An Integrated Bioinformatics Approach to Identify Network-Derived Hub Genes in Starving Zebrafish

Simple Summary In this study, in order to provide a better understanding and new knowledge about biochemical phenomena and gene interactions in the zebrafish transcriptome, the notion of integrative bioinformatics was used. To do so, we used co-expression and protein-protein interaction (PPI) networks followed by Bayesian network (BN) construction. We provide an integrative bioinformatics pipeline to explore crucial transcriptional insights from zebrafish starvation-induced transcriptome data in a user-friendly way with minimal programming skill requirements. In this way, many genes, e.g., skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a, were identified as hub genes affecting gene expression in the liver of starving zebrafish. These results can be applied in many different areas of zebrafish genomic studies. The present study was aimed at identifying causative hub genes within modules formed by co-expression and protein-protein interaction (PPI) networks, followed by Bayesian network (BN) construction in the liver transcriptome of starved zebrafish. To this end, the GSE11107 and GSE112272 datasets from the GEO databases were downloaded and meta-analyzed using the MetaDE package, an add-on R package. Differentially expressed genes (DEGs) were identified based upon expression intensity N(mu = 0.2, sigma(2) = 0.4). Reconstruction of BNs was performed by the bnlearn R package on genes within modules using STRINGdb and CEMiTool. ndufs5 (shared among PPI, BN and COEX), rps26, rpl10, sdhc (shared between PPI and BN), ndufa6, ndufa10, ndufb8 (shared between PPI and COEX), skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a (shared between COEX and BN) were identified as causative hub genes affecting gene expression in the liver of starving zebrafish. Future work will shed light on using integrative analyses of miRNA and DNA microarrays simultaneously, and performing in silico and experimental validation of these hub-causative (CST) genes affecting starvation in zebrafish.

Automated summary

This study used integrative bioinformatics to investigate biochemical phenomena and gene interactions in the zebrafish transcriptome, and provided a user-friendly pipeline to explore crucial transcriptional insights from zebrafish starvation-induced transcriptome data. Several hub genes affecting gene expression in the liver of starving zebrafish were identified. These findings have important implications for zebrafish genomic studies.