Cribbs Sushma K, Park Youngja, Guidot David M, Martin Greg S, Brown Lou Ann, Lennox Jeffrey, Jones Dean P
1 Department of Medicine, Division of Pulmonary, Allergy and Critical Care, Emory University , Atlanta, Georgia .
AIDS Res Hum Retroviruses. 2014 Jun;30(6):579-85. doi: 10.1089/aid.2013.0198. Epub 2014 Feb 10.
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 C18 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.
尽管采用了抗逆转录病毒疗法，但肺炎球菌等病原体引起的肺炎在HIV-1感染者中仍会导致显著的发病率和死亡率。即便CD4细胞计数较高且病毒载量较低，呼吸道感染仍会发生；因此，有必要更好地了解肺部免疫和感染预测因素。我们测试了代谢组学（一种用于分子指纹识别的综合生物系统方法）是否能够区分这些个体特征。从其他方面健康的HIV-1感染者和健康对照者中收集支气管肺泡灌洗液（BALf）。采用液相色谱-高分辨率质谱法检测BALf中的代谢物。统计和生物信息学分析使用错误发现率（FDR）和正交校正偏最小二乘判别分析（OPLS-DA）来识别分组判别因素，即对HIV-1感染者和对照者95%的分离贡献最大的前5%的代谢物。我们纳入了24名HIV-1感染者（CD4中位数=432）和24名对照者。来自C18和AE分析的总共115个精确质量m/z特征在HIV-1感染者和对照者之间存在显著差异（FDR=0.05）。层次聚类分析揭示了代谢物簇，其根据HIV-1状态区分样本（FDR=0.05）。其中一些代谢物与代谢组学数据库中的任何代谢物都不匹配；质荷比325.065（M+H）在HIV-1感染者的BAL中显著更高（FDR=0.05），且与绿脓菌素匹配，绿脓菌素是铜绿假单胞菌产生的一种铁载体。BALf中的代谢谱区分了健康的HIV-1感染者和对照者。与已知人类代谢物缺乏关联以及与一种细菌代谢物匹配表明，这些差异可能反映了宿主的肺部微生物群和/或与HIV-1感染者的亚临床感染有关。
