Promoters adopt distinct dynamic manifestations depending on transcription factor context

Abstract Cells respond to external signals and stresses by activating transcription factors (TF), which induce gene expression changes. Prior work suggests that signal‐specific gene expression changes are partly achieved because different gene promoters exhibit distinct induction dynamics in respons...

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Main Authors: Anders S Hansen, Christoph Zechner
Format: Article
Language:English
Published: Wiley 2021-02-01
Series:Molecular Systems Biology
Subjects:
Online Access:https://doi.org/10.15252/msb.20209821
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spelling doaj-96fd96158ac24282a2c0a6047b3248912021-08-02T16:04:06ZengWileyMolecular Systems Biology1744-42922021-02-01172n/an/a10.15252/msb.20209821Promoters adopt distinct dynamic manifestations depending on transcription factor contextAnders S Hansen0Christoph Zechner1Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USAMax Planck Institute of Molecular Cell Biology & Genetics Dresden GermanyAbstract Cells respond to external signals and stresses by activating transcription factors (TF), which induce gene expression changes. Prior work suggests that signal‐specific gene expression changes are partly achieved because different gene promoters exhibit distinct induction dynamics in response to the same TF input signal. Here, using high‐throughput quantitative single‐cell measurements and a novel statistical method, we systematically analyzed transcriptional responses to a large number of dynamic TF inputs. In particular, we quantified the scaling behavior among different transcriptional features extracted from the measured trajectories such as the gene activation delay or duration of promoter activity. Surprisingly, we found that even the same gene promoter can exhibit qualitatively distinct induction and scaling behaviors when exposed to different dynamic TF contexts. While it was previously known that promoters fall into distinct classes, here we show that the same promoter can switch between different classes depending on context. Thus, promoters can adopt context‐dependent “manifestations”. Our analysis suggests that the full complexity of signal processing by genetic circuits may be significantly underestimated when studied in only specific contexts.https://doi.org/10.15252/msb.20209821Bayesian inferencemanifestationMsn2promoter class switchingtranscription factor dynamics
collection DOAJ
language English
format Article
sources DOAJ
author Anders S Hansen
Christoph Zechner
spellingShingle Anders S Hansen
Christoph Zechner
Promoters adopt distinct dynamic manifestations depending on transcription factor context
Molecular Systems Biology
Bayesian inference
manifestation
Msn2
promoter class switching
transcription factor dynamics
author_facet Anders S Hansen
Christoph Zechner
author_sort Anders S Hansen
title Promoters adopt distinct dynamic manifestations depending on transcription factor context
title_short Promoters adopt distinct dynamic manifestations depending on transcription factor context
title_full Promoters adopt distinct dynamic manifestations depending on transcription factor context
title_fullStr Promoters adopt distinct dynamic manifestations depending on transcription factor context
title_full_unstemmed Promoters adopt distinct dynamic manifestations depending on transcription factor context
title_sort promoters adopt distinct dynamic manifestations depending on transcription factor context
publisher Wiley
series Molecular Systems Biology
issn 1744-4292
publishDate 2021-02-01
description Abstract Cells respond to external signals and stresses by activating transcription factors (TF), which induce gene expression changes. Prior work suggests that signal‐specific gene expression changes are partly achieved because different gene promoters exhibit distinct induction dynamics in response to the same TF input signal. Here, using high‐throughput quantitative single‐cell measurements and a novel statistical method, we systematically analyzed transcriptional responses to a large number of dynamic TF inputs. In particular, we quantified the scaling behavior among different transcriptional features extracted from the measured trajectories such as the gene activation delay or duration of promoter activity. Surprisingly, we found that even the same gene promoter can exhibit qualitatively distinct induction and scaling behaviors when exposed to different dynamic TF contexts. While it was previously known that promoters fall into distinct classes, here we show that the same promoter can switch between different classes depending on context. Thus, promoters can adopt context‐dependent “manifestations”. Our analysis suggests that the full complexity of signal processing by genetic circuits may be significantly underestimated when studied in only specific contexts.
topic Bayesian inference
manifestation
Msn2
promoter class switching
transcription factor dynamics
url https://doi.org/10.15252/msb.20209821
work_keys_str_mv AT andersshansen promotersadoptdistinctdynamicmanifestationsdependingontranscriptionfactorcontext
AT christophzechner promotersadoptdistinctdynamicmanifestationsdependingontranscriptionfactorcontext
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