Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations.

Authors

Peter Rogan, The University of Western OntarioFollow
Ruipeng Lu, The University of Western OntarioFollow

Document Type

Article

Publication Date

1-1-2019

Journal

F1000Research

Volume

7

First Page

1933

URL with Digital Object Identifier

https://doi.org/10.12688/f1000research.17363.2

Abstract

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results: The glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Notes

Available open access in F1000Research at https://doi.org/10.12688/f1000research.17363.2

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Citation of this paper:

Lu R and Rogan PK. Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations [version 2; peer review: 2 approved]. F1000Research 2019, 7:1933 (https://doi.org/10.12688/f1000research.17363.2)