Genetics and the HPA axis
Genetics of stress responses
Basal activity of the HPA axis and the response to stress are strongly influenced by genetic factors (Mormede et al., 2002; Redei, 2008; DeRijk, 2009). Pig populations show much functional variation (e.g. (Geverink et al., 2006; Foury et al., 2007)) and divergent genetic selection for the HPA axis response to various stimuli has been successful in a wide range of species: trout (confinement stress: (Fevolden et al., 1999; Pottinger and Carrick, 1999), chickens (adrenal response to ACTH: (Edens and Siegel, 1975); social stress: (Gross and Siegel, 1985), turkeys (cold stress: (Brown and Nestor, 1973), Japanese quail (immobilization stress: (Satterlee and Johnson, 1988) and mice (restraint stress: (Touma et al., 2008). The response to selection is usually very strong, with realized heritability between 0.4 and 0.5.
Adrenal cortex sensitivity to ACTH
Genetic variation is present in every component of the system, at the level of hormone production, bioavailability and action. The production rate of cortisol is primarily regulated by the sensitivity of the adrenal cortex to ACTH. Hennessy et al. (1988) demonstrated in pigs that the adrenal response to ACTH is variable among individuals but stable across time for a given animal. Similar differences in cortisol secretion were shown in response to CRH (Zhang et al., 1990), physical exercise or insulin-induced hypoglycaemia (Zhang et al., 1992), although the ACTH response was not different among individuals, so that the effect must have been due to adrenal sensitivity to ACTH. Metabolic clearance of cortisol bears no relationship with the response to ACTH (Zhang et al., 1993). Altogether, these data show that adrenal sensitivity to ACTH is a key index of individual differences in HPA function. As noted previously, the magnitude of the adrenal response to ACTH is negatively correlated with body weight and growth rate (Hennessy and Jackson, 1987), but did not show, in this paper, any relationship to body fat content or muscle pH. The adrenal response to ACTH is highly heritable (h2 = 0.51; (Larzul et al., 2010), data obtained in a Large White pig population). Indeed, as mentioned in the previous paragraph, Brown and Nestor (1973) could select divergent lines of turkey based on their response to ACTH injection, with a realized heritability of 0.28. The same kind of variability in adrenal response to ACTH has been shown in humans (Bertagna et al., 1994; Coste et al., 1994).
Differential gene expression studies in pigs (Hazard et al., 2008; Li et al., 2008a and 2008b; Jouffe et al., 2009) and chickens (Bureau et al., 2009) have produced candidate genes for differences in sensitivity to ACTH.
Bioavailability of GR hormones
Bioavailability of GR hormones is regulated by metabolic enzymes and carrier proteins. The enzymes 11β-hydroxysteroid dehydrogenase 1 and 2 convert cortisol and corticosterone into their inactive 11-oxo derivatives (type 2) and back (type 1). This mechanism is an important regulator of GR hormone activity (Remer et al., 2008). Although research in this field is very active towards the development of drugs for the control of obesity and metabolic diseases in humans (Hale and Wang, 2008), very little information is available in farm animals. An association between a single nucleotide polymorphism (SNP) in the HSD11B1 gene and production traits has been described in two pig populations, although these effects may be not be due to the causative mutation (Otieno et al., 2005).
In plasma, GR hormones bind with high affinity to a specific glycoprotein, corticosteroid-binding globulin (CBG) and with a lower affinity to albumin, so that the free, active fraction of the hormones is small and highly regulated by CBG levels. A genetic mapping experiment in an F2 intercross between Meishan and Large White pig breeds showed an association between a locus on pig chromosome 7q26 and cortisol levels, especially after the pigs were exposed to the stress of a novel environment (Desautes et al., 2002). By comparative genomics, the gene encoding CBG (SERPINA6) appeared to be a good candidate, and further research strongly supported the implication of mutations in the SERPINA6 gene in cortisolaemia, carcass composition and meat quality (Ousova et al., 2004; Geverink et al., 2006; Guyonnet-Duperat et al., 2006). Several recent animal and human studies confirm the role of CBG or its genomic locus in traits related to neuropsychiatry, obesity and diabetes, immunity and inflammation, as well as growth (see Moisan, 2010, for a review).
Receptors and transduction mechanisms
Large genetic variations in the efficiency of corticosteroid hormones have been described (e.g. Harizi et al., 2007, in mice). In humans and laboratory animals, numerous molecular variations have been described in the sequence of receptors, with functional consequences for health and disease (van Rossum and Lamberts, 2004; DeRijk, 2009), but very little information is available in farm animal species (Perreau et al., 1999).
This review of the literature shows that GR secretion and function is highly variable due to numerous genetic differences in all the components of the HPA axis. Several molecular variations in gene structure have functional consequences on various traits related to stress responses, production and robustness. Data in farm animals are still incomplete, but research is active in this field. We also need more information about the systems genetics of the HPA axis for a more integrated understanding of its functioning and effects. Indeed, several sources of genetic variability are usually found in the same model (Marissal-Arvy et al., 2004), but very little is known about the interactions among various sources of variability within the axis, and how they eventually compensate for or potentiate each other. Some data indicate that the effect of a single mutation (Carter et al., 2009), or the consequence of GR hormone removal (de Jong et al., 2007), is strongly dependent on genetic background. It is also necessary to explore the functional significance of various parameters classically used to evaluate HPA axis activity. For instance, when comparing the response of three mouse strains to various stressors, the strain with the largest response of plasma corticosterone displayed the lowest biological response as measured by the increase of glucose or decrease of interleukin-6 plasma levels (Harizi et al., 2007). At the present state of knowledge, selection should aim at an increase of cortisol production (sensitivity of the adrenal cortex to ACTH), of hormone bioavailability (via increased SERPINA6 expression, for instance) and of transduction mechanisms efficiency. Modelling the various sources of genetic variability and their functional consequences should provide insight in the best use of DNA markers to influence the function of the HPA axis towards the breeding goals of improved robustness without negative effects on production traits.
It must be noticed here that cortisol concentrations does not generally equate to stress level: the relationship between the two is genotype-specific, depending on genetic influences on HPA axis function, not only in terms of cortisol production, as measured by hormone concentrations in plasma or other body fluids (saliva, urine, faeces), but also in the efficiency of cortisol effects on its targets, due to genetic influences on hormone bioavailability and receptor/post-receptor mechanisms. As a consequence, selection for higher HPA axis activity or functionality should not increase stress as measured by psycho-behavioural consequences of environmental stimuli (Dantzer and Mormede, 1983). Indeed, recent selection experiments in mice, based on their corticosterone response to restraint stress, showed that the low line (with lower corticosterone response to stress) displayed a ‘depressed’ behavioural phenotype and more aggressive tendencies (Touma et al., 2008). It will be worth investigating in farm animals the changes in psycho-behavioural responses to stress as a result of the genetic selection towards a more active HPA axis.