Molecular genetics of stress responses and robustness in pig – SUSoSTRESS

The first component consists of a divergent genetic selection on the basis of the response of the adrenal gland to stress. After three generations of selection both obtained lines (low and high response) will be compared for many parameters in relation to production (growth rate, carcass composition,...) and robustness (survival of newborns, behavior, coping skills). The second component is an in-depth analysis of the genetic differences in responses to different stress using high-throughput molecular techniques (transcriptome, metabolome, neuroendocrine profile). All these data will be integrated in a mathematical model of the variability in the responses to stress also integrating production and robustness parameters. This model will serve as a support to the strategies of selection for more robust animals with an improved welfare.

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ROBUSTNESS in farm animals was defined by Knap as ‘the ability to combine a high production potential with resilience to stressors, allowing for unproblematic expression of a high production potential in a wide variety of environmental conditions’. The importance of robustness-related traits in breeding objectives is progressively increasing towards the production of animals with a high production level in a wide range of climatic conditions and production systems, together with a high level of animal welfare. Current strategies to increase robustness include selection for ‘functional traits’, such as skeletal and cardiovascular integrity, disease resistance and mortality in various stages. It is also possible to use global evaluation of sensitivity to the environment (e.g. reaction norm analysis or canalization), but these techniques are difficult to implement in practice.
STRESS is defined as the non-specific response of the organisms to any challenge. In vertebrate food-producing animal species, the hypothalamic–pituitary–adrenocortical (HPA) axis is the most important stress-responsive neuroendocrine system. Cortisol (or corticosterone) released by the adrenal cortices exerts a large range of effects on metabolism, the cardiovascular system, inflammatory processes and brain function, for example. Large individual variations have been described in the HPA axis activity with important physiopathological consequences. In terms of animal production, higher cortisol levels have negative effects on growth rate and feed efficiency and increase the fat/lean ratio of carcasses. On the contrary, cortisol has positive effects on traits related to robustness and adaptation. Intense selection for lean tissue growth during the last decades has concomitantly reduced cortisol production, which may be responsible for the negative effects of selection on robustness traits. The strategy that we propose is to change the balance between production and robustness by selecting animals with higher HPA axis activity (Mormède et al. Animal 5:651, 2011).
The first aim of the present project SUSoSTRESS is to produce experimental evidence supporting this strategy. The proof of concept will be given by the study of production and robustness traits in two lines of pigs divergently selected for their cortisol response to ACTH stimulation. The critical advantage of this strategy for animal breeding is that it relies on a single, well-defined physiological system to increase robustness and adaptability, making genetic selection more easily reachable as well as the generalisation from the species under study, the pig, to other farm animal species.
Numerous candidate genes and molecular polymorphisms have been described for genetic-based individual differences in HPA axis function, including hormone production and release by the adrenal cortices, bioavailability of hormones as well as receptor and post-receptor mechanisms (Mormède et al. ANYAS 1220:127, 2011). In order to use this molecular information for genetic selection, we need an integrated systems genetic approach. The second aim of the project is the elaboration of a model of genetic variation (genetic architecture) of the HPA axis and its physiological activity in relation to animal performance on both robustness and production traits, as the basis for genomic selection.
The third aim of the project is to deliver practical information for genomic selection of more robust animals, by combination (integration) of the genetic model, the thorough bioinformatics analysis of the HPA axis components and targets, and the high-density genotyping of the bi-directionally selected animals. This research is therefore an exemplar of basic research with clear application for farm animal selection.