Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function

  1. Jeffrey Molendijk
  2. Ronnie Blazev
  3. Richard J Mills
  4. Yaan-Kit Ng
  5. Kevin I Watt
  6. Daryn Chau
  7. Paul Gregorevic
  8. Peter J Crouch
  9. James BW Hilton
  10. Leszek Lisowski
  11. Peixiang Zhang
  12. Karen Reue
  13. Aldons J Lusis
  14. James Hudson
  15. David E James
  16. Marcus M Seldin
  17. Benjamin L Parker  Is a corresponding author
  1. University of Melbourne, Australia
  2. QIMR Berghofer Medical Research Institute, Australia
  3. University of California, Irvine, United States
  4. University of Sydney, Australia
  5. University of California, Los Angeles, United States

Abstract

Improving muscle function has great potential to improve the quality of life. To identify novel regulators of skeletal muscle metabolism and function, we performed a proteomic analysis of gastrocnemius muscle from 73 genetically distinct inbred mouse strains, and integrated the data with previously acquired genomics and >300 molecular/phenotypic traits via quantitative trait loci mapping and correlation network analysis. These data identified thousands of associations between protein abundance and phenotypes and can be accessed online (https://muscle.coffeeprot.com/) to identify regulators of muscle function. We used this resource to prioritize targets for a functional genomic screen in human bioengineered skeletal muscle. This identified several negative regulators of muscle function including UFC1, an E2 ligase for protein UFMylation. We show UFMylation is up-regulated in a mouse model of amyotrophic lateral sclerosis, a disease that involves muscle atrophy. Furthermore, in vivo knockdown of UFMylation increased contraction force, implicating its role as a negative regulator of skeletal muscle function.

Data availability

The proteomics data generated in this study are deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) under the identifiers PXD032729, PXD034913 and PXD035170. The code used for downstream analysis of proteomic data can be found at: https://github.com/JeffreyMolendijk/skeletal_muscle.

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

Article and author information

Author details

  1. Jeffrey Molendijk

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6575-504X
  2. Ronnie Blazev

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
  3. Richard J Mills

    QIMR Berghofer Medical Research Institute, Brisbane, Australia
    Competing interests
    No competing interests declared.
  4. Yaan-Kit Ng

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
  5. Kevin I Watt

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
  6. Daryn Chau

    Department of Biological Chemistry, University of California, Irvine, Irvine, United States
    Competing interests
    No competing interests declared.
  7. Paul Gregorevic

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
  8. Peter J Crouch

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7777-4747
  9. James BW Hilton

    Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
  10. Leszek Lisowski

    Children's Medical Research Institute, University of Sydney, Sydney, Australia
    Competing interests
    No competing interests declared.
  11. Peixiang Zhang

    Department of Human Genetics/Medicine, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
  12. Karen Reue

    Department of Human Genetics/Medicine, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
  13. Aldons J Lusis

    Department of Human Genetics/Medicine, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
  14. James Hudson

    QIMR Berghofer Medical Research Institute, Brisbane, Australia
    Competing interests
    No competing interests declared.
  15. David E James

    School of Life and Environmental Science, University of Sydney, Sydney, Australia
    Competing interests
    David E James, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5946-5257
  16. Marcus M Seldin

    Department of Biological Chemistry, University of California, Irvine, Irvine, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8026-4759
  17. Benjamin L Parker

    Department of Anatomy and Physiology, University of Melbourne, Melbourne, Australia
    For correspondence
    ben.parker@unimelb.edu.au
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1818-2183

Funding

National Health and Medical Research Council (APP1184363)

  • Karen Reue
  • Marcus M Seldin
  • Benjamin L Parker

National Institute of Health (HL147883)

  • Aldons J Lusis

National Institute of Health (DK117850)

  • Aldons J Lusis

Weary Dunlop Foundation (NA)

  • Benjamin L Parker

The ALS Association (21-DDC-574)

  • Paul Gregorevic
  • Peter J Crouch

National Health and Medical Research Council (APP2009642)

  • Benjamin L Parker

National Health and Medical Research Council (APP2013189)

  • Richard J Mills

National Health and Medical Research Council (APP1156562)

  • Paul Gregorevic
  • Benjamin L Parker

National Institute of Health (HL138193)

  • Marcus M Seldin

National Institute of Health (DK130640)

  • Marcus M Seldin

National Institute of Health (DK097771)

  • Marcus M Seldin

National Institute of Health (GM115318)

  • Karen Reue

National Institute of Health (AG070959)

  • Aldons J Lusis

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

Reviewing Editor

  1. Charles Farber, University of Virginia, United States

Ethics

Animal experimentation: All rAAV6 intramuscular injection mouse experiments were approved by The University of Melbourne Animal Ethics Committee (AEC ID1914940) and conformed to the National Health and Medical Research Council of Australia guidelines regarding the care and use of experimental animals. All studies involving the use of SOD1G37R mice and non-transgenic littermates were approved by a University of Melbourne Animal Experimentation Ethics Committee (approval #2015124) and conformed with guidelines of the Australian National Health and Medical Research Council.

Version history

  1. Preprint posted: August 22, 2022 (view preprint)
  2. Received: August 24, 2022
  3. Accepted: November 29, 2022
  4. Accepted Manuscript published: December 6, 2022 (version 1)
  5. Version of Record published: January 11, 2023 (version 2)

Copyright

© 2022, Molendijk 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

  • 1,565
    views
  • 229
    downloads
  • 9
    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. Jeffrey Molendijk
  2. Ronnie Blazev
  3. Richard J Mills
  4. Yaan-Kit Ng
  5. Kevin I Watt
  6. Daryn Chau
  7. Paul Gregorevic
  8. Peter J Crouch
  9. James BW Hilton
  10. Leszek Lisowski
  11. Peixiang Zhang
  12. Karen Reue
  13. Aldons J Lusis
  14. James Hudson
  15. David E James
  16. Marcus M Seldin
  17. Benjamin L Parker
(2022)
Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function
eLife 11:e82951.
https://doi.org/10.7554/eLife.82951

Share this article

https://doi.org/10.7554/eLife.82951

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Andrea I Luppi, Pedro AM Mediano ... Emmanuel A Stamatakis
    Research Article

    How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a ‘synergistic global workspace’, comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain’s default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Ardalan Naseri, Degui Zhi, Shaojie Zhang
    Research Article Updated

    Runs-of-homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE (runs-of-homozygous diplotype cluster enumerator), to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 single nucleotide polymorphisms (SNPs) and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended human leukocyte antigen (HLA) region and autoimmune disorders. We found an association between a diplotype covering the homeostatic iron regulator (HFE) gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (p-value = 1.82 × 10−11). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale.