Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data

• Published in: Scientific reports Vol. 14; no. 1; p. 9019
• Main Authors: Videla Rodriguez, E. A., Mitchell, John B. O., Smith, V. Anne
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.04.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/2114; 631/114/2164; 631/114/2397; 631/601/1737; Animals; Bayes Theorem; Bayesian analysis; Bayesian network; Brain; Chicken; Chickens; Chickens - genetics; Chickens - metabolism; Datasets; Design; Endoplasmic reticulum; Gene; Gene expression; Gene Expression Profiling - veterinary; Gene Regulatory Networks; Heat shock proteins; Heat-Shock Response - genetics; Humanities and Social Sciences; multidisciplinary; Physiology; Poultry; Science; Science (multidisciplinary); Stress; Chicken; Gene; Bayesian network; Stress
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-58679-3

Bayesian networks represent a useful tool to explore interactions within biological systems. The aims of this study were to identify a reduced number of genes associated with a stress condition in chickens ( Gallus gallus ) and to unravel their interactions by implementing a Bayesian network approach. Initially, one publicly available dataset (3 control vs. 3 heat-stressed chickens) was used to identify the stress signal, represented by 25 differentially expressed genes (DEGs). The dataset was augmented by looking for the 25 DEGs in other four publicly available databases. Bayesian network algorithms were used to discover the informative relationships between the DEGs. Only ten out of the 25 DEGs displayed interactions. Four of them were Heat Shock Proteins that could be playing a key role, especially under stress conditions, where maintaining the correct functioning of the cell machinery might be crucial. One of the DEGs is an open reading frame whose function is yet unknown, highlighting the power of Bayesian networks in knowledge discovery. Identifying an initial stress signal, augmenting it by combining other databases, and finally learning the structure of Bayesian networks allowed us to find genes closely related to stress, with the possibility of further exploring the system in future studies.