Control of tissue development and cell diversity by cell cycle dependent transcriptional filtering

  1. Maria Abou Chakra
  2. Ruth Isserlin
  3. Thinh N. Tran
  4. Gary D Bader  Is a corresponding author
  1. University of Toronto, Canada

Abstract

Cell cycle duration changes dramatically during development, starting out fast to generate cells quickly and slowing down over time as the organism matures. The cell cycle can also act as a transcriptional filter to control the expression of long gene transcripts which are partially transcribed in short cycles. Using mathematical simulations of cell proliferation, we identify an emergent property, that this filter can act as a tuning knob to control gene transcript expression, cell diversity and the number and proportion of different cell types in a tissue. Our predictions are supported by comparison to single-cell RNA-seq data captured over embryonic development. Additionally, evolutionary genome analysis shows that fast developing organisms have a narrow genomic distribution of gene lengths while slower developers have an expanded number of long genes. Our results support the idea that cell cycle dynamics may be important across multicellular animals for controlling gene transcript expression and cell fate.

Data availability

All data generated during this study are included in the manuscript and supporting files. Source file for the code is available athttps://github.com/BaderLab/Cell_Cycle_Theory

The following previously published data sets were used

Article and author information

Author details

  1. Maria Abou Chakra

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4895-954X
  2. Ruth Isserlin

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Thinh N. Tran

    The Donnelly Centre, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Gary D Bader

    The Donnelly Centre, University of Toronto, Toronto, Canada
    For correspondence
    gary.bader@utoronto.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0185-8861

Funding

Canada First Research Excellence Fund

  • Gary D Bader

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

Reviewing Editor

  1. Wenying Shou, University College London, United Kingdom

Publication history

  1. Received: November 17, 2020
  2. Accepted: July 1, 2021
  3. Accepted Manuscript published: July 2, 2021 (version 1)
  4. Version of Record published: July 14, 2021 (version 2)

Copyright

© 2021, Abou Chakra 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

  • 1,637
    Page views
  • 178
    Downloads
  • 3
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

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. Maria Abou Chakra
  2. Ruth Isserlin
  3. Thinh N. Tran
  4. Gary D Bader
(2021)
Control of tissue development and cell diversity by cell cycle dependent transcriptional filtering
eLife 10:e64951.
https://doi.org/10.7554/eLife.64951

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Laura M Doherty et al.
    Research Article

    Deubiquitinating enzymes (DUBs), ~100 of which are found in human cells, are proteases that remove ubiquitin conjugates from proteins, thereby regulating protein turnover. They are involved in a wide range of cellular activities and are emerging therapeutic targets for cancer and other diseases. Drugs targeting USP1 and USP30 are in clinical development for cancer and kidney disease respectively. However, the majority of substrates and pathways regulated by DUBs remain unknown, impeding efforts to prioritize specific enzymes for research and drug development. To assemble a knowledgebase of DUB activities, co-dependent genes, and substrates, we combined targeted experiments using CRISPR libraries and inhibitors with systematic mining of functional genomic databases. Analysis of the Dependency Map, Connectivity Map, Cancer Cell Line Encyclopedia, and multiple protein-protein interaction databases yielded specific hypotheses about DUB function, a subset of which were confirmed in follow-on experiments. The data in this paper are browsable online in a newly developed DUB Portal and promise to improve understanding of DUBs as a family as well as the activities of incompletely characterized DUBs (e.g. USPL1 and USP32) and those already targeted with investigational cancer therapeutics (e.g. USP14, UCHL5, and USP7).

    1. Computational and Systems Biology
    2. Neuroscience
    Rany Abend et al.
    Research Article Updated

    Influential theories implicate variations in the mechanisms supporting threat learning in the severity of anxiety symptoms. We use computational models of associative learning in conjunction with structural imaging to explicate links among the mechanisms underlying threat learning, their neuroanatomical substrates, and anxiety severity in humans. We recorded skin-conductance data during a threat-learning task from individuals with and without anxiety disorders (N=251; 8-50 years; 116 females). Reinforcement-learning model variants quantified processes hypothesized to relate to anxiety: threat conditioning, threat generalization, safety learning, and threat extinction. We identified the best-fitting models for these processes and tested associations among latent learning parameters, whole-brain anatomy, and anxiety severity. Results indicate that greater anxiety severity related specifically to slower safety learning and slower extinction of response to safe stimuli. Nucleus accumbens gray-matter volume moderated learning-anxiety associations. Using a modeling approach, we identify computational mechanisms linking threat learning and anxiety severity and their neuroanatomical substrates.