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Fecal Glucocorticoid Analysis: Non-invasive Adrenal Monitoring in Equids
08:02

Fecal Glucocorticoid Analysis: Non-invasive Adrenal Monitoring in Equids

Published on: April 25, 2016

Elucidating molecular networks that either affect or respond to plasma cortisol concentration in target tissues of

Siriluck Ponsuksili1, Yang Du, Eduard Murani

  • 1Research Group "Functional Genome Analysis," Leibniz Institute for Farm Animal Biology, 18196 Dummerstorf, Germany.

Genetics
|August 21, 2012
PubMed

Insights

This study identifies genetic variants influencing cortisol levels in pigs using advanced analysis. These findings enhance understanding of the genetic basis for growth, health, and well-being traits regulated by the glucocorticoid system.

Area of Science:

  • Genomics and Molecular Biology
  • Endocrinology
  • Animal Science

Background:

  • Cortisol, a key glucocorticoid hormone, regulates immune and metabolic functions, with its signaling showing significant interindividual genetic variation.
  • Dysregulation of glucocorticoid signaling negatively impacts growth, health, and well-being.
  • The specific genetic variants underlying cortisol regulation remain largely unknown.

Purpose of the Study:

  • To identify candidate genes in porcine liver and muscle that influence or respond to plasma cortisol levels.
  • To elucidate the genetic architecture of cortisol regulation and its impact on complex traits.

Main Methods:

  • Trait-correlated expression analysis to characterize transcript activities.
  • Screening for expression quantitative trait loci (eQTL) to identify genes regulated by genetic variation.
  • Genome-wide association (GWA) studies and causality modeling (Network Edge Orienting - NEO) to predict gene effects on cortisol levels.

Main Results:

  • Trait-correlated expression analysis revealed numerous biological functions associated with cortisol levels in liver and muscle.
  • Network Edge Orienting predicted 26 genes affecting and 70 genes responding to plasma cortisol levels in the liver.
  • In muscle, 2 genes were predicted to affect and 25 to respond to plasma cortisol levels, among those correlated and regulated by eQTL.

Conclusions:

  • Comprehensive data integration successfully identified candidate genes and elucidated molecular networks involved in cortisol regulation.
  • Distinguishing upstream and downstream effects of transcripts provides a deeper understanding of the genetic basis for growth, health, and well-being traits influenced by cortisol.

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