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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.
Highlights
Biological and regulatory mechanisms underlying most multi-gene expression-based disease biomarkers are often not readily evident
The goal of this study was to analyze a gene regulatory network (GRN) to identify the most significant set of key transcription factors (TFs) regulators of the set of genes constituting a separately identified biomarker, namely our asthma biomarker. This is complementary to investigating the constituent genes of the biomarker individually, as well as only identifying TF regulators associated with the target disease or phenotype using methods like Master Regulator Analysis (MRA)
Since this network was inferred from nasal gene expression data, it is expected to be directly relevant to our nasal brush-based asthma biomarker as well as to asthma overall, given shared biology between the nasal and bronchial airways[3,25,26]
Summary
Biological and regulatory mechanisms underlying most multi-gene expression-based disease biomarkers are often not readily evident. We describe a novel framework that combines network analyses with RNA sequence (RNAseq) data to identify transcription factors (TFs) significantly regulating a disease biomarker This framework, named NeTFactor (Network-identified Transcription Factor), uses a computationally inferred context-specific gene regulatory network (GRN)[8] to guide the analysis. Such a GRN consists of directed edges denoting interactions between regulators (e.g. TFs) and their target(s) (e.g. gene(s) they regulate). The goal of this study was to analyze a GRN to identify the most significant set of key TF regulators of the set of genes constituting a separately identified biomarker, namely our asthma biomarker This is complementary to investigating the constituent genes of the biomarker individually, as well as only identifying TF regulators associated with the target disease or phenotype using methods like MRA. We used computational and systems biology principles[19,20,21] to develop a novel framework that integrates machine learning- and network-based analyses of complex biomolecular data
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