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

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 m...

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Autores principales: Ahsen, Mehmet Eren, Chun, Yoojin, Grishin, Alexander, Grishina, Galina, Stolovitzky, Gustavo, Pandey, Gaurav, Bunyavanich, Supinda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737052/
https://www.ncbi.nlm.nih.gov/pubmed/31506535
http://dx.doi.org/10.1038/s41598-019-49498-y
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author Ahsen, Mehmet Eren
Chun, Yoojin
Grishin, Alexander
Grishina, Galina
Stolovitzky, Gustavo
Pandey, Gaurav
Bunyavanich, Supinda
author_facet Ahsen, Mehmet Eren
Chun, Yoojin
Grishin, Alexander
Grishina, Galina
Stolovitzky, Gustavo
Pandey, Gaurav
Bunyavanich, Supinda
author_sort Ahsen, Mehmet Eren
collection PubMed
description 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.
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spelling pubmed-67370522019-09-20 NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers Ahsen, Mehmet Eren Chun, Yoojin Grishin, Alexander Grishina, Galina Stolovitzky, Gustavo Pandey, Gaurav Bunyavanich, Supinda Sci Rep Article 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. Nature Publishing Group UK 2019-09-10 /pmc/articles/PMC6737052/ /pubmed/31506535 http://dx.doi.org/10.1038/s41598-019-49498-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ahsen, Mehmet Eren
Chun, Yoojin
Grishin, Alexander
Grishina, Galina
Stolovitzky, Gustavo
Pandey, Gaurav
Bunyavanich, Supinda
NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
title NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
title_full NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
title_fullStr NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
title_full_unstemmed NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
title_short NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
title_sort netfactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737052/
https://www.ncbi.nlm.nih.gov/pubmed/31506535
http://dx.doi.org/10.1038/s41598-019-49498-y
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