Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection

Journal article

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.

Authors: Bharucha, T; Gangadharan, B; Kumar, A; Myall, A; Ayhan, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Chemical Society
• Journal: Journal of Proteome Research
• Volume: 22
• Issue: 6
• Pages: 1614–1629
• Publication date: 2023-05-23
• Acceptance date: 2023-03-28
• DOI: 10.1021/acs.jproteome.2c00563
• EISSN: 1535-3907
• ISSN: 1535-3893
• Language: English
• Keywords: data independent acquisition; predictive modelling; Japanese encephalitis virus; central nervous system infection; network analysis; mass spectrometry; FFR; Lao PDR; encephalitis; neurological infection; machine learning analysis; diagnosis; tandem mass tagging; flavivirus; clinical proteomics