Metabolomics of Bronchoalveolar Lavage Differentiate Healthy HIV-1-Infected Subjects from Controls

Abstract

Despite antiretroviral therapy, pneumonias from pathogens such as pneumococcus continue to cause significant morbidity and mortality in HIV-1-infected individuals. Respiratory infections occur despite high CD4 counts and low viral loads; therefore, better understanding of lung immunity and infection predictors is necessary. We tested whether metabolomics, an integrated biosystems approach to molecular fingerprinting, could differentiate such individual characteristics. Bronchoalveolar lavage fluid (BALf) was collected from otherwise healthy HIV-1-infected individuals and healthy controls. A liquid chromatography-high-resolution mass spectrometry method was used to detect metabolites in BALf. Statistical and bioinformatic analyses used false discovery rate (FDR) and orthogonally corrected partial least-squares discriminant analysis (OPLS-DA) to identify groupwise discriminatory factors as the top 5% of metabolites contributing to 95% separation of HIV-1 and control. We enrolled 24 subjects with HIV-1 (median CD4 = 432) and 24 controls. A total of 115 accurate mass m/z features from C-18 and AE analysis were significantly different between HIV-1 subjects and controls (FDR = 0.05). Hierarchical cluster analysis revealed clusters of metabolites, which discriminated the samples according to HIV-1 status (FDR = 0.05). Several of these did not match any metabolites in metabolomics databases; mass-to-charge 325.065 ([M + H](+)) was significantly higher (FDR = 0.05) in the BAL of HIV-1-infected subjects and matched pyochelin, a siderophore-produced Pseudomonas aeruginosa. Metabolic profiles in BALf differentiated healthy HIV-1-infected subjects and controls. The lack of association with known human metabolites and inclusion of a match to a bacterial metabolite suggest that the differences could reflect the host's lung microbiome and/or be related to subclinical infection in HIV-1-infected patients.