Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Abstract The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease,...

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Autores principales:	Daniel Morgan, Matthew Studham, Andreas Tjärnberg, Holger Weishaupt, Fredrik J. Swartling, Torbjörn E. M. Nordling, Erik L. L. Sonnhammer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/82d92cd771e2430780af2d03d54742cb
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spelling	oai:doaj.org-article:82d92cd771e2430780af2d03d54742cb2021-12-02T19:02:37ZPerturbation-based gene regulatory network inference to unravel oncogenic mechanisms10.1038/s41598-020-70941-y2045-2322https://doaj.org/article/82d92cd771e2430780af2d03d54742cb2020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-70941-yhttps://doaj.org/toc/2045-2322Abstract The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.Daniel MorganMatthew StudhamAndreas TjärnbergHolger WeishauptFredrik J. SwartlingTorbjörn E. M. NordlingErik L. L. SonnhammerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Morgan Matthew Studham Andreas Tjärnberg Holger Weishaupt Fredrik J. Swartling Torbjörn E. M. Nordling Erik L. L. Sonnhammer Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
description	Abstract The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in cross-validated benchmarks and for an independent dataset of the same genes under a different perturbation design. The inferred GRN captures many known regulatory interactions central to cancer-relevant processes in addition to predicting many novel interactions, some of which were experimentally validated, thus providing mechanistic insights that are useful for future cancer research.
format	article
author	Daniel Morgan Matthew Studham Andreas Tjärnberg Holger Weishaupt Fredrik J. Swartling Torbjörn E. M. Nordling Erik L. L. Sonnhammer
author_facet	Daniel Morgan Matthew Studham Andreas Tjärnberg Holger Weishaupt Fredrik J. Swartling Torbjörn E. M. Nordling Erik L. L. Sonnhammer
author_sort	Daniel Morgan
title	Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
title_short	Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
title_full	Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
title_fullStr	Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
title_full_unstemmed	Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
title_sort	perturbation-based gene regulatory network inference to unravel oncogenic mechanisms
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/82d92cd771e2430780af2d03d54742cb
work_keys_str_mv	AT danielmorgan perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms AT matthewstudham perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms AT andreastjarnberg perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms AT holgerweishaupt perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms AT fredrikjswartling perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms AT torbjornemnordling perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms AT erikllsonnhammer perturbationbasedgeneregulatorynetworkinferencetounraveloncogenicmechanisms
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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

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