Shared enhancer gene regulatory networks between wound and oncogenic programs

  1. Swann Floc'hlay
  2. Ramya Balaji
  3. Dimitrije Stanković
  4. Valerie M Christiaens
  5. Carmen Bravo González-Blas
  6. Seppe De Winter
  7. Gert J Hulselmans
  8. Maxime De Waegeneer
  9. Xiaojiang Quan
  10. Duygu Koldere
  11. Mardelle Atkins
  12. Georg Halder
  13. Mirka Uhlirova
  14. Anne Classen  Is a corresponding author
  15. Stein Aerts  Is a corresponding author
  1. VIB-KU Leuven Center for Brain and Disease Research, Belgium
  2. University of Freiburg, Germany
  3. University of Colognee, Germany
  4. KU Leuven, Belgium
  5. Sam Houston State University, United States
  6. VIB-KU Leuven Center for Cancer Biology, Belgium
  7. University of Cologne, Germany

Abstract

Wound response programs are often activated during neoplastic growth in tumors. In both wound repair and tumor growth, cells respond to acute stress and balance the activation of multiple programs including apoptosis, proliferation, and cell migration. Central to those responses are the activation of the JNK/MAPK and JAK/STAT signaling pathways. Yet, to what extent these signaling cascades interact at the cis-regulatory level, and how they orchestrate different regulatory and phenotypic responses is still unclear. Here, we aim to characterize the regulatory states that emerge and cooperate in the wound response, using the Drosophila melanogaster wing disc as a model system, and compare these with cancer cell states induced by rasV12scrib-/- in the eye disc. We used single-cell multiome profiling to derive enhancer Gene Regulatory Networks (eGRNs) by integrating chromatin accessibility and gene expression signals. We identify a 'proliferative' eGRN, active in the majority of wounded cells and controlled by AP-1 and STAT. In a smaller, but distinct population of wound cells, a 'senescent' eGRN is activated and driven by C/EBP-like transcription factors (Irbp18, Xrp1, Slow border, and Vrille) and Scalloped. These two eGRN signatures are found to be active in tumor cells, at both gene expression and chromatin accessibility levels. Our single-cell multiome and eGRNs resource offers an in-depth characterisation of the senescence markers, together with a new perspective on the shared gene regulatory programs acting during wound response and oncogenesis.

Data availability

Single-cell sequencing data and aligned matrices have been deposited in GEO (accession code GSE205401)

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Swann Floc'hlay

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  2. Ramya Balaji

    Faculty of Biology, University of Freiburg, Freiburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Dimitrije Stanković

    Institute for Genetics, University of Colognee, Cologne, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Valerie M Christiaens

    Department of Human Genetics, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  5. Carmen Bravo González-Blas

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  6. Seppe De Winter

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7907-1247
  7. Gert J Hulselmans

    Department of Human Genetics, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  8. Maxime De Waegeneer

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  9. Xiaojiang Quan

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  10. Duygu Koldere

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  11. Mardelle Atkins

    Department of Biological Sciences, Sam Houston State University, Texas, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0245-2452
  12. Georg Halder

    VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  13. Mirka Uhlirova

    Institute for Genetics, University of Cologne, Cologne, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5735-8287
  14. Anne Classen

    Faculty of Biology, University of Freiburg, Freiburg, Germany
    For correspondence
    anne.classen@zbsa.uni-freiburg.de
    Competing interests
    The authors declare that no competing interests exist.
  15. Stein Aerts

    VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
    For correspondence
    stein.aerts@kuleuven.be
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8006-0315

Funding

European Research Council (724226_cisCONTROL)

  • Valerie M Christiaens
  • Gert J Hulselmans
  • Stein Aerts

Fonds Wetenschappelijk Onderzoek (G0C0417N)

  • Xiaojiang Quan
  • Duygu Koldere

Fonds Wetenschappelijk Onderzoek (G094121N)

  • Swann Floc'hlay

Deutsche Forschungsgemeinschaft (EXC-2189)

  • Anne Classen

Deutsche Forschungsgemeinschaft (CL490/3-1)

  • Anne Classen

Deutsche Forschungsgemeinschaft (EXC 2030)

  • Mirka Uhlirova

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

Reviewing Editor

  1. Utpal Banerjee, University of California, Los Angeles, United States

Publication history

  1. Received: June 17, 2022
  2. Accepted: May 2, 2023
  3. Accepted Manuscript published: May 3, 2023 (version 1)

Copyright

© 2023, Floc'hlay 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

  • 367
    Page views
  • 118
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, 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. Swann Floc'hlay
  2. Ramya Balaji
  3. Dimitrije Stanković
  4. Valerie M Christiaens
  5. Carmen Bravo González-Blas
  6. Seppe De Winter
  7. Gert J Hulselmans
  8. Maxime De Waegeneer
  9. Xiaojiang Quan
  10. Duygu Koldere
  11. Mardelle Atkins
  12. Georg Halder
  13. Mirka Uhlirova
  14. Anne Classen
  15. Stein Aerts
(2023)
Shared enhancer gene regulatory networks between wound and oncogenic programs
eLife 12:e81173.
https://doi.org/10.7554/eLife.81173

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Kai J Sandbrink, Pranav Mamidanna ... Alexander Mathis
    Research Article

    Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical role of proprioception is representing the body state. We hypothesize that the proprioceptive system could also be critical for high-level tasks such as action recognition. To test this theory, we pursued a task-driven modeling approach, which allowed us to isolate the study of proprioception. We generated a large synthetic dataset of human arm trajectories tracing characters of the Latin alphabet in 3D space, together with muscle activities obtained from a musculoskeletal model and model-based muscle spindle activity. Next, we compared two classes of tasks: trajectory decoding and action recognition, which allowed us to train hierarchical models to decode either the position and velocity of the end-effector of one's posture or the character (action) identity from the spindle firing patterns. We found that artificial neural networks could robustly solve both tasks, and the networks'units show tuning properties similar to neurons in the primate somatosensory cortex and the brainstem. Remarkably, we found uniformly distributed directional selective units only with the action-recognition-trained models and not the trajectory-decoding-trained models. This suggests that proprioceptive encoding is additionally associated with higher-level functions such as action recognition and therefore provides new, experimentally testable hypotheses of how proprioception aids in adaptive motor control.

    1. Computational and Systems Biology
    Yujian Wen, Jielong Huang ... Hao Zhu
    Tools and Resources Updated

    Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality. Causal discovery (also called causal inference) infers causal interactions between objects from observational data. Reported causal discovery methods and single-cell datasets make applying causal discovery to single cells a promising direction. However, evaluating and choosing causal discovery methods and developing and performing proper workflow remain challenges. We report the workflow and platform CausalCell (http://www.gaemons.net/causalcell/causalDiscovery/) for performing single-cell causal discovery. The workflow/platform is developed upon benchmarking four kinds of causal discovery methods and is examined by analyzing multiple single-cell RNA-sequencing (scRNA-seq) datasets. Our results suggest that different situations need different methods and the constraint-based PC algorithm with kernel-based conditional independence tests work best in most situations. Related issues are discussed and tips for best practices are given. Inferred causal interactions in single cells provide valuable clues for investigating molecular interactions and gene regulations, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions.