Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif

  1. Md Zulfikar Ali
  2. Vinuselvi Parisutham
  3. Sandeep Choubey
  4. Robert C Brewster  Is a corresponding author
  1. University of Massachusetts Medical School, United States
  2. Max Planck Institute for the Physics of Complex Systems, Germany

Abstract

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.

Data availability

Data was deposited to the Image Data Resource (https://idr.openmicroscopy.org) under accession number idr0095. Code used to generate figures and simulation code is available on github at https://github.com/zulfikgp/Autoregulation.

The following data sets were generated

Article and author information

Author details

  1. Md Zulfikar Ali

    Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7054-0059
  2. Vinuselvi Parisutham

    Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0349-4072
  3. Sandeep Choubey

    Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7387-6148
  4. Robert C Brewster

    Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States
    For correspondence
    Robert.brewster@umassmed.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7656-4086

Funding

National Institute of General Medical Sciences (R35GM128797)

  • Md Zulfikar Ali
  • Vinuselvi Parisutham
  • Robert C Brewster

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2020, Ali et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,297
    views
  • 158
    downloads
  • 14
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Md Zulfikar Ali
  2. Vinuselvi Parisutham
  3. Sandeep Choubey
  4. Robert C Brewster
(2020)
Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif
eLife 9:e56517.
https://doi.org/10.7554/eLife.56517

Share this article

https://doi.org/10.7554/eLife.56517

Further reading

    1. Computational and Systems Biology
    2. Ecology
    Lenore Pipes, Rasmus Nielsen
    Tools and Resources

    Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Natanael Spisak, Gabriel Athènes ... Aleksandra M Walczak
    Tools and Resources

    B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and 𝑑𝑁∕𝑑𝑆 ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.