Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease
Wang S, Kojima K, Mobley JA, West AB.. EBioMedicine, 2019-06-20
Topic: Brain proteins, microglia and lab models
Background Extracellular vesicles (EVs) harbor thousands of proteins that hold promise for biomarker development. Usually difficult to purify, EVs in urine are relatively easily obtained and have demonstrated efficacy for kidney disease prediction. Herein, we further characterize the proteome of urinary EVs to explore the potential for biomarkers unrelated to kidney dysfunction, focusing on Parkinson's disease (PD). Methods Using a quantitative mass spectrometry approach, we measured urinary EV proteins from a discovery cohort of 50 subjects. EVs in urine were classified into subgroups and EV proteins were ranked by abundance and variability over time. Enriched pathways and ontologies in stable EV proteins were identified and proteins that predict PD were further measured in a cohort of 108 subjects. Findings Hundreds of commonly expressed urinary EV proteins with stable expression over time were distinguished from proteins with high variability. Bioinformatic analyses reveal a striking enrichment of endolysosomal proteins linked to Parkinson's, Alzheimer's, and Huntington's disease. Tissue and biofluid enrichment analyses show broad representation of EVs from across the body without bias towards kidney or urine proteins. Among the proteins linked to neurological diseases, SNAP23 and calbindin were the most elevated in PD cases with 86% prediction success for disease diagnosis in the discovery cohort and 76% prediction success in the replication cohort. Interpretation Urinary EVs are an underutilized but highly accessible resource for biomarker discovery with particular promise for neurological diseases like PD.
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APA 7th
Wang, S., Kojima, K., Mobley, J. A., & West, A. B. (2019). Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease. EBioMedicine. https://doi.org/10.1016/j.ebiom.2019.06.021
Vancouver
Wang S, Kojima K, Mobley JA, West AB. Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease. EBioMedicine. 2019. doi:10.1016/j.ebiom.2019.06.021. PMID: 31229437.
BibTeX
@article{wang2019-31229437,
author = {Wang, S. and Kojima, K. and Mobley, J. A. and West, A. B.},
title = {{Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease}},
journal = {EBioMedicine},
year = {2019},
doi = {10.1016/j.ebiom.2019.06.021},
pmid = {31229437},
url = {https://doi.org/10.1016/j.ebiom.2019.06.021}
}
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Key findings
- Usually difficult to purify, EVs in urine are relatively easily obtained and have demonstrated efficacy for kidney disease prediction.
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Study details
- Participants50
- Evidence strengthModerate
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 30 of the 58 points we could judge it on, which is 52 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (EBioMedicine) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 50 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31229437) |
| Free to read | 2 / 2 | Free to read |
| Points | 30 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 30 of the 58 points we could judge it on is 52 out of 100. |
| Total | 52 / 100 | — |
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