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Peer-reviewed veterinary case report

Proteomics of In Vivo models of ischemic stroke: A systematic review with a systems biological perspective.

Journal:
Ageing research reviews
Year:
2026
Authors:
Gogoi, Anaekshi et al.
Affiliation:
Yenepoya (Deemed to be University) · India

Abstract

BACKGROUND AND PURPOSE: To discover therapeutic targets and biomarkers for ischemic stroke, a systems-level understanding of the altered brain proteome is necessary from various types of in vivo models. In the last two decades, despite accumulating numerous large-scale proteomics datasets on in vivo models of ischemic stroke, systematic classification and summarization of these data have not been attempted yet. METHODS: Following PRISMA, ARRIVE, and CAMARADES guidelines, we conducted a systematic review of proteomics studies done on various in vivo models of ischemic stroke (ivProt studies). RESULTS: Of 680 studies, 120 ivProt studies were shortlisted. They were classified based on TOAST stroke subtypes, model category, study duration, and sampled brain location. We collated 288 proteins deregulated in the brain tissue of MCAO-affected animals, 44 of which were reported in minimum two ivProt studies. A comparison of deregulated proteome between ivProt and clinical proteomics studies revealed consistent deregulation for 85 % of the commonly quantified proteins. The Gene Ontology analysis of the consensus stroke-perturbed proteome (n = 90) showed enrichment of annotation terms related to blood coagulation, HIF-signaling and secretory pathway. About 88 % of these proteins were found listed in an exosome database (ExoCarta), while 42 % overlapped with the therapeutic target database. Notably, this list contained eight successful drug targets (ANXA1, C3, DPYSL2, IDH1, IDH3A, PRKCE, PRKCG, and SERPINA1). CONCLUSION: A web resource (ivProt_Repository) was created that may stimulate clinical validation of shortlisted proteins for biomarker discovery or proof-of-concept studies for therapeutic target discovery and help to establish novel pipelines for big data integration.

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Original publication: https://pubmed.ncbi.nlm.nih.gov/41207327/