Bharucha T., Gangadharan B., Kumar A., Myall A. C., Ayhan N., Pastorino B., Chanthongthip A., Vongsouvath M., Mayxay M., Sengvilaipaseuth O., Phonemixay O., Rattanavong S., O'Brien D. P., Vendrell I., Fischer R., Kessler B., Turtle L., de Lamballerie X., Dubot Pérès Audrey, Newton P. N., Zitzmann N. (2023). Deep proteomics network and machine learning analysis of human cerebrospinal fluid in japanese encephalitis virus infection. Journal of Proteome Research, 22 (6), p. 1614-1629. ISSN 1535-3893.
Titre du document
Deep proteomics network and machine learning analysis of human cerebrospinal fluid in japanese encephalitis virus infection
Année de publication
2023
Auteurs
Bharucha T., Gangadharan B., Kumar A., Myall A. C., Ayhan N., Pastorino B., Chanthongthip A., Vongsouvath M., Mayxay M., Sengvilaipaseuth O., Phonemixay O., Rattanavong S., O'Brien D. P., Vendrell I., Fischer R., Kessler B., Turtle L., de Lamballerie X., Dubot Pérès Audrey, Newton P. N., Zitzmann N.
Source
Journal of Proteome Research, 2023,
22 (6), p. 1614-1629 ISSN 1535-3893
Japanese encephalitis virus is a leading cause of neurologicalinfection in the Asia-Pacific region with no means of detection inmore remote areas. We aimed to test the hypothesis of a Japanese encephalitis(JE) protein signature in human cerebrospinal fluid (CSF) that couldbe harnessed in a rapid diagnostic test (RDT), contribute to understandingthe host response and predict outcome during infection. Liquid chromatographyand tandem mass spectrometry (LC-MS/MS), using extensive offlinefractionation and tandem mass tag labeling (TMT), enabled comparisonof 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 4,805human proteins and 265 pathogen proteins. Feature selection and predictivemodeling using TMT analysis of 147 patient samples enabled the developmentof a nine-protein JE diagnostic signature. This was tested using DIAanalysis of an independent group of 16 patient samples, demonstrating82% accuracy. Ultimately, validation in a larger group of patientsand different locations could help refine the list to 2-3 proteinsfor an RDT. The mass spectrometry proteomics data have been depositedto the ProteomeXchange Consortium via the PRIDE partner repositorywith the dataset identifier PXD034789 and 10.6019/PXD034789.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Santé : généralités [050]
;
Entomologie médicale / Parasitologie / Virologie [052]
Description Géographique
LAOS
Localisation
Fonds IRD [F B010088170]
Identifiant IRD
fdi:010088170
