RNA-Seq Profiling between Commercial and Indigenous Iranian Chickens Highlights Differences in Innate Immune Gene Expression.

The purpose of the current study was to examine transcriptomic-based profiling of differentially expressed innate immune genes between indigenous and commercial chickens. In order to compare the transcriptome profiles of the different chicken breeds, we extracted RNA from blood samples of the Isfahan indigenous chicken (as indigenous) and Ross broiler chicken (as commercial) breeds. RNA-Seq yielded totals of 36,763,939 and 31,545,002 reads for the indigenous and commercial breeds, respectively, with clean reads then aligned to the chicken reference genome (Galgal5). Overall, 1327 genes were significantly differentially expressed, of which 1013 genes were upregulated in the commercial versus the indigenous breed, while 314 were more highly expressed in the indigenous birds. Furthermore, our results demonstrated that the SPARC, ATP6V0D2, IL4I1, SMPDL3A, ADAM7, TMCC3, ULK2, MYO6, THG1L and IRG1 genes were the most significantly expressed genes in the commercial birds and the PAPPA, DUSP1, PSMD12, LHX8, IL8, TRPM2, GDAP1L1, FAM161A, ABCC2 and ASAH2 genes were the most significant in the indigenous chickens. Of notable finding in this study was that the high-level gene expressions of heat-shock proteins (HSPs) in the indigenous breeds could serve as a guideline for future genetic improvement. This study identified genes with breed-specific expression, and comparative transcriptome analysis helped understanding of the differences in underlying genetic mechanisms between commercial and local breeds. Therefore, the current results can be used to identify candidate genes for further breed improvement.

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

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(µ = 0.2, σ2 = 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.