Identifying the combinatorial control of signal-dependent transcription factors.

The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strateg...

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Autores principales:	Ning Wang, Diane Lefaudeux, Anup Mazumder, Jingyi Jessica Li, Alexander Hoffmann
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a2f43821778447608578d614dd1a5847
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spelling	oai:doaj.org-article:a2f43821778447608578d614dd1a58472021-11-25T05:40:35ZIdentifying the combinatorial control of signal-dependent transcription factors.1553-734X1553-735810.1371/journal.pcbi.1009095https://doaj.org/article/a2f43821778447608578d614dd1a58472021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009095https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.Ning WangDiane LefaudeuxAnup MazumderJingyi Jessica LiAlexander HoffmannPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009095 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ning Wang Diane Lefaudeux Anup Mazumder Jingyi Jessica Li Alexander Hoffmann Identifying the combinatorial control of signal-dependent transcription factors.
description	The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.
format	article
author	Ning Wang Diane Lefaudeux Anup Mazumder Jingyi Jessica Li Alexander Hoffmann
author_facet	Ning Wang Diane Lefaudeux Anup Mazumder Jingyi Jessica Li Alexander Hoffmann
author_sort	Ning Wang
title	Identifying the combinatorial control of signal-dependent transcription factors.
title_short	Identifying the combinatorial control of signal-dependent transcription factors.
title_full	Identifying the combinatorial control of signal-dependent transcription factors.
title_fullStr	Identifying the combinatorial control of signal-dependent transcription factors.
title_full_unstemmed	Identifying the combinatorial control of signal-dependent transcription factors.
title_sort	identifying the combinatorial control of signal-dependent transcription factors.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a2f43821778447608578d614dd1a5847
work_keys_str_mv	AT ningwang identifyingthecombinatorialcontrolofsignaldependenttranscriptionfactors AT dianelefaudeux identifyingthecombinatorialcontrolofsignaldependenttranscriptionfactors AT anupmazumder identifyingthecombinatorialcontrolofsignaldependenttranscriptionfactors AT jingyijessicali identifyingthecombinatorialcontrolofsignaldependenttranscriptionfactors AT alexanderhoffmann identifyingthecombinatorialcontrolofsignaldependenttranscriptionfactors
_version_	1718414534516932608

Identifying the combinatorial control of signal-dependent transcription factors.

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