Marie-Pierre Ellies-Oury

Herbivores

Mohammed Gagaoua

Jérôme Saracco

Marie Chavent

Brigitte Picard

Bringing Excellence in Open Access

Keywords: Breeding practices, Carcass characteristics, Clustering of variables, Biomarkers, m. Semitendinosus, m. Longissimus thoracis Longissimus Thoracis; ST: Semitendinosus

This work sets out a methodological approach to assess the relationships that exist between four sets of data: breeding practices, carcass characteristics, m. Longissimus thoracis biomarkers and m. Semitendinosus biomarkers. Seventy-one young bulls of 3 breeds were characterized by 78 variables.

The variables in each set were arranged into 4, 3, 5 and 3 homogeneous clusters respectively using the clust of Var approach. A second clustering of variables established from these 15 clusters was then used to build 5 pools of clusters that were precisely described to characterize the interaction between the 4 sets of variables. Some practices have a universal impact on biomarker patterns eg. increasing straw proportion in the diet leads to an increase in MyLC-1F and Hsp40 biomarkers whatever the breed and the muscle. Nevertheless, most breeding practices appear to have varying impacts on biomarkers depending on animal type and muscle. For example, ST biomarkers are less related to breeding practices and/or carcass characteristics than in LT. Moreover, some biomarker expressions appear to be breeddependant, especially for the Angus breed which is very specific in comparison to continental breeds. The breed where muscle biomarkers are the most closely linked to breeding practices and carcass characteristics is the Blonde d' Aquitaine. In this breed, MDH1 and DJ-1 biomarkers are positively linked to muscle yield and carcass development in both muscles and some other LT biomarkers might also be considered as good indicators of carcass fatness (small Hsp, ENO3, Capz-β, MyBP-H).

Several of the tested biomarkers have been identified as being relevant for carcass quality prediction suggesting that biomarker abundance could represent an interesting possibility for the early management of carcass and meat quality.

INTRODUCTION

Farm management systems, especially during the finishing period, are known to impact final carcass and meat quality traits. In order to predict the most important meat quality traits as early as possible, it is therefore useful to reveal the gene networks involved in the development of the desired quality phenotype. The modern beef industry currently lacks fast, objective and noninvasive tools to estimate and/or predict the beef quality of live animals or online in abattoirs, paying specific attention on sensory traits. The identification of relevant genetic and genomic markers that could permit the detection of live animals' potential desired quality traits would be helpful for beef producers to help them select the most appropriate production systems, animal types, and markets.

Gene expression controls the biological characteristics of muscles and thanks to advances in genomics, researchers have identified a number of genes that are associated with different meat qualities [START_REF] Bernard | New indicators of beef sensory quality revealed by expression of specific genes[END_REF][START_REF] Goddard | Mapping genes for complex traits in domestic animals and their use in breeding programmes[END_REF][START_REF] Dunnera | Association of genes involved in carcass and meat quality traits in 15 European bovine breeds[END_REF][START_REF] Sun | The identification of 14 new genes for meat quality traits in chicken using a genome-wide association study[END_REF]. Over the last few decades, the implementation of genomic research programs has been very important in revealing the underlying biological mechanisms behind several meat quality traits [START_REF] Picard | Recent advances in omic technologies for meat quality management[END_REF]. These programs concern DNA level, RNA level and also protein expression. As an emerging method, proteomics has been applied to characterize the

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physiology behind animal growth and development, reproduction, welfare and beef products and to identify potential meat quality markers [START_REF] Picard | Skeletal muscle proteomics in livestock production[END_REF][START_REF] Picard | Chapter 13: Proteomics as a tool to explain/predict meat and fish quality[END_REF]. These markers, which may be over-or underexpressed depending on meat quality grade, are involved in many different biological mechanisms, such as energy metabolism, muscle structure, stress and oxidative stress reactions, cell cycle, proteolysis and apoptosis [START_REF] Picard | Chapter 13: Proteomics as a tool to explain/predict meat and fish quality[END_REF][START_REF] Ouali | Biomarkers of meat tenderness: present knowledge and perspectives in regards to our current understanding of the mechanisms involved[END_REF].

The aim of the present report is to study the link between breeding practices and/or beef carcass characteristics with the abundances of a series of muscle biomarkers. In an attempt to establish, if possible, universal links and / or predictive equations, two muscles (differing in their metabolic and contractile characteristics) were studied in young bulls from three continental breeds differing in terms of their earliness and physiological characteristics.

MATERIALS AND METHODS

Animals, Management, Slaughtering and Sampling

The method was developed using data from the EU FP6 Integrated Project Pro Safe Beef (FOODCT-2006-36241) under the INRA reference AQ284. This study used 71 young bulls of three pure breeds: Aberdeen Angus (n A =22), Blond d' Aquitaine (n BA =24) and Limousin (n Lim =25).

The French Blond d' Aquitaine breed is highly muscled with low intramuscular fat content ; the Aberdeen Angus breed is known to be precocious and thus fat with a marbled meat; the Limousin meat is intermediate between the other two breeds [START_REF] Picard | Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle[END_REF]. Animals (12 months-old at the beginning of the experiment) were assigned during the finishing period (105 days before slaughter) to a feeding regime given ad libitum based on straw (25%) and concentrate (75%) supplemented with antioxidant or not. They were housed in groups of four animals of the same breed in 6 × 6 m pens with straw bedding. Individual daily feed intake and body weight were recorded every 2 weeks. They were slaughtered at about 17 months of age at a live weight of around 665 kg.

The study was carried out in compliance with current French welfare recommendations for the use of experimental animals.

All animals were slaughtered under standard conditions in the experimental abattoir of the INRA Research Centre, after 24 hours of food deprivation. Slaughter was carried out in compliance with French welfare regulations. The carcasses were not electrically stimulated and they were chilled and stored at 4 °C up to 24 h post-mortem (p-m). Final pH was recorded between the 6 th and 7 th rib using a pH meter equipped with a glass electrode at 24h p-m.

Animal measurements and carcass characteristics

Age, live weight and empty body weight were recorded before slaughter. Average daily gain (ADG), feed conversion efficiency (ADG / dry matter) and feed conversion efficiency based on net energy intake (ADG / net energy intake) were calculated using initial live weight and feed consumption (expressed as dry matter or energy intake) during the finishing period. Carcass weight was determined at slaughter. The 6 th rib joint was removed and dissected in order to estimate the composition of the carcass (content and proportions) based on the regression equations previously established [START_REF] Robelin | Estimation de la composition de la carcasse des taurillons a partir de la composition de la 6eme cote[END_REF]. Carcass and meat yield were estimated using the determined weights. The carcass was dissected in order to weigh the various organs and tissues (leather weight; head weight; digestive and urinary tract content; shinbone weights). Measurements were also recorded (total carcass length; thigh thickness; length shank-symphysis; sirloin thickness, kidney length) yielding measurement ratios (sirloin thickness / kidney length; thigh thickness / total length; thigh thickness / length shank-symphysis). In this way, weights, measures and ratios yielded 36 variables characterizing animal performances (Table 1).

Meat quality evaluation

Muscle samples from m. longissimus thoracis (LT, mixed fast oxido-glycolytic muscle: 23% of type I fibers, 36% IIA and 39% IIX) and m. semitendinosus (ST, mixed fast glycolytic muscle: 8% of type I fibers, 24% of IIA and 64% of IIX) [START_REF] Picard | Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle[END_REF][START_REF] Robelin | Estimation de la composition de la carcasse des taurillons a partir de la composition de la 6eme cote[END_REF] were excised from the carcass of each animal within 15 min p-m.

The 21 protein biomarkers analyzed for LT and ST muscle are given in Table [START_REF] Bernard | New indicators of beef sensory quality revealed by expression of specific genes[END_REF]. The conditions for use and specificity of primary antibodies against these 21 proteins in bovine muscle were previously determined by [START_REF] Guillemin | La maîtrise de la tendreté de la viande bovine: identification de marqueurs biologiques[END_REF] using Western blotting.

Total protein extractions were performed according to the method of [START_REF] Bouley | Mapping of bovine skeletal muscle proteins using two-dimensional gel electrophoresis and mass spectrometry[END_REF]) in a denaturation extraction buffer containing 8.3 M urea, 2 M thiourea, 1% DTT and 2% CHAPS. The protein concentration was determined using the Bradford protein assay [START_REF] Bradford | A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding[END_REF]. The protein extractions were stored at -20 °C until use.

Relative abundances of the 21 protein biomarkers were evaluated following the Dot-Blot technique as described by Gagaoua et al. [START_REF] Gagaoua | Coherent correlation networks among protein biomarkers of beef tenderness: what they reveal[END_REF]. Briefly, protein samples were spotted in quadruplicate on a nitrocellulose membrane with the Minifold I dot blot from Schleicher & Schuell Biosciences (Germany) and hybridized with the specific antibody of each protein in the conditions recently reported by [START_REF] Picard | Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle[END_REF]. Secondary fluorescent conjugated IR Dye 800CW antibodies (anti-mouse) were supplied by LI-COR Biosciences (Lincoln, NE, USA) and used at 1/20000. Subsequently, membranes were scanned by an Odyssey (LI-COR Biosciences) scanner at 800 nm. The relative abundance of protein for each sample, given in arbitrary units, was standardized by comparison to a reference sample constituted by mixing all samples from the three young bulls in equal proportions.

Statistical Analysis

For each breed, the statistical analysis was organised in 3 steps as shown following the method recently reported by our group [START_REF] Ellies-Oury | An innovative approach combining Animal Performances, nutritional value and sensory quality of meat[END_REF] and highlighted in Figure [START_REF] Bernard | New indicators of beef sensory quality revealed by expression of specific genes[END_REF].

For each set of variables (breeding practices / animal measurements and carcass performances / LT biomarkers /ST biomarkers), a hierarchical clustering of variables was applied to assess the links between the different variables making up the set. To do this, we used a combination of two dimension reduction approaches (implemented in R software): i) the Clust of Var approach, developed to arrange variables into homogeneous

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clusters and to allow dimension reduction and variable selection, and ii) principal component analysis (PCA). Neither pretreatment, nor normalisation techniques was applied before PCA. However, the methodology of the PCA implies to center and reduce the variables that are characterised by different units and dispersion profiles. A bootstrap approach allowed to evaluate the stability of the partitions of variables and to determine the appropriate number of clusters. Moreover, this method is useful to identify the links between the variables and to highlight redundancy in a data set. Thus, for each set of data, we different clusters.

Step 1: For each cluster, a central quantitative synthetic variable, defined as the first principal component of PCA applied to all the variables within the cluster, was calculated. To characterize each cluster, only the variables having a square correlation with the central quantitative synthetic variables greater than 0.50 were taken into consideration. Nevertheless, all variables were taken into account in the following steps of the analysis. To visualize and evaluate the correlation between the different clusters established for each set of variables, a PCA and different correlation tests were carried out (data not shown).

Step 2: To establish the links between breeding practices / animal measurements and carcass performances / LT biomarkers / ST biomarkers, a Clust of Var PCA was carried out on the different clusters taken together (all of the clusters obtained in Step 1, corresponding to the four data sets). Different pools of clusters were created according to the first component of the PCA applied to the clusters and characterized, using the variables having a square correlation with the central quantitative synthetic variables greater than 0.50.

Step 3: To visualize and evaluate the correlation between the four pools of clusters, a PCA was performed.

RESULTS

A preliminary analysis was carried out on only 2 sets of variables (LT biomarkers and ST biomarkers) in order to evaluate the relationships between the list of protein biomarkers from both muscles (see supplementary material). This allowed us to establish that different biological mechanisms may underlie protein expression depending on the muscle. Moreover, although some generic results can be obtained by taking all breeds together, it appears more pertinent to establish the links between breeding practices, carcass performances and muscles biomarkers breed by breed as biomarker abundances appear to be breed-specific. These results confirm those reported by our group [START_REF] Picard | Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle[END_REF][START_REF] Gagaoua | Proteomic biomarkers of meat colour of Blonde d' Aquitaine young bulls?: towards a better comprehension of the biological mechanisms[END_REF][START_REF] Gagaoua | Understanding early post-mortem biochemical processes underlying meat color and ph decline in the longissimus thoracis muscle of young blond d' Aquitaine bulls using protein biomarkers[END_REF], indicating a muscle-dependent and a breed-dependent evolution of meat quality biomarkers hence the decision to study the relations between the 4 set of variables breed per breed.

Step 1: arrangement of variables in each set of variables into homogeneous clusters and description of each cluster As indicated above, each cluster of variables is characterized by variables highly correlated to a central quantitative synthetic variable. Using variable clustering thus aims to maximize the homogeneity within a cluster. For each breed (n = 3) and each set of variables (number of set of variable = 4; breeding practices BP, carcass characteristics CC, LT biomarkers (LTB), ST biomarkers (STB)), variable clusters were constituted (Table 2).

As regards breeding practices, the clusters were quite similar and more or less coherent from breed to breed i.e. a higher concentrate intake in the feeding ration was positively linked with higher energy (UF) and protein (PDIN -PDIE) intake (A-BP-3, L-BP-2, BA-BP-2); the proportions of concentrates and straw in the feeding ration was negatively correlated (A-BP-1, L-BP-1, BA-BP-1).

As regards carcass characteristics, variable associations were also found to be relatively stable from one breed to another and also consistent with the expectations: adipose tissues (TFT, CFT, 5QF, Trim Fat) expressed in amount and in proportion being opposed to muscle and bone development (A-CC-1, L-CC-1, BA-CC-1). The increase in muscle and bone weight are in line with the increase in global animal development (EBW, CCW, SBW, HW; A-CC-2, L-CC-2, BA-CC-2). In continental breeds, muscle and bone development is also associated with carcass (Limousin) or muscle yields (Blond d' Aquitaine). On the other hand, in the Angus breed, these yields appear to be independent.

In the Longissimus thoracis muscle, some biomarkers are similarly associated in both continental breeds: Hsp70-1, Hsp70-8, µ-calpain, DJ-1 and MDH1 for example. The other clusters were not stable among breeds, confirming the conclusions established in the first phase and justifying a study breed per breed.

In the Semitendinosus muscle, only one pair of biomarkers is associated in the same manner in the two continental breeds: Hsp20 and Hsp70-Grp75. However, great similarity is noted when the Angus and Blond d' Aquitaine breeds are considered together, with two identical associations: on the one hand Hsp70-Grp75 and µ-calpain and on the other hand MyHC-II, MyBP-H and MDH1.

Step 2: evaluation breed per breed of the interaction between the 4 sets of clusters, and constitution of 4 to 5 pools of clusters per breed i) Links between LT and ST biomarkers Whatever the breed, the clusters constituted on ST biomarkers were located in very close proximity, with 3 pools (Pool-A5, -L4, -B4) and an association of respectively 11, 6 and 11 ST biomarkers in Angus, Limousin and Blond d' Aquitaine breeds (Figure 1). In the two continental breeds, Hsp70-1B, MyHC-I, Hsp70-8 and µ-calpain biomarkers of both ST and LT muscles were shared by the same pools (Pool-L1 and -B1). Even though the retained biomarkers are not similar from one breed to another. For example, in the Angus breed, ST and LT biomarkers are not included in the same pool (Table 3).

Links between biomarkers and breeding practices

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linked to the LT MyHC-II biomarker in the Angus breed. Thus, breeding practices appear to have various impacts on biomarkers depending on the animal type and the muscle studied. The results revealed that depending on breeding practices, the Angus muscles appear to have opposite reactions to those from continental breeds.

Protein and energy content of the diet has no significant impact on muscle biomarkers except for Blond d' Aquitaine animals. For this breed, the increase of the diet value is linked to an increase in enolase 3, small heat shock proteins (Hsp27, Hsp20) and two structural proteins (CapZ-β and MyBP-H)..

Links between biomarkers and carcass characteristics

Whatever the breed, increasing the diet in concentrate, UF and PDI content infers an increase in adipose tissue development in the whole animal, in the carcass and in the 5 th quarter of the carcass (Table 3). In the Blond d' Aquitaine breed which is known to be rather lean, the fatter carcasses (higher CFT, TFT, 5QF, leather weight, %TFT, % CFT) are characterized by a higher expression of previously indicated biomarkers (Hsp27, Hsp20, enolase 3, CapZ-β and MyBP-H), whereas no linear relation appears between these parameters in the other two breeds.

In the Limousin breed, variation in studied carcass characteristics does not appear to be linked to muscle biomarker modification. In the other two breeds, muscle and bone development (including carcass, total bone, head and shin bone weights) appears to be positively linked to MyHC-I biomarker abundance in LT muscle.

When considering the three breeds as one data set, it appears that the most significant links between breeding practices and biomarkers, as well as between carcass characteristics and biomarkers, are found in the Blond d' Aquitaine breed.

In this breed, muscle (weight and yield) and bone development is linked to an increase in MDH1 and DJ-1 biomarkers in both muscles. In LT muscle these carcass characteristics are linked to MyHC-I biomarker abundance, whereas in ST muscle they are positively correlated with the abundance of MyHC-II, confirming as indicated before that the response of biomarkers to variation factors is muscle dependant. Finally, in LT muscle, bone and muscle development is also correlated with large Hsp (Hsp70-1B and Hsp70-8) and oxidative resistance (DJ-1, PRDX6) proteins.

Step 3: evaluation breed per breed of the links among the different pools of clusters

A principal component analysis was performed breed per breed in order to evaluate the proximities between the different pools of clusters (Figure 2). It allowed us to establish in the Angus breed that:

-pools A2, A3 and A5 are positively linked together on the first axis (33.4 % inertia, Figure 2). The positive values are associated with richer diets (UF, PDI, DMI, concentrate and straw ingested) and the fatter carcasses (% CFT, % TFT, CFT, TFT, 5QF, Leather weight). The diets richer in straw (amount and proportion) and the carcasses with a higher development (% Muscle and % Bone) are located on the opposite side of the axis Some biomarkers are negatively correlated with fatter carcasses on both muscles, namely µ-calpain, Hsp70-Grp75, MyHC-II and LDH-B. Fatter carcasses are also negatively linked to Hsp70-8, CapZ-β and MyBP-H in ST muscle. Only the SOD1 biomarker of the LT muscle is positively linked with the carcass characteristics.

-pools A1 and A4 are positively linked on the second axis (26.6 % inertia), comparing heavier carcasses (EBW, CCW, SBW, HW, Muscle and Bone weights; positive values) versus yields (carcass and muscle) and animal efficiency (ADG, Feed efficiency ; negative values) (Figure 2). The MyHC-I biomarker of the LT muscle is well represented on this second axis (positive values).

In the Limousin breed, the first axis is linked to pools L2 and L3 (27.2 % inertia). As previously observed for the Angus breed, the richer diets and fatter carcasses were positively linked to SOD1, Hsp40 and MyLC-1F of LT muscle. On the second axis (19.4% inertia), only pool L1 appears to be well represented.

Finally, in the Blond d' Aquitaine breed, the first axis associated 7/10 pools B1 and B4 (39.1 % inertia), whereas the second axis is only associated with pool B3 (30.3 % inertia). These associations confirmed the similarities reported previously between some protein abundances in both muscles (Hsp70-8, µ-calpain, Hsp70-1B, DJ-1, PRDX6 and MDH1). These proteins are higher in heavier (EBW, CCW, HW, SBW) and higher meat yield carcasses. Some other LT muscle biomarkers (Hsp20, Hsp27, CapZ-β, enolase 3 and MyBP-H) were positively associated with carcass fat development (%CFT, %TFT, CFT, TFT, 5QF, leather weight), in accordance with an increase in diet intake (UF, PDI, DMI, concentrates) and a higher daily gain (ADG).

DISCUSSION AND CONCLUSION

The aim of the present work was to study the evolution of the abundances of a list of 21 biomarkers in two divergent beef muscles depending on animal breeding practices and carcass characteristics using an innovative clustering approach recently reported by our group [START_REF] Ellies-Oury | An innovative approach combining Animal Performances, nutritional value and sensory quality of meat[END_REF][START_REF] Gagaoua | Proteomic biomarkers of meat colour of Blonde d' Aquitaine young bulls?: towards a better comprehension of the biological mechanisms[END_REF][START_REF] Gagaoua | Understanding early post-mortem biochemical processes underlying meat color and ph decline in the longissimus thoracis muscle of young blond d' Aquitaine bulls using protein biomarkers[END_REF]. The findings revealed large differences between the muscles studied and confirmed the well documented literature data that the two analyzed muscles have different characteristics in terms of muscle fibre composition [START_REF] Totland | Distribution patterns of muscle fibre types in major muscles of the bull (Bos taurus)[END_REF]. In comparison to LT, the ST muscle was reported to have lower oxidative and higher glycolytic It also lower proportion of slow fibres, a lower oxidative metabolism and a higher fibre cross-sectional area than LT muscle [START_REF] Schreurs | Meta-analysis of the effect of animal maturity on muscle characteristics in different muscles, breeds, and sexes of cattle[END_REF].

This study showed that the impacts of breeding practices on muscle biomarkers are diverse depending on muscle and/or breed. Indeed, it seems that it is not easy to quantify the impact of these breeding practices but they appear to have a significant impact on ST biomarkers in the Blond d' Aquitaine, whereas in the two other breeds, the impact is more pronounced on the LT muscle.

The findings suggest that whatever the breed, ST biomarkers are less related to breeding practices and/or not linked to carcass characteristics than in LT. The rather limited impact of breeding practices on ST biomarkers was unexpected. It has been reported in the past that breeding practices affect muscles differently and certain young bull muscles, namely the rather glycolytic ST has been considered more responsive than the more oxidative ones (such as the m. Supraspinatus) [START_REF] Vestergaard | Influence of feeding intensity, grazing and finishing feeding on muscle fibre characteristics and meat colour of semitendinosus, longissimus dorsi and supraspinatus muscles of young bulls[END_REF]. Accordingly, it could be hypothesized that the feeding diets were not contrasted enough in the present work to underline the various biomarker expressions in the ST muscle.

Otherwise, some practices appear to have an unchanged impact on biomarker patterns whatever the breed and the muscle. For example, increasing the proportion of straw in the diet leads to an increase in MyLC-1F and Hsp40 biomarkers both in the LT of Limousin and Angus and in the ST of Blond d' Aquitaine. This may be in part related to protein turnover [START_REF] Therkildsen | Muscle Protein Turnover[END_REF]. The proteomic investigations on the effect of feeding and feed intake in beef muscles are very scarce and the few reported studies concentrate on other species. For example, Almeida et al. [START_REF] Almeida | The Effect of Weight Loss on the Muscle Proteome in the Damara, Dorper and Australian Merino Ovine Breeds[END_REF] showed that three structural and contractile apparatus proteins (among them a Myosin Light Chain spot) in sheep are affected when subjected to restricted feed. Moreover, MyLC has been associated with higher muscle deposition ability in cattle [START_REF] Bouley | Proteomic analysis of bovine skeletal muscle hypertrophy[END_REF] and its expression increases in animals fed on pasture vs. concentrate [START_REF] Shibata | Differential expression of the skeletal muscle proteome in grazed cattle[END_REF][START_REF] Almeida | The longissimus thoracis muscle proteome in Alentejana bulls as affected by growth path[END_REF]. However, most of the biomarkers evolve differently in the two muscles according to breeding practices. The muscle-type effect detected in this study confirms those found in the literature for the same muscles, indicating a muscle effect on 14 proteins among the 24 proteins tested [START_REF] Guillemin | Functional analysis of beef tenderness[END_REF]. These differences according to muscle type are in accordance with previous conclusions established on the links between biomarkers and meat quality traits. These two different muscles being related for example to tenderness through two distinct molecular networks [START_REF] Guillemin | Functional analysis of beef tenderness[END_REF].

Even though this "universal impact" is true for some biomarkers, it does not always hold for others, especially as biomarker expression seems to be breed-dependant. Breed differences were also investigated using comparative proteomic profiling on two muscles from five different pure pig breeds and the findings showed that there are potential biomarkers for breed classification [START_REF] Mach | Comparative proteomic profiling of 2 muscles from 5 different pure pig breeds using surface-enhanced laser desorption/ionization time-of-flight proteomics technology[END_REF]. Indeed, the patterns of some Angus biomarkers when breeding practices are modified are the opposite of those of continental ones. For example, increasing straw proportion in the diet leads to an increase/a decrease in SOD1 biomarker, or an increase in oxidative/glycolytic biomarkers depending on the breed under consideration (Limousin vs. Angus and Blond d' Aquitaine vs. Angus, respectively). The reverse impact of straw proportion increase on the SOD1 biomarker depending on the muscle could be explained by a differing expression of oxidative resistance proteins, irrespective of contractile and metabolic muscle type. This hypothesis was formulated previously by [START_REF] Guillemin | Functional analysis of beef tenderness[END_REF], who indicated that the glycolytic ST has a lower oxidative protection system and that in case of stress, a strong and active pathway could be engaged to protect cells against reactive oxygen species (ROS) by Cu/Zn superoxide dismutase (SOD1). Moreover, the fibre type, especially the slow twitch ones may be the source of these differences. As reported above, there are more type I fibres in LT (23%) compared to ST muscle (8%) and these fibres are known to contain high levels of mitochondria and to be involved in the scavenging mechanisms of ROS [START_REF] Mach | Comparative proteomic profiling of 2 muscles from 5 different pure pig breeds using surface-enhanced laser desorption/ionization time-of-flight proteomics technology[END_REF][START_REF] Kozakowska | The role of oxidative stress in skeletal muscle injury and regeneration: focus on antioxidant enzymes[END_REF]. Furthermore, it has been reported that oxidative and glycolytic fibres show marked differences even in ROS metabolism and enzymatic activities of SOD (lower in fast glycolytic muscles) [START_REF] Picard | Email[END_REF][START_REF] Picard | Mitochondrial functional specialization in glycolytic and oxidative muscle fibers: tailoring the organelle for optimal function[END_REF]. Finally, it is worth mentioning that cattle with lower levels of IMF were reported to have a lower muscle oxidative capacity and more glycolytic capacity [START_REF] Lin | Differences in mitochondrial gene expression profiles, enzyme activities and myosin heavy chain types in yak versus bovine skeletal muscles[END_REF] and the breeds investigated in this study have different IMF content (Angus > Limousin ≈ Blond d' Aquitaine [START_REF] Gagaoua | Sensory quality of meat from eight different types of cattle in relation with their biochemical characteristics[END_REF]. It was proposed that individual cattle with a lower level of MyLC expression during growth have more IMF deposition because the lower MyLC level promoted the proliferation of myoblasts, which can be transdifferentiated into IMF when nutritionally rich diets are given during the fattening period and thus increase deposition of IMF [START_REF] Holst | Roles of peroxisome proliferator-activated receptors delta and gamma in myoblast transdifferentiation[END_REF][START_REF] Zhang | Differentially expressed proteins during fat accumulation in bovine skeletal muscle[END_REF].

In the Blond d ' Aquitaine breed, biomarkers are somewhat more closely linked with breeding practices and carcass characteristics than in the two other breeds. Thus, it might be supposed that it would be easier to predict breeding practices and carcass characteristics using biomarker abundance in that breed than in the other two, which might make it possible to direct the carcass towards the most suitable market earlier. Among the interesting biomarkers retained, we found MDH1 and DJ-1 that are both positively linked to muscle yield and carcass development (weights of the different parts) in both LT and ST muscles. These findings confirm those obtained by Guillemin et al. [START_REF] Guillemin | Variations in the abundance of 24 protein biomarkers of beef tenderness according to muscle and animal type[END_REF] revealing no significant muscle-effect on the MDH1 oxidative enzyme biomarker. It is well-known that MDH1 plays a pivotal role in the malate-aspartate shuttle operating between cytosol and mitochondria and to be involved in the contractile function [START_REF] Lo | Developmental regulation and cellular distribution of human cytosolic malate dehydrogenase (MDH1)[END_REF][START_REF] Liu | Protein Profiles for Muscle Development and Intramuscular Fat Accumulation at Different Post-Hatching Ages in Chickens[END_REF]. So, the involvement of MDH1 with an increase in expression is required for the high demands of energy metabolism in developing tissues, especially in skeletal muscle as previously reported [START_REF] Ding | Two regulatory elements in the 5 region of the mouse mitochondrial malate dehydrogenase gene[END_REF]. Once again, the associations of these two potential biomarkers may be related to the production of ROS associated with increased metabolic activity during muscle (carcass) development. During oxidative stress, DJ-1 (a highly conserved protein involved in the regulation of oxidative stress by directly quenching ROS upon oxidative modification of a conserved cysteine residue) is re-localized to the mitochondria, where it has a key role in scavenging mitochondrial H 2 O 2 and limiting mitochondrial fragmentation. MDH1 is involved in the final steps of glycolysis before malate enters the mitochondrion; hence we suggest that the relationship between DJ-1 and MDH-1 with carcass development and other animal characteristics may be relatively direct.

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In the LT muscle, these carcass properties are also positively linked to the large Hsp proteins (Hsp70-1 and Hsp70-8) and once again with the oxidative resistance biomarkers (DJ-1 and PRDX-6). These biomarkers could be particularly interesting as they are known as good biomarkers for meat tenderness and other meat traits [START_REF] Picard | LBiomarkers of beef tenderness, moving towards analytical tools[END_REF]. Concerning large Hsp proteins, they are reported to be important for p-m muscle changes and to correlate in numerous studies with tenderness [START_REF] Picard | Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle[END_REF][START_REF] Guillemin | Functional analysis of beef tenderness[END_REF][START_REF] Rinalducci | Love me tender: an Omics window on the bovine meat tenderness network[END_REF]. Large Hsp proteins are involved in folding of newly or denatured proteins and promote the recovery of cell membranes, thus maintaining cell homeostasis [START_REF] Mayer | Hsp70 chaperone dynamics and molecular mechanism[END_REF].

Moreover, it appears that Hsp70-1 could be a relatively general biomarker of tenderness whatever the breed and the muscle considered. Thus, it is interesting to establish that in the LT muscle large HSP proteins could be considered as a good indicator of carcass quality. Hsp70-1 being linked both to carcass characteristics and to meat tenderness suggests that evaluating Hsp70-1 abundance could offer a way to predict and to control these two parameters. Thus, we confirm a number of profiling proteomic studies,that have reported the involvement of large Hsp proteins in the determination of tenderness and other quality traits [START_REF] Picard | Chapter 13: Proteomics as a tool to explain/predict meat and fish quality[END_REF].

Finally, muscle yield is positively linked to MyHC-I in the LT and MyHC-II in ST muscle. The contradiction between the two muscles has already been observed and discussed above. Indeed, in the LT muscle of young bull and cows muscles, tenderness appears to be positively correlated with the slow oxidative state while in the ST muscle, tenderness is positively correlated with glycolytic properties [START_REF] Ellies-Oury | An innovative approach combining Animal Performances, nutritional value and sensory quality of meat[END_REF]. Thus, it could be supposed that muscle yield is positively linked to tenderness whatever the muscle being considered.

In the Blond d' Aquitaine breed, some LT muscle biomarkers might be considered as good indicators of carcass fatness: namely small Hsp, ENO3, CapZ-β and MyBP-H. When considering previous results established with the same experiment, it appears that some of these biomarkers could be of great value for meat tenderness prediction since small Hsp by Hsp20, Hsp27 or αB-crystallin, the glycolytic ENO3 enzyme (involved in the development and regeneration of muscle) and the structural protein CapZ-β, are all reported to be positively related to meat tenderness [START_REF] Robelin | Estimation de la composition de la carcasse des taurillons a partir de la composition de la 6eme cote[END_REF] and meat color [START_REF] Gagaoua | Proteomic biomarkers of meat colour of Blonde d' Aquitaine young bulls?: towards a better comprehension of the biological mechanisms[END_REF].

In the Angus breed, considered as precocious and relatively fat in comparison to the late and lean Blond d' Aquitaine breed, the relationship between fat development and muscle biomarkers may involve only the ST muscle. The links between fat development and biomarkers are inversed in this muscle, as fatter carcasses were negatively linked to ST Hsp70-8, CapZ-β and MyBP-H proteins. This may confirm our previous findings showing that relationships between breeding practices and/or carcass characteristics and biomarkers are muscle and breed dependent. Also, it may be due to the particularity of Angus breed which manifested specific patterns in comparison to continental breeds, probably linked to its precociousness and its higher and earlier fat development. In agreement with our findings, various recent proteomic studies have previously reported that numerous proteins are involved in fat development. These papers revealed some of the biological pathways that determine the bovine intramuscular fat (IMF) deposition [START_REF] Zhang | Differentially expressed proteins during fat accumulation in bovine skeletal muscle[END_REF][START_REF] Kim | Proteins in longissimus muscle of Korean native cattle and their relationship to meat quality[END_REF][START_REF] Mao | Beef quality with different intramuscular fat content and proteomic analysis using isobaric tag for relative and absolute quantitation of differentially expressed proteins[END_REF].

Finally, it should be noted that for a given breed (eg. Blond d' Aquitaine), some biomarkers are impacted by breeding practices in the same way. For example, DJ-1 or MDH1 are positively correlated from one muscle to the other. On the contrary, other biomarkers appear to be negatively correlated. For example, the increase in MyHC-I biomarker in the mixed fast oxido-glycolytic LT muscle are to be associated with the increase of MyHC-II biomarker in the mixed fast glycolytic ST muscle. These data suggest that several animal factors impact muscle protein expression, some of which are common between muscles, whereas other differ. The findings of this study are quite coherent with previous conclusions reported in the significant literature by most of the studies conducted by our group [START_REF] Picard | Chapter 13: Proteomics as a tool to explain/predict meat and fish quality[END_REF][START_REF] Picard | Inverse relationships between biomarkers and beef tenderness according to contractile and metabolic properties of the muscle[END_REF].

The present paper's originality resides in the fact that the topic is still a new one. Indeed, the relationships between breeding practices and biomarker abundance have not, to our knowledge, really been explored in the literature, the relations between carcass performances and biomarker abundance being even less fully developed in previous studies. Thus, the combination of the topic and the statistical method used in this study appears to be creative and unlocks new perspectives for early prediction of carcass and meat quality.

Overall, several of the tested biomarkers have been identified as being relevant for the traits of interest, and their relationships with these desired traits are discussed in terms of the possible underlying biological mechanisms. Before these biomarkers can be used as an industrial tool, further developments are needed, above all the investigation of the breed and muscle impacts, protein per protein [START_REF] Williams | Genetic control of meat quality traits[END_REF][START_REF] Lametsch | Identification of protein degradation during post-mortem storage of pig meat[END_REF][START_REF] Guillemin | Different phenotypic and proteomic markers explain variability of beef tenderness across muscles[END_REF][START_REF] Chaze | Recherche de marqueurs de tendret de la viande de jeunes bovins de races viande par analyse protomique[END_REF][START_REF] Bouley | Mapping of bovine skeletal muscle proteins using two-dimensional gel electrophoresis and mass spectrometry[END_REF]. To better understand the similarities but also the differences that may exist among breeds/muscles, a similar study is in progress using a large database of more than 40 experiments bringing together about 20 breeds and a large number of muscles. Finally these explorations might allow early (at the beginning of the finishing period, by a muscular biopsy or a blood sampling) determination of potential carcass performances

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and meat quality traits. This could facilitate the management of carcass and meat quality by breeding practices, using prediction equations involving the tested muscle biomarkers.

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Figure 1

 1 Figure 1 Details of the 3 steps used in the statistical approach for each of the three breeds.

Table 1 :

 1 Variables of each of the four sets of variables investigated in this study. Straw intake (kg DM) Concentrate intake (kg DM) Dry matter Intake (kg) % straw % concentrate Energy level of the ration (expressed in UF) Protein level of the ration (expressed in g of PDIN and g of PDIE) Initial live weight (kg) Slaughter live weight (kg) Average daily gain (g / day) Feed efficiency (expressed per kg of DMI) Feed efficiency (expressed per UF) Carcass yield (% empty body weight) Muscle yield (% empty body weight) Trim fat weight (kg) Shin bone weigh (SBW; kg) Leather weight (kg) Liver weight (kg) Head weight (HW; kg) Empty body weight (EBW; kg) Cold carcass weight (CCW; kg) 5th quarter fat (5QF; kg) Carcass fat tissues (CFT; kg) Carcass fat tissues (CFT ; % of carcass fat) Muscles (kg) Muscles (% of carcass weight) Bone (kg) Bone (% of carcass weight) Total fat tissues (TFT; kg) Total fat tissues (TFT ; % empty body weight) Structure CapZ-β (F-actin-capping protein subunit β) α-actin MyLC-1F (myosin light chain 1F) MyBP-H (myosin binding protein H) MyHC-I (myosin heavy chain-I) MyHC-II (myosin heavy chain-II) MyHC-IIx (myosin heavy chain-IIx) Structure CapZ-β (F-actin-capping protein subunit β) α-actin MyLC-1F (myosin light chain 1F) MyBP-H (myosin binding protein H) MyHC-I (myosin heavy chain-I) MyHC-II (myosin heavy chain-II) MyHC-IIx (myosin heavy chain-IIx) Oxidative resistance DJ-1 (Parkinson disease protein 7) PRDX6 (cis-peroxiredoxin) SOD1 (superoxide dismutase Cu/ Zn) Proteolysis µ-calpain Central Bringing Excellence in Open Access   Ellies-Oury et al. (2017) Email: J Bioinform, Genomics, Proteomics 2(1): 1013 (2017)

10 Table 2 :

 102 None of the ST biomarker clusters established in Angus and Limousin were associated with breeding practices or carcass characteristics clusters. This may indicate that ST biomarkers are not linearly correlated to the breeding practices implemented in this work. Whatever the breed, increasing straw proportion in the diet at the expense of concentrates is linked to a modification in biomarker expression in the ST muscle (for Blond d' Aquitaine) or LT muscle (for the other two breeds). The proportion of straw in Central Bringing Excellence in Open Access   Ellies-Oury et al. (2017) Email: J Bioinform, Genomics, Proteomics 2(1): 1013 (2017) 5/Variables associated in each cluster and direction of the evolution, depending on the variable domain and breed. Breed Variable domain Characterization of each cluster (only the well represented variables in each cluster (R² > 0.5) are given) Angus (A) BP A-BP-1 :  Straw intake ;  % Straw ;  % Concentrates A-BP-2 :  ADG ;  Feed Efficiency per DMI ;  Feed Efficiency per UF A-BP-3 :  Concentrate intake ;  DMI ;  UF ;  PDIN ;  PDIE CC A-CC-1 :  % Muscle ;  % Bone ;  % CFT ;  % TFT ;  CFT ;  TFT ;  5QF ;  Leather weight A-CC-2 :  HW ;  SBW ; CCW ;  EBW ;  Muscles ;  Bone A-CC-3 :  Carcass yield ;  Muscle yield LTB A-LTB-1 :  MyHC-I A-LTB-2 :  µ-calpain ;  ;  SOD1 A-LTB-3 :  MyHC-II ;  MyLC-1F ;  LDH-B STB A-STB-1 :  Hsp70-8 A-STB-2 :  B-crystallin ;  MyHC-I ;  CapZ-β ;  DJ-1 A-STB-3 :  MyHC-II ;  MyBP-H ;  µ-calpain ;  LDH-B ;  MDH1 ;  Hsp70-Grp75 Limousin (L) BP L-BP-1 :  Straw intake ;  % Straw ;  % Concentrates L-BP-2 :  Concentrate intake ;  DMI ;  UF ;  PDIN ;  PDIE L-BP-3 :  SLW ;  ADG ;  Feed Efficiency per DMI ;  Feed Efficiency per UF CC L-CC-1 :  % Muscle ;  % Bone ;  % CFT ;  % TFT ;  CFT ;  TFT ;  5QF ;  Leather weight L-CC-2 :  HW ;  SBW ; CCW ;  EBW ;  Muscles ;  Bone ;  Carcass yield LTB L-LTB-1 :  Hsp20 ;  DJ-1 ;  MyHC-II ;  MDH1 L-LTB-2 :  B-crystall ;  Hsp70-1B ;  MyHC-I ;  Hsp70-8 ;  µ-calpain L-LTB-3 :  SOD1 ;  Hsp40 ;  MyLC-1F STB L-STB-1 :  SOD1 ;  Eno 3 L-STB-2 :  Hsp20 ;  DJ-1 ;  Hsp70-Grp75 L-STB-3 :  MyHC-I ;  MyLC-1F ;  PRDX6 Blond d'Aquitaine (BA) BP BA-BP-1 :  Straw intake ;  % Straw ;  % Concentrates BA-BP-2 :  Concentrate intake ;  DMI ;  UF ;  PDIN ;  PDIE ;  ADG CC BA-CC-1 :  % Muscle ;  % CFT ;  % TFT ;  CFT ;  TFT ;  5QF ;  Leather weight BA-CC-2 :  HW ;  SBW ; CCW ;  EBW ;  Muscles ;  Bone ;  Muscle yield LTB BA-LTB-1 :  Hsp27 ;  Hsp20 ;  CapZ-β ;  Eno3 ;  MyBP-H BA-LTB-2 :  MyHC-I ;  DJ-1 ;  MDH1 BA-LTB-3 :  Hsp70-1B ;  Hsp70-8 ;  MyLC-1F ;  µ-calpain ;  PRDX6 STB BA-STB-1 :  αB-crystall ;  Hsp70-1B BA-STB-2 :  Hsp27 ;  SOD1 ;  MyHC-IIx BA-STB-3 :  Hsp20 ;  CapZ-β ;  Hsp70-8 ;  µ-calpain ;  PRDX6 ;  Hsp70-Grp75 BA-STB-4 :  MyHC-I ;  Hsp40 ;  MyLC-1F BA-STB-5 :  DJ-1 ;  MyHC-II ;  MyBP-H ;  MDH1 Abbreviations: BP: Breeding Practices ; CC : Carcass Characteristics ; LTB : LT Biomarkers ; STB : ST Biomarkers Identification of each cluster: Breed -Set of variable -Number of the Cluster in the set of variable. Example: A-BP-1 for the first cluster of breeding practices in the Angus Breed.

Figure 2

 2 Figure 2 Evaluation breed per breed of the links among the different pools of clusters for Angus (A), Limousin (L) and Blond d'Aquitaine (B). Details of clusters and pools are given in Tables2 and 3 respectively

Table 3 : 1 ;

 31 Constitution of pool of clusters after a clustering of variable on the clusters. BA-STB-2 ; BA-STB-3 (only the well represented clusters in each pool (R² > 0.5) are given except for that indicated with an asterisk (R² < 0.5); all the clusters of a single pool are positively correlated ; composition of clusters is specified in Table2) Identification of each pool: Breed -Number of the Pool; Example: Pool-A1 for the first pool in the Angus Breed.Central Bringing Excellence in Open Access  Ellies-Oury et al. (2017) Email: J Bioinform, Genomics, Proteomics 2(1): 1013 (2017)

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ACKNOWLEDGEMENTS

This work was part of the EU FP6 Integrated Project ProSafeBeef, contract no. FOODCT-2006-36241. Funding by the European Union is gratefully acknowledged with respect to animal production and laboratory analyses. We thank Pascal Faure and Denis Roux (INRA UERT, Theix) for animal management and slaughter. We also thank Jean-François Hocquette (INRA UMRH1213, Theix) for the project management, but also all of the people involved in this project for their assistance in data collection, the management and slaughtering of animals and muscle sampling. Finally, we thank Karine Méteau (INRA Le Magneraud), for sensory analysis and Michel Doreau (INRA UMRH1213, Theix) for his help in the writing of the manuscript.


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Pigprogress Sows

A Large White sow -this

breed is currently part of trials to create more heat-resistant animals.

In this series, every author looks at the problem of 'heat stress' from a different angle. In Pig Progress, issue 33.07, veterinarian Steve McOrist considered a veterinarian approach of the issue. In this article, it is time for a genetic approach. How can heat stress be alleviated by choosing the right breeds? Heat resistance in pigs can be defined as the ability of an animal to develop thermoregulatory responses to mitigate the effects of heat stress on its physiological system. It can alternatively be viewed as the ability of a pig to maintain its production level under thermal stress. Three types of heat stress may occur in pig farming systems:

• Chronic heat stress encountered in warm climates such as intertropical countries;

• Short-term heat stress during two or three summer months in temperate areas;

• Acute heat-stress as it occurs during days of heat waves.

The animal responses to heat stress vary according to the duration and the intensity of the thermal challenge. Heat stress alters several metabolic processes which adversely affect pig performance, health and welfare and thus reduce the economic viability of pig farming. There are many reasons for considering heat stress in future pig breeding programmes.

The global market

Pig breeding is more and more an international business, with major breeding organisations providing genotypes in several types of production environments. Furthermore, more than 50% of pig production takes place in warm climates, such as South America, South and East Asia, with the prediction of faster growth in the future than in temperate areas.

Increased sensitivity

There is a growing body of evidence that selection for increased production and reproduction traits has reduced the environmental flexibility of pigs.

Genetic variability of heat stress

Several studies have shown the existence of a genetic variability for heat tolerance both between and within pig populations, so that there is room for genetic improvement of heat tolerance.

Climate change

An increase in global average temperature is expected to increase in the next decades, which will accentuate heat stress related problems in both hot and temperate areas.

Read also: Heat stress series: A vet's view on heat stress in breeding

Breeding goals for heat tolerance

In terms of general breeding strategy, the most common approach for selecting pigs in heterogeneous environments is to use standard breeding goals and criteria. Using this approach, the most relevant strategy will strongly depend on the level of genotype-byenvironment interactions (GxE) for traits of interest.

If GxE interactions are low to moderate, selecting for robust animals that are able to perform well in the different production environments is likely to be the most relevant choice, as it allows a limited number of large populations to be selected. On the opposite side, with strong GxE interactions, the hierarchy of breeding values is likely to be modified, so that a good candidate in one environment may turn out to be worse in another environment. In such a case, the selection of specific pig populations well adapted to the different types of production environments is likely to be the strategy of choice. Yet, it should be emphasised that this strategy leads to an increase in the number of populations and a reduction in the size of each population, which might affect selection intensities and efficiency.

Use of new phenotypes

An alternative or complementary strategy is the use of 'new phenotypes' such as thermoregulatory response indicators, which would be combined with standard traits in a global index, taking into account production efficiency and adaptation to heat stress. Among traits that could be of interest, some are indicators of the total thermoregulation process, such as rectal temperature, or traits related to heat dissipation, such as respiratory rate or skin temperature.

More complex traits, such as the time necessary to return to normal rectal or skin temperature or respiratory rate, which are indicators of resilience, could also be of interest, but such traits require continuous, high throughput, measurements using probes. The inheritance of traits directly related with thermoregulatory responses is still poorly known. INRA studies on tropical data from Large White lactating sows showed that thermoregulatory responses such as rectal temperature or respiratory rate are lowly to moderately heritable, suggesting that selecting for a better thermoregulation is possible. Lower heritability values were obtained in growing pigs. Nevertheless, the genetic correlations with production traits such as sow daily feed intake, litter growth rate or feed efficiency were low.

To our knowledge, there is no practical commercial breeding program that includes thermoregulation traits in the selection index. The inclusion of such traits in conventional breeding schemes is not easy because of the complexity to estimate the economic weight to put in the breeding index for such type of the trait. For instance, what is the financial cost of a decrease of ten breaths per minute of the standard deviation of respiratory rate of lactating sows? Nevertheless, thermoregulation traits could be included as criteria of selection to improve the prediction without having economic weights.

Genomic tools

Genomic tools are being increasingly used in pig breeding schemes with the development of marker assisted or genomic selection. These new tools make it possible to identify chromosomal regions associated with variations in heat tolerance, paving the way for genomic based selection for both performance and heat tolerance traits. For instance, a simple option would be to use SNP associated with thermotolerance to select heat tolerant sires among the candidates with high breeding values for production. The implementation of this alternative requires a good reference population in order to insure the accuracy of genomic breeding values and low genotyping costs to achieve the economic efficiency of the scheme.

Functional genomic research studies on the physiological mechanisms underlying heat tolerance in pigs could contribute to identify new phenotypes. A research programme is currently performed in this field, using an experimental back-cross design between a tropical heat-adapted pig breed, the Creole pig breed and the European Large White breed.

Breeding schemes

Genetic improvement programmes for heat tolerance can be addressed either through genetic selection or crossbreeding or both. For instance, the heat tolerance of local tropical breeds can contribute to improve heat tolerance in a mainstream commercial line or breed (by crossbreeding or by introgressing 'heat adaptation' genes). Little has been published on this topic in pigs, but there are some success stories in beef cattle reared in tropical countries. Many native pig breeds are located in tropical areas, but many of them are poorly characterised. It is necessary to implement breeding programmes for the conservation and improvement of these locally heat-adapted breeds.

A complex issue

Breeding for heat tolerance traits in pigs is a complex issue. Selecting animals with highproduction level under heat stress can be achieved with success in different ways. It is important to note that there are other aspects that could interact with breeding for heat tolerance such as disease resistance, feed efficiency, purebreds-crossbred interactions. Additional research is still required to better understand:

• The genetic determinism of heat tolerance;

• The level of GxE interactions of economically important traits;

• The physiological mechanism underlying heat tolerance.

These topics (structural and functional genomics) are likely to feed each other in close future.

This article is a second in the heat stress series, which has been made in cooperation with Animine.

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