Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data
Abstract
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.
Citation
Videla Rodriguez , E A , Mitchell , J B O & Smith , V A 2024 , ' Robust identification of interactions between heat-stress responsive genes in the chicken brain using Bayesian networks and augmented expression data ' , Scientific Reports , vol. 14 , 9019 . https://doi.org/10.1038/s41598-024-58679-3
Publication
Scientific Reports
Status
Peer reviewed
ISSN
2045-2322Type
Journal article
Description
Funding: This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 812777.Collections
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