Genomic approaches to economic trait loci and tissue expressionprofiling: application to muscle biochemistry and beef quality

Andre Eggena,*, Jean-Francois Hocquetteb

aBiochemical Genetics and Cytogenetics Unit, Department of Animal Genetics, INRA, 78350 Jouy-en-Josas, FrancebHerbivore Research Unit, Department of Animal Husbandry and Nutrition, INRA, 63122 Saint-Genes-Champanelle, France

Received 3 October 2002; accepted 2 December 2002

Abstract

Genetic and environmental factors profoundly alter the phenotypes of animals. Nowadays, genomics allows large-scale analysisof gene characteristics (structural genomics) and expression (functional genomics). Genome mapping, comparative genomics and

identification of quantitative trait loci and polymorphisms are the subject of active investigation to gain a better knowledge of thestructure and function of genes. Gene expression profiling using DNA microarrays and proteomics holds great promise for thestudy of regulatory events which control the final biological functions. Combined with classical genetics and muscle biochemistry toform an integrative biology, these new approaches will bring a better understanding of complex traits and physiological processes.

Major applications in meat science could be, for cattle, (1) the identification of new predictors of quality traits (for instance, ten-derness), (2) the monitoring of beef quality (including traceability) through the production systems (nutrition level, growth path,grass-feeding), and (3) the improvement of animal selection (markers and gene assisted selection) which may also include quality

traits.# 2003 Elsevier Ltd. All rights reserved.

Keywords: Genomics; Animal selection; Meat science; QTL; Mapping; Functional genomics

1. Introduction

The most significant change in science during the lastdecade has been the development of ‘‘genomics’’, forexample, large-scale approaches to gene analysis andexpression. Indeed, the wealth of information comingfrom genome sequences or cDNA libraries coupled withpowerful chip technologies (Jordan, 1998) has openedthe way to integrated analysis of gene characteristicsand expression. Large-scale analysis of nucleotidesequences, combined with DNA microarrays, ‘‘pro-teomics’’ (Celis et al., 2000), ‘‘metabolomics’’ and alsobiochemical modeling (Fiehn, 2001) are indeed chan-ging the way biologists are studying gene expression.Scientists will be able to obtain simultaneously all therequested molecular information related to the biologi-cal function they are looking at, or to discover new keygenes involved in a specific physiological function.

Combined with classical physiology using modelingtools, genomics is expected to advance our knowledgeof cell biology by leaps and bounds. These newapproaches are nowadays strongly encouraged in var-ious biological areas applied, for instance, to variousimportant medical issues, for example, muscle pathol-ogy (Pietu et al., 1999; Tkatchenko et al., 2001), obesityor diabetes (Nadler & Attie, 2001).In this paper, the meat industry, and especially the

beef industry, is taken as a representative example of theexpected outcomes and benefits arising from genomicapproaches applied to bovine muscle biochemistry.

2. Genomic approaches

Genomics is divided into two basic areas, the char-acterization of the physical nature of whole genomes(structural genomics) and the characterization of theoverall patterns of gene expression at the mRNA orprotein levels (functional genomics). The first approachis an absolute prerequisite for expression studies. How-

0309-1740/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/S0309-1740(03)00020-2

Meat Science 66 (2003) 1–9

www.elsevier.com/locate/meatsci

* Corresponding author. Tel.: +33-1-34-65-24-24; fax: +33-1-34-

65-24-78.

E-mail address: eggen@jouy.inra.fr (A. Eggen).

ever, from a physiological point of view, the true exci-tement is not the knowledge concerning the genome perse, but understanding which genes are transcribed andtranslated into protein products and how these pro-cesses are regulated. Both, structural and functionalapproaches are relevant to the mapping, the detailedcharacterization of economic trait loci and the identifi-cation of the relevant gene(s) controlling traits of inter-est. The combination of both approaches with classicalphysiology leads to a modern form of biology, namely,‘‘Integrative biology’’.

2.1. Structural genomics

Let us start with a phenotype of interest in an animalpopulation and postulate that no prior knowledge couldlead to the direct discovery of the underlying gene(s). Inthat case, the identification of the genetic determinantsof a particular phenotype will most often start with theidentification of pedigrees in which the traits of interestare segregating in order to develop a positional cloningapproach. This strategy takes advantage of the struc-tural knowledge of the genome based on cytogenetics,genetics, radiation hybrids and physical maps of a gen-ome: a trait of interest will be mapped to a particularchromosomal region through the identification of linkedmarkers and genes. The region of interest will then be

fine mapped and new genetic markers will be identifiedin order to reduce the confidence interval containing thegene of interest. The next step will focus on the con-struction of a set of overlapping clones (contig) for theentire region to perform a gene inventory leading to theidentification of the gene itself and finally of the causalmutation (Fig. 1). This approach is often combined witha candidate gene approach where potential functionalcandidate genes are investigated in the defined intervalof localization of the studied trait (Eggen, 2000; Milan,2000). Using this positional cloning approach, manygenome mapping programs world-wide have identifiedchromosomal regions (Quantitative Trait Loci or QTL)harboring genes associated with specific economic traits(Georges et al., 1995; Milan et al., 2000).As genomic approaches are emerging in different spe-

cies, data from several genomes are being produced andcombined into a relatively new discipline, namely com-parative genomics: through comparative mapping,chromosome similarities among species are identifiedmaking it possible to extrapolate mapping data fromgenomes with high resolution gene maps (human andmouse) to genomes with low resolution gene maps (i.e.livestock species; Gautier, Hayes, & Eggen, 2002;Hayes, 1995). Another step in comparative genomics isthe comparison of sequences, which could be provenvery useful to confirm functional annotations or com-

Fig. 1. The four steps involved in positional cloning (modified from Bernot, 1999).

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putational gene-finding results and to identify novelgenes in a genome (Roest et al., 2000; Thomas &Touchman, 2002). In addition to comparison of codingregions, comparative sequencing has the potential ofidentifying conserved sequences outside the codingregions that could control gene expression (Hardison,2000). One can hypothesize that part of the conservedsequences could be of potential functional significance.Another important aspect of the genome knowledge is

the identification of gene polymorphisms, for instancesingle nucleotide polymorphims (SNP). SNP may bedetected within functional candidate genes or withinchromosomal regions containing QTL which have beenpreviously identified as important for the phenotype ofinterest. Genotyping of SNPs is a rapidly developingfield and the association of the existence of SNP withthe variability of a phenotype will be useful in identify-ing key genes involved in the determination of char-acters (Garcia, Canon, & Dunner, 2002; Vignal, Milan,SanCristobal, & Eggen, 2002).

2.2. Functional genomics

A major effort in genomics is the identification ofcoding sequences or transcript units on a whole genomebasis and the discovery of those that are trait-associatedand expressed in a specific organ or tissue. These codingsequences are therefore responsible for a particularfunction and/or a specific phenotype.One recent technological advance in this area is the

DNA microarray. In this technique, DNA fragmentscorresponding to either known or undefined genes arespotted at a high density to generate a microarray(DNA chip). Oligonucleotide arrays can also be used.Samples of messenger RNA extracted from target tis-sues are then labeled during reversed transcription andhybridized to the microarray. The next step is thequantification of the amount of hybridized material perspot by using powerful image analysis software. Theresults will give a gene expression ‘‘fingerprint’’. Bycomparing samples from two or more tissues or thesame tissue with different treatments, it is possible toassociate the expression of particular genes with thephenotype of interest. Depending on the previousknowledge of DNA sequences used in this type ofexperiment, this technology allows either the analysis ofimportant known genes or the discovery of new genesinvolved in the determination of the phenotypes ofinterest. This is the reason why two different strategiesare used: (1) the first is based on microarrays with arelatively small number of key genes known to be cru-cial for the studied biological functions; modeling theexpression of these key genes leads to an overallknowledge of the studied function; (2) the second strat-egy is to work with a high number of genes, most ofthem being undefined; the genes which are differentially

expressed when the phenotypes change are consideredto be new key genes and are then further characterizedin terms of biological function. The DNA technologyhas recently been tested and several papers have suc-cessfully demonstrated that this methodology can pro-vide insights into complex biological systems, forexample, muscle biology (Campbell, Gordon, Carlson,Pattison, Hamilton, & Booth, 2001). For instance, newgenes related to the myogenic differentiation arrest ofhuman rhabdomyosarcoma cells have been identified(Astolfi et al., 2001). This is of great importance inmedical science since rhabdomyosarcoma is a highlymalignant tumor of skeletal muscle origin accountingfor 5–10% of childhood cancers and for more than 50%of pediatric soft tissue sarcomas. Similarly, differentialscreening of gene expression in skeletal muscles fromDuchenne muscular dystrophy patients allowed theidentification of novel genes up- or down-regulated inthe case of mutations in the dystrophin gene (Tkatch-enko et al., 2001). Similar experiments of functionalgenomics are encouraged to identify genes which maybe differentially regulated following mutations in themyostatin gene in double-muscled cattle (Grobet et al.,1998). Indeed, many events in muscle physiology(Deveaux, Cassar-Malek, & Picard, 2001) or energymetabolism of adipose tissues for instance (Hocquette,Bas, Bauchart, Vermorel, & Geay, 1999) are altered indouble-muscled cattle.

2.3. Towards proteomics

However, it is the products of the genes that give lifeto the cells. For this reason measuring protein expres-sion will considerably advance our understanding of cellbiology (Matthews, 2001). ‘‘Proteomics’’ refers to acollection of technologies with the common goal ofidentifying key proteins playing crucial roles in complexbiological samples. One of the most widely used pro-teomic technologies is two-dimension gel electrophor-esis. This technology is based on the electrophoreticseparation by charge and size of a complex proteinmixture extracted from target tissues. The result is theseparation of hundreds to thousands of proteins intodiscrete spots. The spots of interest are determined byquantifying their relative levels between two or morebiological samples using image analysis software. Pro-teins expressed differentially between different pheno-types are considered to be important, and thereforesubjected to further identification and characterizationusing modern technology (Edman sequencing, massspectrometry, etc.). Again several recent papers havesuccessfully demonstrated that this approach can bevery useful to gain a better understanding of biologicalfunction of various tissues with different scientificobjectives (Dierick, Dieu, Remacle, Raes, Roepstorff, &Toussaint, 2002; Isfort, 2002; Westergren-Thorson,

A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9 3

Malmstrom, & Marko-Varga, 2002). However, as forthe mRNA studies, some protein biochips are nowadaysavailable. They are based on high-density arrays ofimmobilized antibodies or immobilized functional pro-teins. In the first case, large-scale immunoassay systemsare used for protein expression profiling. In the secondcase, the major applications are screening for protein–protein interactions, identifying the substrates of pro-tein kinases, and identifying the protein targets of smallmolecules (MacBeath & Schreiber, 2000; Wilson &Nock, 2002).

3. Integrative biology

The most powerful scientific strategies are certainlybased on the combination of structural, comparative,and functional genomics at the genome, mRNA andprotein levels. Such approaches combined with classicalbiochemical, physiological and nutritional studies couldprove to be very useful in the near future as detailed inthe two next examples.The first example is accessing metabolic pathways:

physiological and biochemical expertise of a trait ofinterest (phenotype) could lead to the identification ofcandidate metabolic pathways and therefore candidategenes involved in the control of the entire pathway.Taking the human genome as master genome, humangene sequences for all known genes of a pathway makeit possible to identify species-specific coding sequencesand therefore species-specific large insert clones (Bac-terial Artificial Chromosome) for which polymorphism,namely SNP, could be detected: this polymorphismwould then be used to test a putative association foreach gene of the pathway and the trait of interest. Inparallel, the discovery of differentially expressed genesat the mRNA (Phelps, Palumbo, & Beliaev, 2002) or theprotein levels (Celis et al., 2000) may help to understandthe metabolic networks. A well known example is theregulation of glucose metabolism in yeast (DeRisi, Iver,& Brown, 1997) and this is a major long-term perspec-tive in the field of energy metabolism of farm animals(Ortigues-Marty et al., 2001).The second example is making use of model organ-

isms: in order to identify potential genes involved inmuscle biochemistry, a model organism like Caenorha-bidtis elegans could help identify and characterize theinteraction of the genes involved in the development ofthe muscle tissue since the complete genome is nowsequenced and cell differentiation has been well studied(Birchmeier & Brohman, 2000; Guhathakurta, Schrie-fer, Hresko, Waterston, & Stormo, 2002; Roy, Stuart,Lund, & Kim, 2002).In conclusion, we have to consider that new tools and

methodologies are now available to researchers for thestudy of the genetic determinants of specific traits and

phenotypes. However, particular efforts have to bemade on the characterization and the dissection of spe-cific phenotypes into simple biological units for whichthe genetic determinants are easier to identify: a com-plex trait would then be the interaction of several ‘‘sim-pler’’ biological units.

4. Major issues which need to be addressed in the

meat industry

The meat industry accounts for a high proportion ofgross agricultural output, at least in developed coun-tries. In addition, the demand for meat in developingcountries is increasing rapidly, especially in China (Gill,1999). Thus, the world demand for meat is supposed toincrease 1.65-fold between 1993 and 2020, and that ofbeef 1.5-fold (Corbett, 2001). However, beef consump-tion is decreasing in developed countries due to a largenumber of reasons: high cost, very irregular sensorytraits (especially tenderness), low dietetic values due tohigh saturated fat contents, increasing concern for ani-mal welfare and a confidence crisis at consumer level.Indeed, various safety crises including bovine spongi-form encephalopathy (BSE) have had a significantimpact on the livestock industry all over the World.These changes forced the meat industry (and especiallythe beef industry) to rethink its business. So far, themeat industry response at least in Europe and USA hasbeen the elaboration of charts for high quality produc-tion systems, i.e. the proliferation of labels and thedevelopment of market segmentation.We would like to argue here that the understanding of

molecular and genetic control of muscle biochemistry,and hence of important beef quality traits, can help forcattle selection or animal management and nutrition(Fig. 2) in order to satisfy the consumers’ wishes. Theultimate objective is to produce healthier beef of desiredand constant quality in appropriate production systems.Our discussion will be based on three different examples

Fig. 2. Towards a comprehensive view of the genetic, nutritional and

environmental determinants of meat quality.

4 A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9

which illustrate different views of the system: quality,animal husbandry and nutrition, and finally geneticselection.

4.1. How could genomics help for quality prediction?

Consumers are looking for safe and consistently highquality products (Tarrant, 1998). Meat quality can bedefined in terms of safety, technological traits, sensorytraits (tenderness, juiciness, flavor, color, etc.), dietetictraits (fat composition and content, etc.) and imagetraits linked to subjective considerations (animal wel-fare, natural production systems). Many factors affectthe quality of meat, including the way the animals arefed, managed and then slaughtered and the way thecarcasses and the meat are processed. Here we will dis-cuss, as one representative example, a major qualitytrait: tenderness, which is of most concern to the meatindustry.The critical control points (CCPs) of tenderness from

the production, pre-slaughter, processing and valueadded sectors of the beef supply chain were identifiedfor the Australia domestic beef market using beef sam-ples which have been tested by consumers (12,700 in1999 to 55,000 samples in 2002). These CCPs have beenincorporated in a model to predict palatability for indi-vidual bovine muscles. The accuracy of the model topredict palatability was tested by looking at its ability tocorrectly classify samples into consumer grades. About49–68% of the samples were correctly classified(Thompson, 2002). These numbers are relatively low,but the results fit well with various studies conducted inFrance which indicated that pre-slaughter factors alone(breed, diet, growth path), explain less than one third toa quarter of the variability in mechanical strength orbeef tenderness (Renand, Picard, Touraille, Berge, &Lepetit, 2001). In addition, the contribution of pre-slaughter muscle characteristics to tenderness differs alot between muscle types and production systems (ani-mal type � management) (Brouard, Renand, & Turin,2001; Dransfield et al., 2002; Renand, Havy, & Turin,2002). However, despite this moderate impact on finaltenderness, it is well established that area and type ofmuscle fiber (Picard, Lefaucheur, Berri, & Duclos,2002), collagen characteristics and intramuscular fatplay a significant role in beef tenderness (Geay, Bau-chart, Hocquette, & Culioli, 2001). Furthermore, it ishypothesized that other muscle characteristics (e.g.contents of type III collagen or of various proteo-glycans) may be important for beef tenderness(Harper, 1999; Hocquette, Cassar-Malek, Listrat, Jurie,Jailler, & Picard, 2001), but their relative contributionsare currently a matter of debate due to our limitedknowledge of muscle physiology and to the lack ofreliable and precise laboratory methods to study theseparameters.

Taken all together, this underlines the need to (1)identify new muscle characteristics which may beimportant for tenderness, and to (2) develop high qual-ity statistical and modeling tools to predict meat ten-derness from all the available information. The firstobjective can be clearly achieved by large-scale analysisof gene characteristics and expression in bovine muscle.Indeed, the functional genomics approach has openedthe way to an almost exhaustive analysis of geneexpression in various physiological conditions. The sec-ond objective can by achieved by the development ofbioinformatic tools to store and to analyze by appro-priate statistical methods the great number of resultswhich will become available from genomics. Bioinfor-matic tools will also be essential to track the relation-ships between gene characteristics (e.g. SNP) andexpression (at the mRNA and/or protein levels) withmuscle characteristics and meat quality traits.In practice, we must also underline the limits of

genomics. Indeed, transcriptomic analysis based onmRNA studies must be carried out on fresh samplesto avoid any degradation of mRNA. By contrast,proteomics analysis can be carried out on musclestaken after slaughter and during ageing. This is ofparticular interest since meat tenderness depends, to alarge extent, on post-mortem tenderization. Thisstrategy has been successfully developed to study meatquality of pigs (Lametsch & Bendixen, 2001). On theother hand, the technical variability of ‘‘proteomics’’analysis is intuitively greater than for transcriptomicanalysis simply because they are based on very differ-ent principles: physical separation for proteomics vsmolecular hybridization for transcriptomics (Celis etal., 2000).

4.2. How could genomics help to monitor meat qualitythrough the production systems?

The earlier mentioned muscle biochemical character-istics which determine meat quality traits (muscle fibers,connective tissue, intramuscular fat) can be modified bynutritional and physiological factors. This has beenextensively reviewed by various authors (Geay et al.,2001; Hocquette, Cassar-Malek et al., 2001; Oddy,Harper, Greenwood, & McDonagh, 2001).Briefly, it has been firstly demonstrated by different

physiological and biochemical approaches that growthin utero plays a very important role in myogenesis.Compared with other mammals, bovine muscle is verymature at birth from the point of view of its contractileand metabolic properties (Picard et al., 2002). In addi-tion, the proliferation of myoblasts and muscle differ-entiation before birth are susceptible to maternalnutritional manipulations. Changes in the hormonalstatus (hormone synthesis and metabolism, regulationof hormone receptors) are thought to be mediators of

A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9 5

this nutritional adaptation (Dauncey, White, Burton, &Katsumata, 2001). A global gene expression profiling atmRNA or protein level will provide a better under-standing of the gene regulation that underlies myogen-esis and its control by nutrition. Preliminary studies offunctional genomics conducted in France confirmedthat, in cattle, many genes are regulated around 6months of fetal life and around birth, which indicatesthat the last 3 months of gestation are important forbovine muscle myogenesis (Sudre et al., 2000).Secondly, it has also been demonstrated by biochem-

ical studies that muscle characteristics can be modifiedby changes in nutrition or in growth path. For instance,the type of diet (hay vs grass silage) modifies musclecollagen and fiber characteristics, which may changetenderness (Listrat, Rakadjiyski, Jurie, Picard, Tour-aille, & Geay, 1999). Similarly, a grass-based diet atpasture compared to a corn-based diet indoors inducesan orientation of energy metabolism towards the oxi-dative type. Muscle mitochondria are more active(Ortigues-Marty et al., 2002), and, again, this maychange meat quality traits. Re-feeding after a restrictedperiod results in superior growth rate, termed compen-satory growth. The restriction period was characterizedby a mild hypothyroidism (Cassar-Malek, Kahl, Jurie,& Picard, 2001), which may induce muscle fiber plasti-city (Brandstetter, Picard, & Geay, 1998) which is mus-cle-specific (Listrat et al., 1999; Cassar-Malek, Listrat etal., 2001). However, our knowledge of gene regulationby animal husbandry and nutrition is limited due to thesmall number of genes or muscle characteristics whichhave been studied so far.Thirdly, the BSE crisis and other safety problems

have increased consumer concerns regarding the con-stituents of animal origin in feedstuffs, the type of dietgiven to the herbivores and the geographic origin of themeat-producing mammals. Analytical methods capableof solving these problems by objective criteria are vitalfor successful regulatory control in order to restoreconsumer confidence in beef production. For instance,methodologies using DNA markers have been proposedfor beef identification in different countries (Arana,Soret, Lasa, & Alfonso, 2002, Choy, Oh, & Kang, 2001;Sancristobal-Gaudy, Renand, Amigues, Bosher, Leve-ziel, & Bibe, 2000; Verkaar, Nijman, Boutaga, & Len-stra, 2002). Methods of traceability of grass-feedinghave also been developed on the basis of the signatureof carotenoid pigments in herbivore meat, milk andcheese (Prache & Theriez, 1999; Prache et al., 2002) oron the basis of the terpenoid profile in fats of dairyproducts or meat (Cornu, Kondjoyan, Begnaud, Micol,Renou, & Berdague, 2001). Given that nutrients regulatemetabolic activity by modifying gene expression in herbi-vores (Hocquette, Ortigues-Marty, & Vermorel, 2001),it is likely that the gene expression profile will be usefulto develop new methods of traceability of grass-feeding.

Thanks to the development of the ‘‘functional geno-mics’’ approach, scientists can now study the influenceof production systems on meat quality traits from acompletely different point of view, namely throughlarge-scale approaches to gene expression regulation atthe mRNA or protein levels by nutritional and physi-ological factors whatever the genotypes of the animalsmay be. One of the major objectives of genomics isindeed to gain a better knowledge of environmentalfactors regulating gene expression. This may help tomonitor quality by optimizing production systems andto develop new methods of traceability.

4.3. How could genomics help to select animals withsuperior genetic potential?

If producers can adapt their production systems toensure high quality products, breeders have to exploitthe variability among animals by selecting those thathave superior genetic potential for the economic traitsof interest. However, the selected traits must be easy tomeasure and sufficiently inheritable to be selected. Inlivestock species, significant advances in the selectionprocess for economically important characters havebeen achieved over the past decades (Dekkers & Hospi-tal, 2002). Such progress was based on the phenotypicalrecording of individual performances for traits of inter-est and on the compilation of these observations, toge-ther with the genealogical information concerning acertain ‘‘breeding value’’ of candidates for selection.Most of the economic traits considered in animal selec-tion are quantitative traits. The genetic variation ofthose traits is thought to be based on the cumulativeinteraction of different alleles of several genes alongwith environmental factors: indeed for quantitative traitloci, a generally accepted hypothesis is that the geneticarchitecture of a QTL consists of a large number ofgenes, each having a small effect on the phenotype(Flint & Mott, 2001). Therefore, achieving a reasonableunderstanding of the relevant genes controlling a spe-cific phenotype and of their interaction remains animpossible challenge with traditional methods. How-ever, recent studies have demonstrated that in somecases, a small number of genetic loci contribute to alarge proportion of the variance of the trait of interest(Hilbert et al., 1991; Jacob et al., 1991). Moreover, sev-eral limitations of these methods for genetic improve-ment based on population genetics and statistics arebecoming evident with time: firstly, efficiency decreaseswhen the traits are difficult to measure or have a lowinheritability; secondly, animal selection has been gen-erally limited to traits that can be correctly measured ina large number of animals and, thirdly, new selectioncriteria have to be integrated in the selection processsuch as adequacy of the processed product (i.e. milk forcheese production, carcasses for meat production, etc.),

6 A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9

quality and acceptance of the final product by the con-sumer, and animal welfare. Unfortunately, low to mod-erate heritabilities and inconsistent variation within andbetween breeds for beef quality traits related to muscletoughness have been recorded. This suggests thatgenetic improvement in beef tenderness may be a chal-lenge for the near future (Burrow, Moore, Johnston,Barendsse, & Bindon, 2001). Similarly, in pigs, onlybetween 10 and 30% of the variation in meat qualitytraits is determined by the genetic basis of the animal(Klont, Plastow, Wilson, Garnier, & Sosnicki, 2002).However, genetic variability and heritability of musclefiber number and size may be sufficient to include thesetraits in farm animal selection for improving lean meatcontent and meat quality (Rehfeldt, Fiedler, Dietl, &Ender, 2000).Up until now, genetic selection of beef cattle has been

directed in favor of muscle growth. Since consumersseek products of high and reliable quality, the con-sequences of growth selection on muscle biochemicaland molecular characteristics were examined usingDNA array technology. Selection on growth is asso-ciated with lower oxidative muscle metabolism and avariation in expression profile of 26 out of 400 genes.Most of them have an unknown biological function(Sudre et al., 2002).By combining the structural and functional genomic

approaches, geneticians will be able to study phenotypedifferences between animals, and therefore differences inthe final product, from a completely different point ofview, namely the nucleotide sequence. One of the majorgoals of genomics is indeed to gain an exhaustiveunderstanding of the structure and the function of genesthrough a detailed molecular characterization of wholegenomes, including polymorphisms (e.g. SNP). There-fore genomic approaches can help in the dissection ofthe genetic components of qualitative, quantitative andcomplex traits. These efforts will lead to a better under-standing of the relationships between genetic variationand biological functions. Thus, new putative applica-tions for animal selection can be considered in the nearfuture (i.e. marker assisted selection).

5. Conclusions

The world meat business is constantly undergoingchanges. For beef, these changes are nowadays forcedby increasing concern from the consumers for safety,sensory quality, healthiness, traceability and naturalfeeding of herbivores, due to the recent crisis (BSE,etc.). A set of very promising new tools based on struc-tural and functional genomics is revolutionizing the wayscientists study the biology of a specific function or anorganism in general. The applications may not only bein human medicine (Celis et al., 2000), but also in

muscle biochemistry applied to meat quality. The use-fulness of these new tools has been previously discussedfor pig meat quality (Klont, Plastow, Wilson, Garnier,& Sasnicki, 2002). In beef, the use of these new methodswill be essential to understand the control of a widevariety of phenotypes of genetic, environmental ornutritional origin. One of the main challenges in thefuture will be to solve the problems posed by the analy-sis, interpretation and access to large amount of datathat will become available from structural (QTL, SNP)and functional (mRNA or protein levels) genomics.

Acknowledgements

The authors thanks Drs. Isabelle Cassar-Malek,Christine Leroux, Anne Listrat, Brigitte Picard (INRA,Theix), Patrice Martin (INRA, Jouy-en-Josas), HubertLeveziel (INRA, Limoges University) and the entirebovine genomics team of the Biochemical genetics andCytogenetics Unit (INRA, Jouy-en-Josas) for helpfuldiscussions and their implications in AGENAE, theINRA program related to structural and functionalgenomics applied to Animal Science.

References

Arana, A., Soret, B., Lasa, I., & Alfonso, L. (2002). Meat traceability

using DNA markers: application to the beef industry.Meat Science,

61, 367–373.

Astolfi, A., De Giovanni, C., Landuzzi, L., Nicoletti, G., Ricci, C.,

Croci, S., Scopece, L., Nanni, P., & Lollini, P. L. (2001). Identifica-

tion of new genes related to the myogenic differentiation arrest of

human rhabdomyosarcoma cells. Gene, 274, 139–149.

Bernot, A. (1999). L’analyse des genomes: cartographie, sequencage,

identification des genes (2nd ed.). Paris, France: Nathan universite.

Birchmeier, C., & Brohman, H. (2000). Genes that control the devel-

opment of migrating muscle precursor cells. Current Opinion in Cell

Biology, 12, 725–730.

Brandstetter, A. M., Picard, B., & Geay, Y. (1998). Muscle fibre

characteristics in four muscles of growing bulls. II. Effect of castra-

tion and feeding level. Livestock Production Science, 53, 25–26.

Brouard, S., Renand, G., & Turin, F. (2001). Relationships between

longissimus lumborum muscle characteristics and tenderness of three

local beef breeds. Rencontres Recherches Ruminants, 8, 49–52.

Burrow, H. M., Moore, S. S., Johnston, D. J., Barendse, W., & Bin-

don, B. M. (2001). Quantitative and molecular genetic influences on

properties of beef: a review. Australian Journal of Experimental

Agriculture, 41, 893–919.

Campbell, W. G., Gordon, S. E., Carlson, C. J., Pattison, J. S.,

Hamilton, M. T., & Booth, F. W. (2001). Differential global gene

expression in red and white skeletal muscle. American Journal of

Physiology, 280, C763–C768.

Cassar-Malek, I., Kahl, S., Jurie, C., & Picard, B. (2001). Influence of

feeding level during post-weaning growth on circulating concentration

of thyroid hormones and extrathyroidal 50-deiodination in steers.

Journal ofAnimal Science, 79, 2679–2687.

Cassar-Malek, I., Listrat, A., Jurie, C., Jailler, R., Hocquette, J. F.,

Bauchart, D., Ouali, A., Lamarre, M., & Picard, B. (2001). Influ-

A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9 7

ence of compensatory growth on muscular characteristics of steers.

Rencontres Recherches Ruminants, 8, 111.

Celis, J. E., Kruhoffer, M., Gromova, I., Frederiksen, C., Ostergaard,

M., Thykjaer, T., Gromov, P., Yu, J., Palsdottir, H., Magnusson,

N., & Orntoft, T. F. (2000). Gene expression profiling: monitoring

transcription and translation products using microarrays and pro-

teomics. FEBS Letters, 480, 2–16 (review).

Choy, Y. H., Oh, S. J., & Kang, J. O. (2001). Application of RAPD

methods in meat for beef identification. Asian-Australasian Journal

of Animal Science, 14, 1655–1658.

Corbett, J. L. (2001). The challenging demand for increases in meat

and milk production: enhancing the contributions of grazing live-

stock. Asian-Australasian Journal of Animal Science, 14, 1–12

(review).

Cornu, A., Kondjoyan, N., Begnaud, F., Micol, D., Renou, J. P., &

Berdague, J. L. (2001). Terpenes in meat: tracers of animal feed and

geographical origin. Rencontres Recherches Ruminants, 8, 61.

Dauncey, M. J., White, P., Burton, K. A., & Katsumata, M. (2001).

Nutrition–hormone receptor-gene interactions: implications for

development and disease. Proceedings of the Nutrition Society, 60,

63–72 (review).

Dekkers, J. C. M., & Hospital, F. (2002). The use of molecular genet-

ics in the improvement of agricultural populations. Nature Reviews

Genetics, 3, 22–32.

DeRisi, J. L., Iver, V. R., & Brown, P. O. (1997). Exploring the

metabolic and genetic control of gene expression on a genomic

scale. Science, 278, 680–686.

Deveaux, V., Cassar-Malek, I., & Picard, B. (2001). Comparison of

contractile characteristics of muscle from Holstein and double-

muscled Belgian Blue fetuses. Comparative Biochemistry and

Physiology, 131A, 21–29.

Dierick, J. F., Dieu, M., Remacle, J., Raes, M., Roepstorff, P., &

Toussaint, O. (2002). Proteomics in experimental gerontology.

Experimental Gerontology, 37, 721–734 (review).

Dransfield, E., Abouelkaram, A., Bauchart, D., Culioli, J., Jurie, C.,

Lepetit, J., Listrat, A., Martin, J. –F., & Picard, B. (2002). Eating

quality of three muscles from young bulls and cull cows from four

breeds from Central France. Rencontres Recherches Ruminants, 9,

255–258.

Eggen, A. (2000). Cartographie fine d’un gene et clonage positionnel.

INRA Productions Animales, Hors Serie, 133–136.

Fiehn, O. (2001). Combining genomics, metabolome analysis, and

biochemical modelling to understand metabolic networks. Com-

parative and Functional Genomics, 2, 155–168 (review).

Flint, J., & Mott, R. (2001). Finding the molecular basis of quantita-

tive traits: successes and pitfalls. Nature Reviews Genetics, 2, 437–

445.

Garcıa, D., Canon, J., & Dunner, S. (2002). Genetic location of

inheritable traits through association studies: a review. Current

Genomics, 3, 188–200.

Gautier, M., Hayes, H., & Eggen, A. (2002). An extensive and compre-

hensive radiation hybrid map of bovine chromosome 15: comparison

with human chromosome 11.Mammalian Genome, 13, 316–319.

Geay, Y., Bauchart, D., Hocquette, J. F., & Culioli, J. (2001). Effect of

nutritional factors on biochemical, structural and metabolic char-

acteristics of muscles in ruminants; consequences on dietetic value

and sensorial qualities of meat. Reproduction Nutrition Development,

41, 1–26 (review)(Erratum, 41, 377).

Georges, M., Nielsen, D., Mackinnon, M., Mishra, A., Okimoto, R.,

Pasquino, A. T., Sargeant, L. S., Sorensen, A., Steele, M. R., &

Zhao, X., et al. (1995). Mapping quantitative trait loci controlling

milk production in dairy cattle by exploiting progeny testing.

Genetics, 139, 907–920.

Gill, M. (1999). Meat production in developing countries. Proceedings

of the Nutrition Society, 58, 371–376 (review).

Grobet, L., Poncelet, D., Royo, L. J., Brouwers, B., Pirottin, D.,

Michaux, C., Menissier, F., Zanotti, M., Dunner, S., & Georges, M.

(1998). Molecular definition of an allelic series of mutations dis-

rupting the myostatin function and causing double-muscling in cat-

tle. Mammalian Genome, 9, 210–213.

Guhathakurta, D., Schriefer, L. A., Hresko, M. C., Waterston, R. H.,

& Stormo, G. D. (2002). Identifying muscle regulatory elements and

genes in the nematode Caenorhabditis elegans. Pacific Symposium on

Biocomputing, 425–436.

Hardison, R. C. (2000). Conserved noncoding sequences are reliable

guides to regulatory sequences. Trends in Genetics, 16, 369–372.

Harper, G. S. (1999). Trends in skeletal muscle biology and the

understanding of toughness in beef. Australian Journal of Agri-

cultural Research, 50, 1105–1129 (review).

Hayes, H. (1995). Chromosome painting with human chromosome-

specific DNA libraries reveals the extend and distribution of con-

served segments in bovine chromosomes. Cytogenetics Cell Genetics,

71, 168–174.

Hilbert, P., Lindpaintner, K., Beckmann, J. S., Serikawa, T., Soubrier,

F., Dubay, C., Cartwright, P., De Gouyon, B., Julier, C., & Taka-

hasi, S., et al. (1991). Chromosomal mapping of two genetic loci

associated with blood-pressure regulation in hereditary hypertensive

rats. Nature, 353, 521–529.

Hocquette, J. F., Bas, P., Bauchart, D., Vermorel, M., & Geay, Y.

(1999). Fat partitioning and biochemical characteristics of fatty tis-

sues in relation to plasma metabolites and hormones in normal and

double-muscled young growing bulls. Comparative Biochemistry and

Physiology A, 122, 127–138 (Erratum, 123, 311–312).

Hocquette, J. F., Ortigues-Marty, I., & Vermorel, M. (2001). Manipu-

lation of tissue energy metabolism in meat-producing ruminants.

Asian-Australasian Journal of Animal Science, 14, 720–732 (review).

Hocquette, J. F., Cassar-Malek, I., Listrat, A., Jurie, C., Jailler, R., &

Picard, B. (2001). Some recent advances in muscle biology and its

regulation by nutrition: consequences on bovine meat quality.

Recent Advances in Animal Nutrition in Australia, 13, 135–143

(review).

Isfort, R. J. (2002). Proteomic analysis of striated muscle. Journal of

Chromatography B, 771, 155–165 (review).

Jacob, H. J., Lindpaintner, K., Lincoln, S. E., Kusumi, K., Bunker,

R. K., Mao, Y. P., Ganten, D., Dzau, V. J., & Lander, E. S. (1991).

Genetic mapping of a gene causing hypertension in the stroke-prone

spontaneously hypertensive rat. Cell, 67, 213–224.

Jordan, B. R. (1998). Large-scale expression measurement by hybridi-

sation methods: from high-density membranes to ‘‘DNA chips’’.

Journal of Biochemistry, 124, 251–258 (review).

Klont, R. E., Plastow, G. S., Wilson, E. R., Garnier, J. P., & Sosnicki,

A. A. (2002). In Proceedings of the 48th International Congress of

Meat Science and Technology, Rome, 25–30 August (pp. 6–11).

Lametsch, R., & Bendixen, E. (2001). Proteome analysis applied to

meat science: characterizing post-mortem changes in porcine mus-

cle. Journal of Agricultural Food Chemistry, 49, 4531–4537.

Listrat, A., Rakadjiyski, N., Jurie, C., Picard, B., Touraille, C., &

Geay, Y. (1999). Effect of the type of diet on muscle characteristics

and meat palatability of growing Salers bulls. Meat Science, 53,

115–124.

MacBeath, G., & Schreiber, S. L. (2000). Printing proteins as micro-

arrays for high-throughput function determination. Science, 289,

1760–1763.

Matthews, D. E. (2001). What is more exciting than ‘genomics’? ‘Pro-

teomics’. Current Opinion in Clinical Nutrition and Metabolism Care,

4, 339 (editorial comment).

Milan, D. (2000). Notion de gene candidat. INRA Productions Ani-

males, Hors Serie, 119–123 (revue).

Milan, D., Jeon, J. T., Looft, C., Amarger, V., Robic, A., Thelander,

M., Rogel-Gaillard, C., Paul, S., Iannuccelli, N., Rask, L., Ronne,

H., Lundstrom, K., Reinsch, N., Gellin, J., Kalm, E., Roy, P. L.,

Chardon, P., & Andersson, L. (2000). A mutation in PRKAG3

associated with excess glycogen content in pig skeletal muscle.

Science, 288, 1248–1251.

8 A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9

Nadler, S. T., & Attie, A. D. (2001). Please pass the chips: genomic

insights into obesity and diabetes. Journal of Nutrition, 131, 2078–

2081 (review).

Oddy, V. H., Harper, G. S., Greenwood, P. L., & McDonagh, M. B.

(2001). Nutritional and developmental effects on the intrinsic prop-

erties of muscles as they relate to meat quality. Australian Journal of

Experimental Agriculture, 41, 921–942 (review).

Ortigues-Marty, I., Hocquette, J. F., Vermorel, M., (2001). Meth-

odologies and techniques used in the study of energy metabolism:

elements of discussion and perspectives. 15th Symposium on Energy

Metabolism in Animals. 11–16 September 2000, Snekkersten (Den-

mark). In A. Chwalibog, & K. Jakobsen, (Eds.), Energy metabolism

in animals, 2001. EAAP Publication No. 103, Wageningen Pers.

(The Netherlands), pp. 81-89.

Ortigues-Marty, I., Jurie, C., Hocquette, J. F., Picard, B., Cassar-

Malek, I., Listrat, A., Jailler, R., Bauchart, D., Dozias, D., &

Micol D. (2002). The use of principal component analysis (PCA)

to characterize beef steers. 19th General Meeting of the european

Grassland Federation. 27–30 May 2002, La Rochelle (France). In

J. L. Durand, J. C. Emile, C. Huyghe, & C. Lemaire (Eds.),

Multi-function grasslands. Quality forages, animal products and

landscapes, 2202. EGF, Vol. 7; pp. 584–585, Grassland Science in

Europe.

Phelps, T. J., Palumbo, A. V., & Beliaev, A. S. (2002). Metabolomics

and midroarrays for improved understanding of phenotypic char-

acteristics controlled by both genomics and environmental con-

straints. Current Opinion in Biotechnology, 13, 20–24.

Picard, B., Lefaucheur, L., Berri, C., & Duclos, M. (2002) Myo-

genesis in farm animals. Reproduction Nutrition Development, 42,

415–431 (review).

Pietu, G., Eveno, E., Soury-Segurens, B., Fayein, N. A., Mariage-

Samson, R., Matingou, C., Leroy, E., Dechesne, C., Krieger, S.,

Ansorge, W., Reguigne-Arnould, I., Cox, D., Dehejia, A., Poly-

meropoulos, M. H., Devignes, M. D., & Auffray, C. (1999). The

genexpress IMAGE knowledge base of the human muscle tran-

scriptome: a resource of structural, functional, and positional can-

didate genes for muscle physiology and pathologies. Genome

Research, 12, 1313–1320.

Prache, S., & Theriez, M. (1999). Traceability of lamb production

systems: carotenoids in plasma and adipose tissue. Animal Science,

69, 29–36.

Prache, S., Priolo, A., Tournadre, H., Jailler, R., Dubroeucq, H.,

Micol, D., & Martin, B. (2002). Traceability of grass-feeding by

quantifying the signature of carotenoid pigments in herbivores

meat, milk and cheese. 19th General Meeting of the european

Grassland Federation. 27–30 May, La Rochelle (France). In J. L.

Durand, J. C. Emile, C. Huyghe, C. Lemaire. (Eds.), Multi-function

grasslands. Quality forages, animal products and landscapes, 2202.

EGF (Vol. 7). Grassland Science in Europe.

Rehfeldt, C., Fiedler, I., Dietl, G., & Ender, K. (2000). Myogenesis

and postnatal skeletal muscle cell growth as influenced by selection.

Livestock Production Science, 66, 177–188.

Renand, G., Havy, A., & Turin, F. (2002). Beef traits and meat quality

characterisation in three French beef production systems with

Salers, Aubrac and Gascons bulls. INRA Production Animales, 15,

171–183.

Renand, G., Picard, B., Touraille, C., Berge, P., & Lepetit, J. (2001).

Relationship between muscle characteristics and meat quality traits

of young Charolais bulls. Meat Science, 59, 49–60.

Roest, C. H., Jaillon, O., Bernot, A., Dasilva, C., Bouneau, L.,

Fischer, C., Fizames, C., Wincker, P., Brottier, P., Quetier, F.,

Saurin, W., & Weissenbach, J. (2000). Estimate of human gene

number provided by genome-wide analysis using Tetraodon nigro-

viridis DNA sequence. Nature Genetics, 25, 235–238.

Roy, P. J., Stuart, J. M., Lund, J., & Kim, S. K. (2002). Chromosomal

clustering of muscle-expressed genes in Caenorhabditis elegans.

Nature, 418, 975–979.

Sancristobal-Gaudy, M., Renand, G., Amigues, Y., Boscher, M. Y.,

Leveziel, H., & Bibe, B. (2000). Tracabilite individuelle des viandes

bovines a l’aide de marqueurs genetiques. INRA Production Ani-

males, 13, 269–276.

Sudre, K., Jurie, C., Listrat, A., Leroux, C., Auffray, C., Renand, G.,

Martin, P., Hocquette J. F. (2002). Molecular and biochemical

muscle characteristics putatively associated with meat quality in

Charolais bulls divergently selected for growth. Book of Abstracts of

the 53rd Annual Meeting of the European Association of Animal

Production (EAAP), Cairo (Egypt). No. 8, p. 148.

Sudre, K., Leroux, C., Petit, E., Renand, G., Cassar-Malek, I., Auf-

fray, C., Pietu, G., Martin, P., Hocquette, J. F. (2000). Analyse

preliminaire de l’expression des genes dans deux muscles squelet-

tiques de bovin au cours du developpement a l’aide de filters a haute

densite. 8th Journees des Sciences du Muscle et Technologies de la

Viande. Paris (France), 21–22 novembre. Viandes et Produits

Carnes, Numero Hors Serie (pp. 77–80).

Tarrant, P. V. (1998). Some recent advances and future priorities in

research for the meat industry. Meat Science, 49(Suppl. 1), S1–S16

(review).

Thomas, J. W., & Touchman, J. W. (2002). Vertebrate genome

sequencing: building a backbone for comparative genomics. Trends

in Genetics, 18(2), 104–108.

Thompson, J. (2002). Managing meat tenderness. Meat Science, 62,

295–308.

Tkatchenko, A. V., Pietu, G., Cros, N., Gannoun-Zaki, L., Auffray,

C., Leger, J. J., & Dechesne, C. A. (2001). Identification of altered

gene expression in skeletal muscles from Duchenne muscular dys-

trophy patients. Neuromuscular Disorders, 11, 269–277.

Verkaar, E. L. C., Nijman, I. J., Boutaga, K., & Lenstra, J. A. (2002).

Differentiation of cattle species in beef by PCR–RFLP of mito-

chondrial and satellite DNA. Meat Science, 60, 365–369.

Vignal, A., Milan, D., SanCristobal, M., & Eggen, A. (2002). A review

on SNP and other types of molecular markers and their use in ani-

mal genetics. Genetics Selection Evolution, 34, 275–305.

Westergren-Thorsson, G., Malmstrom, J., & Marko-Varga, G. (2002).

Proteomics—the protein expression technology to study connective

tissue biology. Journal of Pharmaceutical and Biomedical Analysis,

24, 815–824.

Wilson, D. S., & Nock, S. (2002). Functional protein microarrays.

Current Opinion in Chemical Biology, 6, 81–85 (review).

A. Eggen, J.-F. Hocquette /Meat Science 66 (2003) 1–9 9