing the time of the test to 70 days has a distinct advantage in reducing the cost of the test but it is still relatively expensive to measure at approximately AUS$500 per animal.
Arthur et al. (2004) came to three conclusions regarding the selection of residual feed intake in cattle. First, there is genetic variation for feed efficiency measured as
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RFI in Australian beef cattle. Second, RFI is moderately heritable. Third, the cost of beef production can be reduced by selecting low RFI (ie more efficient) cattle.
There have only been a few studies in beef cattle to locate QTL. Sherman et al.
(2008; 2009) found associations between SNP underlying RFI QTL on five bovine chromosomes (BTA 2, 5, 10, 20, and 29). The cattle in the study were Angus,
Charolais or Alberta hybrid bulls crossed to hybrid dams. Five QTL with significant effects on RFI were also found on BTA 1, 6, 8, 11 and 20 in the Jersey x Limousin progeny utilised herein (Fenton, 2004). As these QTL are located on different cattle chromosomes in the two studies with one exception, BTA 20, it is likely that different genetic backgrounds will yield different DNA variants affecting residual feed intake.
Alternatively, the genes are not of large effect and therefore, are not consistently found.
Nevertheless, locating QTL can assist genomic prediction, which aims to improve selection by exploiting information on the transmission of chromosome fragments.
For genomic prediction to influence beef cattle breeding programs and the cost of genetic gain, training analyses must be undertaken. Training involves statistical analyses that exploit individuals with high-density genotypes and recorded performance. The amount of data required for training depends upon a number of factors including the heritability of the trait (Garrick, 2011).
A few studies have attempted to identify candidate genes associated with RFI in beef cattle (Karisa et al., 2013; Fonseca et al., 2015). However, these studies have been almost exclusively based on differences in gene expression and there is little or no evidence that genetic variation in these genes is associated with RFI.
Therefore, the study described herein and that of Naik (2007) are among the few
159 where positional candidate genes have been examined for genetic variation related to residual feed intake.
7.2 Residual feed intake and mitochondrial function
The results from the previous studies on RFI help to better understand the mechanisms behind variation. One of the most studied mechanisms has been the efficiency of mitochondria in animals that have been selected for either low or high
RFI (Bottje et al., 2004; Kolath et al., 2006; Sharifabadi et al., 2012). This is not surprising since mitochondria function would be expected to reflect the ability to produce ATP.
The electron transport chain comprising of Complex I to V are acknowledged to be responsible for the transfer of electrons simultaneously, producing the ATP as the energy resource for the cell. Inefficiency in the pathways of these Complexes might initiate consequences in the overall production of ATP and the animals would require a higher feed intake to compensate.
Bottje et al. (2002, 2006) discovered an association of mitochondrial function with residual feed intake in broiler chickens. The breast and leg muscle mitochondria from low RFI chickens exhibited less respiratory chain uncoupling and higher
Complex I and II activities than the high RFI chickens. It was suggested that higher enzyme activities might be related to defects in the mitochondria. In 2004, Bottje et al. came to the conclusion that low RFI was associated with a greater activity in respiratory chain complexes (Complex I, II, III and IV) in chicken breast muscle and liver.
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The relationship of mitochondrial function and RFI has been also studied in cattle. In a study in the Angus steers, the decreased plasma glucose concentration observed in the low RFI steers was presumed to be the results of a lower feed intake by these animals (Kolath et al., 2006). Kolath et al. (2006) also came to a conclusion that the rate of mitochondrial respiration is greater in low RFI cattle than in high RFI cattle.
The relationship between hepatic mitochondrial function and RFI was examined in beef cattle using two trials with different cattle breeds (Lancaster et al., 2014). It was hypothesized that cattle with low RFI would have improved efficiency of hepatic mitochondrial respiration compared with high RFI cattle. Results showed that ADP has greater control of oxidative phosphorylation in liver mitochondria of cattle with low RFI (Lancaster et al., 2014).
Naik (2007) conducted a study on proteomics to examine the variation in proteins related to feed intake in the Trangie Angus cattle selection lines. The results from the proteomics study indicated that the levels of stress related proteins such as albumin, catalase, heat-shock proteins 70kDA and 60kDA were elevated in the low
RFI relative to high RFI cattle. In the same study, the level of some oxidative phosphorylation subunits was higher in the low RFI cattle (Naik, 2007). Therefore, the findings herein that the Complex I and Complex III enzymes activities were also higher in the low RFI cattle is consistent.
The advancement of new technologies has made the study of the transcriptome, including mRNA and miRNA, possible. By examining gene expression levels in the skeletal muscle of Yorkshire boars, Jing et al. (2015) concluded that decreasing mitochondrial energy metabolism, possibly through AMPK-PGC-1α pathways, and increasing muscle growth through IGF-1/2 and TGF-α signalling pathways, are
161 potential strategies for the improvement of feed efficiency in pigs and possibly other livestock.
The possibility of manipulating the AMPK pathway to improve feed efficiency is supported by the evidence that the regulation of hormones involved in appetite control affects AMPK activity and food intake in the Jersey x Limousin cattle (Lee,
2005). The AMPK gene was also discovered to be in one of the RFI QTL, on BTA
11 (Naik, 2007). Furthermore, results from the study herein demonstrated a nearly significant association of the SNP in the AMPK (PRKAA1) gene with residual trait intake trait (p=0.065, Chapter 4).
Recent work by Fonseca et al. (2015) selected four genes known to be involved in mitochondrial function (PGC-1α, TFAM, UCP2 and UCP3) for a gene expression study using quantitative real-time PCR. Tissues used for the analysis were the liver and muscle tissue from two groups of Nellore cattle ranked for RFI. It was discovered that the TFAM and UCP2 genes had increased expression in the liver tissue in the low RFI animals. However, in the muscle tissue, the expression of
TFAM was reduced in the low RFI group. Based on these results, Fonseca et al.
(2015) suggested the use of the TFAM and UCP genes as candidate genes in breeding programs for net feed efficiency but no genetic variants in these genes were identified or tested for their relationship with RFI.
One of the genes known to affect the efficiency of mitochondrial function is the uncoupling protein 3 (UCP3) gene. Amongst the latest studies on this gene, the gene expression was measured in the Limousin x Friesian heifers differing in RFI.
The mRNA expression of UCP3 was down-regulated by 2.2 fold in the low RFI animals compared to the high RFI animals (p = 0.06). Another gene of interest was
162 the transcription factor PCG-1α (PPARGC1A), which is a transcriptional coactivator that regulates the genes involved in energy metabolism. The PGC-1α mRNA transcripts were 1.7 fold higher in the low RFI animals (Kelly et al., 2011).
7.3 Residual feed intake candidate genes
Candidate genes are genes that are likely to control a particular phenotypic trait based on their known function. By sequencing the candidate genes, one may detect
DNA variants, which includes single nucleotide polymorphisms (SNPs) and insertions and deletions (in/dels) (Kim and Misra, 2007). SNPs are polymorphisms caused by point mutations that give rise to different alleles containing alternative bases at a given nucleotide position within a locus. The use of SNPs in molecular marker development are crucial since they are the most abundant type of polymorphism in any organism and are adaptable to automated genotyping (Liu and
Cordes, 2004). SNPs with significant effects on RFI could act as DNA markers for the selection of net feed efficiency.
While many studies on mitochondrial function and RFI have been conducted, there are limited studies on positional candidate genes for residual feed intake that are based on the location of the genes within QTL and their role in mitochondrial function and/or energy metabolism. Hence, this study focused on these candidate genes as well as the interactions of the genes that might affect residual feed intake.
Ten potential candidate genes were identified as being associated with RFI and mitochondrial function initially (Zulkifli et al., 2009). The genes were selected based on their location on the BTA QTL and their function in the mitochondria (Table 7.1).
The 10 candidate genes were AK1, ALDOB, CAT, HADHB, GHRL, NDUFA8,
NDUFB5, SOD1, SOD2 and SUCLG1.
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The 10 candidate genes were then sequenced herein in order to discover DNA variants that could be screened for associations with RFI related traits. A total of
54347 bases were sequenced in the 10 genes and 58 DNA variants were found.
Fourteen SNPs from 9 of the genes with sequence variants were analysed for association with RFI related traits and specific fat depot traits, haplotype effects and epistatic effects among the candidate genes. Two genes (HADHB and SOD1) had significant effects on RFI. To verify additional candidate genes, 31 SNPs from a previous study (Naik, 2007) were selected for further analysis. Seven of these SNPs also had significant or nearly significant effects on RFI. Despite the inclusion of the the new SNPs in the analyses, the false discovery rate of 5% was retained because the number of SNPs and traits was still limited.
Table 7.1 Residual feed intake candidate genes
Candidate genes BTA Function NADH Dehydrogenase Beta BTA 1 Involved in electron transport chain Subcomplex5, 16kDa (NDUFB5) Superoxide dismutase 1, soluble BTA 1 Involved in binding copper and zinc ions (SOD1) and destroying free superoxide radicals Aldolase B (ALDOB) BTA 8 Involved in fructose metabolism Superoxide dismutase 2, (SOD2) BTA 9 Involved in destroying radicals Adenylate kinase (AK1) BTA 11 Involved in maintaining cellular energetic economy NADH Dehydrogenase Alpha BTA 11 Involved in electron transport chain Subcomplex 8, 19kDa (NDUFA8) Hydroxyacyl-co- A BTA 11 Involved in synthesising mitochondrial Dehydrogenase β Subunit trifunctional protein (HADHB) Succinyl Co-A synthetase BTA 11 Involved in generating high energy (SUCLG1) phosphate Catalase (CAT) BTA 15 Involved in reactive oxygen species (ROS) metabolic pathway Ghrelin (GHRL) BTA 22 Involved in growth regulation, has an appetite-stimulating effect
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Based on the Pathway Commons analysis, it was predicted that because some genes were the same pathway and/or directly interact, there may be epistatic effects between the genes. Pathway Commons includes data from biochemical reactions, complex assembly, transport and catalysis events, as well physical interactions involving proteins, DNA, RNA, small molecules and complexes (Cerami et al., 2011) and allows one to determine if any two molecules have a shared link, common control mechanisms, or other material connection. Only four of the interactions from the pathway analysis had epistatic effects. This was not necessarily unexpected as the phenotypic effect caused by one variant does not inevitably affect the phenotype of another variant. For example, the effects could simply be additive.
The positional candidate gene study by Naik (2007) identified a number of potential candidate genes that might affect residual feed intake based on their location in RFI
QTL in the Jersey x Limousin herd. Of these, 31 were confirmed in an association study in the Trangie Angus selection line cattle and seven were verified herein to affect RFI in the Jersey x Limousin progeny. Karisa et al. (2013) using Angus and
Charolais beef steers also identified candidate genes that might affect residual feed intake. Amongst the genes of interest which might be associated with mitochondrial function are myosin-X (MYO10), cytochrome P450 subfamily 2B (CYP2B4), aldehyde oxidase (AOX1), and ubiquitin-like modifier activating enzyme 5 (UBA5) genes.
It can be concluded that genes related mitochondrial function and energy metabolism are reasonable candidates for residual feed intake. However, these genes must be verified in much larger cattle populations which poses the problem of finding a sufficient number of animals with adequate phenotypic data. The problem is compounded by the fact that the DNA variants may be unique to the breed and/or sire family as they are unlikely to be causative. Therefore, future studies should
165 considered that investigate the biochemical pathways of these genes as well as the gene expression because it may be that the manipulation of these pathways may be more fruitful than selecting DNA variants.
7.4 Residual feed intake and body composition
Recent work using the Angus Trangie RFI selection line data implicated body composition as affecting RFI (Lines et al., 2014). It was found that the low RFI animals were leaner than the high RFI animals. Thus, 10 additional specific fat depots traits were also analysed for SNP associations with the candidate genes and
4 specific fat depots traits were analysed for epistatic effects. However, none of the
SNPs that affected RFI also significantly affected the fat traits.
To determine if RFI and fatness are related in the Jersey x Limousin progeny, as an aside, a regression analysis was conducted to examined the correlation between
RFI and six traits (hscw, rib fat, imf, ema, seam fat and ossification). In the Jersey x
Limousin population, RFI was not correlated with any of six fat traits. A regression analysis was also conducted using these traits for the Trangie Angus animals and as expected, RFI and rib fat depth were correlated (p=0.001). The conclusion is that while RFI in the Trangie Angus selection line cattle is related to fatness, this is not the case for the Jersey x Limousin cattle. Therefore, DNA variants identified for RFI from the Jersey x Limousin progeny are unlikely also to affect body composition.
However, another factor is that the Trangie Angus selection lines were measured post-weaning for RFI while the Jersey x Limousin progeny were measured for RFI in the feedlot after finishing on pasture and just prior to slaughter. Trait differences between these measurement times have been shown (Barwick, et al., 2009) and this could account for the fact that body composition appears to be less related to
RFI in the Jersey x Limousin cattle.
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Based on the data obtained by Lines, et al., (2014) from the Trangie Angus RFI selection lines, the authors hypothesized that the selection for RFI resulted in changes in appetite and hence, in body composition differences. The changes in body composition, namely fatness, accounted for most of the variation in RFI in these animals. However, it did not account for all of the variation. The authors speculated that the remaining variation in RFI may be due to activity differences.
The results herein though suggest that at least some of the remaining variation may be actually due to differences in mitochondrial efficiency.
The study herein has demonstrated a relationship between mitochondrial activity and RFI both at a genetic level and at a biochemical level. A study by Lines et al.,
(2014) suggested that differences in RFI can be attributed to the appetite or energy consumed by an animal. Since the genes did not also affect fatness, it seems unlikely that there is a relationship with appetite and RFI in the Jersey x Limousin progeny. However, Fenton (2004) demonstrated that RFI and feeding behaviour was correlated in these animals. The low RFI animals ate less frequently and ate for shorter times suggesting a decreased appetite driving the differences in feed intake.
It should be acknowledged though that this is not necessarily casual..
Nevertheless, the results in the current study indicate that there may be actual differences in cellular efficiency between differing in RFI, leading to differences in the efficiency of maintenance. In the Jersey x Limousin current trial, the feed test was conducted on older animals and the residual correlation between RFI and maintenance feed requirement (0.91, kg feed per kg body weight) was much greater than that with feed conversion efficiency (0.08, kg feed per kg weight gain). Thus, to a large degree RFI in this trial does reflect maintenance efficiency. True maintainence efficiency is usually measured as basal metabolic rate and it is
167 recommended that in future studies, basal metabolic rate is measured in a range of cattle breeds and related to RFI to confirm these findings
7.5 Conclusion
Residual feed intake is an important trait to investigate for genotype-assisted selection as it will help reduce cost for production. Being able to select animals with low residual feed intake, which are more efficient, will be more accurate if the genotypes of the genes controlling the trait are included in selection programs.
Selecting animals with lower feed intake using genotypes will be optimal if the genes and their causative variants regulating the mechanisms behind net feed efficiency are known.
It is concluded that the results from this study support the previous literature in that there is a relationship between residual feed intake and mitochondrial function. The results herein also indicate that the majority of the variation in residual feed intake is not necessarily accounted for by appetite as was suggested by Lines, et al., (2014).
There appears to be cattle population differences in the source of the RFI variation.
Thus, in addition to growth and appetite regulation, mitochondrial function and energy metabolism appear to be related to net feed efficiency in cattle and their study should be continued in this regard.
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Appendices
169
Appendix A.1.1
Jersey x Limousin RFI Phenotyping (Adapted from Naik, 2007)
The Davies Cattle Gene Mapping project was established with the intention of identifying DNA markers for QTL affecting beef carcass composition and meat quality and to study the mode of inheritance of important carcass related traits. The project comprised a double back-cross design using two extreme Bos taurus breeds
[the Jersey (J) dairy breed and Limousin (L) beef breed] (Morris et al, 2006). These breeds are known to differ in many traits including carcass composition, fat colour, marbling, body size and meat tenderness (Cundiff et al, 1986). Jersey cattle were crossed with Limousin to produce F1 progeny. Three pairs of first-cross (F1=X) half- brothers were generated, with one of each pair used for mating in Australia and the other used for mating in NZ to both pure Jersey (J) and pure Limousin (L) dams, creating in total 784 three-quarter XJ and XL backcross progeny (range 120-156 progeny per sire). In Australia, 366 calves were bred by natural mating over three breeding seasons (1997, 1998, and 1999) and reared on their dams in the one location (Martindale, South Australia). Calves were grown out on pasture until approximately 28 months of age and then finished on grain for at least 6 months as part of an intensive feed efficiency trial (age at slaughter was 34-40 months). In total, 210 purebred Limousin and Jersey dams were used to generate the Australian backcross progeny.
In New Zealand, 418 calves were born over two years (1996-1997). They were grown out on pasture without grain concentrates and slaughtered at 24-28 months of age. Approximately 300 traits have been measured in the two locations, ranging from carcass composition, behaviour, biochemical traits to feed intake and efficiency. The New Zealand progeny were not measured for feed intake because they were finished on grass and will not be discussed further.
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There were 80 animals in the first cohort (1996 heifers and steers) which were measured for feed intake at the Tullimba Research Feedlot located in Armidale,
New South Wales. The following two years (1997 and 1998) of cattle, the heifers and steers were measured separately. These four cohorts were measured at the
Struan Research Feedlot located in Naracoorte, South Australia.
Both feedlots utilised Ruddweigh electronic feeders. Cattle in a feeding pen were tagged with electronic ear tags that produce a signal for a unique number. Feed intake was calculated as the weight of the food after feeding subtracted from weight of the feed before feeding commenced and summed for each day. Also the time taken feeding (ET), the number of feeding sessions (ES) per day and body weight were recorded on a daily basis. Different parameters contributing to NFE such as daily feed intake, metabolic mid-weight, maintenance requirement, gross efficiency and eating rate were also calculated individually for each animal.
The data were analysed by calculating the least-squares means for each animal over a test period. Day was included in the model to allow for weather, personnel, time of feeding and any other factors that could affect the intake of all cattle.
Average daily gain was calculated as the regression coefficient (slope) of weight against day of test. Residual feed intake was calculated after modelling daily feed intake for metabolic body weight (MMWT) and average daily gain while on the feed intake test . The initial equation used to calculate daily feed intake (DFI) included the main effect of cohort and interactions between MMWT and ADG.
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Table A.1: Summary of phenotypic data for the Australian Davies cattle
(Fenton, 2004)
Trait Total number Mean CV% Min. Max.
of animals
MWt (Kg) 324 625.6 13 382.6 824.3
MMWt (Kg0.73) 324 109.7 10 76.8 134.5
ADG (Kg/day) 324 0.94 61 -1.43 2.35
ADG (Kg/day) 324 12.94 17 6.37 18.95
RFI(Kg/day) 324 0.05 14 -5.04 7.23
Cohort was defined as the combination of year of birth (1996-1998) and sex (heifer or steer). However, both interaction terms and cohort were not significant. Thus, a simple model comprising only MMWT and ADG was used (equation 2.2). It should be noted that this simple model inflated the phenotypic variance.
DFI = Cohort + MMWT + ADG + Cohort.MMWT +Cohort. ADG + RFI(Equation 2.1)
DFI = Cohort + MMWT + ADG + ε (Equation 2.2) where, DFI is daily feed intake (g feed/day), ADG is average daily gain (g body weight/day), MMWT is the metabolic mid-weight (g average body weight0.73) and
RFI is the residual feed intake or residual error term (Fenton, 2004). Net feed efficiency is the same as residualfeed intake but opposite in direction since animals that eat less are more efficient.
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Appendix A.1.2
Angus Trangie RFI Selection Lines (Adapted from Naik, 2007)
Experimental animals for mitochondrial biochemical studies came from the Angus
Elite Progeny Testing Program. Industry Angus stud bulls were crossed with Angus cows from the residual feed intake selection lines at Trangie, NSW (Department of
Primary Industry, NSW). Data collected on the calves included growth performance, structural soundness and ultrasound measurements of fat depth, eye muscle area and marbling. The steer progeny was finished on grain and slaughtered, with additional measurements taken on residualfeed intake and carcass traits.
Samples were collected in 2 consecutive years in 2005 and 2006. 132 steers from
12 sires in year 2005 and 136 steers from 12 sires in year 2006 were used for sample collection. The steers were born at the Trangie Agricultural Research
Station, New South Wales, in the months of July-August in 2003 and 2004. They were weaned in March 2004 and 2005, respectively. Animals born in 2003 were backgrounded at Glen Innes (NSW) or Rutherglen (Victoria), before transfer to
Tullimba in November 2004 for feedlot finishing and RFI testing. They were slaughtered at AMH Beef City, Queensland in April 2005. Animals born in 2004 were backgrounded at Grafton, Queensland and then at Glen Innes, NSW. At Glen
Innes, they were spilt into two groups and maintained in a feedlot in those two groups throughout the feed intake testing at Tullimba, before transfer to Warwick,
Queensland in May 2006 for slaughter. The steers were adapted to the feeders for
14 days before starting the test for 70 days. During the test period, the steers were weighed every 14 days. The test was designed to allow animals to feed ad lib and record electronically how much the animal ate each time it entered the feeders. RFI was calculated for all the steers. At slaughter, data on carcass weight, eye muscle area, rib-fat, marbling, weight of bone (radius-ulna), chuck weight, meat pH and meat colour were recorded. Average body weight of the animals was 600 kg.
173
Appendix A.2
Purification Kit Protocol (UltraClean® PCR Clean-Up Kit, Mo Bio Laboratories Inc)
1. Prepare 1.5 ml (labelled) tubes for transferring PCR products from 0.2 ml tubes
2. Add 300 µl of SpinBind to the tubes with PCR products prior to vortexing and
spinning down.
3. Transfer the SpinBind/PCR mixture to a labeled Spin Filter unit and centrifuge
at 10,000 x g for 30 seconds.
4. Remove the Spin Filter basket and discard flow through.
5. Replace the Spin Filter basket in the same tube before adding 300 µl of
SpinClean buffer.
6. Centrifuge at 10,000 x g for 30 seconds before removing the Spin Filter basket
to discard flow through.
7. Replace the Spin Filter basket in the same tube before an additional 30
seconds spin.
8. Transfer the Spin Filter basket to a 2ml collection tube.
9. Add 30 µl of Elution Buffer (10mM Tris) onto the centre of the Spin Filter
membrane.
10. Centrifuge for 1 minute at 10,000 x g.
11. Discard the Spin Filter basket. Purified PCR products in the 2 ml collection tube
should be stored in 4°C for further use.
174
Appendix A.3
Purification of sequencing products
1. Transfer the sequencing products to 1.5 ml tubes.
2. Add 80µl of 75% isopropanol in each tube.
3. Vortex the tube and leave at room temperature for 15 minutes.
4. Centrifuge the tubes at 13,000 rpm for 20 minutes.
5. Remove the supernatant. Be careful not to disturb the pellet.
6. Add 250µl of 75% isopropanol to the tube.
7. Centrifuge at 13,000 rpm for 20 minutes.
8. Discard the supernatant carefully.
9. Dry the sample in a 37°C incubator.
Appendix A.4
Ice-cold Lysis Buffer
Tris-HCl (pH 7.4) 20 mM
Triton x-100 1%
NaCl 50 mM
Sucrose 250 mM
Sodium fluoride 50 mM Sodium pyrophosphate 5 mM
Dithiothreitol (DTT) 2 mM
Leupeptin 4 mg/L
Trypsin inhibitor 50 mg/L
Benzamide 2 µM
Phenyl methyl sulfonyl fluoride (PMSF) 0.5mM/L
175
Appendix A.5
Isolation buffer (pH 7) d-mannitol 220 mM
Sucrose 70 mM
HEPES 2 mM
Appendix A.6
Coomassie Blue Solution
1. Dissolve 100mg Coomassie Brilliant Blue G-250 in 50 ml of 95% ethanol in a 1- liter volumetric flask.
2. Add 100 ml of phosphoric acid.
3. Bring to volume with water.
4. Filter through Whatman No 1 filter paper.
5. Store at 4°C.
Appendix A.7
Complex I activity reagent mix
Potassium phosphate, MgCl2 (pH 7.2) 25 mM, 5 mM
Bovine Serum Albumin (BSA) 2.5mg/ml
NADH 0.13 mM Potassium cyanide (KCN) 2 mM
Antimycin A in ethanol 2 µg/ml
Ubiquinone in ethanol 65 µM
176
Appendix A.8
Complex III activity reagent mix
Potassium phosphate, MgCl2 (pH 7.2) 25 mM, 5 mM
Bovine Serum Albumin (BSA) 2.5 mg/ml
Potassium cyanide (KCN) 2 mM
Ubiquinol in ethanol 35 µM
Cytochrome C (oxidised) 15 µM n-dodecyl-β-D-maltoside 0.6 mM
Rotenone in ethanol 2 µg/ml
Appendix A.9
Complex IV activity reagent mix
Potassium phosphate (pH 7.0) 20 mM n-dodecyl-β-D-maltoside 0.45 mM
Cytochrome c (reduced) 15 µM
177
Appendix A.10
Protein Carbonyl Assay
1. Transfer 100 µl of sample to two 2 ml tube. One tube will be the sample tube.
2. Add 400 µl of DNPH to the sample tube.
3. Add 400 µl of 2.5 M HCl to the control tube.
4. Incubate both tubes in the dark at room temperature for one hour. Vortex each
tube briefly every 15 minutes.
5. Add 500 µl of 20% TCA to each tube.
6. Vortex tubes and place on ice for 5 minutes.
7. Discard supernatant and resuspend pellet in 500 µl of 10% TCA.
8. Place tubes on ice for 5 minutes.
9. Centrifuge tubes for 10 minutes at 10,000 x g at 4°C.
10. Discard supernatant and resuspend pellet in 500 µl of (1:1) ethanol/ethyl
acetate mixture.
11. Vortex thoroughly and centrifuge tubes at 10,000 x g for 10 minutes at 4°C.
12. Repeat step 10 and 11 two more times.
13. Add 250 µl of guanidine hydrochloride and resuspend the protein pellets by
vortexing.
14. To remove any remaining debris, centrifuge at 10,000 x g for 10 minutes at 4
°C.
15. Transfer 220 µl of supernatant from the tube to the well of the 96-well plate.
16. Measure the absorbance at a wavelength between 360-385 nm using a plate
reader.
178
Appendix B
List of primers designed for PCR
Gene Forward primer Reverse primer
AK1 ex1 GGACTTGGGAGGGAAACAG CGTGTCCTTGTTTGCCTTG
AK1 ex2.3 CTCGCTCTGGGCTGACTC GTGTGTCATGGGCTTTGCT
AK1 ex4 AATGAATGGATGAATGAATGAATG CAGTTGGAGACACGGAGGTTC
AK1 ex5 CCCAGAACGAGCAGAGACC CCTACGGGGCTTGGTGA
AK1 ex6 CCCTCACTGGTTGGACAAG GATGTGTTCTGCCCTCCC
ALD ex1 ATGCTTCCCAGTTTTCCTTG TCCCTAAGCCAAAGACTCAAA
ALD ex2 CATCCCACCTTCATCCATTA ATCCTTGGCTGGCTTTACAC
ALD ex3 CCCCCGATAAAATGACAGTAG GGTGAGGATGGAAAGGAGTG
ALD ex4 TCTTACACGGGCACCTCTTC AGCAGTGTCCAGACCCAAGT
ALD ex5 ACAGCCCCCAGTAAAGTCA AGTGGTGAGAGAAAAGGAAGG
ALD ex6 GGCTAAACCACAGATGCTCA TGAGGACTTTCTTGCTTTCTTAC
ALD ex7 ATGGAGGCTGATTTGGATGT GCTGAGGAGAGGACGGATTT
ALD ex8 TCAACATTTCACTGCCCTCTT TGCTTTTCTTGGTGGCTTCT
ALD ex9 GGATAAAAGCATAAAAGGAAGGA ATCCAACGAGCAACCAATGT
CAT ex1 CCTATCTCTTCCCAGCCTCTC CTCTGCCTCTTTCCCCATAA
CAT ex2 TAGACCAGAGGGGGAGCA TGATGGACAGTTTTGCCTTT
CAT ex3 AGGTGGTTGTTTGGCTCTGT TGCTTGTTTCCTATTGTCTGGT
CAT ex4 GTGAAATGCAAAAGGAAATGAA AATCCAGCCAATCCCAATC
CAT ex5 GGACACAACTGAGCGACTGA TGAATGGGGAAAGATGAATAA
CAT ex6 TAGGGAAATGAAGGGAAAGTAG GAGAGCCAGCACAAAATGAG
CAT ex7 GAAAAGAATCAGGATAAGCAAAC GCAACAGCAAACAGGAGTAAA
179
CAT ex8 CAGCATCTCCCTTCCAGTTC CAGGACTAAAGACCCAAACCA
CAT ex9 GATTTGTGGCTCTTTGGCTG GCAGTAGCCAGGGTGAAAGA
CAT ex10 CCCAACTGAAACCCTCCA ATGCACTGGAGGAGGAAAT
CAT ex11 GTGGGGTTTGGGTCAGTTT GCAGGACCCACATTTTCATAC
CAT ex12 CCCCCATCCTACCCGTC AAAACCCACTGTGCCTATCC
CAT ex13.1 AAGGGTACTGGAGTGGGTTGT GCAGGAATAAACATCAAGCCA
CAT ex13.2 GGCTTTTAATCCTACTTTCCTGT CTGGAGAAGGAAATGGCAAC
HAD ex1 AGCGGACGAAAGTGGAAA AAAGCCACACAACCGAGATT
HAD ex2.3 CAAATCATAGCTCTGCCTCTTAC CTCCTGCCAACATCCTCAA
HAD ex4 AGTAGTCACCAACCAGATGCC GGGGGCAACAGAAAATAAGA
HAD ex5 CGGATGAAGGGTATGAAGGA GCACACACGGCTAAAATGAA
HAD ex6 GTTGTTTCTCTTTTCTTTTTGACT AAACATATCCAAAACCAGCTC
HAD ex7 TTTCATCATTCCACCAACCAT AAACTCCTCCTGCCTCAAATC
HAD ex8 TGTCCCTGTAGCCTGAATGAA TCCGCAAGGCATCTCTAAAA
HAD ex9 CTTCCCTTGTCCTAATCCTGA TTACCAAATACGAAAGCAAACTC
HAD ex10 TTGCTTTCGTATTTGGTAACATC TTTGTCCTTTCTTCCTCCTTG
HAD ex11.12 CAAAATGGGTCAGTGTGTGTG TTGTGAGGAAGGGAGAGGAG
HAD ex13 ACTCCTCTCCCTTCCTCACAA TGCAAACCAATCAGAATCCA
HAD ex14 GAGGAAATCCAGGGCTACAC CCAGGAGGTATTTTTCAACTAAG
HAD ex15 AACAGGGCTCTTGGCTTTC TTTTGTATTTTCCATCATTTCCA
HAD ex16.1 GCCCCATTTTACAGAGGAAGT TCGTTAGGTCACAAATAGGAATG
HAD ex16.2 TTCCGGTGTTCTGAGCTTT CTAGCAGCCTCATGGTGGA
ND8 ex1 CAGTCACGGATGTTCAAGGA GTTGAGGGCATTGGGTTG
ND8 ex2 CATTGGTCCCTTCTGGTCTC AAAAAGAAAAAGAACATGCCAA
ND8 ex3 GGCTGTGTGTTTGAGGTCTGT AACCAACAAGAAACCTGCTCA
180
ND8 ex4 GTGCCTCCCTCCCTATCTG AAAACAGAGAGAGCGAGGGAG
ND5 ex1 AAGTTGGGGAAGTGGACAGA AAAGAATCGTGTGCCTGAGA
ND5 ex3.4.5.6 TTCCTCCTTCTCAAACCAGC GCCAGCATTAGTATCAGGACAA
ND5 ex7 GAGCATAGTGTAATAAGAACGAGCA GGGAGAAGGGGAAGGAGAG
ND5 ex8.1 AGAACAGGGACAGGAGGAGAG GAAAGCCCCAAACCAGAAA
ND5 ex8.2 GGTTTTGTGTGCGATTTCATT GGCACAAGGGTATTCAAAAG
SOD1 ex1 TACCTCCCTGTCCCACTTT CACCCCCACACACACAAA
SOD1 ex2 TAGCCACACACGAGCACAAC TTTGTCCCTGAGGCTGATTT
SOD1 ex3 GTGCTCAGGAGTGCCATTTT CGCTACAAAACCCAAATCGT
SOD1 ex4 TGCACCCCAAGTCTTTCC ATGCCCTGGAGAAGAAAATG
SOD1 ex5 TGGGTTTCTCGTCTTTTTGA TCCCTAAAGCTAACAAAGAATG
SOD2 ex1 ATCTCTTTCGGGGGCTGG CGGAGAGGAGGAGGAGGAG
SOD2 ex2 CGCATCGTCTCAGGTCAG GGGGGCACTGGTCTTTAC
SOD2 ex3 TTTTCTGTCCTTGTTTTCTTTGTC ATTGTGTGTGTGCGTGTGTG
SOD2 ex4 CACGCACACACACAATACCA AATTCTCCAGCTCCCCATAC
SOD2 ex5.6.7 TTCATTCATGGGTGGGTTTT AGATCCGGGGCAGTGAGT
SOD2 ex8.9 AGAAGGCTGTGATTGGTGAA GATTCGCCCAGCAAGATTA
SOD2 ex10 AGCATACACATAGCCACCCC AACAAGCCACACTCAGAAACAC
SUC ex1 CGGGGAAAAGCCAAGTTAT TTCTCAACCCACTCACCCTC
SUC ex2 TGGGTCAAATAGGGAAAGCA AGGGATTTTCGGGGTAACAA
SUC ex3 GATGAGGATGCTTGAAAATATG GCTGACACGCACACTTCTAA
SUC ex4 GGAGAGGAGGCCACACTTAA CTTTGGGGAGTTTTGAGCAT
SUC ex5 CGGGTCAAGCACAAACT TCAATGGGAGAATGGACA
SUC ex6 TGATGGTGTTGTTTGTTTGGA TGCCAAGACAGTGACCAAAG
SUC ex7 ACAAGACCCAACCACCATTTA AGCCAGCCTTGACAAAGAGA
SUC ex8 CAGTTTTGCTTCAGGATTGTTT TTTTCAACCTCTGTCCCTGG
181
SUC ex9 CTGAGGTTCTTGACGTTTTGTT AAAGACACATCCATGCACACA
GHR ex1 AGGAGGAGGGGAGCAGAA GAGATGGTGGCTGGTGGA
GHR ex2 TCAGCCCTCCTCCAGAAAC GCTCCTCCTTCCTGACTATCTG
GHR ex3 AAGGATCGTGGAGTGGTAAG AACGCAAGAGGTCAAGTCAA
GHR ex4 AACCCGTGCCCCTGTAGT CTCCTTTCCCCTTCCTGTTC
182
Appendix C.1
Haplotype SNP genotype frequencies*
*Highlighted cells indicates combinations with less than 5 animals.
183
Appendix C.2
List of additional SNPs
No SNP Gene Sequence 1 mho031 TGATTAGTTT(T/-)CTTTCTTTAAT 2 ampk1 AMP -Activate Kinase Alpha 1 Subunit ATGATTAATTA(G/A)TTGACTCTTA 3 igf1snp2 Insulin-Like Growth Factor I TCTGCAKCTAKGGAGCCAAGG 4 rdhe2e13 Retinal Short Chain Dehydrogenase AAGCTGGAGCCAC(G/T)AGGGTCTAT Reductase 5 atp2b432 ATPase, Ca++ Transporting, Plasma 5' TTTCTTTGCTRACAGCCCTTT Membrane 4 6 bcdo2snp3 Beta-Carotene Oxygenase 2 TAGTTATTAT(C/G)AGAAGAAAAA 7 IL2 Interleukin 5' CCAGAGAGACMTCTTTTATAA 8 umps2 Uridine Monophosphate Synthetase 5' CACAGTGCTGYGTATTCTTGA 9 ppargcia10 Peroxisome Proliferator-Activated Receptor GAGAGAGACC(G/A)CGGAGGTGAG Gamma, Coactivator 1 Alpha 10 tek16 Tyrosine Kinase, Endothelia TTACTTGTAAAGGAA(A/G)TATTTCCCTTA 11 ghr5 Growth Hormone Receptor AACTTTCTTC(T/C)TCACACA 12 tek2 Tyrosine Kinase, Endothelia TTTGGCTTTATTATTATT(A/T)TTTTTTTTTT 13 ass1 Argininosuccinate Synthase 1 5' TTCCTCTTTAYACTTTCCCAC 14 bcmo14 Beta-Carotene Oxygenase 1 AAAGGGAGGA(A/C)CATGAATCTA 15 map1b Microtubule-Associated Protein 1B 5' AGACCGGAGAYTATGAAGAGA 16 umps1 Uridine Monophosphate Synthetase 5' TTTGGGAAACYGCTGAGGTTC 17 si_3 Sucrase-Isomaltase 5' AATTTCAATTYCTATTCCTAG 18 ahsg2 Alpha-2-HS-Glycoprotein GCAGCCTAGC(A/-)TTCCTGGAGG 19 pi3k_2 Phosphatidylinositol 3-kinase P-85 Alpha AATTAATCAA(A/-)ATATTTCACC Subunit 20 hadha_1 Mitochondrial Trifunctional Protein, Alpha 5' AAATTATGTTSAAACATTAAT Subunit 21 capn1snp530 Calpain 1, (Mu/I) Large Subunit 22 elong_3 Elongation Protein 3 Homolog ACATTCCGTTT(C/T)GAATTGGTT 23 foll1 Follistatin Precursor 5' CTGCTACGACYGCCAAATCAC 24 pi3k_1 Phosphatidylinositol 3-kinase P-85 Alpha 5' TAGAACATACYGGCGTTTGTT Subunit 25 si_1 Sucrase-Isomaltase 5' ACTAAAGAACKCTGCGAAGAA 26 ahsg1 Alpha-2-HS-Glycoprotein 5' ACACTTTCTCYGGGGTGGCCT 27 fhsr2 Follicle Stimulating Hormone Receptor 5' ATCCAAGGAAYGGCCACTGCC 28 fhsr1 Follicle Stimulating Hormone Receptor 5' AGGTCAGAAASCTCATCCACT
184
29 ghr1 Growth Hormone Receptor 5' CGAGGTAGACRCCAAAAAGTA 30 ghr_2 Growth Hormone Receptor 5' GAGTTTCATCRTTTTTTACCT 31 foll2 Follistatin Precursor 5' TGTCACCACCRGGCCCGTCCT
185
Appendix D.1 Epistatic SNP genotype frequencies (examples but includes all with less than 5 animals as highlighted)
186
Appendix D.2
Result of pathway analysis: No interactions
CAT + ALDOB No interaction CAT + AK1 No interaction CAT + SUCLGI No interaction CAT + GHRL No interaction ALDOB + SOD2 No interaction ALDOB + NDUFB5 No interaction ALDOB + NDUFA8 No interaction ALDOB + AK1 No interaction ALDOB + SUCLG1 No interaction ALDOB + GHRL No interaction HADHB + GHRL No interaction SOD1 + SUCLG1 No interaction SOD1 + GHRL No interaction SOD2 + AK1 No interaction SOD2 + SUCLG1 No interaction SOD2 + GHRL No interaction NDUFB5 + AK1 No interaction NDUFB5 + SUCLG1 No interaction NDUFB5 + GHRL No interaction NDUFA8 + AK1 No interaction NDUFA8 + SUCLG1 No interaction NDUFA8 + GHRL No interaction AK1 + GHRL No interaction SUCLG1 + GHRL No interaction PRKAA1 + CAT No interaction PRKAA1 + HADHB No interaction PRKAA1 + NDUFB5 No interaction PRKAA1 + NDUFA8 No interaction PRKAA1 + SUCLG1 No interaction PRKAA1 + GHRL No interaction PRKAA1 + IGF1 No interaction PRKAA1 + IL2 No interaction PRKAA1 + MAP1B No interaction PRKAA1 + SI No interaction PRKAA1 + HADHA No interaction PRKAA1 + PIK3CA No interaction IGF1 + CAT No interaction IGF1 + ALDOB No interaction IGF1 + HADHB No interaction IGF1 + SOD2 No interaction IGF1 + NDUFB5 No interaction IGF1 + NDUFA8 No interaction IGF1 + SUCLG1 No interaction IGF1 + MAP1B No interaction IGF1 + HADHA No interaction IL2 + CAT No interaction
187
IL2 + ALDOB No interaction IL2 + HADHB No interaction IL2 + SOD1 No interaction IL2 + SOD2 No interaction IL2 + NDUFB5 No interaction IL2 + NDUFA8 No interaction IL2 + SUCLG1 No interaction IL2 + GHRL No interaction IL2 + MAP1B No interaction IL2 + SI No interaction MAP1B + CAT No interaction MAP1B + ALDOB No interaction MAP1B + HADHB No interaction MAP1B + SOD2 No interaction MAP1B + NDUFB5 No interaction MAP1B + NDUFA8 No interaction MAP1B + AK1 No interaction MAP1B + SUCLG1 No interaction MAP1B + GHRL No interaction MAP1B + HADHA No interaction MAP1B + PIK3CA No interaction SI + SOD2 No interaction SI + SUCLG1 No interaction SI + PIK3CA No interaction HADHA + GHRL No interaction PIK3CA + CAT No interaction PIK3CA + ALDOB No interaction PIK3CA + NDUFA8 No interaction PIK3CA + SUCLG1 No interaction
188
Appendix D.3
Result of pathway analysis: catalyze two conversions connected via a common molecule
GENES OTHER GENES RELATED WITH PATHWAY INTERACTION CAT + HADHB ACADM, MYO5B CAT + SOD1 CAT + SOD2 INS CAT + NDUFB5 MT-ND6, NDUFAB1, MT-ND4 CAT + NDUFA8 NDUFAB1 HADHB + SOD1 MYO5B, ACAA1, ACAA2 HADHB + SOD2 MYO5B, ACADM, HADH, HSD17B10 HADHB + NDUFB5 MT-ND4, ACADM, HADH, NDUFAB1, MYO5B, HSD17B10 HADHB + NDUFA8 NDUFAB1, MYO5B, ACADM, HSD17B10, HADH HADHB + AK1 MYO5B HADHB + SUCLG1 ACADM, HADH, SUCLA2, HSD17B10, ACAT2 SOD1 + SOD2 AOC2, AOC3, INS, POR SOD1 + NDUFB5 NDUFAB1, NDUFA2, NDUFA6, NDUFA13, NDUFV1, NDUFS4, NDUFS7, MT-ND1, MT-ND2, MT-ND4, MT-ND5, MT-ND6 SOD1 + NDUFA8 NDUFAB1, NDUFA6, NDUFS7, MT-ND1, MT-ND2, MT-ND4, MT-ND5, MT-ND6 SOD1 + AK1 AK2, AK5, AK7 SOD2 + NDUFB5 NDUFAB1, NDUFA2, NDUFA13, NDUFS7, MT-ND1, MT-ND2, MT-ND4, MT-ND5, MT-ND6, LMNB1 SOD2 + NDUFA8 NDUFAB1. NDUFA2, NDUFA13, NDUFS7, MT-ND1, MT-ND4, MT-ND5, MT-ND6 NDUFB5 + NDUFA8 NDUFAB1, NDUFA2, NDUFA6, NDUFA13, NDUFS7, MT-ND1, MT-ND2, MT-ND4, MT-ND5, MT-ND6,TUSC3 AK1 + SUCLG1 AK2, AK7, SUCLA2, SUCLG2 PRKAA1 + SOD1 PRKAA1 + SOD2 IGF1 + AK1 IL2 + AK1 SI + CAT SI + HADHB MYO5B SI + SOD1 SI + NDUFB5 SI + NDUFA8 SI + AK1 AK2, AK5, AK7 SI + HADHA HADH, HSD17B10 HADHA + CAT MYO5B, ACADM, HADH HADHA + HADHB MYO5B, HADH, ACADM, HSD17B10, ACAT1, ACAT2, ACOX3 HADHA + SOD1 HADH, HSD17B10 HADHA + SOD2 MYO5B, HADH, ACADM HADHA + NDUFB5 MYO5B, HADH, ACADM, HSD17B10, NDUFAB10, MT-ND1, MT- ND4, MT-ND5, MT-ND6 HADHA + AK1 HSD17B10, HADH, AK5, EGHS1, AK2, AK7 HADHA + SUCLG1 HADH, SUCLA2, ACADM, HSD17B10, ACAT2, TUBA8
189
HADHA + PIK3CA PIK3CA + HADHB PIK3CD, PIK3CG, MYO5B PIK3CA + SOD1 PIK3CD PIK3CA + SOD2 PIK3CD PIK3CA + NDUFB5 PIK3CA + AK1
190
Appendix D.4
SNP interactions between candidate genes without myostatin f94l genotype in the model*
INTERACTION bone% dfi ema fat% ftbone heart% heartwt hscw kid% kidwt liver% liverwt meat% mtbone ossi adg rfi AK1SNP1xALDSNP3 0.014 0.009 0.019 0.033 0.011 0.084
AK1SNP1xALDSNP8 0.043 0.077 0.007 0.007 0.001 0.067 0.033
AK1SNP1xCATSNP8
AK1SNP1xHADSNP2 0.055 0.076
AK1SNP1xHADSNP4 0.068 0.094 0.022
AK1SNP1xHADSNP7 0.029 0.042 0.091
AK1SNP1xND5SNP5'
AK1SNP1xND5SNP8.2 0.039 0.038 0.037 0.056
AK1SNP1xND8SNP1 0.085 0.084
AK1SNP1xSUCSNP4 0.094 <0.001 <0.001 0.069 0.004 0.039
ALDSNP3xCATSNP8 0.056 0.003 0.088
ALDSNP3xCATSNP12 0.069 0.052 0.068 0.041
ALDSNP3xHADSNP2 0.079 0.009
ALDSNP3xHADSNP4 0.072 0.071 0.045 0.085
ALDSNP3xHADSNP7 0.031 0.088
ALDSNP3xND5SNP5' 0.003
ALDSNP3xND5SNP8.2 0.034 0.052
ALDSNP3xND8SNP1 0.066
ALDSNP3xSOD2SNP3 0.086 0.006 0.024 0.087
ALDSNP3xSUCSNP4 0.051 0.014 0.056 0.024 0.070 0.058 0.048 0.079
191
INTERACTION bone% dfi ema fat% ftbone heart% heartwt hscw kid% kidwt liver% liverwt meat% mtbone ossi adg rfi ALDSNP8xHADSNP2 0.002
ALDSNP8xHADSNP4 0.029 0.078 0.063
ALDSNP8xHADSNP7 0.087
ALDSNP8xND5SNP5' 0.013 0.054 0.023
ALDSNP8xND5SNP8.2 0.092
ALDSNP8xND8SNP1 0.056
ALDSNP8xSOD1SNP3 0.036
ALDSNP8xSOD2SNP3 0.029 0.036 0.091 0.028
ALDSNP8xSUCSNP4 0.051 0.032
CATSNP8xHADSNP2 0.097 0.033
CATSNP8xHADSNP4 0.092 0.033 0.092 0.026
CATSNP8xHADSNP7
CATSNP8xND5SNP5'
CATSNP8xND5SNP8.2 0.089 0.095 0.042
CATSNP8xSOD1SNP3 0.036 0.037
CATSNP8xSOD2SNP3 0.090
CATSNP8xSUCSNP4
CATSNP12xAK1SNP1
CATSNP12xHADSNP2
CATSNP12xHADSNP4
CATSNP12xHADSNP7 0.011 0.077
CATSNP12xND5SNP8.2
CATSNP12xND5SNP5' 0.012 0.034
CATSNP12xSOD2SNP3 0.086
CATSNP12xSUCSNP4 0.037 0.053 0.065 0.088
HADSNP2xND5SNP5' 0.096 0.069
192
INTERACTION bone% dfi ema fat% ftbone heart% heartwt hscw kid% kidwt liver% liverwt meat% mtbone ossi adg rfi HADSNP2xND5SNP8.2 0.099
HADSNP2xND8SNP1 0.063 0.094
HADSNP2xSOD1SNP3
HADSNP2xSOD2SNP3 0.083 0.013 0.028
HADSNP2xSUCSNP4 0.015 0.013 0.019 0.030 0.005 0.005 0.008 0.004
HADSNP4xND5SNP5' 0.056 0.085
HADSNP4xND8SNP1 0.073
HADSNP4xSOD1SNP3
HADSNP4xSOD2SNP3 0.052 0.070
HADSNP7xND5SNP8.2 0.082
HADSNP7xSOD1SNP3 0.056
HADSNP7xSOD2SNP3 0.031 0.042
ND5SNP5'xSOD1SNP3 0.037 0.037 0.039
ND5SNP5'xSOD2SNP3
ND5SNP5'xSUCSNP4 0.018 0.085
ND5SNP8.2xND8SNP1 0.070 0.086
ND5SNP8.2xSOD1SNP3 0.068
ND5SNP8.2xSOD2SNP3 0.006
ND8SNP1xSOD1SNP3 0.025
ND8SNP1xSOD2SNP3 0.085 0.076
ND8SNP1xSUCSNP4 0.049
SOD1SNP3xSUCSNP4 0.008 0.078 0.016 0.098
SOD2SNP3xSUCSNP4 0.017 0.032 0.041 *P < 0.10 only. Trait abbreviations in Table 4.4
193
Appendix E
List of high and low residual feed intake animals according to mid-parent RFI EBV
High feed intake animals Low feed intake animals
No Kill ID MIDP RFI No Kill ID MIDP RFI 1 77 0.98 1 9 -0.85 2 123 0.94 2 48 -0.81 3 87 0.92 3 16 -0.80 4 105 0.87 4 42 -0.77 5 128 0.86 5 6 -0.76 6 118 0.85 6 1 -0.74 7 86 0.83 7 43 -0.68 8 95 0.82 8 15 -0.67 9 75 0.81 9 14 -0.66 10 102 0.78 10 4 -0.65 11 120 0.74 11 37 -0.64 12 91 0.73 12 23 -0.61 13 94 0.72 13 12 -0.60 14 101 0.71 14 67 -0.59 15 133 0.69 15 2 -0.57 16 79 0.68 16 10 -0.54 17 108 0.67 17 32 -0.53 18 136 0.66 18 33 -0.51 19 126 0.65 19 51 -0.50 20 129 0.63 20 8 -0.48
194
References
195
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