Time course of the response to ACTH in pig: biological and transcriptomic study

Abstract

BackgroundHPA axis plays a major role in physiological homeostasis. It is also involved in stress and adaptive response to the environment. In farm animals in general and specifically in pigs, breeding strategies have highly favored production traits such as lean growth rate, feed efficiency and prolificacy at the cost of robustness. On the hypothesis that the HPA axis could contribute to the trade-off between robustness and production traits, we have designed this experiment to explore individual variation in the biological response to the main stress hormone, cortisol, in pigs. We used ACTH injections to trigger production of cortisol in 120 juvenile Large White (LW) pigs from 28 litters and the kinetics of the response was measured with biological variables and whole blood gene expression at 4 time points. A multilevel statistical analysis was used to take into account the longitudinal aspect of the data.ResultsCortisol level reached its peak 1 h after ACTH injection. White blood cell composition was modified with a decrease of lymphocytes and monocytes and an increase of granulocytes (FDR<0.05). Basal level of cortisol was correlated with birth and weaning weights. Microarray analysis identified 65 unique genes of which expression responded to the injection of ACTH (adjusted P<0.05). These genes were classified into 4 clusters with distinctive kinetics in response to ACTH injection. The first cluster identified genes strongly correlated to cortisol and previously reported as being regulated by glucocorticoids. In particular, DDIT4, DUSP1, FKBP5, IL7R, NFKBIA, PER1, RGS2 and RHOB were shown to be connected to each other by the glucocorticoid receptor NR3C1. Most of the differentially expressed genes that encode transcription factors have not been described yet as being important in transcription networks involved in stress response. Their co-expression may mean co-regulation and they could thus provide new patterns of biomarkers of the individual sensitivity to cortisol.ConclusionsWe identified 65 genes as biological markers of HPA axis activation at the gene expression level. These genes might be candidates for a better understanding of the molecular mechanisms of the stress response.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2118-8) contains supplementary material, which is available to authorized users.

ACTH Induces Oxidative Stress and Decreases DNA Methylation of Fkbp5 in Suhuai Sow

“…It has been demonstrated that glucocorticoids increase the transcription of DUSP1 gene by binding to GRE region in A549 human lung adenocarcinoma cells (Shipp et al, 2010). CXCR4, IL7R, and TXNIP genes were differentially expressed in response to ACTH in pigs (Sautron et al, 2015). Fkbp5 is identified as glucocorticoid-responsive genes and is regulated by the binding GR to the sequences corresponding to the GRE in Fkbp5 gene region (U et al, 2004).…”

“…ACTH activated the expression of glucocorticoidresponsive genes: Five genes, namely CXCR4, DUSP1, IL7R, TXNIP, and Fkbp5, have been proposed as markers of differential regulation of glucocorticoid-responsive elements (Donn et al, 2007;Sautron et al, 2015). In order to study whether ACTH activates GR-related pathways and affects downstream gene expression, we examined the mRNA levels of these genes.…”

Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs

“…Venn diagram was edited to enlighten commonalities between the lists of unique genes identified as VIP for the three traits protein family which plays a role in immune regulation and basic cellular processes involving protein folding and trafficking. In juvenile pigs, FKBP5 was proposed as a candidate gene for a better understanding of the stress response, notably due to its connexion with the glucocorticoid receptor [27]. Interestingly, it is known that FE is associated with the susceptibility to stress in cattle [28], and that RFI and cortisol response are also positively associated in rams [29].…”

Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy

“…PCA was performed with EZinfo (version 3.0.3). Multilevel PCA 14 was performed using mixOmics (version 6.1.3). Pareto scaling was used in PCA and mPCA modelling.…”