Dysbiosis of gut microbiota and metabolites during AIDS: implications for CD4 + T cell reduction and immune activation.

OBJECTIVE: Identifying the gut microbiota associated with host immunity in the AIDS stage. DESIGN: We performed a cross-sectional study. METHODS: We recruited people with HIV (PWH) in the AIDS or non-AIDS stage and evaluated their gut microbiota and metabolites by using 16S ribosomal RNA (rRNA) sequencing and liquid chromatography-mass spectrometry (LC-MS). Machine learning models were used to analyze the correlations between key bacteria and CD4 + T cell count, CD4 + T cell activation, bacterial translocation, gut metabolites, and KEGG functional pathways. RESULTS: We recruited 114 PWH in the AIDS stage and 203 PWH in the non-AIDS stage. The α-diversity of gut microbiota was downregulated in the AIDS stage ( P  < 0.05). Several machine learning models could be used to identify key gut microbiota associated with AIDS, including the logistic regression model with area under the curve (AUC), sensitivity, specificity, and Brier scores of 0.854, 0.813, 0.813, and 0.160, respectively. The decreased key bacteria ASV1 ( Bacteroides sp.), ASV8 ( Fusobacterium sp.), ASV30 ( Roseburia sp.), ASV37 ( Bacteroides sp.), and ASV41 ( Lactobacillus sp.) in the AIDS stage were positively correlated with the CD4 + T cell count, the EndoCAb IgM level, 4-hydroxyphenylpyruvic acid abundance, and the predicted cell growth pathway, and negatively correlated with the CD3 + CD4 + CD38 + HLA-DR + T cell count and the sCD14 level. CONCLUSION: Machine learning has the potential to recognize key gut microbiota related to AIDS. The key five bacteria in the AIDS stage and their metabolites might be related to CD4 + T cell reduction and immune activation.

The altered metabolites contributed by dysbiosis of gut microbiota are associated with microbial translocation and immune activation during HIV infection.

Background: The immune activation caused by microbial translocation has been considered to be a major driver of HIV infection progression. The dysbiosis of gut microbiota has been demonstrated in HIV infection, but the interplay between gut microbiota and its metabolites in the pathogenesis of HIV is seldom reported. Methods: We conducted a case-controlled study including 41 AIDS patients, 39 pre-AIDS patients and 34 healthy controls. Both AIDS group and pre-AIDS group were divided according to clinical manifestations and CD4 + T cell count. We collected stool samples for 16S rDNA sequencing and untargeted metabolomics analysis, and examined immune activation and microbial translocation for blood samples. Results: The pre-AIDS and AIDS groups had higher levels of microbial translocation and immune activation. There were significant differences in gut microbiota and metabolites at different stages of HIV infection. Higher abundances of pathogenic bacteria or opportunistic pathogen, as well as lower abundances of butyrate-producing bacteria and bacteria with anti-inflammatory potential were associated with HIV severity. The metabolism of tryptophan was disordered after HIV infection. Lower level of anti-inflammatory metabolites and phosphonoacetate, and higher level of phenylethylamine and polyamines were observed in HIV infection. And microbial metabolic pathways related to altered metabolites differed. Moreover, disrupted metabolites contributed by altered microbiota were found to be correlated to microbial translocation and immune activation. Conclusions: Metabolites caused by dysbiosis of gut microbiota and related metabolic function are correlated to immune activation and microbial translocation, suggesting that the effect of microbiota on metabolites is related to intestinal barrier disruption in HIV infection.