Molecular & Cellular ProteomicsNeurosciencePeer-reviewed journal article
Proteomic portraits reveal evolutionarily conserved and divergent responses to spinal cord injury
Michael A. SkinnideriD, Jason C. RogalskiiD, Seth TigchelaariD, Neda Manouchehri, Anna PrudovaiD, Angela JacksoniD, Karina Nielsen, Jaihyun Jeong, Shalini ChaudharyiD, Katelyn Shortt, Ylonna Gallagher-Kurtzke, Kitty So, Allan FongiD, Rishab Gupta, Elena B. OkoniD, Michael Anthony RizzutoiD, Kevin DongiD, Femke StreijgeriD, Lise M. BélangeriD, Leanna RitchieiD, Angela TsangiD, Sean ChristieiD, Jean‐Marc Mac‐ThiongiD, Christopher S. BaileyiD, Tamir T. AiloniD, , Brian K. KwoniD
Abstract
Despite the emergence of promising therapeutic approaches in preclinical studies, the failure of large-scale clinical trials leaves clinicians without effective treatments for acute spinal cord injury (SCI). These trials are hindered by their reliance on detailed neurological examinations to establish outcomes, which inflate the time and resources required for completion. Moreover, therapeutic development takes place in animal models whose relevance to human injury remains unclear. Here, we address these challenges through targeted proteomic analyses of CSF and serum samples from 111 acute SCI patients and, in parallel, a large animal (porcine) model of SCI. We develop protein biomarkers of injury severity and recovery, including a prognostic model of neurological improvement at six months with an AUC of 0.91, and validate these in an independent cohort. Through cross-species proteomic analyses, we dissect evolutionarily conserved and divergent aspects of the SCI response, and establish the CSF abundance of glial fibrillary acidic protein (GFAP) as a biochemical outcome measure in both humans and pigs. Our work opens up new avenues to catalyze translation by facilitating the evaluation of novel SCI therapies, while also providing a resource from which to direct future preclinical efforts.
- organism: human
- organism: multiple species
- assay: proteomics
- research focus: differential expression
- approach: statistical model
- Citations
- 29
- FWCI
- 3.33
- Field percentile
- top 7%
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