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

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, 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, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  3. Anita Yee

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

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

    School of Biomedical Sciences, The University of Hong Kong, 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

    Centre for PanorOmic Sciences (CPOS), The University of Hong Kong, Hong Kong, Hong Kong
    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, 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.

Reviewing Editor

  1. Subburaman Mohan, Loma Linda University, United States

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)

Version 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

  • 2,456
    views
  • 392
    downloads
  • 36
    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. 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

Share this article

https://doi.org/10.7554/eLife.64940

Further reading

    1. Computational and Systems Biology
    David Geller-McGrath, Kishori M Konwar ... Jason E McDermott
    Tools and Resources

    The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Kenya Hitomi, Yoichiro Ishii, Bei-Wen Ying
    Research Article

    As the genome encodes the information crucial for cell growth, a sizeable genomic deficiency often causes a significant decrease in growth fitness. Whether and how the decreased growth fitness caused by genome reduction could be compensated by evolution was investigated here. Experimental evolution with an Escherichia coli strain carrying a reduced genome was conducted in multiple lineages for approximately 1000 generations. The growth rate, which largely declined due to genome reduction, was considerably recovered, associated with the improved carrying capacity. Genome mutations accumulated during evolution were significantly varied across the evolutionary lineages and were randomly localized on the reduced genome. Transcriptome reorganization showed a common evolutionary direction and conserved the chromosomal periodicity, regardless of highly diversified gene categories, regulons, and pathways enriched in the differentially expressed genes. Genome mutations and transcriptome reorganization caused by evolution, which were found to be dissimilar to those caused by genome reduction, must have followed divergent mechanisms in individual evolutionary lineages. Gene network reconstruction successfully identified three gene modules functionally differentiated, which were responsible for the evolutionary changes of the reduced genome in growth fitness, genome mutation, and gene expression, respectively. The diversity in evolutionary approaches improved the growth fitness associated with the homeostatic transcriptome architecture as if the evolutionary compensation for genome reduction was like all roads leading to Rome.