Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues

Abstract

Transcription factors (TFs) are a special class of DNA-binding proteins that orchestrate gene transcription by recruiting other TFs, co-activators or co-repressors. Their combinatorial interplay in higher organisms maintains homeostasis and governs cell identity by finely controlling and regulating tissue-specific gene expression. Despite the rich literature on the importance of cooperative TFs for deciphering the mechanisms of individual regulatory programs that control tissue specificity in several organisms such as human, mouse, or Drosophila melanogaster , to date, there is still need for a comprehensive study to detect specific TF cooperations in regulatory processes of cattle tissues. To address the needs of knowledge about specific combinatorial gene regulation in cattle tissues, we made use of three publicly available RNA-seq datasets and obtained tissue-specific gene (TSG) sets for ten tissues (heart, lung, liver, kidney, duodenum, muscle tissue, adipose tissue, colon, spleen and testis). By analyzing these TSG-sets, tissue-specific TF cooperations of each tissue have been identified. The results reveal that similar to the combinatorial regulatory events of model organisms, TFs change their partners depending on their biological functions in different tissues. Particularly with regard to preferential partner choice of the transcription factors STAT 3 and NR 2 C 2, this phenomenon has been highlighted with their five different specific cooperation partners in multiple tissues. The information about cooperative TFs could be promising: i) to understand the molecular mechanisms of regulating processes; and ii) to extend the existing knowledge on the importance of single TFs in cattle tissues.

In Silico Identification of the Complex Interplay between Regulatory SNPs, Transcription Factors, and Their Related Genes in Brassica napus L. Using Multi-Omics Data

“…However, the selection of the promoter regions is crucial: (i) to avoid the redundancy between sequences which could lead to the overestimation of some TFBSs [ 68 ] (ii) to address the inaccuracy of transcription start site (TSS) positions resulting from their imprecise prediction. To overcome these issues, we followed a similar strategy to those suggested in previous studies [ 7 , 49 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ] and accordingly extracted two sets of promoter sequences for each tissue ranging from bp to bp relative to the TSS using the reference genome version 4.1 and gene annotation given in [ 63 ]. While the first sequence set refers to the promoter sequences of the DEGs (foreground set), the second set contains the promoter sequences of genes having the same GC-content as the foreground set (background set) [ 75 ].…”

Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis

“…In order to gain a better understanding of the underlying molecular mechanism of AAT in different tissues and compare the results of both breeds, we created cooperation networks for each tissue based on its specific TF pairs as suggested in our previous studies [26,47,66,67]. The nodes represent the TFs and the edges represent their co-operation in these networks which are presented in Figure 3.…”

“…However, their prediction performance is prone to high rates of false positive predictions [30,45,46]. In order to eliminate the false predictions to some extent in our analysis, we manually created a specific PWM library following our previous study [47]. For this purpose, we first obtained all available cattle TFs from AnimalTFDB 2.0 [48] and identified the expression values (TPM values) of their corresponding TF genes in the gene expression dataset, under study.…”

agReg-SNPdb: A Database of Regulatory SNPs for Agricultural Animal Species

“…In contrast to the process of translation, the transcriptional machinery and its regulatory mechanisms are far from being deciphered [ 1 ]. These mechanisms are mainly governed by a special class of regulatory proteins, the transcription factors (TFs), and their combinatorial interplay [ 2 , 3 ]. TFs regulate the transcription as a response to specific environmental conditions by binding to short degenerate sequence motifs known as transcription factor binding sites (TFBSs) in promoter regions of their target genes and, thereby, enhance or repress gene transcription.…”