DIPPER, a spatiotemporal proteomics atlas of human intervertebral discs for exploring ageing and degeneration dynamics

  1. Vivian Tam
  2. Peikai Chen
  3. Anita Yee
  4. Nestor Solis
  5. Theo Klein
  6. Mateusz Kudelko
  7. Rakesh Sharma
  8. Wilson CW Chan
  9. Christopher M Overall
  10. Lisbet Haglund
  11. Pak C Sham
  12. Kathryn Song Eng Cheah
  13. Danny Chan  Is a corresponding author
  1. The University of Hong Kong, Hong Kong
  2. University of British Columbia, Canada
  3. McGill University, Canada
  4. The University of Hong Kong, China

Abstract

The spatiotemporal proteome of the intervertebral disc (IVD) underpins its integrity and function. We present DIPPER, a deep and comprehensive IVD proteomic resource comprising 94 genome-wide profiles from 17 individuals. To begin with, protein modules defining key directional trends spanning the lateral and anteroposterior axes were derived from high-resolution spatial proteomes of intact young cadaveric lumbar IVDs. They revealed novel region-specific profiles of regulatory activities, and displayed potential paths of deconstruction in the level- and location-matched aged cadaveric discs. Machine learning methods predicted a 'hydration matrisome' that connects extracellular matrix with MRI intensity. Importantly, the static proteome used as point-references can be integrated with dynamic proteome (SILAC/degradome) and transcriptome data from multiple clinical samples, enhancing robustness and clinical relevance. The data, findings and methodology, available on a web interface (www.sbms.hku.hk/dclab/DIPPER), will be valuable references in the field of IVD biology and proteomic analytics.

Data availability

The mass spectrometry proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE repository with the following dataset identifiers for cadaver samples (PXD017774), SILAC samples (PXD018193), and degradome samples (PXD018298000). The RAW data for the transcriptome data has been deposited on NCBI GEO with accession number GSE147383.

The following data sets were generated

Article and author information

Author details

  1. Vivian Tam

    School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0457-3477
  2. Peikai Chen

    School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  3. Anita Yee

    School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  4. Nestor Solis

    Centre for Blood Research, Faculty of Dentistry, University of British Columbia, Vancouver, Canada
    Competing interests
    No competing interests declared.
  5. Theo Klein

    Centre for Blood Research, Faculty of Dentistry, University of British Columbia, Vancouver, Canada
    Competing interests
    Theo Klein, Theo Klein is affiliated with Triskelion BV. The author has no financial interests to declare.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8061-9353
  6. Mateusz Kudelko

    School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  7. Rakesh Sharma

    Proteomics and Metabolomics Core Facility, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  8. Wilson CW Chan

    School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  9. Christopher M Overall

    Centre for Blood Research, Faculty of Dentistry, University of British Columbia, Vancouver, Canada
    Competing interests
    No competing interests declared.
  10. Lisbet Haglund

    Faculty of Medicine, Department of Surgery, McGill University, Montreal, Canada
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1288-2149
  11. Pak C Sham

    Department of Psychiatry, The University of Hong Kong, Hong Kong, China
    Competing interests
    No competing interests declared.
  12. Kathryn Song Eng Cheah

    School of Biomedical Sciences, The University of Hong Kong, Hong Kong, Hong Kong
    Competing interests
    Kathryn Song Eng Cheah, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0802-8799
  13. Danny Chan

    School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
    For correspondence
    chand@hku.hk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3824-5778

Funding

Research Grants Council, University Grants Committee (E-HKU703/18)

  • Danny Chan

Research Grants Council, University Grants Committee (T12-708/12N)

  • Kathryn Song Eng Cheah

Research Grants Council, University Grants Committee (AoE/M-04/04)

  • Kathryn Song Eng Cheah

Ministry of Science and Technology of the People's Republic of China ((973") (2014CB942900)")

  • Danny Chan

Canadian Institutes of Health Research (FDN-148408)

  • Christopher M Overall

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

Ethics

Human subjects: Ethics Statement: Clinical specimens were obtained with approval by the Institutional Review Board (references UW13-576 and EC 1516-00 11/01/2001) and with informed consent in accordance with the Helsinki Declaration of 1975 (revision 1983)

Reviewing Editor

  1. Subburaman Mohan, Loma Linda University, United States

Publication history

  1. Received: November 16, 2020
  2. Accepted: December 30, 2020
  3. Accepted Manuscript published: December 31, 2020 (version 1)
  4. Accepted Manuscript updated: January 26, 2021 (version 2)
  5. Version of Record published: February 2, 2021 (version 3)

Copyright

© 2020, Tam 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

  • 1,660
    Page views
  • 240
    Downloads
  • 14
    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. Vivian Tam
  2. Peikai Chen
  3. Anita Yee
  4. Nestor Solis
  5. Theo Klein
  6. Mateusz Kudelko
  7. Rakesh Sharma
  8. Wilson CW Chan
  9. Christopher M Overall
  10. Lisbet Haglund
  11. Pak C Sham
  12. Kathryn Song Eng Cheah
  13. Danny Chan
(2020)
DIPPER, a spatiotemporal proteomics atlas of human intervertebral discs for exploring ageing and degeneration dynamics
eLife 9:e64940.
https://doi.org/10.7554/eLife.64940

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.