Integrated systems analysis reveals conserved gene networks underlying response to spinal cord injury

eLife, 2018 · DOI: https://doi.org/10.7554/eLife.39188.001 · Published: October 2, 2018

Spinal Cord Injury Neurology Bioinformatics

Simple Explanation

Spinal cord injury (SCI) is a devastating condition lacking effective treatments. The researchers used a systems biology approach to integrate existing research with new data from human spinal cords. This revealed a gene regulatory network associated with the response to SCI. The study identified a conserved gene subnetwork that is upregulated with increasing SCI severity and downregulated during functional recovery. This subnetwork was validated in rodents using transcriptomic and proteomic analyses. The analysis provides a systems-level understanding of the molecular processes activated after SCI, potentially leading to new therapeutic targets and biomarkers for injury severity.

Study Duration

6 months post-injury

Participants

71 post-mortem human spinal cords, hundreds of microarray samples from mouse (n = 414) and rat (n = 267) spinal cords

Evidence Level

Systems biology approach integrating literature review, transcriptomic and proteomic data

Key Findings

1
Systematic literature analysis identified 695 unique human genes associated with the response to SCI by small-scale experiments.
2
Gene coexpression network analysis of human spinal cord identified five highly conserved and reproducible modules, two of which (M3 and M7) are significantly enriched for literature-curated genes.
3
Meta-analysis revealed a consensus network signature associated with the response to SCI, and M3 was the sole module enriched for genes positively correlated to injury severity.

Research Summary

This study uses an integrated systems biology approach to understand the molecular mechanisms underlying spinal cord injury (SCI) pathophysiology. Large-scale RNA-seq data from healthy subjects was leveraged to reveal gene regulatory relationships in the human spinal cord. Multiple gene expression datasets from experimental models of SCI were integrated, identifying gene subnetworks implicated in the pathophysiological response. These signatures were reproduced at both transcriptomic and proteomic levels in an animal trial. The study identified evolutionarily conserved and reproducible gene subnetworks with differential regulation following SCI, providing a genome-wide view of the pathophysiological processes triggered by SCI.

Practical Implications

Biomarker Discovery

Identification of Anxa1 (annexin A1) as a strong candidate biomarker for SCI severity, potentially improving patient stratification in clinical trials.

Therapeutic Target Identification

Highlighting M3 as a potential therapeutic target, as its expression pattern is reversed with administration of NT-3, a treatment that promotes motor and sensory recovery.

Preclinical Lead Discovery

The identification of drugs that reverse transcriptional changes associated with SCI has the potential to provide a new strategy for preclinical lead discovery.

Study Limitations

1
The study's literature curation approach assumes that the molecular organization of the transcriptome in healthy human subjects is informative about the biological processes dysregulated by SCI.
2
While M3 shows substantial conservation between human and rat at the systems level, individual genes may diverge in their expression following acute SCI between human and rodents.
3
Limited power to detect module preservation at the proteomic level due to the small size of the proteomic sample (n = 15).

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