objective: Herpes simplex keratitis (HSK), caused by type 1 herpes simplex virus (HSV) reactivation, is a severe infectious disease that leads to vision loss. HSV can trigger metabolic reprogramming in the host cell and change the extracellular vesicles (EV) cargos; however, little is known about the EV metabolic signatures during ocular HSV infection. Here, we aimed to depict the EV-associated metabolic landscape in HSK patients' tears.
methods: We collected 82 samples from 41 participants with unilateral HSK (contralateral unaffected tears were set as negative control), including subtype cohorts of 13 epithelial, 20 stromal, and 8 endothelial HSK. We isolated tear EVs via our previously established platform a... More
objective: Herpes simplex keratitis (HSK), caused by type 1 herpes simplex virus (HSV) reactivation, is a severe infectious disease that leads to vision loss. HSV can trigger metabolic reprogramming in the host cell and change the extracellular vesicles (EV) cargos; however, little is known about the EV metabolic signatures during ocular HSV infection. Here, we aimed to depict the EV-associated metabolic landscape in HSK patients' tears.
methods: We collected 82 samples from 41 participants with unilateral HSK (contralateral unaffected tears were set as negative control), including subtype cohorts of 13 epithelial, 20 stromal, and 8 endothelial HSK. We isolated tear EVs via our previously established platform and conducted metabolic analysis using LC-MS/MS. The metabolic signatures for recognizing HSK and subtypes were assessed through differential analysis and machine learning algorithms.
results: Hypopsia and increased extracellular CD63 levels were observed in affected eyes. We identified 339 metabolites based on sEVs isolated from tears. Differential analysis revealed alterations in energy and amino acid metabolism, as well as the infectious microenvironment. Furthermore, we observed dysregulated metabolite such as methyldopa, which is associated with inappropriate neovascularization and corneal sensation loss, contributing to the HSK severity particularly in the stromal subtype. Moreover, machine learning classification also suggested a set of EV metabolic signatures that have potential for pan-keratitis detection.
conclusions: Our findings demonstrate that tear EV metabolites can serve as valuable indicators for comprehending the underlying pathological mechanisms. This knowledge is expected to facilitate the development of liquid biopsy means and therapeutic target discovery.
