Article pp.143-152 du Vol.27 n°2 (2007)

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FOCUS : JSMTV

A general approach combining QTL research and gene expression profiling

to identify genes controlling chicken meat quality

E. Le Bihan-Duval¹, C. Berri¹, F. Pitel², J. Nadaf¹, V. Sibut³, C. Jenkins¹, M.-J. Duclos¹

1 – INTRODUCTION: ECONOMIC AND SCIENTIFIC CONTEXT

Besides productivity gains brought by a better knowledge of the dietary needs, a progress of the veterinary treatments or a rationalization of the rearing conditions, genetics widely contributed to the development of the poultry production (BEAUMONT et al., 2004). It allowed considerable progress in growth performances and body composition of meat-type poultry. After decades of development, economic data are less favourable with a decline of 17%

between 1998 and 2005 of the French poultry production (MAGDELAINE, 2006).

Nevertheless, the national market widely diversified and perspectives of development still remain, mainly for elaborated products. In the case of chicken, their market share should grow of more than 5% between 2005 and 2010, increasing from 23% to 28% (MAGDELAINE, 2006).

The improvement of technological quality of the meat has become a major stake for the poultry industry, in order to propose products well adapted to processing and responding to the consumer demand of quality. As in the pig, the post-mortem pH fall in muscle is determining for the processing and storage ability of poultry meat, even if it does not exclude the impact of other fac- tors. So, meats with low ultimate pH or with a high rate of pH fall are characterized by a low water holding capacity associated to a tough and dry texture after cooking (BARBUT 1996, 1997). They are often qualified as PSE (for Pale, Soft, Exudative) meats. Meats with high ultimate pH are poorly adapted to storage because of a risk of accelerated microbial development (ALLEN et al., 1997, 1998). Recent studies carried out in French slaughter plants showed that the final pH of chicken breast meat (the most valuable part of the carcass) is extremely variable, even within the same slaughter batch (BRUNEL et al., 2006;

figure 1). Technical advances, such as sorting meat on its colour (POPOT et al.,

1. INRA – Unité de Recherches Avicoles – 37380 Nouzilly – France.

2. INRA – Laboratoire de Génétique Cellulaire – 31326 CASTANET-TOLOSAN cedex – France.

3. ITAVI – Unité de Recherches Avicoles – 37380 NOUZILLY – France.

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2006), are thus envisaged to better control the processing yields and to improve the quality of the elaborated products. In addition, substantial gains could be obtained by an improvement of the quality of the raw meat.

Muscle post-mortem metabolism is influenced by numerous parameters (BERRI, 2000). The ultimate pH of the meat is largely determined by the muscle glycogen stores at death, as shown by the very strong genetic correlation of - 0.97 between muscle glycolytic potential and breast meat pH ultimate obtained in a heavy chicken line (DEBUT et al., 2005a). The initial rate of pH fall is mainly influenced by the behavioural and physiological responses of the bird to pre- slaughter stress (DEBUT et al., 2005b; BERRI et al., 2005b). Recent data obtained in chicken indicate a strong genetic determinism of muscle and meat characteristics, strengthening the interest of selection to improve meat quality in poultry.

Currently, the efficiency of selection methods is lowered by the necessity of sacrificing animals to assess meat quality. It also implies high costs of measure- ment. Identifying markers or genes related to meat quality seems thus necessary to improve selection methods. If selection for meat quality could first be envisaged for the standard production which is nowadays the main source of meat for processing industry, at term it could also concern the alternative label or certified productions. Currently, these productions are mainly dedicated to the carcass market (more than 65% of their market shares), but at term the processed product market could also be a future opening for label and certified birds.

5,64 5,45

5,61 5,55

5,43 5,7

5,52 5,44 5,43

5,99 5,77

6,05 5,95 5,84

6,05

5,85 5,86 5,91

6,43 6,18

6,51

6,39 6,36

6,54

6,44 6,36

6,54

5 5,2 5,4 5,6 5,8 6 6,2 6,4 6,6 6,8

1 2 3 4 5 6 7 8 9

Total Figure 1

Variation in breast meat ultimate pH (mean is in blue, minimum in red, and maximum in black) within eight different slaughter hatches of chickens (N=280 birds measured per hatch) slaughtered in a commercial processing plant

(BRUNEL et al., 2006).

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2 – CONTRIBUTION OF THE EXPERIMENTAL AND COMMERCIAL CHICKEN LINES FOR GENOMIC STUDIES ON MEAT QUALITY

The diversity of poultry productions is largely based on the diversity of the genotypes, which exhibit a wide range of growth and body composition performances. In France, if a standard chicken reaches its market weight (approximately 2 kg) in 6 weeks, it will take a minimum of 12 weeks for a slow-growing label chicken. Between-line variation of body composition also exists, some standard lines having been selected for high meat yield and low carcass fatness. Recent studies on divergent experimental lines or within a commercial population have showed that muscle characteristics and meat quality may vary according to growth performances and body composition. In a heavy commercial chicken line, high breast development was found to be related to fibre hypertrophy which came along with decreased muscle glycogen stores (BERRI

et al., 2007). The genetic correlation between breast weight and level of glyco- gen stores at death was -0.64 ± 0.09, and that between breast weight and meat ultimate pH 0.86 ± 0.07 (DEBUT et al., 2005a). As a consequence, in this genotype increasing the genetic potential for muscle development led to a breast meat which was less pale, less exudative and more tender after cooking.

Besides, the comparison of two chicken lines selected for or against abdominal fatness (LECLERCQ et al., 1980) showed that the lean birds exhibited lower glycogen content in the breast muscle than the fat birds (BERRI et al., 2005b). As a consequence, meat of lean birds had higher ultimate pH, and was less pale and exudative (table 1). This relationship between carcass fatness and meat quality had already been suggested in another experimental broiler line, by a genetic correlation of -0.54 between the abdominal fat percentage and the breast meat ultimate pH (LE BIHAN-DUVAL et al., 2001).

Selection for growth rate can also induce change in breast meat quality, as shown by the comparison of two experimental chicken lines divergently selected for high or low growth rate (RICARD, 1975). As shown in table 1, in addition to body weight these two lines also strongly differed for body composition. The breast meat of the high growth line was characterized by a higher rate of initial pH fall (revealed by a lower pH at 15 min post-mortem), a lower ultimate pH and a less coloured aspect. These variations of the post- mortem metabolism seemed partially due to the variations of muscle glycogen stores at death but also those of physical activity of birds before slaughter. So, the high growth line exhibited higher glycogen content in breast muscle and a higher struggle activity on the shackle line, leading to both faster and more pronounced post-mortem muscle acidification. First used as models to study growth and body composition, these experimental lines of chicken appear also relevant for studying genes involved in variability of chicken meat quality.

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Table 1

Body weight, body composition and breast meat parameters

(mean ± standard deviation) in the high growth (HGL) and low growth (LGL) lines, as well as in the fat (FL) and lean (LL) lines compared at 9 wks of age.

3 – CONTRIBUTION OF THE POSITIONAL GENOMICS: DETECTION OF QTLS

The development of molecular biology has allowed the construction of genetic maps in many species of agronomic interest, including chicken. These maps include different linkage groups containing several anonymous genetic markers, used as “tags” on the genome. These genetic markers may be poly- morphic and present several “alleles”. Even though the availability of the chicken genome sequence will soon allow the development of quite numerous SNP (Single Nucleotide Polymorphism) markers, currently the most frequently used markers are microsatellites, whose polymorphism corresponds to variable numbers of repeated short DNA sequences (2 - 4 pb). There were approximately 800 in the consensus map published in 2000 (GROENEN et al., 2000). Well distributed on the whole genome, these markers have been widely used in glo- bal approaches to scan the genome. It is notably the case for the research of QTLs (Quantitative Trait Loci), which correspond to chromosomal regions containing one or several mutated genes controlling the characters (or phenotypes) of interest. As shown in figure 2, the detection of QTLs is based on the comparison of mean performances between two groups of offspring having received from their parents (noted F1) either the allele M1 or the allele M2 for a given marker. If a difference exists, we can expect that the marker M is physi- cally transmitted with the gene Q affecting the phenotype (LE ROY, 2001). The

Traits LGL

(n=56)

HGL (n=53)

line effect (p value)

LL (n=60)

FL (n=60)

line effect (p value) Growth and body composition

body weight (g) 683 ± 67 1922 ± 157 <.0001 2522 ± 193 2627 ± 162 <.001 abdominal fat percentage (%) 0.2 ± 0.2 2.5 ± 0.7 <.0001 1.4 ± 0.5 3.9 ± 0.7 <.001 breast yield (%) 10.44 ± 0.75 11.37 ± 0.84 <.0001 12.8 ± 0.9 11.5 ± 0.9 <.001

thigh yield (%) 22.03 ± 0.68 23.24 ± 0.86 <.0001 NA NA NA

Breast meat parameters

lightness (L*) 45.6 ± 1.8 48.3 ± 3.2 <.0001 44.9 ± 2.6 47.4 ± 2.7 <.001 redness (a*) 1.6 ± 0.7 -0.2 ± 0.8 <.0001 -0.3 ± 0.7 -1.0 ± 0.7 <.001 yellowness (b*) 13.3 ± 1.4 9.4 ± 1.2 <.0001 9.3 ± 1.0 8.3 ± 1.3 <.001 pH15 6.33 ± 0.16 6.20 ± 0.22 0.0004 6.38 ± 0.21 6.36 ± 0.22 NS

pHu 6.14 ± 0.14 5.74 ± 0.09 <.0001 5.79 ± 0.12 5.66 ± 0.11 <.001 drip loss (%) 2.1 ± 1.5 2.3 ± 1.2 NS 1.1 ± 0.6 1.4 ± 0.6 <.05 NS = non significant; NA = non available.

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analysis of the variance or Maximum Likelihood tests (ELSEN, 2001; KAO, 2000) which confront the two hypotheses: H1 segregation of a QTL versus H0 absence of QTL, allow concluding about the existence of a QTL within a given chromosomal region.

In chickens, QTLs related to growth and body composition have been reported (HOCKING, 2005), while almost no study is available for meat quality parameters. That is why INRA and several American teams initiated a program of QTL search for production traits including meat quality traits. This program used as models the high growth and low growth lines, as well as the fat and lean lines selected at INRA. For each of these two models, about 700 F2 offsprings as well as their F0 and F1 parents were genotyped for approximately 120 microsatellite markers. Measurements of growth, shrank size, body composition, plasma parameters (glycaemia, IGF and free fatty acids) and quality of the meat were performed in the F2 population. Meat quality was estimated through measure- ment of pH at 15 min and 24 hours post mortem, lightness (L*), redness (a*), yellowness (b*) and drip loss. At this stage of the project, several QTLs controlling growth, body composition or meat quality have already been identified on the cross between high growth and low growth lines. The first results do not indicate that the regions controlling growth performance and meat quality are the same. Regarding meat quality, a very highly significant QTL controlling meat colour (a* and b*) was identified on chromosome 11 (figure 3). Two significant QTL for pH15 were also identified on chromosomes 1 and 2, the one on chromosome 1 being close to a QTL affecting drip loss (Nadaf et al., 2007). Additional multivariate analyses combining the information on several traits are still in progress with QTLMAP software (LE ROY et al., 1998), in order to refine the

0 5 10 15 20 25 30 35 40

Distribution of F2 phenotypes

M1 M2

Q1 Q2

F1

F2

Offspring with M2 marker Offspring with

M1 marker

Figure 2

Principal of the detection of a QTL (Q) thanks to the information on marker (M).

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localization of QTLs as well as their pleiotropic effects on several characters (GILBERT and LE ROY, 2003). These first regions have now to be validated within a cross between fat and lean lines, which will also allow identifying new QTLs.

The localization of the QTLs remains nevertheless very imprecise: several dozens of centimorgans (from 20 to 50), containing several hundreds of putative candidate genes. As underlined by Bidanel and Genêt (2001), this low resolution does not preclude the use of these results, for example for marker assisted selection (MAS) or for introgression of favourable alleles in a population. How- ever, at long term, efficiency of such selection programs will be lowered by the loss of association between markers and QTL. Ideally, it would be necessary to identify the causal mutation which could allow a simple typing of the reproduc- ers. In a near future, the most significant QTLs for the quality of the meat must be refined within the experimental crosses by developing new microsatellite or SNP markers in the regions of interest. If the experimental populations consti- tute relevant supports for first QTL research, the use of these results in selection program inevitably requires a validation in the commercial populations. This will be carried out in a commercial heavy chicken genotype, already widely characterized for muscle metabolism and meat quality (DEBUT et al., 2005a;

BERRI et al., 2007).

Besides the QTL search, we will benefit from the knowledge acquired in other species, for example by testing in poultry the effect of major genes identified in the pig. The rapid rates of post-mortem pH fall observed in chicken or in turkey are indeed analogous to the so called PSE syndrome largely described in the pig. In this species, the PSE syndrome is due to a C/T mutation in the gene coding for the muscle RYR1 protein, a calcium pump also called ryanodine receptor. In chicken muscle, the two isoforms RYR1 and RYR3 coexist (OTTINI

et al., 1996) and their respective genes have been positioned. The development of molecular markers near or within these two genes should allow clarifying

65 75 85 95 105 115 125 135 145 155 165

18 23 28 33 38 43 48 53 58 63 68

Map position (cM)

LRT

Figure 3

Likelihood Ratio Test (LRT) for the QTLs affecting breast meat yellowness (higher curve) and redness (lower curve) on GGA11. Significant thresholds at 1%

on a genome-wide basis are indicated.

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their potential impact on the rate of pH fall in the chicken. In a similar way, it will be interesting to test the implication of the AMP kinase PRKAG3 gene also called RN gene, responsible for an excess of muscle glycogen and meat with poor processing yield in the pig (MILAN et al., 2000).

4 – CONTRIBUTION OF THE FUNCTIONAL GENOMICS

The recent development in agronomic species of tools for large-scale analysis of gene expression profiling offers new possibilities to identify the genes involved in meat quality variations. DNA chips (or microarrays) with imprint of about 14000 unique genes were notably developed at the University of Dela- ware (COGBURN et al., 2004). They allowed initiating a program of comparison of gene expression profiles of the Pectoralis muscle, between the high growth and low growth lines as well as between the fat and lean lines. Analyses were con- ducted on the first model at 1, 3, 5, 7, 9 and 11 weeks of age and showed that a rather important number of genes (from about 100 to more than 1000 according to the stage) are differentially expressed between the two genotypes. Differ- ences in gene expression observed on DNA chips are now validated by real time RT-PCR. In parallel, the characterization of muscle gene expression profiles of the fat and lean birds is running and will soon be completed by that of commercial chickens characterized by either high or low muscle glycogen stores. These programs will benefit from the availability for chicken of oligonu- cleotide chips (ARK genomics; CRB-GADIE at INRA), which are more sensitive and contain a higher number of genes (approximately 22000 unique genes).

Microarray analyses should end in a list of genes differentially expressed and potentially involved in variations of muscle and meat characteristics between the different groups of animals. In parallel, QTL research will allow identifying chromosomal regions responsible for quality variations and thus to generate a list of genes likely to carry some causal mutations. Bioinformatics will be useful to integrate the information from QTL analyses on the positional candidate genes and that of gene expression profiling on the functional candidate genes. Numerous information on the gene function can be supplied by data banks either non-specialized or specialized, or with annotations (such as GeneOntology). Recent tools, such as Genomatix software (http://www.genomatix.de/company/people.html), have been developed to facilitate the study of the regulatory pathways and to find a possible link between positional and functional candidate genes. A graphic representation of networks including the candidate genes (for example those co-located within the same QTL region) and the genes co-quoted in the literature can be obtained (figure 4). These networks can allow identifying but also completing groups of genes presenting similar patterns of expression. The analysis can also provide information on the various transcripts and the corresponding promoters. This will allow determining the regulatory pathways involved in meat quality and in fine revealing the role of some positional candidate genes.

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A functional characterization of QTLs involved in meat quality can also be envisaged through an experimental “eQTL” approach. If QTL term is used for qualifying chromosomal regions responsible for the variability of a quantitative trait (for example a meat quality indicator), the eQTL term relates to regions controlling the variability of expression of a gene. Already applied with success to the yeast (BREM et al., 2002) and the mouse (SCHADT et al., 2003), this approach is currently experienced in chicken for studying growth and adiposity (GENANIMAL, 2004) as well as breast meat quality (GENANIMAL QualViVol, 2005). These two projects are using back-cross or F2 crosses between the experimental lines mentioned above (table 1). In practice, the study on the meat quality will particularly focus on genes involved in variations of muscle glycogen stores and thus meat ultimate pH. It will consist in measuring the expression of functional candidate genes by real time RT-PCR in muscles of extreme animals of the F2 families. The co-localization of QTL regions controlling quality parameters and eQTL regions involved in the expression of genes should allow to identify regulatory pathways and to progress towards the identification of causal genes. Thanks to this approach we will estimate up to what point meas- ures of RNA expression can help to precise localization of the QTLs because of a better characterization of the phenotype.

Figure 4

Graphical representation of the genes from input list (in blue)

as well as co-quoted genes from the literature (BiblioSphere from Genomatix).

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5 – CONCLUSIONS

Researches undertaken to identify the genes involved in Poultry meat quality benefit of the considerable progress in molecular biology and bioinformatics.

These tools allow integrative research projects which imply additional disci- plines such as genetics, physiology and bioinformatics. The knowledge acquired on the animal models is also a crucial point for the success of this research by identifying the most relevant experimental conditions and clarifying the underlying physiological mechanisms.

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