Investigative ophthalmology & visual science · 2025
Metabolomic Profiling of Ocular Fluid Identifies Host Response Factors for Risk Stratification in Bacterial Endophthalmitis.
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Shetty Naren, Mahendradas Padmamalini, Kannan Ramaraj, Das Samayitree, Sathe Priyanka, Shetty Rohit, Ghosh Arkasubhra, Deshpande Vrushali
Аннотация
PURPOSE: This study aimed to identify metabolite biomarkers in ocular fluid that can serve as indicators of infection severity, guide risk stratification, and monitor the host response to bacterial endophthalmitis.
METHODS: We performed untargeted metabolomics to profile ocular fluids from 26 patients diagnosed with bacterial endophthalmitis and 17 control samples. Multivariate data analysis and pathway enrichment of metabolites were performed using the MetaboAnalyst R package.
RESULTS: The volcano plot analysis revealed significantly altered m/z values (log fold change > 2; P < 0.05), with 1824 m/z values upregulated and 3854 m/z values downregulated species. Notably, the levels of methyl linolenate significantly decreased from grade 2 to grade 5, whereas no significant alterations were observed in grade 1 compared with controls. The peak intensities of palmitoleamide also showed a decrease from grade 2 to grade 5 compared with controls. Conversely, myristoleoyl carnitine exhibited significantly increased levels in both grade 2 and grade 5. Methyl linolenate (AUC, 0.885), palmitoleamide (AUC, 0.885), and myristoleoyl carnitine (AUC, 0.869) were identified as the three metabolites with the highest classification capacity for bacterial endophthalmitis in tear samples.
CONCLUSIONS: This report is the first of a set of metabolite biomarkers in ocular fluid that correlate with the severity of bacterial endophthalmitis. These biomarkers have the potential to aid in risk stratification and personalized treatment approaches. The metabolomic profiles generated in this study provide insight into the host response mechanisms and the pathophysiology of the infection in human eyes.
METHODS: We performed untargeted metabolomics to profile ocular fluids from 26 patients diagnosed with bacterial endophthalmitis and 17 control samples. Multivariate data analysis and pathway enrichment of metabolites were performed using the MetaboAnalyst R package.
RESULTS: The volcano plot analysis revealed significantly altered m/z values (log fold change > 2; P < 0.05), with 1824 m/z values upregulated and 3854 m/z values downregulated species. Notably, the levels of methyl linolenate significantly decreased from grade 2 to grade 5, whereas no significant alterations were observed in grade 1 compared with controls. The peak intensities of palmitoleamide also showed a decrease from grade 2 to grade 5 compared with controls. Conversely, myristoleoyl carnitine exhibited significantly increased levels in both grade 2 and grade 5. Methyl linolenate (AUC, 0.885), palmitoleamide (AUC, 0.885), and myristoleoyl carnitine (AUC, 0.869) were identified as the three metabolites with the highest classification capacity for bacterial endophthalmitis in tear samples.
CONCLUSIONS: This report is the first of a set of metabolite biomarkers in ocular fluid that correlate with the severity of bacterial endophthalmitis. These biomarkers have the potential to aid in risk stratification and personalized treatment approaches. The metabolomic profiles generated in this study provide insight into the host response mechanisms and the pathophysiology of the infection in human eyes.