NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers

Mehmet Eren Ahsen, Yoojin Chun, Alexander Grishin, Galina Grishina, Gustavo Stolovitzky, Gaurav Pandey, Supinda Bunyavanich

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biological and regulatory mechanisms underlying many multi-gene expression-based disease biomarkers are often not readily evident. We describe an innovative framework, NeTFactor, that combines network analyses with gene expression data to identify transcription factors (TFs) that significantly and maximally regulate such a biomarker. NeTFactor uses a computationally-inferred context-specific gene regulatory network and applies topological, statistical, and optimization methods to identify regulator TFs. Application of NeTFactor to a multi-gene expression-based asthma biomarker identified ETS translocation variant 4 (ETV4) and peroxisome proliferator-Activated receptor gamma (PPARG) as the biomarker's most significant TF regulators. siRNA-based knock down of these TFs in an airway epithelial cell line model demonstrated significant reduction of cytokine expression relevant to asthma, validating NeTFactor's top-scoring findings. While PPARG has been associated with airway inflammation, ETV4 has not yet been implicated in asthma, thus indicating the possibility of novel, disease-relevant discovery by NeTFactor. We also show that NeTFactor's results are robust when the gene regulatory network and biomarker are derived from independent data. Additionally, our application of NeTFactor to a different disease biomarker identified TF regulators of interest. These results illustrate that the application of NeTFactor to multi-gene expression-based biomarkers could yield valuable insights into regulatory mechanisms and biological processes underlying disease.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450379649
DOIs
StatePublished - Sep 21 2020
Externally publishedYes
Event11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020 - Virtual, Online, United States
Duration: Sep 21 2020Sep 24 2020

Publication series

NameProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020

Conference

Conference11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
CountryUnited States
CityVirtual, Online
Period9/21/209/24/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Biomedical Engineering
  • Health Informatics

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