Integrated analysis of microbiome and host transcriptome unveils correlations between lung microbiota and host immunity in bronchoalveolar lavage fluid of pneumocystis pneumonia patients.

OBJECTIVE: The lung microbiota of patients with pulmonary diseases is disrupted and impacts the immunity. The microbiological and immune landscape of the lungs in patients with pneumocystis pneumonia (PCP) remains poorly understood. METHODS: Multi-omics analysis and machine learning were performed on bronchoalveolar lavage fluid to explore interaction between the lung microbiota and host immunity in PCP. Then we constructed a diagnostic model using differential genes with LASSO regression and validated by qPCR. The immune infiltration analysis was performed to explore the landscape of lung immunity in patients with PCP. RESULTS: Patients with PCP showed a low alpha diversity of lung microbiota, accompanied by the elevated abundance of Firmicutes, and the differential expressed genes (DEGs) analysis displayed a downregulation of MAPK signaling. The MAPK10, TGFB1, and EFNA3 indicated a potential to predict PCP (AUC = 0.86). The lung immune landscape in PCP showed the lower levels of naïve CD4+ T cells and activated dendritic cells. The correlation analysis of the MAPK signaling pathway-related DEGs and the differential microorganisms at the level of phylum showed that the Firmicutes was negatively correlated with these DEGs. CONCLUSION: We profiled the characteristics of lung microbiota and immune landscape in PCP, which may contribute to elucidating the mechanism of PCP.

Lipid metabolism and identification of biomarkers in asthma by lipidomic analysis.

BACKGROUND: Lipids participate in many important biological functions through energy storage, material transport, signal transduction, and molecular recognition processes. Studies have reported that asthmatic patients have abnormal lipid metabolism. However, there are limited studies on the characterization of lipid metabolism in asthmatic patients by lipidomics. METHODS: We characterized the plasma lipid profile of 28 healthy controls and 33 outpatients with asthma (18 mild, 15 moderate) by liquid chromatography mass spectrometry/mass spectrometry-based lipidomics. RESULTS: We determined 1338 individual lipid species in the plasma. Significant changes were identified in ten lipid species in asthmatic patients than in healthy controls (all P < 0.05). Phosphatidylethanolamine (PE) (18:1p/22:6), PE (20:0/18:1), PE (38:1), sphingomyelin (SM) (d18:1/18:1), and triglyceride (TG) (16:0/16:0/18:1) positively correlated with the severity of asthma (all P < 0.05). Phosphatidylinositol (PI) (16:0/20:4), TG (17:0/18:1/18:1), phosphatidylglycerol (PG) (44:0), ceramide (Cer) (d16:0/27:2), and lysophosphatidylcholine (LPC) (22:4) negatively correlated with the severity of asthma (all P < 0.05). Correlation analysis showed a significant correlation between all ten lipid species (all P < 0.05). From the area under the curve of the receiver operating characteristic curve analysis, PE (38:1) was the major lipid metabolite that distinguished asthmatic patients from healthy controls, and may be considered a potential lipid biomarker. PE (20:0/18:1) and TG (16:0/16:0/18:1) might be related to IgE levels in asthmatic patients. CONCLUSIONS: Our results indicated the presence of abnormal lipid metabolism, which correlated with the severity and IgE levels in asthmatic patients.