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

Abstract

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. Author summaryJapanese encephalitis virus (JEV) is a leading cause of brain infection in Asia and the Pacific, with recent introduction 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. The disease is difficult to diagnose, and there are no rapid tests that may be performed in remote areas that it exists such that we remain unclear of the burden of disease and the effects of control measures. We aimed to apply a relatively novel method to analyse the proteins in patients with JE as compared to other neurological infections, to see if this could be useful for making a diagnosis. We tested the brain fluid of 163 patients recruited as part of the Laos central nervous system infection study. We used a method, liquid chromatography mass spectrometry that does not require prior knowledge of the proteins present, that is you do not target any specific protein. Over 5,000 proteins were identified, and these were analysed by various methods. We grouped the proteins into different clusters that provided insight into their function. We also filtered the list to 10 proteins that predicted JE as compared to other brain infections. Future work will require confirmation of the findings 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.