Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37219084", "kind": "science-record", "revision": 3, "content_hash": "sha256:719044d9524737051dfe65377ce8cd8803875135e2d963c7af9db2f1fc952c51", "created_at": "2026-11-01T19:01:55Z", "updated_at": "2026-11-01T22:09:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.", "doi": "10.1021/acs.jproteome.2c00563", "pmid": "37219084", "authors": ["Tehmina Bharucha", "Bevin Gangadharan", "Abhinav Kumar", "Ashleigh C Myall", "Nazli Ayhan", "Boris Pastorino", "Anisone Chanthongthip", "Manivanh Vongsouvath", "Mayfong Mayxay", "Onanong Sengvilaipaseuth", "Ooyanong Phonemixay", "Sayaphet Rattanavong", "Darragh P O'Brien", "Iolanda Vendrell", "Roman Fischer", "Benedikt Kessler", "Lance Turtle", "Xavier de Lamballerie", "Audrey Dubot-Pérès", "Paul N Newton", "Nicole Zitzmann", "SEAe Consortium"], "journal": "Journal of proteome research", "year": 2023, "abstract": "Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC-MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2-3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.", "claims": [{"id": "claim-method", "statement": "Differential expression between the protein abundance in the JE vs non-JE patient samples was performed using a t test and Benjamini–Hochberg correction for multiple testing.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37219084", "locator": "sections[8] \"TMT LC–MS/MS Data Differential Protein Expression\" paragraph 2", "quote": "Differential expression between the protein abundance in the JE vs non-JE patient samples was performed using a t test and Benjamini–Hochberg correction for multiple testing."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium92 via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789. All other data underlying this article are available in the article and in its online Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37219084", "locator": "sections[27] \"notes\" paragraph 1", "quote": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium92 via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789. All other data underlying this article are available in the article and in its online Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A nine-protein JE diagnostic signature developed using TMT analysis of 147 patient samples achieved 82% accuracy when tested by DIA analysis in an independent group of 16 patient samples.", "kind": "quantitative", "target": {"value": 82.0, "unit": "per cent", "tolerance": null, "source": "This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy."}, "evidence": [{"asset": "source:pmid:37219084", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature."}, {"asset": "source:pmid:37219084", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A nine-protein JE diagnostic signature developed using TMT analysis of 147 patient samples achieved 82% accuracy when tested by DIA analysis in an independent group of 16 patient samples.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37219084"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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