Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

Patel, N.; Bush, W.

doi:10.1101/2021.06.18.449031

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bioRxiv · 10.1101/2021.06.18.449031

Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

Patel, N.·Bush, W.

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Abstract

BackgroundTranscription factor(TF) interactions are known to regulate target gene(TG) expression in eukaryotes via TF regulatory modules(TRMs). Such interactions can be formed due to co-localizing TFs binding proximally to each other in the DNA sequence or over long distances between distally binding TFs via chromatin looping. While the former type of interaction has been characterized extensively, long distance TF interactions are still largely understudied. Furthermore, most prior approaches have focused on characterizing physical TF interactions without accounting for their effects on TG expression regulation. Understanding TRM based TG expression regulation could aid in understanding diseases caused by disruptions to these mechanisms. In this paper, we present a novel neural network based TRM detection approach that consists of using multi-omics TF based regulatory mechanism information to generate features for building non-linear multilayer perceptron TG expression prediction models in the GM12878 immortalized lymphoblastoid cells. ResultsWe estimated main effects of 149 individual TFs and interaction effects of 48 distinct combinations of TFs forming TRMs based on their influence on TG expression. We identified several well-known and discovered multiple previously uncharacterized TF interactions within our detected set of TRMs. We further characterized the pairwise TRMs using long distance chromatin looping and motif co-occurrence data. We found that nearly all the TFs constituting TRMs detected by our approach interacted via chromatin looping, and that these TFs further interacted with promoters to influence TG expression through one of four possible regulatory configurations. ConclusionHere, we have provided a framework for detecting TRMs using neural network models containing multi-omics TF based regulatory features. We have also described these TRMs based on their regulatory potential along with presenting evidence for the possibility of TF interactions forming the TRMs occurring via chromatin looping.

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Patel, N., Bush, W.. 2021-06-20. Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models. https://doi.org/10.1101/2021.06.18.449031

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