Abstract

Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides a systems-level view of the coordinated molecular processes activated in response to SCI.

Data availability

Sequencing data have been deposited in GEO under accession code GSE115067. They can be accessed at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115067. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD010192. They can be accessed at https://www.ebi.ac.uk/pride/archive/projects/PXD010192.

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

Article and author information

Author details

  1. Jordan W Squair

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  2. Seth Tigchelaar

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Kyung-Mee Moon

    Centre for High-Throughput Biology, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Jie Liu

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Wolfram Tetzlaff

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3462-1676
  6. Brian K Kwon

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  7. Andrei V Krassioukov

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  8. Christopher R West

    International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  9. Leonard J Foster

    Centre for High-Throughput Biology, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8551-4817
  10. Michael A Skinnider

    Centre for High-Throughput Biology, University of British Columbia, Vancouver, Canada
    For correspondence
    michaelskinnider@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2168-1621

Funding

Heart and Stroke Foundation of Canada

  • Christopher R West

Canadian Institutes of Health Research

  • Michael A Skinnider

Canadian Institutes of Health Research

  • Jordan W Squair

Genome Canada

  • Leonard J Foster

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

Reviewing Editor

  1. Ole Kiehn, Karolinska Institutet, Sweden

Ethics

Animal experimentation: Ethical approval was obtained by the University of British Columbia Behavioural Research Ethics Board (A14-0152) and all procedures strictly adhere to the guidelines issues by the Canadian Council for Animal Care.

Version history

  1. Received: June 14, 2018
  2. Accepted: September 24, 2018
  3. Accepted Manuscript published: October 2, 2018 (version 1)
  4. Version of Record published: October 5, 2018 (version 2)

Copyright

© 2018, Squair 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

  • 4,092
    views
  • 518
    downloads
  • 26
    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. Jordan W Squair
  2. Seth Tigchelaar
  3. Kyung-Mee Moon
  4. Jie Liu
  5. Wolfram Tetzlaff
  6. Brian K Kwon
  7. Andrei V Krassioukov
  8. Christopher R West
  9. Leonard J Foster
  10. Michael A Skinnider
(2018)
Integrated systems analysis reveals conserved gene networks underlying response to spinal cord injury
eLife 7:e39188.
https://doi.org/10.7554/eLife.39188

Share this article

https://doi.org/10.7554/eLife.39188

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan Ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.