A comprehensive manually-curated compendium of bovine transcription factors

Abstract

Transcription factors (TFs) are pivotal regulatory proteins that control gene expression in a context-dependent and tissue-specific manner. In contrast to human, where comprehensive curated TF collections exist, bovine TFs are only rudimentary recorded and characterized. In this article, we present a manually-curated compendium of 865 sequence-specific DNA-binding bovines TFs, which we analyzed for domain family distribution, evolutionary conservation, and tissue-specific expression. In addition, we provide a list of putative transcription cofactors derived from known interactions with the identified TFs. Since there is a general lack of knowledge concerning the regulation of gene expression in cattle, the curated list of TF should provide a basis for an improved comprehension of regulatory mechanisms that are specific to the species.

Potential Biomarkers for Feed Efficiency-Related Traits in Nelore Cattle Identified by Co-expression Network and Integrative Genomics Analyses

“…(B) The table to the right describes the coefficients obtained from a linear model for the hub genes' expression overlapping simultaneously average daily gain (ADG), feed conversion ratio (FCR), feed efficiency ratio (FE), Kleiber index (KI), and relative growth ratio (RGR). hub genes reported here were previously associated with feed efficiency-related traits as QTLs (de Oliveira et al, 2014), as DE genes in contrasting RFI groups (Tizioto et al, 2016), as well as putative regulatory elements (Cesar et al, 2018) and TFs described for bovine (de Souza et al, 2018).…”

“…To further identify putative regulatory elements linked to the hub genes, we built a gene list containing the genes reported in previous studies using the same population, with genes identified as DE in muscle between efficient and inefficient groups (n = 73) (Tizioto et al, 2016), QTLs associated with feed efficiency-related traits (n = 36) (de Oliveira et al, 2014), genes reported in eQTL analysis (n = 1643 genes, 1268 cis and 10,334 trans-eQTLs) (Cesar et al, 2018), and TFs described for bovine (n = 865) (de Souza et al, 2018). Genes identified in the 1Mb QTL windows reported by de Oliveira et al 2014were retrieved using the R package Biomart version 3.5 (Durinck et al, 2009).…”

Genetic regulators of mineral amount in Nelore cattle muscle predicted by a new co-expression and regulatory impact factor approach

“…expressed genes (DEGs) for contrasting mineral amount sample groups 9,10 , transcription factors (TF) 17 and genes affected by Expression Quantitative Trait Locus (eQTLs) 18 . This information, along with genes with significant RIFs, was used as node attributes and included in the network analyses ( Fig.…”

“…In this analysis we estimated the correlations between the expression of a gene or miRNA, which was found to be correlated to a mineral in the present work, and DEGs previously identified for the same mineral 9,10 . This analysis was carried out for each mineral separately and included the same genes with regulatory potential as in the previous section (DEGs 9,10 , TFs 17 , genes affected by eQTLs 18 and genes with significant RIF). To identify elements with regulatory potential, we then selected the genes that were network hubs or that were significant according to RIF (see methods).…”

“…Significant correlations obtained from PCIT 15 analysis between genes or miRNAs expressions and minerals were used to build a co-expression network with the Cytoscape software 78 . We overlapped the gene list from our network with the genes previously reported by our research group to be differentially expressed for at least one mineral, based on the same population evaluated here 9,10 , TFs 17 , genes affected by cis or trans eQTLs 18 and with significant RIF in the present work. These features were used as attributes in the network.…”