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PreprintCited by 17Gene Regulatory Network Analysisstochastic dynamics and bifurcationBacterial Genetics and Biotechnology

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Short residence times of DNA-bound transcription factors can reduce gene expression noise and increase the transmission of information in a gene regulation system

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Azpeitia, Eugenio, Wagner, Andreas·2019·Frontiers in Molecular Biosciences
DOI 10.1101/776955PMC PMC7198700

Preprint — published as 10.3389/fmolb.2020.00067 · Paper page

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Abstract

Gene expression noise is not just ubiquitous but also variable, and we still do not understand some of the most elementary factors that affect it. Among them is the residence time of a transcription factor (TF) on DNA, the mean time that a DNA-bound TF remains bound. Here, we use a stochastic model of transcriptional regulation to study how this residence time affects gene expression. We find that the effect of residence time on gene expression depends on the level of induction of the gene. At high levels of induction, residence time has no effect on gene expression. However, as the level of induction decreases, short residence times reduce gene expression noise. The reason is that fast on-off TF binding dynamics prevent long periods where proteins are predominantly synthesized or degraded, which can cause excessive fluctuations in gene expression. As a consequence, short residence times can help a gene regulation system acquire information about the cellular environment it operates in. Our predictions are consistent with the observation that experimentally measured residence times are usually modest and lie between seconds to minutes.

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FIGURE 1 Schematic description of the model and main concepts. (A) ka and kd correspond to the association and dissociation rate, respectively; k1 and k2 correspond to the mRNA and protein synthesis rate, respectively; d1 and d2 correspond to the mRNA and protein degradation rate, respectively. Relationships of both residence time and affinity with (B) the stability of the TF-DNAbs complex, and (C) TF-DNAbs binding probability. (D) Relationship of affinity and TF concentration with the level of induction.
FIGURE 1 Schematic description of the model and main concepts. (A) ka and kd correspond to the association and dissociation rate, respectively; k1 and k2 correspond to the mRNA and protein synthesis rate, respectively; d1 and d2 correspond to the mRNA and protein degradation rate, respectively. Relationships of both residence time and affinity with (B) the stability of the TF-DNAbs complex, and (C) TF-DNAbs binding probability. (D) Relationship of affinity and TF concentration with the level of induction.CC BY · PubMed Central
FIGURE 2 Residence time and noise. (A) Mean fluctuation size in the number of protein molecules (y axis) at different levels of induction as a function of residence time (x axis). (B–D) Example time trajectories of the number of protein molecules NP at three different levels of induction. Analyses of (E) mean number of protein (N¯P) (F) mean and coefficient of variation of the frequency of protein production events. (B–D) Red and blue lines show data for short (1 s) and a long (1 h) residence times, respectively. (A,E,F) High (TF = 10–7M), intermediate (TF = 10–9M), and low (TF = 10–11M) levels of induction are indicated in the color legend below the figure.
FIGURE 2 Residence time and noise. (A) Mean fluctuation size in the number of protein molecules (y axis) at different levels of induction as a function of residence time (x axis). (B–D) Example time trajectories of the number of protein molecules NP at three different levels of induction. Analyses of (E) mean number of protein (N¯P) (F) mean and coefficient of variation of the frequency of protein production events. (B–D) Red and blue lines show data for short (1 s) and a long (1 h) residence times, respectively. (A,E,F) High (TF = 10–7M), intermediate (TF = 10–9M), and low (TF = 10–11M) levels of induction are indicated in the color legend below the figure.CC BY · PubMed Central
FIGURE 3 Residence time and information. (A) Schematic explanation of the relationship between noise and acquired information. The panel shows hypothetical response protein distributions produced by two different stimuli at a high (right) and a low (left) level of gene expression noise. (B) Acquired information at different residence times as a function of the affinity (Keq) between TF molecules and DNA. (C,D) Distributions of the number of expressed proteins NP at three different TF concentrations (see color legend) with a long (C), and a short (D) residence time. In (C) and (D), Keq = 10–9M.
FIGURE 3 Residence time and information. (A) Schematic explanation of the relationship between noise and acquired information. The panel shows hypothetical response protein distributions produced by two different stimuli at a high (right) and a low (left) level of gene expression noise. (B) Acquired information at different residence times as a function of the affinity (Keq) between TF molecules and DNA. (C,D) Distributions of the number of expressed proteins NP at three different TF concentrations (see color legend) with a long (C), and a short (D) residence time. In (C) and (D), Keq = 10–9M.CC BY · PubMed Central

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Metadata

Authors
Azpeitia, E., & Wagner, A.
Affiliations
SIB Swiss Institute of Bioinformatics (CH)
Corresponding author
Wagner, Andreas
ORCID
Venue
Frontiers in Molecular Biosciences
Publisher
openRxiv
Published
2019-09-20
Volume
7
Pages
67-67
ISSN
2296-889X (ISSN-L)
DOI
10.1101/776955
PMC
PMC7198700
OpenAlex
W3023727038
Cited by
17
References
1
Citations by year
2026: 1 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 3
FWCI
1.13
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Molecular Biosciences (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • National Science Foundation (31003A_172887)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (739874, 172887)
  • H2020 European Research Council (739874)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
Keywords
stochastic gene expression, transcriptional regulation, stochastic gene expression models, information transmission, gene expression regulation

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