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Multi-omics links microbial dysbiosis, systemic inflammation, and metabolomic disruptions to SNAE risk in treated HIV
Christopher M. Basting, Jodi Anderson, Kevin Escandón, Garritt Wieking, Candace Guerrero, Jarrett Reichel, Ross T. Cromarty, Erik Swanson, Ty Schroeder, Elaina Creagan, Maura Barrett, Fernanda Torres-Ruiz, Maribel Soto-Nava, Lady Carvajal-Ruiz, Karla Krystel Ordaz-Candelario, Olivia Briceño, Nicholas Funderburg, Melanie Graham, Peter Hunt, Santiago Avila-Rios, Gonzalo Salgado Montes de Oca, Timothy W. Schacker, Nichole R. Klatt
Christopher M. Basting, Jodi Anderson, Kevin Escandón, Garritt Wieking, Candace Guerrero, Jarrett Reichel, Ross T. Cromarty, Erik Swanson, Ty Schroeder, Elaina Creagan, Maura Barrett, Fernanda Torres-Ruiz, Maribel Soto-Nava, Lady Carvajal-Ruiz, Karla Krystel Ordaz-Candelario, Olivia Briceño, Nicholas Funderburg, Melanie Graham, Peter Hunt, Santiago Avila-Rios, Gonzalo Salgado Montes de Oca, Timothy W. Schacker, Nichole R. Klatt
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Research Article AIDS/HIV Inflammation Microbiology

Multi-omics links microbial dysbiosis, systemic inflammation, and metabolomic disruptions to SNAE risk in treated HIV

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Abstract

Serious non-AIDS events (SNAEs), including non-AIDS malignancies, cardiovascular disease, and hepatic complications, remain major causes of mortality in treated HIV infection. These outcomes are driven by persistent immune activation, systemic inflammation, and metabolic dysfunction despite effective viral suppression with antiretroviral therapy (ART). To investigate mechanisms underlying SNAE pathogenesis, we performed a cross-site multi-omic analysis integrating plasma proteins, plasma metabolites, and mucosal microbiomes in 82 ART-treated people with HIV (PWH) and 10 people without HIV from the United States and Mexico. Geography was the dominant source of variation, particularly across lipid classes. However, individuals at high risk for SNAEs, defined by low CD4+ T cell counts and low CD4/CD8 ratios, shared a consistent signature of systemic inflammation, mitochondrial dysfunction, and microbial dysbiosis, including elevated plasma IL-6 and ω-oxidation products (adipic and suberic acids) and depletion of short-chain fatty acid–producing commensals in the gut mucosa, including Akkermansia muciniphila, Bacteroides uniformis, and Ruminococcus. A. muciniphila abundance correlated with lower IL-6 levels, fewer HIV RNA-producing cells in lymph nodes, and higher CD4/CD8 ratios. These findings identify a shared inflammatory and metabolic phenotype in PWH and implicate A. muciniphila as a potential microbiome-based target to mitigate immune activation and SNAE risk in treated HIV.

Authors

Christopher M. Basting, Jodi Anderson, Kevin Escandón, Garritt Wieking, Candace Guerrero, Jarrett Reichel, Ross T. Cromarty, Erik Swanson, Ty Schroeder, Elaina Creagan, Maura Barrett, Fernanda Torres-Ruiz, Maribel Soto-Nava, Lady Carvajal-Ruiz, Karla Krystel Ordaz-Candelario, Olivia Briceño, Nicholas Funderburg, Melanie Graham, Peter Hunt, Santiago Avila-Rios, Gonzalo Salgado Montes de Oca, Timothy W. Schacker, Nichole R. Klatt

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Figure 5

Supervised machine learning of SNAE risk groups.

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Supervised machine learning of SNAE risk groups.
(A) Scores plot and top...
(A) Scores plot and top loading vectors on component 1 for the protein block PLS-DA, metabolome block sparse PLS-DA (sPLS-DA), rectum block sPLS-DA, and ileum block sPLS-DA. Loading vectors are colored by the group with the highest mean value. (B) Scores plot of the integrated DIABLO model, combining all 4 data blocks and showing the weighted average of components 1 and 2 according to their correlation with SNAE risk group. (C) Top loading scores on components 1 and 2 for each block in the DIABLO model; bars are colored by the corresponding block the variable belongs to. (D) Correlation circle plot of the DIABLO model, showing variables with correlations (>0.5) on components 1 and 2. (E) A variable’s location depicts both its correlation on each component and its correlation with other variables in proximity. Performance of each model in predicting SNAE risk based on AUROC, determined by 5-fold cross-validation repeated 50 times.

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ISSN 2379-3708

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