domain: sdv info

text:
Keywords: breeding values, direct-maternal genetic correlation, growth, pig, rabbit, sheep

INTRODUCTION

Maternal effects occur when an animal's phenotype is influenced by its mother's phenotype in addition to the genes it inherits. [START_REF] Willham | The role of maternal effects in animal breeding: III. Biometrical aspects of maternal effects in animals[END_REF] proposed to model such traits by decomposing the phenotype into the sum of unobserved direct and maternal phenotypes, which are the sum of both a genotypic and an environmental value. Generally, the variance and covariance components estimated with such models are direct and maternal genetic variances, the maternal permanent environmental variance, the residual variance, and directmaternal genetic covariance [START_REF] Dodenhoff | Comparison of models to estimate genetic effects for weaning weight of Angus cattle[END_REF]. To estimate variance components, such models require the use of both appropriate methods [START_REF] Thompson | The estimation of maternal genetic variances[END_REF] and appropriate data structure [START_REF] Gerstmayr | Impact of the data structure on the reliability of the estimated genetic parameters in an animal model with maternal effects[END_REF]. Still, in some cases, the model produces surprising results such as a strong negative direct-maternal genetic correlation [START_REF] Robinson | Estimation and interpretation of direct and maternal genetic parameters for weights of Australian Angus cattle[END_REF][START_REF] Koerhuis | Models to estimate maternal effects for juvenile body weight in broiler chickens[END_REF]. Various explanations for such bias have been proposed in the literature: misidentification of sires (Lee and Pollack, 1997a), lack of genetic connectedness between herds [START_REF] Clement | simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects[END_REF], omission of the random sire × herd × year effect (Lee and Pollack, 1997b;[START_REF] Hagger | Litter, permanent environmental, ram-flock, and genetic effects on early weight gain of lambs[END_REF], or environmental covariances among dam-offspring records [START_REF] Koch | The role of maternal effects in animal breeding: VI. Maternal effects in beef cattle[END_REF][START_REF] Bijma | Estimating maternal genetic effects in livestock[END_REF]. However, in practice, perfectly designed data that fits complex models is not always available when estimating the genetic parameters to use for genetic evaluation [START_REF] Dodenhoff | Comparison of models to estimate genetic effects for weaning weight of Angus cattle[END_REF], so some uncertainty about the true value of the direct-maternal genetic correlation remains. Choosing the direct-maternal ABSTRACT: Obtaining unbiased estimates of the direct-maternal genetic correlation proves far from straightforward for several reasons. Consequently, the use of such over-or underestimated correlations may introduce errors in genetic evaluation models. The objective of our study was to evaluate how the value of the direct-maternal genetic correlation affects EBV. Direct, maternal, and total breeding values were predicted for the ADG or weight at weaning for 3 different species (sheep, rabbits, and pigs) using models that differ depending on the fixed value of the directmaternal genetic correlation (ranging from -0.9 to 0.9) as well as a model in which the correlation was estimated. The results were consistent between species. The direct-maternal genetic correlation had a greater impact on the estimated maternal genetic effects than on direct effects. The lowest correlations between maternal breeding values obtained with different models were -0.20, -0.01, and -0.72 in pigs, sheep, and rabbits, respectively, whereas for the direct breeding value, the lowest correlations were 0.45, 0.90, and 0.95 in pigs, sheep, and rabbits, respectively. The total EBV, calculated as the unweighted sum of direct and maternal genetic effects, did not differ greatly between the models, the lowest correlations between total breeding values being 0.93, 0.98, and 0.97 for pigs, sheep, and rabbits, respectively. Given the uncertainty associated with estimating the direct-maternal genetic correlation, setting its value to 0 in genetic evaluation models appears to be a good compromise. genetic correlation for a genetic evaluation model can, therefore, be challenging. This article attempts to answer the question "does the use of a wrong direct-maternal genetic correlation seriously impact breeding value (EBV) predictions?" by comparing EBV for growth obtained with different direct-maternal genetic correlations in 3 different species: pigs, sheep, and rabbits.

MATERIALS AND METHODS

The ADG or the weight at weaning was analyzed in 3 different species (pig, sheep, and rabbit). Rearing facilities are described in detail in [START_REF] Banville | Genetic parameters for litter size, piglet growth and sow's early growth and body composition in the Chinese-European line Tai Zumu[END_REF], [START_REF] David | Heterogeneity of variance components for preweaning growth in Romane sheep due to the number of lambs reared[END_REF][START_REF] Piles | Survival analysis in two lines of rabbits selected for reproductive traits[END_REF] for pigs, sheep, and rabbits, respectively. Briefly, pig data consisted of 15,572 ADG records of Tai Zumu piglets born in 3 GENE+ (Erin, France) [START_REF] Ricordeau | La souche ovine INRA 401: Un exemple de souche synthetique[END_REF]. All animals were bred in the same system. Lambs were weighed at birth and at 44.8 ± 3.8 d of age using a standardized method (i.e., same animal restraint method and same weight scale). Resulting weights were used to calculate the ADG between birth and 45 d (258 ± 62 g/d). Rabbit data consisted of the weaning weights of 62,139 young rabbits. This experimental rabbit population was bred at the experimental INRA farm Pôle d'Expérimentation Cunicole Toulousain (Castanet-Tolosan, France). Kittens were weighed at weaning, that is, at approximately 31 d of age (31.1 ± 1.0 d). Data are summarized in Table 1.

Let y ijk be the ADG or weaning weight of animal i nursed by female j in litter k. For all species, the linear mixed models used to study y ijk can all be decomposed from the following general model:

y ijk = μ ijk + d i + m j + p j + l k + ε ijk ,
in which μ ijk represents the fixed effects, d i and m j represent the direct and maternal genetic effects, p j represents the permanent maternal effect, l k represents the litter random effect, and ε ijk represents the residual. All random effects were distributed as centered normal distributions with variance-covariance matrices equal to

2 2 d dm dm m s s s s   ⊗     A
for the genetic effects, in which A is the relationship matrix, I p p s 2 is for the maternal permanent effects, I l l s 2 is for the litter effect, and I ε ε σ 2 is for the residual effects, in which I are identity matrices of appropriate sizes. The fixed effects included in the models were those used for genetic evaluation purposes (rabbits) or those that had been used in previous studies (pigs and sheep): litter size (20 levels), age at weighting (10 levels), sex (2 levels), and the combination herd × year × season (48 levels) in pigs; type of birth (3 levels) in interaction with sex of the lamb (2 levels), year × season (36 classes) combination, age of the dam (8 levels), and litter size (2 levels) in sheep; and age at weighing (6 levels), litter size at 21 d old (10 classes), kindling rank (5 classes), and the combination year × month of birth (105 levels) in rabbits.

The goal of the analysis was to compare EBV obtained with models that differ according to the value of the direct-maternal genetic correlation (ρ σ σ σ = ( )

dm d m / / 2 2 1 2
) and/or the value of the direct or maternal genetic variance. Twenty-two different models were fitted to the data. In the first model, all the parameters of the general model described above had to be estimated (called EST model). The second model included only a maternal genetic effect; i.e., s s ) and varied from -0.9 to 0.9 with a 0.1 step. We designated these models MOD_ρ, in which ρ is the fixed value of the genetic correlation. All models were fitted using ASReml software [START_REF] Gilmour | ASReml user guide release 3.0[END_REF]. Heritabilities were computed based on the resulting estimates of variance and covariance components:

σ σ σ σ σ σ σ ε m m d d m p l 2 2 2 2 2 2 / + + + + + ( ) for maternal heritabil- ity, σ σ σ σ σ σ σ ε d m d d m p l 2 2 2 2 2 2 / + + + + + ( ) for direct heritabil- ity, and σ σ σ σ σ σ σ σ σ ε d m dm m d dm p l 2 2 2 2 2 2 2 2 + + ( ) + + + + + ( ) /
for total heritability [START_REF] Eaglen | Genetic parameters of direct and maternal effects for calving ease in Dutch Holstein-Friesian cattle[END_REF]. The correlations between the direct, maternal, or total EBV ( ˆi d , ˆi m , and total

 ˆÊBVi i i d m
= + , respectively, for individual i) were calculated between each model to evaluate the change in EBV depending on the model.

RESULTS

The estimates for variance components and heritabilities obtained with the EST model are shown in Table 2. Maternal heritabilities were low for all species (ranging from 0.06 to 0.09). Direct heritability was low for pigs and sheep (0.10 and 0.13, respectively) and moderate for rabbits (0.26). The genetic correlation between direct and maternal effects for ADG was strongly negative in pigs (-0.54) and slightly positive in sheep (+0.21), but the accuracy of the estimates was low. The genetic correlation between direct and maternal effects obtained for the weight at weaning in rabbits was negative (-0.36). The total heritability was low for pigs (0.08) and moderate for sheep and rabbits (0.22 and 0.20, respectively). Both heritability estimates and log likelihood (LogL) values are presented for the different models in Fig. 1, 2, and 3 for pigs, sheep, and rabbits, respectively. In the MOD_ρ models, the maternal genetic variance could not be estimated for several extreme values of the genetic correlation between direct and maternal effects. For -0.9 ≤ ρ ≤ -0.7 in sheep and 0.8 ≤ ρ ≤ 0.9 in rabbits, the model failed to converge and maternal genetic variance was fixed at a boundary (i.e., 0) during the estimation process. The results from these models were, therefore, not used when comparing EBV. Results were consistent between species. The EST model showed the highest LogL value. Omitting the direct genetic effects (MEf model) led to a significantly lower LogL value associated with a substantial decrease of the total heritability in comparison with the EST model. The changes observed when maternal ge-netic effects were excluded (DEf model) were less substantial. The results obtained with the MOD_ρ models showed that direct and maternal heritabilities decreased with the value of the genetic correlation whereas total heritabilities increased.

Figures 4 to 6 illustrate the correlations between the maternal EBV obtained with the different models (DEf model excluded). To help with reading Fig. 4 to 11, all values of ρ were not used to draw the correlation matrices. Maternal EBV varied with models. For the MOD_ρ models, the correlation between maternal EBV decreased when the absolute difference between fixed genetic correlations increased, the lowest value being -0.20, -0.01, and -0.72 for pigs, sheep, and rabbits, respectively. When direct genetic effects were excluded (MEf model), maternal EBV were close to those obtained with the MOD_ρ models that had a moderately positive fixed genetic correlation. The correlation between the maternal EBV obtained with the EST model and the MOD_ρ models decreased symmetrically with the absolute difference between the estimated and fixed genetic correlation (|Δ|) in sheep and pigs (the lowest values being 0.11 and 0.60 in pigs and sheep, respectively). In rabbits, the decrease was greater when Δ was positive than when it was negative (the lowest correlation being -0.30). The correlations between the direct EBV obtained with the different models (except for the MEf model) are also shown in Fig. 4 to 6 for the 3 species. As for the maternal EBV, the correlation between direct EBV decreased, although to a lesser extent, when the absolute difference between the fixed genetic correlations increased in MOD_ρ models (the lowest correlations being 0.45, 0.90, and 0.96 in pigs, sheep, and rabbits, respectively). The direct EBV generated by the EST model were highly correlated with the direct EBV obtained with the different MOD_ρ models in sheep and rabbits (the lowest correlations being 0.96 and 0.98 in sheep and rabbits, respectively). In pigs, slightly lower values were found for the same comparison (the lowest value being 0.76). The correlations between the total EBV obtained with MOD_ρ models were high for all species (Fig. 7 to 9), the lowest correlation being 0.93 between MOD_0.9 and MOD_-0.9 in pigs. Omitting maternal effects from the model (DEf model) did not have a significant impact on total EBV in rabbits and sheep (correlations with other models >0.96). However, more substantial changes were observed when direct effects were excluded (MEf model; correlations < 0.96 in all cases). The same pattern was observed in pigs although correlation values were found to be lower. The total EBV obtained with the EST model were strongly correlated with the total EBV of MOD_ρ models whatever the value of ρ (>0.98 in sheep and rabbits and >0.95 in pigs).

DISCUSSION

We chose to compare the EBV obtained between and within 3 different sets of models (EST, MOD_ρ, and DEf/ MEf). The EST model, in which all the parameters of the general model are estimated, was considered to be the reference model. Its variance and covariance parameters would have been used to predict EBV in genetic evaluation procedures. Nonetheless, given the potential sources of bias in the estimation [START_REF] Gerstmayr | Impact of the data structure on the reliability of the estimated genetic parameters in an animal model with maternal effects[END_REF]Lee and Pollack, 1997a,b;[START_REF] Clement | simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects[END_REF][START_REF] Bijma | Estimating maternal genetic effects in livestock[END_REF], we cannot actually ascertain that this is the model that provides the EBV closest to the true values. The EBV generated by the various MOD_ρ models practically covered the range of values that can be obtained whatever the value of the direct-maternal genetic correlation. The DEf and MEf models were fitted to the data to evaluate the impact of applying a "wrong" model on the EBV when the trait studied is governed by direct and maternal genetic effects.

We chose to apply the models to several different species to have a wide range of data types and be able to draw conclusions that are not data specific. Depending on the species, the structure of the data was variable with large (pigs and rabbits) or limited (sheep) litter sizes. The variance and covariance component values were also The heritabilities obtained with the EST model in pigs were in accordance with [START_REF] Bouwman | Maternal and social genetic effects on average daily gain of piglets from birth until weaning[END_REF], who reported low heritabilities for direct and maternal effects (0.07 and 0.06, respectively), but lower than those reported by [START_REF] Rosendo | Correlated responses of pre-and postweaning growth and backfat thickness to six generations of selection for ovulation rate or prenatal survival in French Large White pigs[END_REF]0.24 and 0.41, respectively), who also reported a highly negative directmaternal genetic correlation (-0.74). There was probably an overestimation of the heritability in this last study that ignored the common environmental effect of the sow in its model. [START_REF] Bouwman | Maternal and social genetic effects on average daily gain of piglets from birth until weaning[END_REF] reported a higher direct-maternal genetic correlation than the one obtained in the present study (-0.21 versus -0.58), which is probably explained by the higher proportion of cross-fostering in their study (29 versus 6%) that helped in the parameter estimations. Our estimates of heritability in sheep are consistent with most of the heritabilities reported in the literature for preweaning ADG. [START_REF] Bromley | Genetic parameters among weight, prolificacy, and wool traits of Columbia, Polypay, Rambouillet, and Targhee sheep[END_REF] reported heritabilities varying from 0.07 to 0.20 for direct effects and from 0.04 to 0.05 for maternal effects, depending on the breed. In a review on such studies, [START_REF] Safari | A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep[END_REF] reported an average heritability of 0.15 for the direct effect and 0.05 for the maternal effect. In another study, however, [START_REF] Snowder | Estimates of genetic parameters and selection strategies to improve the economic efficiency of postweaning growth in lambs[END_REF] reported a low heritability for direct effects (0.03) and a higher heritability for maternal effects (0.28). Previous studies in sheep have reported estimates of the genetic correlation between direct and maternal effects that vary to a great extent, with values ranging from -0.52 [START_REF] Mousa | Genetic parameters for growth traits for a composite terminal sire breed of sheep[END_REF] to 0.52 [START_REF] Bromley | Genetic parameters among weight, prolificacy, and wool traits of Columbia, Polypay, Rambouillet, and Targhee sheep[END_REF]. In rabbits, our total heritability estimate was in line with the values reported by [START_REF] García | Estimation of correlated response on growth traits to selection in litter size of rabbits using a cryopreserved control population and genetic trends[END_REF]0.22) and slightly lower than the values reported by [START_REF] Niranjan | Estimates of direct and maternal effects on growth traits in Angora rabbits[END_REF]0.25) but higher than those provided by [START_REF] Lukefahr | Mass selection for 70-day body weight in rabbits[END_REF]0.04), [START_REF] Drouilhet | Genetic parameters for two selection criteria for feed efficiency in rabbits[END_REF]0.06), [START_REF] Garreau | La sélection des qualités maternelles pour la croissance du lapereau[END_REF]0.10), and Iraqi (2003;0.10). Most of these previous studies did not include maternal genetic effects in their models. However, the comparison is still relevant because the heritabilities estimated with EST and DEf models were quite close in rabbits.

The small changes of the total heritability estimates observed when maternal genetic effects were ignored (DEf model) are consistent with previous studies [START_REF] Hagger | Litter, permanent environmental, ram-flock, and genetic effects on early weight gain of lambs[END_REF][START_REF] Berweger | Comparison of models to estimate maternal effects for weaning weight of Swiss Angus cattle fitting a sire × herd interaction as an additional random effect[END_REF][START_REF] Clement | simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects[END_REF]. On the other hand, when direct genetic effects were ignored (MEf model), the total heritability calculated using σ σ σ σ σ σ ε

m m d p l 2 2 2 2 2 2 / + + + + ( )
substantially decreased. In this specific case, offspring records are considered repeated measurements of their dam. Consequently, similarly to the sire-dam model, part of the dam variance has to be multiplied by 4 to obtain the genetic variance [START_REF] Matos | Genetic analysis of discrete reproductive traits in sheep using linear and nonlinear models: I. Estimation of genetic parameters[END_REF][START_REF] Wolf | Disentangling prenatal and postnatal maternal genetic effects reveals persistent prenatal effects on offspring growth in mice[END_REF]. It can be noted that the magnitude of the decrease of heritability is proportional to the relative importance of the maternal effects obtained using the EST model.

In MOD_ρ models, the increase of the total heritability with the direct-maternal genetic correlation was attenuated by the simultaneous decrease of the direct and maternal genetic variances. However, we observed changes in the total heritability with ρ for all species. The relative SD of the heritability were 0.12, 0.17, and 0.15 for pigs, sheep, and rabbits, respectively, for ρ varying in the reasonable interval [-0.5, 0.5]. Consequently, the expected accuracy of selection varies with ρ.

Comparison of the EBV obtained with the different MOD_ρ models shows that direct EBV are less sensitive to the value of ρ than maternal EBV. If the changes in direct and maternal EBV are considered to be more or less interrelated, 2 hypotheses can be postulated to explain our results. First, direct EBV were derived from the animal's own performance and maternal EBV were not. Consequently, if changes to the value of ρ affect 1 set of EBV, then it would be most likely to have an impact on maternal EBV. Second, in this study, the variance of the maternal genetic effect is generally lower than that of the direct effect. Therefore, small changes to the direct EBV lead to more substantial changes of the maternal EBV. This assumption is consistent with the decrease in the correlation between maternal EBV of the different MOD_ρ models when the relative contribution of direct genetic variance (s s s explains the high correlations between total EBV among several MOD_ρ models observed in rabbits whereas the correlations between maternal EBV were highly negative. Total EBV were not significantly affected by the value of the direct-maternal genetic correlation. Given the difficulty of obtaining reliable estimations of this correlation, we therefore consider that setting the value of the direct-maternal genetic correlation to 0 in genetic evaluation models is a good compromise. Similar recommendations were issued by [START_REF] Phocas | Genetic parameters for birth and weaning traits in French specialized beef cattle breeds[END_REF]. In this study, for the sake of simplicity and in accordance with [START_REF] Eaglen | Genetic parameters of direct and maternal effects for calving ease in Dutch Holstein-Friesian cattle[END_REF] and [START_REF] Willham | The role of maternal effects in animal breeding: III. Biometrical aspects of maternal effects in animals[END_REF], the total EBV was calculated as the sum of the direct and maternal EBV, each trait being considered to have the same weight. Nonetheless, a "classical" selection index procedure in which the traits have different weights [START_REF] Vleck | Selection for direct, maternal and grandmaternal genetic components of economic traits[END_REF]) could be envisaged. Weights are defined depending on the level at which the breeding objective is defined. For example, the following weights are used to calculate the total EBV in some maternal French rabbit lines used in crossbreeding systems: total  ˆÊBV 0.25 0.5 [START_REF] Garreau | Gestion et sélection de la souche INRA 1777: Résultats de trois générations de selection[END_REF]. On the other hand, the following is used in French meat sheep: total  ˆÊBV 0.5

i i i d m = + (
i i i d m = +
(Jacques Bouix, INRA-GenPhySE, personal communication). In that case, the breeding objective is focused on the growing animal and hence the dam contributes half of its genes to the growth of animals. Figures 10 and11 show the correlation between these total EBV in the 2 species, respectively. In both cases, the correlation between total EBV calculated with the EST and all MOD_ρ models is still high (>0.97 in sheep and 0.98 in rabbits). Therefore, a set value of 0 for the direct-maternal genetic correlation remains a good compromise. It is impossible to test and assess how all the possible weight values for the direct and maternal genetic effects might affect the total EBV. Nonetheless, the weights given above (1:2 or 2:1) for the direct and maternal effects correspond, a priori, to the range of weights that might be used for the genetic evaluation of traits controlled by direct and maternal effects.

In this study, we describe how the total EBV is affected by the direct-maternal genetic correlation in the case of only single trait analysis. In the case of multiple trait analysis [START_REF] Wolfová | Breeding objectives for beef cattle used in different production systems: 2. Model application to production systems with the Charolais breed[END_REF], different results may or may not be obtained. The extent of any potential differences will probably be related to the strength of the correlation between the different traits analyzed. In a previous study based on a social model (the maternal model being is a specific kind of social model), [START_REF] Canario | Genetic associations between behavioral traits and directsocial effects of growth rate in pigs[END_REF] studied the changes in the genetic correlation between 2 traits (1 simple trait and a second trait controlled by direct and associative effects) with different set values of the direct-associative correlation in the social genetic model. They showed that for 3 of the 10 simple correlated traits, the genetic correlation between traits varied a little (up to 0.3) with extreme values of the direct-associative correlation. Nonetheless, their study did not provide any clear insight into the effect of the direct-associative correlation on the total EBV or on the total heritability in multiple trait analysis.

To conclude, we showed that the influence of the direct-maternal genetic correlation on the total EBV is minimal. Given that it is difficult to ascertain that the direct-maternal genetic correlation has been estimated without bias, we suggest setting the direct-maternal genetic correlation to 0 for genetic evaluation purposes. Our results were obtained in a context of single trait analysis. Although it is highly probable that same conclusion will be obtained in the multiple trait case, further investigation will be needed to confirm the results for multiple trait analysis with other traits of interest.

LITERATURE CITED

  GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), F-31326 Castanet-Tolosan, France; †Université de Toulouse, INP, ENSAT, GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), F-31326 Castanet-Tolosan, France; ‡Université de Toulouse, INP, ENVT, GenPhySE (Génétique, Physiologie et Systèmes d'Elevage), F-31076 Toulouse, France; §INRA UE0332 Domaine de la Sapinière, F-18390 Osmoy, France; #GENE +, F-62134 Erin, France; and ||INRA, UE1322 Pôle d'Expérimentation Cunicole TOULousain, F-31326 Auzeville, France

  model = general model with direct genetic effects excluded), and the third model excluded a maternal genetic effect; i.e., model with maternal genetic effects excluded). In the last 19 models, the genetic correlation between the direct and maternal genetic effect was fixed (i.e.,

Figure 1 .

 1 Figure 1. Direct, maternal, and total heritabilities and log likelihood (LogL) of the different models in pigs. EST model = all the parameters of the general model had to be estimated; MEf model = general model with direct genetic effects excluded; DE model = general model with maternal genetic effects excluded; MOD_ρ models = general model with fixed value for the direct maternal genetic correlation ρ (on the x-axis).

Figure 2 .

 2 Figure 2. Direct, maternal, and total heritabilities and log likelihood (LogL) of the different models in sheep. EST model = all the parameters of the general model had to be estimated; ME model = only maternal genetic effect; DE model = only direct genetic effect; MOD_ρ models = ρ is the fixed value of the direct-maternal genetic correlation (on the x-axis). Gray box: model did not converge.

Figure 3 .

 3 Figure 3. Direct, maternal, and total heritabilities and log likelihood (LogL) of the different models in rabbits. EST model = all the parameters of the general model had to be estimated; MEf model = general model with direct genetic effects excluded; DEf model = general model with maternal genetic effects excluded; MOD_ρ models = general model with fixed value for the direct maternal genetic correlation ρ (on the x-axis). Gray box: model did not converge.

Figure 4 .

 4 Figure 4. Correlation between maternal EBV (above the diagonal) and between direct EBV (below the diagonal) obtained with the different models in pigs. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Figure 5 .

 5 Figure 5. correlation between maternal EBV (above the diagonal) and between direct EBV (below the diagonal) obtained with the different models in sheep. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Figure 6 .

 6 Figure 6. Correlation between maternal EBV (above the diagonal) and between direct EBV (below the diagonal) obtained with the different models in rabbits. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Figure 7 .

 7 Figure 7. Correlation between total EBV = maternal EBV + direct EBV obtained with the different models in pigs. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Figure 8 .

 8 Figure 8. Correlation between total EBV = maternal EBV + direct EBV obtained with the different models in sheep. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

  variable with strongly negative (pigs), moderately negative (rabbits), or slightly positive (sheep) direct-maternal genetic correlations, as were the proportions of direct genetic effect ( pigs, 68% in sheep, and 74% in rabbits).

  in the EST model) increases (pigs < sheep < rabbits). That also

Figure 9 .

 9 Figure 9. Correlation between total EBV = maternal EBV + direct EBV obtained with the different models in rabbits. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Figure 10 .

 10 Figure 10. Correlation between total EBV = 0.5 × maternal EBV + direct EBV obtained with the different models in sheep. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Figure 11 .

 11 Figure 11. Correlation between total EBV = 0.5 × maternal EBV + 0.25 × direct EBV obtained with the different models in rabbits. EST = all the parameters of the general model had to be estimated; MEf = general model with direct genetic effects excluded; DEf = general model with maternal genetic effects excluded. Figures correspond to the value of ρ in MOD_ρ models.

Table 1 .

 1 Descriptive statistics

	Statistics	Pigs	Sheep	Rabbits
	No. records	15,572	22,807	62,139
	No. dams (% of dams with their own record)	796 (33%)	7,149 (83%)	3,633 (87%)
	No. litters	1,236	21,016	8,080
	No. animals in the pedigree	19,987	37,195	63,858
	Variable of interest	ADG 0-21 d, g/d	ADG 0-45 d, g/d	Weight at 31 d, g
	Mean	223	258	641
	SD	52	62	128
	Minimum	5	31	113
	Maximum	415	523	1,375

Table 2 .

 2 Variance component and heritability estimates obtained with the EST model (all the parameters of the general model had to be estimated)

	Component	Pigs	Sheep	Rabbits
	Direct genetic effect	236	365	2,677
	Maternal genetic effect	189	153	1,143
	Litter	295	309	2,103
	Maternal permanent environmental effect	146	279	723
	Genetic correlation	-0.54 (0.18)	0.21 (0.12)	-0.36 (0.07)
	Direct heritability	0.10 (0.03)	0.13 (0.02)	0.26 (0.02)
	Maternal heritability	0.08 (0.03)	0.06 (0.01)	0.09 (0.01)
	Total heritability	0.08 (0.02)	0.22 (0.02)	0.20 (0.02)


Annie Vincent

Isabelle Louveau

Florence Gondret

Christine Trefeu

Hélène Gilbert

† I Louveau

† F Gondret

† C Tréfeu

† H Gilbert

L Lefaucheur
email: louis.lefaucheur@rennes.inra.fr

M Alix

R Janvier

J Liger

V Piedvache

P Roger

J F Rouault

F Moreews

Divergent selection for residual feed intake affects the transcriptomic and proteomic profiles of pig skeletal muscle 1,2

Keywords: feed efficiency, muscle, pig, proteome, transcriptome

Divergent selection for residual feed intake affects the transcriptomic and proteomic profiles of pig skeletal muscle

aBstraCt: Improving feed efficiency is a relevant strategy to reduce feed cost and environmental waste in livestock production. Selection experiments on residual feed intake (RFI), a measure of feed efficiency, previously indicated that low RFI was associated with lower feed intake, similar growth rate, and greater lean meat content compared with high RFI. To gain insights into the molecular mechanisms underlying these differences, 24 Large White females from 2 lines divergently selected for RFI were examined. Pigs from a low-RFI ("efficient") and high-RFI ("inefficient") line were individually fed ad libitum from 67 d of age (27 kg BW) to slaughter at 115 kg BW (n = 8 per group). Additional pigs of the high-RFI line were feed restricted to the daily feed intake of the ad libitum low-RFI pigs (n = 8) to investigate the impact of selection independently of feed intake. Global gene and protein expression profiles were assessed in the LM collected at slaughter. The analyses involved a porcine commercial microarray and 2-dimensional gel electrophoresis. About 1,000 probes were differentially expressed (P < 0.01) between RFI lines. Only 10% of those probes were also affected by feed restriction. Gene functional classification indicated a greater expression of genes involved in protein synthesis and a lower expression of genes associated with mitochondrial energy metabolism in the low-RFI pigs compared with the high-RFI pigs. At the protein level, 11 unique identified proteins exhibited a differential abundance (P < 0.05) between RFI lines. Differentially expressed proteins were generally not significantly affected by feed restriction. Mitochondrial oxidative proteins such as aconitase hydratase, ATP synthase subunit α, and creatine kinase S-type had a lower abundance in the low-RFI pigs, whereas fructose-biphosphate aldolase A and glyceraldehyde-3-phosphate dehydrogenase, 2 proteins involved in glycolysis, had a greater abundance in those pigs compared with high-RFI pigs. Antioxidant proteins such as superoxide dismutase and glutathione peroxidase 3 at the mRNA level and peroxiredoxin-6 at the protein level were also less expressed in LM of the most efficient pigs, likely related to lower oxidative molecule production. Collectively, both the transcriptomic and proteomic approaches revealed a lower oxidative metabolism in muscle of the low-RFI pigs and all these modifications were largely independent of differences in feed intake.

introduCtion

Improving feed efficiency is a major objective in livestock production to reduce feed cost and environmental waste. Feed efficiency can be assessed by estimating residual feed intake (rFi) of growing animals, defined as the feed consumed above or below what is predicted for growth and maintenance. Therefore, animals with low RFI (rFi -) consume less feed than predicted and are qualified as efficient animals. In pigs, selection experiments have shown that reduced RFI is genetically correlated with lower feed intake and a leaner carcass, without any marked effects on growth rate [START_REF] Gilbert | Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits[END_REF]. In skeletal muscle, larger fast-twitch glycolytic fibers with higher glycogen content and a greater proportion of glycolytic fibers have been reported in RFI -pigs compared with high RFI (rFi + ) pigs [START_REF] Lefaucheur | Muscle characteristics and meat quality traits are affected by divergent selection on residual feed intake in pigs[END_REF]. In addition, lower activities of some glycolytic and oxidative enzymes have been reported in muscle of RFI -pigs (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF][START_REF] Faure | Consequences of divergent selection for residual feed intake in pigs on muscle energy metabolism and meat quality[END_REF], suggesting differences in muscle metabolic orientations for the storage and use of nutrients between lines. In cattle, differences in tissue metabolism have also been shown to contribute to differences in feed efficiency [START_REF] Richardson | Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection[END_REF]. Nevertheless, the underlying biological basis of these differences remains to be fully established.

During the last decade, high-throughput technologies such as transcriptomics and proteomics have been used for large-scale genome expression analysis. These technologies may allow the identification of genes, proteins, or biological pathways that were not described so far to be responsive to the divergent selection for RFI. Therefore, this study aimed to investigate global modifications in gene and protein expressions in skeletal muscle between 2 pig lines divergently selected for RFI. Direct effects of RFI divergence on metabolic orientation was discriminated from the selection-induced differences in feed intake by considering an additional pair-fed RFI + group.

materiaLs and metHods

Animals

The care and use of pigs were performed in compliance with the European Union legislation (directive 86/609/CEE; http://ec.europa.eu/food/fs/aw/ aw_legislation/scientific/86-609-eec_fr.pdf; accessed 27 January 2009) and the French legislation (Décret number 2001-464 29/05/01; http://ethique.ipbs.fr/sdv/ charteexpeanimale.pdf [accessed 29 January 2009]; agreement for animal housing number C-35-27532). Moreover, the technical and scientific staff involved in the experiment got an individual agreement from the French Veterinary Services to experiment on living animals. As previously described [START_REF] Gilbert | Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits[END_REF], ADFI and ADG from 35 to 95 kg were recorded on male candidates to selection, together with live backfat thickness at 95 kg BW. An RFI selection index was computed as a linear combination of those traits to account for growth requirements, with variations of maintenance requirements being ignored between fixed BW. In the present study, pigs from the seventh and eighth generations of divergent selection were considered. Average breeding values for RFI in those pigs were 57.6 and -70.7 g/d for the high-RFI RFI + and RFI -line, respectively, which were not different from the average breeding values calculated in these generations for sibs bred.

Purebred French Large White female pigs in the course of a divergent selection experiment for RFI were produced at the INRA research farms (GenESI at Surgères, Rouillé and Le Magneraud, France). This study was part of a larger contemporary work whose results related to performance, body composition, tissue energy metabolism, and hormonal status have already been published (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF]. Briefly, female piglets from the RFI -(n = 8) and RFI + (n = 16) lines considered in the present study were weaned at 28 d of age and immediately transferred to the experimental facilities of INRA Pegase (Saint-Gilles, France). They were housed in collective pens (1 pen per line) at arrival and had free access to feed. At 67 ± 1 d of age, pigs were individually housed up to commercial slaughter BW of 115 kg. Pigs had free access to a transition diet the first week and were then divided into 3 experimental groups. In the first group, the RFI - pigs were fed ad libitum (RFI -; n = 8). Littermates of the RFI + line were divided into 2 experimental groups having either free access to feed (RFI + ; n = 8) or receiving the same amount of feed per metabolic BW (BW 0.60 ) as RFI -pigs (feed-restricted RFI + pigs [rFi +R ]; n = 8). Pigs were weighed weekly and feed consumption was recorded daily. Daily feed allocated to RFI +R pigs was calculated weekly, based on the average daily feed consumption recorded during the week before in RFI -group, the average BW of RFI - and RFI +R pigs at the day of calculation, and the predicted ADG of RFI -pigs for the week after.

Animals had free access to water and were fed with standard diets formulated for postweaning and growing periods as previously described (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF]. Pigs were weighed weekly and on slaughter day. Feed consumption (feed offered minus refusals) was recorded daily throughout the experiment.

Animal Slaughtering and Sample Collection

Pigs were slaughtered at 115.2 ± 1.5 kg BW by electronarcosis followed by exsanguination 2 h after their first morning meal, at the experimental slaughterhouse of INRA (Saint-Gilles, France). Samples of LM were collected at the last rib level within 20 min after death, cut into small pieces, snap frozen in liquid nitrogen, and stored at -75°C until further analyses.

Ribonucleic Acid Extraction

For total RNA extraction, 80 to 100 mg of frozen muscle was homogenized in Trizol reagent (Invitrogen, Cergy-Pontoise, France) using a TissueLyser (Qiagen, Hilden, Germany) and was then treated as previously described [START_REF] Vincent | Mitochondrial function, fatty acid metabolism, and immune system are relevant features of pig adipose tissue development[END_REF]. The RNA was purified using a silica-membrane technology under vacuum (Nucleospin 8 RNA kit; Macherey Nagel, Hoerdt, France). The quantification of RNA was performed by using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Illkirch, France). Ratios of A260:A280 and A260:A230 were greater than 1.8 in all samples, denoting good purity. The integrity of total RNA was assessed using the Agilent RNA 6000 Nano kit with an Agilent 2100 Bioanalyzer (Agilent Technologies France, Massy, France). Average RNA integrity numbers were 8.1 ± 0.5 (mean ± SD).

Microarray Analysis

Transcriptomics analyses were performed using the porcine commercial Agilent-026440 Oligo Microarray (V2, 4x44K, GPL15007; Agilent Technologies France), containing 43,803 probes. To update annotation of the porcine gene chip probe sets, Agilent 60-mer probes were further analyzed on the basis of probe and contig sequence homology searches using pig transcripts (National Center for Biotechnology Information [nCBi] Sus scrofa RefSeq; http://www.ncbi.nlm.nih. gov/RefSeq/ [accessed 5 March 2012]; UniProt; http:// www.uniprot.org/ [accessed 8 March 2012]) or transcripts from related species (Homo sapiens, Bos taurus, and Mus musculus). Annotation was based on similarity and quality criteria as follows: at least 18 consecutive base pairs within the 60-mer probe sequence and 85% homology [START_REF] Casel | SigReannot: An oligo-set re-annotation pipeline based on similarities with the Ensembl transcripts and Unigene clusters[END_REF]. In total, 33,231 probes (76% of total) were annotated, corresponding to 12,332 unique gene symbols.

Each sample was labeled with Cy3 dye using the Low RNA Input Linear Amplification Kit (Agilent Technologies France) following the manufacturer's instructions. Briefly, a 2-step procedure was used to generate fluorescent complementary RNA by using T7 RNA polymerase. Samples were then purified with an RNeasy MinElute kit (Qiagen). Microarray hybridizations were performed in Agilent's SureHyb hybridization chambers containing 1.65 µg of Cy3-labeled complementary RNA sample per hybridization. Hybridization reactions were performed at 65°C for 17 h using Agilent's Gene Expression Hybridization kit. After washing, microarrays were scanned at 5 µm/ pixel resolution using the Agilent DNA Microarray Scanner G2505C, and images were analyzed with Agilent Feature Extraction Software (version 10.7.3.1; protocol GE1_107_Sep09). All analyses were then performed using the R software version 2.10.0 (R Development Core Team, 2008). Raw spot intensities were first submitted to quality filtration based on 4 criteria: intensity, uniformity, saturation, and outliers detection. Among the 43,803 probes evaluated per microarray, 29,364 probes (67%) were kept after quality filtration. Intensities of filtered spots were log2 transformed, and data were normalized by median centering, that is, subtracting the median value across all probes from all raw values within each sample. Microarray selected data have been deposited into the Gene Expression Omnibus repository and are publicly available through Gene Expression Omnibus accession number GSE47769 (http://www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc=GSE47769; accessed 10 June 2013). Data were then submitted to ANOVA using the fixed effects of the genetic line (RFI -or RFI + ) or feed restriction (RFI +R or RFI + ). Differentially expressed (de) probes with P-values lower than 0.01 were then selected for further functional analysis.

Functional Analysis

Probes that were DE between RFI -and RFI + pigs were then divided into down-or upregulated probes. These 2 lists of probes were further investigated by an enrichment analysis of specific Gene Ontology (go) terms for Biological Processes and Kyoto Encyclopedia Genes and Genomes pathways using the Database for Annotation, Visualization and Integrated Discovery (daVid) bioinformatic resources (http://david.abcc. ncifcrf.gov/home.jsp; accessed 10 April 2012; [START_REF] Huang | DAVID gene ID conversion tool[END_REF][START_REF] De Haer | Relations among individual (residual) feed intake, growth-performance and feed-intake pattern of growing pigs in-group housing[END_REF]. In DAVID analysis, the GO terms_FAT were selected to filter the broadest terms without overshadowing the more specific ones. The lists of probes were uploaded using official gene symbol obtained with DAVID Gene Acession Conversion Tool. The P-values for enrichment were computed by a modified Fisher's exact test, using the porcine microarray as the background. The REVIGO Web server (http://revigo.irb.hr/; accessed 4 February 2013) was also used to reduce redundancy and to find the most representative GO terms [START_REF] Supek | REVIGO Summarizes and visualizes long lists of gene ontology terms[END_REF].

The SimRel was used as the semantic similarity measure within the human GO database [START_REF] Schlicker | A new measure for functional similarity of gene products based on Gene Ontology[END_REF]. Tables were chosen to visualize the results.

Proteome Analysis

Total proteins were extracted from 50 mg of powdered frozen muscle tissue homogenized into 1 mL lysis buffer containing 7.5 M urea, 2 M thiourea, 2% 3-((3-cholamidopropyl)dimethylammonium)-1-propanesulfonate, 40 mM Tris base, 50 mM dithiothreitol (dtt), 1 mM EDTA, 1% immobilized pH gradient (iPg) buffer pH 3 to 11 nonlinear (nL; GE Healthcare, Uppsala, Sweden), 1 µg/mL pepstatin, and 1.5 mg/ mL complete protease inhibitor cocktail (Roche, Mannheim, Germany), using a handheld Teflonpestle-glass Potter-Elvehjem homogenizer. The lysate was incubated at room temperature for 1 h with rotational shaking followed by centrifugation at 60,000 × g for 1 h at 18°C. Supernatant was collected avoiding the fat layer, aliquoted, and stored at -75°C until further analysis. Protein concentration in the supernatants was determined using the Quick Start Bradford kit (Bio-Rad, Marnes-la-Coquette, France) with BSA as the standard. For the first dimension electrophoresis, 300 µg protein was diluted in a rehydration buffer containing 0.5% IPG buffer pH 3 to 11 NL and Destreack solution (GE Healthcare) to a final volume of 450 μL. Precast NL immobilized gradient strips (pH 3-11, 24 cm; GE Healthcare) were rehydrated overnight (16 h) at room temperature in a passive way with the solubilized proteins under 3.5 mL of cover fluid (GE Healthcare). The IPG strips were then subjected to isoelectric focusing on an Ettan IPGphorII system (GE Healthcare) at 20°C with a step gradient protocol ranging from 150 to 8,000 V for 21 h (150 V for 1 h, 300 V for 3 h, 300 to 600 V for 1 h, 600 to 1,000 V for 4 h 30 min, 1,000 to 8,000 V for 3 h, and 8,000 V for 8 h 45 min) to reach a total of 88,600 V•h. At the completion of focusing, the strips were equilibrated by soaking for 15 min in 50 mM Tris-HCl buffer pH 8.8, 6 M urea, 30% glycerol, 2% SDS, and 65 mM DTT followed by an additional 15 min equilibration in the same buffer replacing DTT by 4.5% iodoacetamide and adding 0.03% bromophenol blue sodium salt R-250 (Sigma -Aldrich, Saint-Quentin Fallavier, France). Equilibrated IPG strips were transferred onto the top of a 12.5% uniform SDS-PAGE and sealed with 0.5% (wt/vol) low-melting-point agarose. The SDS-PAGE was performed in a vertical Ettan DALTsix system (GE Healthcare) using upper 2x and lower 1x buffer (number 161-0772; Bio-Rad) at 15°C. Gels were run at 5 W/gel for 45 min followed by 17 W/gel until bromophenol blue front reached the bot-tom of the gel. After migration, gels were fixed overnight in 40% methanol/10% acetic acid, rinsed 3 times for 5 min in water, and stained by 0.12% Coomasie Brilliant Blue G-250 (Bio-Rad) in 10% phosphoric acid, 10% ammonium sulfate, and 20% methanol for 24 h. Following destaining in water, gels were scanned using an UMAX ImageScanner (GE Healthcare) at 300 dots per inch and spot detection and quantification were performed by image analysis (Melanie 2D gel analysis software version 7.0; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland). To take into account experimental variation (protein loading and staining), 2-dimensional (2d) gels were normalized within each gel by dividing each spot volume by the total volume of all valid spots on the gel (spot relative volume expressed as vol/vol × 10 6 ). Highly abundant protein spots such as actin were manually deleted because of inaccurate quantification due to saturation. Data were then analyzed by ANOVA using the GLM procedure of SAS (SAS Inst. Inc., Cary, NC) with the fixed effect of the experimental group (RFI -, RFI + , and RFI +R ). A P-value lower than 0.05 was retained for statistical significance.

Preparative gels with 600 µg of proteins from pooled samples were performed for spot picking. The protein spots that were different between RFI -and RFI + pigs were punched out of preparative gels using a OneTouch 2D gel manual spotpicker (Gel Company, San Francisco, CA). In gel tryptic digestion and protein identification by mass spectrometry were performed in the high-core proteomic facilities of Biogenouest (Rennes, France) as previously described [START_REF] Colinet | Metabolic and proteomic profiling of diapause in the aphid parasitoid praon volucre[END_REF]. Briefly, tryptic peptides were analyzed by nano-liquid chromatography-tandem mass spectrometry (LC-MS/MS) using nano-liquid chromatography system Ultimate 3000 (DIONEX; LC Packings, Amsterdam, The Netherlands) coupled online to a linear ion trap HCT Ultra P Discovery system mass spectrometer (BrukerDaltoniK GmBh, Bremen, Germany). The proteinScape 2.1 software (BrukerDaltoniK GmBh) was used to submit tandem mass spectrometry data to the following database: SwissProt sequence database restricted to mammalia taxonomy (http://www.uniprot.org; accessed 9 October 2012) and NCBI nonredundant sequences databases of mammalian proteins (http://www.ncbi.nlm. nih.gov; accessed 9 October 2012) using the Mascot search engine (Mascot server version 2.2; http:// www.matrixscience.com; accessed 9 October 2012). Parameters were set as follows: trypsin as enzyme with one allowed miscleavage, carbamidomethylation of cysteins as fixed modification, and methionine oxidation as variable modifications. The mass tolerance for parent and fragment ions was set to 1.2 and 0.5 Da, respectively. Results were scored using the probability-based Mowse algorithm, where the protein score is -10 × log(P) and P is the probability that the observed match is a random event. In our conditions, a score greater than 80 indicated a significant identification (P < 0.05) and at least 4 matched peptides per protein and 20% sequence coverage were also required to avoid incorrect identification. The accuracy of the experimental to theoretical isoelectric point and molecular weight were also considered. Identified proteins were then classified according to their biological process terms provided in GO Consortium for Homo sapiens by using correspondence to gene name and their functions proposed on the SwissProt/TrEMBL protein databases (http://www.uniprot.org/; accessed 25 October 2012).

Quantitative Real-Time PCR

Expressions of selected genes found to be DE between RFI lines by transcriptome and proteome analyses or by transcriptome analysis only were further evaluated by quantitative real-time PCR (qPCR). Complementary DNA was synthesized from 1 μg of total RNA previously used in microarray analysis, using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). Primers (Table 1) were designed from porcine sequences using Primer Express software 3.0 (Applied Biosystems). For each primer pair, the amplification efficiency of qPCR reaction was determined using calibration curves generated with 6 decreasing concentrations of cDNA from pooled muscle samples (obtained from 12.5 to 12.5 ×10 -3 ng RNA). Amplification reaction was performed in duplicate in 12.5 μL with 2.5 ng of reverse-transcribed RNA and both forward and reverse primers (200 nM each) in 1x PCR buffer (Fast SYBR Green Master Mix; Applied Biosystems). A StepOnePlus Real Time PCR system (Applied Biosystems) was used. Thermal cycling conditions were as follows: 50°C for 2 min and 95°C for 20 s followed by 40 cycles of denaturation at 95°C for 3 s and annealing at 60°C for 30 s. Specificity of the amplification products was checked by dissociation curves analysis. Three genes were found invariant and, therefore, used as reference for normalization according to the geNorm algorithm [START_REF] Vandesompele | Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes[END_REF]: hypoxanthine phosphoribosyltransferase 1 (HPRT1), peptidylpropyl isomerase A (cyclophilin A) (PPIA), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ). For each sample, a normalization factor (nF) was calculated using the geNorm algorithm and used for subsequent normalization. For each gene, the normalized expression level N was calculated accord-ing to the following formula [START_REF] Pfaffl | A new mathematical model for relative quantification in real-time RT-PCR[END_REF]: N = E -ΔCq (sample -calibrator) /NF, in which E is calculated from the slope of calibration curve, Cq is the quantification cycle, and calibrator is a pool of all muscle samples. Data were analyzed by ANOVA with the fixed effect of genetic line by using the R software version 2.10.0 (R Development Core Team, 2008). A P-value < 0.05 was retained for statistical significance.

resuLts

Residual Feed Intake and Feed Restriction Effects on Performance

The RFI -and RFI +R pigs were older (+10 to 13 d; P < 0.001) than RFI + pigs at the same slaughter BW (Table 2). From 67 d of age to slaughter, ADG did not differ between RFI -and RFI + pigs and was lower in RFI +R pigs (-10%). Daily feed intake was similar in RFI -and RFI +R pigs (P > 0.05), whereas ADFI was markedly lower (P < 0.001) in these groups compared with RFI + pigs when adjusted to metabolic BW (182, 181, and 209 g•d -1 •kg -1 BW 0.60 in RFI -, RFI +R , and RFI + pigs, respectively) or calculated on an as-fed basis (2.23, 2.32 and 2.65 kg/d, respectively). The G:F was greater (P < 0.001) in RFI -than RFI + pigs. Feed restriction in RFI +R was rather moderate (-13% on a metabolic BW basis), so that RFI +R pigs exhibited the same G:F as RFI + pigs (0.35). Carcass lean meat content tended to be greater (P = 0.08) in RFI -and RFI +R pigs than in RFI + pigs.

Residual Feed Intake and Feed Restriction Effects on Muscle Transcriptome

In muscle, 1,013 probes were DE between RFI - and RFI + pigs (P < 0.01) with 82% of them exhibiting a fold change below the absolute value of 1.5 (Table 3). Among these probes, 674 (corresponding to 338 annotated genes) were overexpressed and 339 (corresponding to 188 annotated genes) were underexpressed in RFI -pigs compared with RFI + pigs. The number of DE probes (P < 0.01) between RFI + and RFI +R pigs was lower (727) than the number of DE probes between RFI -and RFI + pigs. Among them, 402 probes were overexpressed in RFI +R pigs compared with RFI + pigs. Only 53 of these probes were also found to be overexpressed in RFI -vs. RFI + pigs. Among the 325 probes downregulated in RFI +R pigs compared with RFI + pigs, only 37 were also downregulated in RFI -vs. RFI + pigs. Therefore, only 10% of the DE probes between RFI lines were also affected by feed restriction. Therefore, the functional analysis was further focused on the effects of genetic differences between RFI lines.

Functional analysis of DE genes between RFI - and RFI + groups showed an overrepresentation of pathways related to oxido-reduction process and energy oxidative metabolism in RFI + pigs (Table 4). Especially, the expression levels of genes encoding glutathione peroxidase 3 (GPX3) and superoxide dismutase 2 (SOD2), 2 enzymes involved in the detoxification of reactive oxygen species (ros), were lower in RFI -than in RFI + pigs. Moreover, many genes involved in the respiratory electron transport chain, the generation of precursor metabolites and energy, and the tricarboxylic acid cycle were downregulated in RFI -pigs compared with RFI + pigs (Table 4). This concerned the expression of genes encoding mitochondrial NADH dehydrogenase 1 (ND1), 2 (ND2), and 4 (ND4); NADH dehydrogenase (ubiquinone) 1 β subcomplex 9 (NDUFB9); isocitrate dehydrogenase 3 (NAD+) α (IDH3A); and cytochrome C (CYCS; Table 4). Electron-transfer-flavoprotein beta polypeptide (ETFB), which serves as a specific electron acceptor for several acyl-CoA dehydrogenases and transfers the electrons to the mitochondrial respira-tory chain via electron-transferring-flavoprotein dehydrogenase, was also underexpressed in RFI -pigs. However, other functionally important genes might have been missed because they did not fit into a cluster. Therefore, additional individual DE genes were also taken into account to extend the global functional analysis (Table 5). Dihydrolipoamide dehydrogenase (DLD) and pyruvate dehydrogenase kinase isozyme 1 (PDK1), 2 genes involved in mitochondrial pyruvate metabolism, were found to be underexpressed in RFI -pigs. Acyl-CoA thioesterase 8 (ACOT8) and lipase A (LIPA), coding for proteins involved in acyl-CoA hydrolysis and lipase activity, respectively, were also underexpressed in RFI -pigs compared with RFI + pigs. Vascular endothelial growth factor A (VEGFA), a gene coding for a growth factor active in angiogenesis and vasculogenesis, was also notably underexpressed in RFI -pigs. The expressions of 2 other genes related to apotosis, CSRNP3 (i.e., a transcriptional factor involved in apoptosis) and OAZ3, an inhibitor of ornithine decarboxylase, which is an enzyme involved in the antioxidative defense and resistance to apoptosis through the generation of polyamines, were also expressed at lower levels in RFI -pigs (Table 5). Finally, 2 F = forward primer; R = reverse primer.

3 Genes used as reference for quantitative real-time PCR normalization.

genes associated with antigen processing and presentation of peptide antigen and endosome transport, such as HLA-A, HLA-B, and HLA-C, were downregulated in RFI -pigs (Table 4). Functional analysis of upregulated genes in RFI +R pigs compared with RFI + pigs reveals significant enrichments of GO terms associated with RNA processing, intracellular protein transport, and ubiquitin-dependent protein catabolic processes. Conversely, translation biological processes such as translation itself, ribonucleoprotein complexes, and mRNA processing were upregulated pathways in RFI - vs. RFI + pigs (Table 4). In particular, genes coding for translation initiation and elongation factors (EEF1A1, EEF1G, EIF2B5, EIF2S2, and EIF6) and RNA processing (WDHD1, POP4, CPSF1, and CPSF3) were overexpressed in RFI -vs. RFI + pigs (Table 5). CAPN2, coding for the millimolar-calpain, a calciumsensitive cysteine protease, was also overexpressed in RFI -pigs. Finally, expression level of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a metabolic enzyme involved in the glycolytic pathway, was greater in RFI -pigs than in RFI + pigs (Table 5).

Residual Feed Intake and Feed Restriction Effects on Muscle Proteome

About 807 spots were detected and matched between the 24 gels (Fig. 1). Among them, 20 spots were found with a differential abundance between RFI -and RFI + pigs (P < 0.05) and 15 spots were less intense in RFI -pigs compared with RFI + pigs. Fourteen spots, corresponding to 11 unique proteins, were successful-ly identified (Table 6). Three proteins, each resolved as double spots exhibiting similar molecular weights but different isoelectric points (Fig. 1), corresponded to ATP synthase subunit α (ATP5A1), creatine kinase S-type (CKMT2), and heat shock protein β-1 (HSPB1). Differences between RFI -and RFI + pigs were similar between double spots for ATP5A1, and CKMT2 and opposite for HSPB1. The 11 identified proteins could be divided into 4 different functional groups: metabolism related (6 proteins), stress related (2 proteins), transport related (2 proteins), and structure related (1 protein; Table 6). Among the metabolism-related proteins, 3 were related to mitochondrial oxidative metabolism (ACO2, ATP5A1, and CKMT2) and were less abundant in RFI -than RFI + animals (P < 0.05). The stress-related protein peroxiredoxin-6 (PRDX6), involved in protection against oxidative stress, was decreased (P < 0.05) in RFI -animals compared with RFI + animals and was downregulated by feed restriction, so that RFI +R pigs and RFI -pigs did not differ. The cytoplasmic glycerol-3-phosphate dehydrogenase (GPD1), an enzyme involved in the glycerol phosphate shuttle, was also less abundant in RFI -than RFI + animals. Conversely, 2 proteins related with glycolytic metabolism (GAPDH and fructose-bisphosphate aldolase A [ALDOA]) had a greater abundance in RFI -than RFI + pigs. These metabolism-related proteins were not influenced by feed restriction in the RFI + pigs. Among transport related proteins, serotransferrin (TF), a secreted protein involved in iron transport, and myoglobin (MB) involved in oxygen transport within myofibers were less abundant in RFI -pigs compared with RFI + pigs (P < 0.05). Relative abundance of myoglobin was not significantly influenced by feed restriction, whereas abundance of TF protein was lower in RFI +R vs. RFI + pigs (P < 0.05). Finally, vascular smooth muscle actin (ACTA2), a structure-related protein, was identified as having a lower abundance in RFI -than RFI + animals (P < 0.05) and was downregulated by feed restriction (RFI +R pigs < RFI + pigs; P < 0.05).

Data Validation by Quantitative Real-Time PCR

Among the DE proteins, only changes in GAPDH were similarly observed both at the mRNA and protein levels between lines. Other DE proteins (ACO2, ATP5A1, CKMT2, ALDOA, GPD1, MB, TF, PRDX6, and HSPB1) were not DE at the mRNA level.

We checked 10 genes showing high fold change in expression levels between RFI -and RFI + pigs and involved in various functional categories: oxidative stress (GPX3, SOD2, and OAZ3), glycolysis (GAPDH), immune response (CD40), apoptose (CSRNP3 and OAZ3), translation (PCIF1 and WDHD1), protein methylation (PRMT3), and water transport (AQP4). The differential expressions between RFI -and RFI + pigs were confirmed by qPCR (Fig. 2) for all tested genes.

disCussion

The current study enriches our knowledge on the molecular mechanisms underlying variations in RFI in pigs selected from a larger group of pigs [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF] by focusing on skeletal muscle transcriptomic and proteomic profiles. To discriminate selection effects from those induced by differences in feed intake, some pigs from the RFI + line were pair fed at the feeding level of pigs from the RFI -line during the growing-finishing period. Although [START_REF] Lkhagvadorj | Gene expression profiling of the short-term adaptive response to acute caloric restriction in liver and adipose tissues of pigs differing in feed efficiency[END_REF] found important effects of an acute caloric restriction on the adipose tissue and liver transcriptomes of pigs divergently selected for RFI, the present study revealed only minor changes in muscle adaptive responses to feed restriction. This discrepancy between our study and the abovementioned one can be related to the organ, variations in experiment duration (several weeks vs. 8 d, respectively), and/or level of feed restriction (13 vs. 75%, respectively). Because only 10% of the DE probes between RFI -and RFI + groups were also affected by feed restriction within the RFI + pigs, our data show that effects of divergent selection for RFI on molecular features of skeletal muscle were quite independent of differences in pig voluntary feed intake between lines.

Lower Muscle Oxidative Metabolism in Low-RFI Pigs

The number of DE mRNA between RFI -and RFI + pigs was much greater than the number of differential proteins. These observations likely result from methodological concerns. Indeed, when the microarray technology allows analyses of all spotted genes, an overwhelming number of proteins cannot be detected on 2D gels because their abundance is below the detection threshold. However, common pathways in which individual genes were involved could be found across methodologies. Thus, both transcriptomic and proteomic data indicate that energy metabolism of the predominantly white glycolytic LM was turned to a less oxidative pattern in RFI -pigs compared with RFI + pigs. This assumption is supported by the finding of an underexpression in the RFI -pigs of dihydrolipoamide dehydrogenase (DLD), a gene that encodes a protein component of the pyruvate dehydrogenase complex regulating the flux of 2-carbon acetyl units from pyruvate through acetyl-CoA into the Krebs cycle and is one of the major enzymes regulating homeostasis of carbohydrate fuels in mammals. This enzyme activity is regulated by a reversible phosphorylation/ dephosphorylation cycle by a specific pyruvate dehydrogenase kinase, and the gene encoding the isoform 1 of this protein was also underexpressed in RFI -pigs. Furthermore, the IDH3A gene catalyzing the allosterically regulated rate-limiting step of the tricarboxylic acid cycle was also downregulated in the RFI -pigs. These findings are further supported by proteomic data showing a lower abundance of aconitase hydratase (ACO2) and ATP synthase subunit α (ATP5A1) in RFI -pigs. Using a similar gel-based proteome analysis in pig LM, [START_REF] Hwang | Assessment of postmortem proteolysis by gel-based proteome analysis and its relationship to meat quality traits in pig longissimus[END_REF] identified the same corresponding spots as ACO2 and ATP5A1, which validates the accuracy of spot identification in the present study. The ACO2 catalyzes the isomerization of citrate to isocitrate via cis-aconitate in the tricarboxylic acid cycle, whereas ATP5A1 is a subunit of the inner mitochondrial membrane ATP synthase complex (F 0 F 1 ATP synthase or Complex V), which produces ATP from ADP in the presence of a proton Table 5. Fold change between the most relevant genes in different biological pathways found between muscles of low (RFI -) and high (RFI + ) residual feed intake pigs The highest P-value is reported when several probes are differentially expressed for a unique gene.

2 The smallest value of expression ratio is reported when several probes are differentially expressed for a gene. Ratios are inversed and preceded by a minus sign for values lower than 1.

Figure 1.

Representative 2-dimensional electrophoresis image of LM separated using an immobilized pH gradient (IPG) 3 to 11 nonlinear strip (GE Healthcare, Uppsala, Sweden) in the first dimension and 12.5% SDS-PAGE in the second dimension. The protein loading was 300 µg and the gel was stained with colloidal Coomassie Brilliant Blue G-250. Numbered spots (14 spots) were identified by nano-liquid chromatography-tandem mass spectrometry (LC-MS/MS) and listed in Table 6.

gradient generated across the inner mitochondrial membrane. Other genes encoding proteins implicated in the mitochondrial electron transport chain such as ND1, ND2, ND4, NDUFB9, CYCS, and ETFB were also found to be underexpressed in RFI -compared with RFI + animals using the transcriptomic analysis. Finally, proteomic data showed less creatine kinase S-type (CKMT2) in RFI -pigs. The CKMT2 is a mitochondrial creatine kinase responsible for the transfer of high-energy phosphate to creatine, which plays a central role in energy transport in tissues with large and fluctuating energy demands such as skeletal muscle [START_REF] Qin | Oxidative myocytes of heart and skeletal muscle express abundant sarcomeric mitochondrial creatine kinase[END_REF]. Importantly, this lower oxidative capacity of RFI -pigs compared with RFI + pigs postulated here based on transcriptomic and proteomic changes in response to RFI selection is supported by enzymatic data previously obtained in a larger group of pigs from the same generation (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF]; lower specific activities of citrate synthase, an indicator of mitochondrial oxidative capacity, and of hydroxy-acyl-CoA dehydrogenase, an indicator of fatty acid β-oxidation potential, have been demonstrated in LM of RFI -pigs compared with RFI + pigs (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF]. On the other hand, Grubbs et al. (2013b) found a greater abundance of the dihydrolipoyl dehydrogenase, aconitate hydratase, and ATP synthase in isolated mitochondria from the red portion of the semitendinosus muscle of RFI -pigs compared with RFI + pigs. Discrepancies between studies are likely due to the fact that Grubbs and colleagues studied intrinsic mitochondrial composition, whereas our results included both differences in mitochondria content as well as differences in their intrinsic composi- 1 Spot numbering as shown in Fig. 1.

2 Protein name and accession numbers were derived from the National Center for Biotechnology Information database (http://www.ncbi.nlm.nih.gov; accessed 9 October 2012).

3 pI = isoelectric point; Mr = molecular weight.

4 Score from MASCOT (Mascot server version 2.2; http://www.matrixscience.com; accessed 9 October 2012).

5 MP = the number of matched peptides in the database search; Sc = protein sequence coverage of the matched peptides in relation to the full-length sequence.

6 Ratio between spot relative volumes of RFI -and RFI + pigs. Ratios are inversed and preceded by a minus sign for values lower than 1.

7 Values are least squares means.

8 RSD = residual SD (corresponding to the root-mean-square error of the full ANOVA model).

tion. The responses of energy metabolism to selection for RFI might be also different between muscle types.

The current study also indicates that the genetic selection for RFI induces changes in the expression levels of various genes and proteins related to vascularization. The proteomic approach reported lower amount of TF, MB, and ACTA2 in RFI -pigs than in RFI + pigs. TF is an iron binding transport protein that is responsible for the transport of iron in the plasma from sites of absorption to sites of storage and utilization, such as in the heme prosthetic group of hemoglobin, myoglobin and cytochromes. MB serves as a transporter of oxygen within muscle fibers and its decrease in RFI -pigs compared with RFI + pigs is then consistent with the reduced oxidative metabolism in the LM of RFI -pigs. Finally, ACTA2 is a vascular smooth muscle actin present in blood vessel walls and its lower abundance in RFI -pigs could be related with a reduced amount of blood vessels [START_REF] Fatigati | Actin and tropomyosin variants in smooth muscles -Dependence on tissue type[END_REF], in accordance with the lower muscle oxidative metabolism in those pigs. The VEGFA gene, a member of the PDGF/VEGF growth factor family encoding a protein that acts on endothelial cells and induces angiogenesis and vasculogenesis [START_REF] Neufeld | Vascular endothelial growth factor (VEGF) and its receptors[END_REF], was also underexpressed in RFI - pigs compared with RFI + pigs by the transcriptomic approach. These differences in muscle oxidative capacity and vascularization may result from selection-induced differences in physical activity, as muscle oxidative metabolism is known to be stimulated by long term physical activity [START_REF] Pette | Mammalian skeletal muscle fiber type transitions[END_REF] and the importance of VEGF in regulating skeletal muscle capillarity has been emphasized in studies dealing with adaptation to exercise [START_REF] Richardson | Exercise adaptation attenuates VEGF gene expression in human skeletal muscle[END_REF] and stimulated skeletal muscle [START_REF] Hang | VEGF gene expression is upregulated in electrically stimulated rat skeletal muscle[END_REF]. In support, RFI -pigs have been shown to exhibit a lower physical activity resulting from a lower eating frequency, a shorter daily feeding time [START_REF] De Haer | Relations among individual (residual) feed intake, growth-performance and feed-intake pattern of growing pigs in-group housing[END_REF][START_REF] Meunier-Salaün | Divergent selection for residual feed intake in group-housed growing pigs: Characteristics of physical and behavioural activity according to line and sex[END_REF], and a lower standing time [START_REF] Meunier-Salaün | Divergent selection for residual feed intake in group-housed growing pigs: Characteristics of physical and behavioural activity according to line and sex[END_REF].

Besides the downregulation of oxidative metabolism in LM of RFI -vs. RFI + pigs, the current transcriptomic and proteomic data also suggest an upregulation of the glycolytic metabolism in RFI -pigs. Indeed, there was an increase in the expression of GAPDH, a glycolytic enzyme, both at the transcriptional and protein levels in those pigs and a greater abundance of ALDOA evidenced only at the protein level. This higher expression of glycolytic enzymes in muscle of RFI -pigs is in agreement with the higher percentage of fast-twitch glycolytic fibers previously reported in the same muscle after 4 generations of selection [START_REF] Lefaucheur | Muscle characteristics and meat quality traits are affected by divergent selection on residual feed intake in pigs[END_REF]. However, previous studies reported a reduced activity of lactate dehydrogenase, an enzyme involved in the terminal route of glycolysis, in LM of pigs selected for RFI -suggesting reduced glycolysis in those pigs (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF][START_REF] Faure | Consequences of divergent selection for residual feed intake in pigs on muscle energy metabolism and meat quality[END_REF]. Using a proteomic approach, Grubbs et al. (2013b) also found a lower abundance of LDHA in the mitochondrial fraction of LM from RFI -pigs but the fact that LDHA is a cytosolic protein prompts us to be careful in interpreting those results. Altogether, the effects of RFI selection on muscle glycolytic metabolism remain rather unclear and deserve further study.

Data are Consistent with a Lower Oxidative Stress and Apoptosis in Low-RFI Pigs

The current study highlights the downregulation of 3 molecules involved in cell defense against ROS in RFI -pigs compared with RFI + pigs. Peroxiredoxin-6 was decreased at the protein level, whereas SOD2 and GPX3 exhibited decreased mRNA levels. Because the metabolic challenge during physical exercise results in an elevated generation of ROS [START_REF] Bo | Mitochondrial redox metabolism in aging: Effect of exercise interventions[END_REF], the observed greater levels of antioxidant molecules in the muscle of RFI + pigs could be an adaptive mechanism to cope with a higher and deleterious ROS production [START_REF] Radak | Exercise and hormesis: Oxidative stress-related adaptation for successful aging[END_REF]. In accordance, a greater ROS production has been reported in mitochondria from semitendinosus muscle in RFI + pigs (Grubbs et al., 2013a) and from breast muscle in broilers with low feed efficiency [START_REF] Bottje | Association of mitochondrial function and feed efficiency in poultry and livestock species[END_REF]. Furthermore, the mitochondria protein profile in LM indicated a greater abundance of proteins in- volved in oxidative and antioxidative stress in the less efficient RFI + line (Grubbs et al., 2013b). Therefore, available data suggest that muscle of RFI -pigs may have less mitochondria, which would be intrinsically more efficient and less prone to produce ROS than those of RFI + pigs. In the present experiment, GPD1 showed a higher abundance in RFI + than RFI -pigs at the protein level. This enzyme could counterbalance ROS production in RFI + pigs while maintaining a high oxidative metabolism. Indeed, GPD1 plays a critical role in carbohydrate and lipid metabolism by catalyzing the reversible conversion of dihydroxyacetone phosphate and NADH to glycerol-3-phosphate and NAD+ in the cytoplasm. Importantly, GPD1 is part of the glycerol phosphate shuttle that facilitates the transfer of reducing equivalents generated during glycolysis in the cytosol (NADH) to coenzyme Q in the mitochondrial electron transport chain, transferring cytoplasmic reducing equivalents through flavin adenine dinucleotide (FadH 2 ) directly to complex II, thus bypassing complex I, a site where ROS are generated. The global reaction is exothermic, driven by the difference in free energy between NADH in the cytosol and reduced FADH 2 in the mitochondria, which could contribute to the greater feed efficiency and lower heat loss of RFI -pigs than of RFI + pigs [START_REF] Barea | Energy utilization in pigs selected for high and low residual feed intake[END_REF]. The underexpression of OAZ3, an inhibitor of ornithine decarboxylase, in RFI -vs. RFI + pigs could also improve the antioxidative defense in RFI - pigs because ornithine decarboxylation is the first step in the synthesis of polyamines that are important as antioxidants. Moreover, cysteine-serine-rich nuclear protein 3 (CSRNP3), a transcriptional factor involved in apoptosis, was also underexpressed at the mRNA level in RFI -pigs. Altogether, available data suggest a reduced ROS production in LM of the RFI -pigs, which might be also associated with lower oxidative stress and apoptosis compared with RFI + pigs.

Transcriptomic Data Suggest a Greater Muscle Protein Synthesis in Low-RFI Pigs

Transcriptomic data highlighted differences in the expression of some genes involved in protein synthesis or degradation. Notably, genes encoding initiation and elongation translation factor subunits (EEF1A1, EEF1G, EIF2S2, EIF2B5, and EIF6), and type I protein arginine N-methyltransferase (PRMT3) involved in the maturation of the 80 and 56S ribosomal proteins were overexpressed in RFI -vs. RFI + pigs. Feed restriction may also affect some genes in protein metabolism. Indeed, protein synthesis and protein catabolic pathways were simultaneously enriched in RFI +R pigs when compared with RFI + pigs. However, it remains elusive to relate these changes observed at the mRNA levels to differences in carcass lean meat content reported between the different groups. Indeed, we failed to identify any differences between lines or due to feed restriction for the relative abundance in these proteins using the proteomic approach. Similarly, it has been reported that markers of protein synthesis did not differ between 2 Yorkshire pig lines selected for divergent feed efficiency [START_REF] Cruzen | Evidence of decreased muscle protein turnover in gilts selected for low residual feed intake[END_REF]. Except for the gene encoding for the millimolar-calpain protease (CAPN2) involved in muscle protein degradation, which was overexpressed in RFI -animals compared with RFI + animals, expression levels of other genes linked to protein catabolism were not modified by selection in the current study. These latter observations are consistent with the lack of difference in activities of enzymes involved in protein catabolism (calpains and proteasome) reported recently on a larger group of animals from the same experiment (Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF]. On the other hand, an increased activity of the calpastatin, an endogenous calpain (calcium-dependent cysteine protease) inhibitor, and a decreased 20S proteasome protein degradation systems have been reported in muscle of a RFI -versus a RFI + pig line [START_REF] Cruzen | Evidence of decreased muscle protein turnover in gilts selected for low residual feed intake[END_REF], suggesting a lower protein degradation rate in RFI -pigs. A reduction in the rate of protein breakdown along with a higher level of calpastatin has been reported in a RFI -line of cattle [START_REF] Mcdonagh | Meat quality and the calpain system of feedlot steers following a single generation of divergent selection for residual feed intake[END_REF]. We have no particular explanation for the discrepancies between our study and other studies, except the fact that different techniques were used. Le [START_REF] Naou | Metabolic changes and tissue responses to selection on residual feed intake in growing pigs[END_REF] measured overall 26S proteasome activity (association of 20S subunit with 1 or 2 19S regulatory complexes), whereas [START_REF] Cruzen | Evidence of decreased muscle protein turnover in gilts selected for low residual feed intake[END_REF] measured the proteolytic core complex 20S. Altogether, further studies are needed to clarify the effects of RFI selection on protein turnover.

Finally, translation needs abundant sources of energy, particularly ATP, which is essentially produced by oxidative metabolism and phosphorylation in mitochondria. When reducing equivalents produced by fatty acid oxidation and tricarboxylic acid cycle are oxidized, both ATP and ROS are produced, and ROS production may limit ATP production. In the present study, superoxide dismutase and glutathione peroxidase 3 at the mRNA level and peroxiredoxin 6 at the protein level were found to be underexpressed in RFI -pigs, which could reflect a lower ROS production in these pigs. In support, recent studies by [START_REF] Bottje | Association of mitochondrial function and feed efficiency in poultry and livestock species[END_REF] and Grubbs et al. (2013a) also showed that genetic selection for RFI -attenuates H 2 O 2 production in mitochondria, limiting ROS production.

Conclusion

Divergent selection for RFI during 7 generations modified gene and protein expressions in pig muscle, so that RFI -pigs exhibited a lower oxidative metabolism in muscle. This was associated with the downregulation of antioxidative molecules, which could reflect a lower muscular oxidative stress in those pigs. The greater lean meat proportion induced by selection in RFI -pigs might be related to a more efficient energy metabolism in muscle, enabling a shift in energy utilization toward lean growth as suggested by the overexpression of genes involved in protein synthesis. The comparisons made in the present study by considering an additional feed-restricted RFI + group indicated that differences between RFI lines were likely not related to differences in feed intake per se. A recent study suggests that the adenosine monophosphate-activated protein kinase pathway could be involved in the underlying mechanisms linking the numerous changes induced by divergent selection on RFI in skeletal muscle [START_REF] Faure | Consequences of divergent selection for residual feed intake in pigs on muscle energy metabolism and meat quality[END_REF] but further studies are needed to establish these relationships.

Figure 2 .

 2 Figure 2. Comparison of microarray and quantitative real-time PCR(qPCR) data for gene expression ratio between low (RFI -) and high (RFI + ) residual feed intake pigs in LM. Ratios lower than 1 were expressed as negative numbers (i.e., a ratio of 0.5 is expressed as -2). All significant differences between RFI -and RFI + microarray data (P < 0.01) were confirmed by qPCR data (P < 0.05), except for superoxide dismutase 2 (SOD2; P = 0.08). AQP4 = aquaporin 4; CD40 = CD40 molecule TNF receptor superfamily member 5; CSRNP3 = cysteine-serine-rich nuclear protein 3; GAPDH = glyceraldehyde-3-phosphate dehydrogenase; GPX3 = glutathione peroxidase 3; OAZ3 = ornithine decarboxylase antizyme 3; PCIF1 = phosphorylated CTD-interacting factor 1; PRMT3 = protein arginine methyltransferase 3; WDHD1 = WD repeat and HMGbox DNA binding protein 1.

Table 1 .

 1 Primer sequences used for analysis of gene expression by quantitative real-time PCR May 2013) or Ensembl project database (http://www.ensembl.org/Sus_scrofa/Info/Index; accessed 14 May 2013) for pig sequences.

	Gene symbol	Description	Accession number 1	Primer sequence (5′-3′) 2
	AQP4	Aquaporin 4	ENSSSCT00000004120	F: CAGAAAAGCCCCTACCTGTTGA
				R: CGTGACAGCGGGATTGATG
	CD40	CD40 molecule, tumor necrosis factor receptor	ENSSSCT00000008144	F: GCCCGCCAGGACAGAAA
		superfamily member 5		R: GGAACTGCAAGGAAGGCATTC
	CSRNP3	Cysteine-serine-rich nuclear protein 3	ENSSSCT00000017319	F: TCAATCCATCCACTTCCAATCA
				R: GCCGCTTCTCCCTTTTGAG
	GAPDH	Glyceraldehyde-3-phosphate dehydrogenase	AF017079.1	F: CATCCATGACAACTTCGGCA
				R: GCATGGACTGTGGTCATGAGTC
	GPX3	Glutathione peroxidase 3	ENSSSCT00000018610	F: GCTTCCCCTGCAACCAATT
				R: GGACATACCTGAGAGTGGACAGAA
	OAZ3	Ornithine decarboxylase antizyme 3	NM_001122996	F: ATTGTATTCGGCTGGGAACCT
				R: GGCGGAAGTGGAAGTCTAGCT
	PCIF1	Phosphorylated CTD-interacting factor 1	XM_003360023	F: CAGCGTGGAGATGCACATG
				R: GAGCCACAGCTTGCTGAAGTAG
	PRMT3	Protein arginine methyltransferase 3	XM_003360703	F: CAGGTGAAGGCTTGAAAGGAA
				R: TCACAATGAGAGAACGTGGATCTT
	SOD2	Superoxide dismutase 2, mitochondrial	NM_214127.2	F: GCGCTGAAAAAGGGTGATGT
				R: ACCGTTAGGGCTCAGATTTGTC
	WDHD1	WD repeat and HMGbox DNA binding protein 1	ENSSSCT00000005569	F: GCCATTCCAGTCAGGTTCTACAC
				R: TCCAATAGAATTCCACACCATGAA
	HPRT1 3	Hypoxanthine phospho-ribosyltransferase 1	DQ845175	F: TACCTAATCATTATGCCGAGGATTT
				R: AGCCGTTCAGTCCTGTCCAT
	PPIA 3	Peptidylpropyl isomerase A	NM_214353	F: AGCACTGGGGAGAAAGGATT
				R: AAAACTGGGAACCGTTTGTG
	YWHAZ 3	Tyrosine 3-mono-oxygenase/tryptophan	XM_001927228	F: ATGCAACCAACACATCCTATC
		5-monooxugenase activation protein, zeta		R: GCATTATTAGCGTGCTGTCTT
		polypeptide

1 Accession number in the National Center for Biotechnology Information database (http://www.ncbi.nlm.nih.gov/gene; accessed 14

Table 2 .

 2 Growth performance and carcass traits in pigs divergently selected for low (RFI -) or high (RFI + ) residual feed intake (RFI) having either free (RFI -and RFI + ) or restricted (RFI +R ) access to feed from 67 d of age to slaughter. Values are least squares means

	Item	RFI -(n = 8)	RFI + (n = 8)	RFI +R (n = 8) RSD 1 P-value
	Growth traits
	67-d-old BW, kg	25.7 b	31.1 a	30.5 a	3.9	0.02
	Final BW, kg	115.8	114.9	115.2	1.6	0.51
	Final age, d	168 a	158 b	172 a	6.23	<0.001
	ADG, g/d	892 a	927 a	819 b	39	<0.001
	Feed intake
	D aily feed intake, g•d -1 •kg BW 0.60	182 a	209 b	181 a	10	<0.001
	G:F	0.40 a	0.35 b	0.35 b	0.02	<0.001
	Carcass traits
	L ean meat	59.0	57.2	58.9	2.0	0.08
	content, %

a,b Within a row, means without a common superscript differ (P < 0.10).

1 RSD = residual SD (corresponding to the root-mean-square error of the full ANOVA model).

Table 3 .

 3 Number of probes differentially expressed (P < 0.01) in muscle of pigs divergently selected for low (RFI -) or high (RFI + ) residual feed intake (RFI) having either free (RFI -and RFI + ) or restricted (RFI +R ) access to feed from 67 d of age to slaughter at 115 kg BW

		RFI -/RFI + pigs	RFI +R /RFI + pigs
	Fold-change	Over-	Under-	Over-	Under-
	groups	expressed	expressed	expressed	expressed
	Total	674	339	402	325
	FC 1 ≥ 1.5	72	110	18	80
	FC ≥ 2	27	32	1	3
	1 FC = fold change.

Table 4 .

 4 Most significant overrepresented Gene Ontology (GO) biological processes identified from the differentially expressed genes between muscles of low (RFI -) and high (RFI + ) residual feed intake pigs having free access to feed from 67 d of age to slaughter

	GO accession	Description	No. 1	Log10 P-value	Gene symbol
	Downregulated in RFI -vs. RFI +
	GO:0055114	Oxidation-reduction process	19	-4.243	ME1, ND1, ND4, LOC396756, NDUFB9, ND2, CYCS, MOSC2,
					IDH3A, SOD2, RDH11, SLC1A3, HSDL2, FMO1, GPX3, DLD,
					HSD17B4, LOC733694, and ETFB
	GO:0048002	Antigen processing and presentation	5	-3.958	SLA-3, HLA-A, HLA-C, HLA-B, and B2M
		of peptide antigen
	GO:0022900	Electron transport chain	8	-3.696	ND1, SLC1A3, ND4, LOC396756, NDUFB9, ND2, CYCS, and ETFB
	GO:0006091	Generation of precursor metabolites	10	-2.248	ND1, SLC1A3, ND4, LOC396756, PGAM4, NDUFB9,
		and energy			ND2, CYCS, ETFB, and IDH3A
	GO:0016197	Endosome transport	4	-1.614	STX16, PIKFYVE, VPS13A, and HOOK3
	GO:0045944	Positive regulation of transcription	8	-1.327	TAF2, CSRNP3, MITF, SIX1, CAND1, TCEA1, TCF7L2, and STAT3
		from RNA polymerase II promoter
	Upregulated in RFI -vs. RFI +
	GO:0006412	Translation	37	-23.118	EIF6, RPL18, RPL14, RPLP0, RPLP1, FAU, RPL10, RPL12,
					RPL36AL, EIF2B5, LOC645139, RPS18, RPS19, RPS12, UBA52,
					RPS26, RPL7, RPL6, RPL8, RPS20, RPS23, RPSA, EEF1A1, PAIP1,
					RPL23A, DENR, RPS6, COPS5, LOC100049695, RPS19P3, RPS3,
					EEF1G, RPS15A, RPL29, RPL23, RPL21, and EIF2S2
	GO:0022613	Ribonucleoprotein complex	12	-3.690	EIF6, GTPBP4, SNUPN, LOC100049695, GNL3L, RPS6,
		biogenesis			SFRS5, RPS19, RPL7, RPLP0, POP4, and TGS1
	GO:0042692	Muscle cell differentiation	9	-2.918	SYNE1, ACTA1, MAPK12, ERBB2, GLMN, SOX6,
					NEURL2, CAPN2, and CACNA1S
	GO:0006379	Messenger RNA cleavage	3	-1.463	POP4, CPSF3, and CPSF1
	GO:0030099	Myeloid cell differentiation	5	-1.4411	RPS19, PSEN1, EPB42, SOX6, and VPS33A
	GO:0006469	Negative regulation of protein kinase	4	-1.346	PSEN1, HEXIM2, SNX6IGF2, and PKIA
		activity

1 Number of genes involved in the GO term.

Table 6 .

 6 Differentially expressed muscle proteins in pigs divergently selected for low (RFI -) or high (RFI + ) residual feed intake (RFI) having either free (RFI -and RFI + ) or restricted (RFI +R ) access to feed from 67 d of age to slaughter

	Spot no. 1	Gene symbol	Protein name 2	Theoretical pI/Mr 3	Score 4 MP/Sc 5	RFI -: RFI +6	Spot relative volume 7 , vol/vol × 10 6 RFI -RFI + RFI +R	RSD 8 P-value 8
	444	ACO2	Aconitase hydratase,	9.0/85.7	1,295	25/25	-1.8	203 a	369 b	343 b	107	0.01
			mitochondrial
	351	ATP5A1	ATP synthase subunit α,	9.7/59.7	1,298	29/35	-1.6	683 a	1,060 b	844 ab	240	0.02
			mitochondrial
	352	ATP5A1	ATP synthase subunit α,	9.7/59.7	932	15/27	-1.6	140 a	219 b	170 ab	56	0.03
			mitochondrial
	303	CKMT2	Creatine kinase S-type,	9.3/47.2	1,301	29/42	-1.5	995 a	1,527 b	1,322 ab	365	0.03
			mitochondrial
	305	CKMT2	Creatine kinase S-type,	9.3/47.2	937	12/35	-1.4	240 a	341 b	246 a	78	0.03
			mitochondrial
	506	GAPDH Glyceraldehyde-3-phosphate	9.3/35.8	732	15/30	1.3	1,563 a	1,184 b	1,410 ab	346	0.04
			dehydrogenase
	246	ALDOA	Fructose-bisphosphate	9.2/39.3	1,289	32/42	1.3	26,459 a 21,055 b 22,877 ab	3,722	0.03
			aldolase A
	268	GPD1	Glycerol-3-phosphate	6.9/37.6	488	8/20	-1.7	369 a	637 b	483 ab	194	0.04
			dehydrogenase [NAD(+)],
			cytoplasmic
	120	PRDX6	Peroxiredoxin-6	5.7/25.0	390	7/44	-1.2	221 a	262 b	218 a	33	0.03
	102	HSPB1	Heat shock protein β-1	6.3/22.9	529	12/31	-1.7	360 a	601 b	447 ab	173	0.03
	108	HSPB1	Heat shock protein β-1	6.3/22.9	290	5/28	1.6	159 a	99 b	101 b	38	0.01
	440	TF	Serotransferrin	7.1/76.9	1,382	28/36	-1.4	444 a	618 b	368 a	162	0.02
	31	MB	Myoglobin	6.9/17.1	682	32/64	-1.4	4,686 a	6,345 b	5,247 ab	1,242	0.04
	300	ACTA2	Actin, aortic smooth muscle	5.1/42.0	600	13/30	-1.4	174 a	251 b	178 a	48	0.01
	a,b Within a row, means without a common superscript differ (P < 0.05).

The study was financially supported by the French National Research Agency (Agence Nationale de la Recherche, ANR-08-GENM038, PIG_FEED project).