Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.
Tehmina BharuchaBevin GangadharanAbhinav KumarAshleigh C MyallNazli AyhanBoris PastorinoAnisone ChanthongthipManivanh VongsouvathMayfong MayxayOnanong SengvilaipaseuthOoyanong PhonemixaySayaphet RattanavongDarragh P O'BrienIolanda VendrellRoman FischerBenedikt Mathias KesslerLance TurtleXavier de LamballerieAudrey Dubot-PérèsPaul N NewtonNicole Zitzmannnull SEAe ConsortiumPublished in: Journal of proteome research (2023)
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.
Keyphrases
- mass spectrometry
- liquid chromatography
- tandem mass spectrometry
- cerebrospinal fluid
- endothelial cells
- machine learning
- gas chromatography
- ultra high performance liquid chromatography
- high resolution mass spectrometry
- high performance liquid chromatography
- end stage renal disease
- induced pluripotent stem cells
- simultaneous determination
- chronic kidney disease
- case report
- big data
- pluripotent stem cells
- newly diagnosed
- artificial intelligence
- high resolution
- capillary electrophoresis
- patient reported outcomes
- deep learning
- prognostic factors
- loop mediated isothermal amplification
- solid phase extraction
- peritoneal dialysis
- patient reported
- subarachnoid hemorrhage
- human immunodeficiency virus
- hiv infected
- quantum dots