Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns

Abstract

Background: Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

Results: We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms.

Conclusions: Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.

Keywords: Transcription factor polymorphism; Transcriptional regulation; Transcriptome imputation.

Fig. 1

An illustration of the two tested mechanisms regarding the assocation between variabiliy in TFs and expression of their transcribed genes. TF-expression model includes associations of TF expression with estimated trans gene expression (A); and TF-binding model includes association of deleterious SNPs within the associated TF with estimated trans gene expression (B)

Fig. 2

An illustration of the pipeline to identify hit genes. We compute the estimated trans GReX on the residulas after removing cis genetic effects and other effects on gene expresssion (A), test their significance relative to background models (B), and conduct a robustness test to validate the results (C)

Fig. 3

Dendrogram of the clustered tissues based on hit genes discovered in the TF-Expression model. Each cluster is in separate color, with black lines denoting signleton clusters

Fig. 4

Dendrogram of the clustered tissues based on hit genes discovered in the TF-Binding model. Each cluster is in separate color, with black lines denoting signleton clusters

Fig. 5

Discussed examples of co-regulation from TF-Expression model (A), where the discussed examples are in yellow and their direct neighbors in blue; and TF-Binding model (B)

Fig. 6

Example of regulation cascade discovered in the TF-Expression model in Mammary breast tissue between IFIT3 (hit gene, in yellow), IRF9 (both TF and hit gene, yellow). Other TFs, such as STAT1 and STAT2, are included in the models for IFIT3, IRF9 or both (orange)