PXD034789-2

PXD034789 is an original dataset announced via ProteomeXchange.

Title	- Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection
Description	- Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus, and leading cause of neurological infection in Asia and the Pacific, with recent emergence in multiple territories in Australia in 2022. Patients may experience devastating socioeconomic consequences; JEV infection (JE) predominantly affects children in poor rural areas, has a 20-30% case fatality rate, and 30-50% of survivors suffer long-term disability. JEV RNA is rarely detected in patient samples, and the standard diagnostic test is an anti-JEV IgM ELISA with sub-optimal specificity; there is no means of detection in more remote areas. We aimed to test the hypothesis that there is a diagnostic protein signature of JE in human cerebrospinal fluid (CSF), and contribute to understanding of the host response and predictors of outcome during infection. We retrospectively tested a cohort of 163 patients recruited as part of the Laos central nervous system infection study. Application of liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labelling, enabled a comparison of the CSF proteome in 68 JE patient vs 95 non-JE neurological infections. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. We incorporated univariate analysis of differential protein expression, network analysis and machine learning techniques to build a ten-protein diagnostic signature of JE with >99% diagnostic accuracy. Pathways related to JE infection included neuronal damage, anti-apoptosis, heat shock and unfolded protein responses, cell adhesion, macrophage and dendritic cell activation as well as a reduced acute inflammatory response, hepatotoxicity, activation of coagulation, extracellular matrix and actin regulation. We verified the results by performing DIA LC-MS/MS in 16 (10%) of the samples, demonstrating 87% accuracy using the same model. Ultimately, antibody-based validation will be required, in a larger group of patients, in different locations and in field settings, to refine the list to 2-3 proteins that could be harnessed in a rapid diagnostic test.
HostingRepository	PRIDE
AnnounceDate	2023-11-14
AnnouncementXML	Submission_2023-11-14_09:00:38.584.xml
DigitalObjectIdentifier	https://dx.doi.org/10.6019/PXD034789
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Supported dataset by repository
PrimarySubmitter	Tehmina Bharucha
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	iodoacetamide derivatized residue
Instrument	Q Exactive

Revision	Datetime	Status	ChangeLog Entry
0	2022-06-21 07:22:18	ID requested
1	2023-06-08 14:41:43	announced
⏵ 2	2023-11-14 09:00:40	announced	2023-11-14: Updated project metadata.
3	2024-10-22 05:47:31	announced	2024-10-22: Updated project metadata.

10.6019/PXD034789;

Bharucha T, Gangadharan B, Kumar A, Myall AC, Ayhan N, Pastorino B, Chanthongthip A, Vongsouvath M, Mayxay M, Sengvilaipaseuth O, Phonemixay O, Rattanavong S, O'Brien DP, Vendrell I, Fischer R, Kessler B, Turtle L, de Lamballerie X, Dubot-P, é, r, è, s A, Newton PN, Zitzmann N, SEAe Consortium, Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection. J Proteome Res, 22(6):1614-1629(2023) [pubmed]

submitter keyword: arbovirus,- Neurological infection, encephalitis, TMT, central nervous system infection, Japanese encephalitis virus, flavivirus, Lao PDR, brain infection, meningitis, cerebrospinal fluid (CSF)

Nicole Zitzmann
contact affiliation	Department of Biochemistry, University of Oxford
contact email	nicole.zitzmann@bioch.ox.ac.uk
lab head
Tehmina Bharucha
contact affiliation	University of Oxford and the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit (LOMWRU)
contact email	t.bharucha@doctors.org.uk
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD034789
     1. Label: PRIDE project
     2. Name: - Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection