Major depletion of insulin sensitivity-associated taxa in the gut microbiome of persons living with HIV controlled by antiretroviral drugs

Abstract

Background: Persons living with HIV (PWH) harbor an altered gut microbiome (higher abundance of Prevotella and lower abundance of Bacillota and Ruminococcus lineages) compared to non-infected individuals. Some of these alterations are linked to sexual preference and others to the HIV infection. The relationship between these lineages and metabolic alterations, often present in aging PWH, has been poorly investigated.

Methods: In this study, we compared fecal metagenomes of 25 antiretroviral-treatment (ART)-controlled PWH to three independent control groups of 25 non-infected matched individuals by means of univariate analyses and machine learning methods. Moreover, we used two external datasets to validate predictive models of PWH classification. Next, we searched for associations between clinical and biological metabolic parameters with taxonomic and functional microbiome profiles. Finally, we compare the gut microbiome in 7 PWH after a 17-week ART switch to raltegravir/maraviroc.

Results: Three major enterotypes (Prevotella, Bacteroides and Ruminococcaceae) were present in all groups. The first Prevotella enterotype was enriched in PWH, with several of characteristic lineages associated with poor metabolic profiles (low HDL and adiponectin, high insulin resistance (HOMA-IR)). Conversely butyrate-producing lineages were markedly depleted in PWH independently of sexual preference and were associated with a better metabolic profile (higher HDL and adiponectin and lower HOMA-IR). Accordingly with the worst metabolic status of PWH, butyrate production and amino-acid degradation modules were associated with high HDL and adiponectin and low HOMA-IR. Random Forest models trained to classify PWH vs. control on taxonomic abundances displayed high generalization performance on two external holdout datasets (ROC AUC of 80-82%). Finally, no significant alterations in microbiome composition were observed after switching to raltegravir/maraviroc.

Conclusion: High resolution metagenomic analyses revealed major differences in the gut microbiome of ART-controlled PWH when compared with three independent matched cohorts of controls. The observed marked insulin resistance could result both from enrichment in Prevotella lineages, and from the depletion in species producing butyrate and involved into amino-acid degradation, which depletion is linked with the HIV infection.

Keywords: HIV; Human gut microbiome; Machine learning; Metabolic status.

Fig. 1

General overview of microbiome composition of ROCnRAL cohort. (A) Gene richness distribution across study groups (n = 25 individuals per group; p-values from pairwise Wilcoxon rank-sum test are shown in brackets). (B) Principal coordinates analysis (PCoA) analyses of samples in panel A with arrows representing the effect sizes of the 30 genus features with highest significant differences across enterotypes (FDR < 0.05; Kruskal-Wallis test) product of environmental fitting over ordination plot. (C) Enterotype compositions in each study group (n = 25 individuals per group). P-value of Chi-squared test is shown in the top of the panel; *=FDR < 0.05, post-hoc analysis for Pearson’s Chi-squared Test for Count Data (D) Clinical variables explaining the microbiome compositional variation across baseline individuals of PWH, MetaCardis and MicroObes groups (n = 25 individuals each; distance-based redundancy analysis, dbRDA; genus-level Bray-Curtis dissimilarity; FDR < 0.05). (E) PCoA of inter-individual differences (genus-level Bray-Curtis beta-diversity) across samples in panel D with samples colored by study group and shaped by enterotypes. Arrows represent effect sizes of the significant variables identified by dbRDA analyses in panel D product of environmental fitting over ordination plot

Fig. 2

Differentially abundant metagenomic species (MGS) across study groups. (A) Heatmap of Cliff’s Delta effect sizes (left panel) of 121 MGS with significant differences in abundance between HIV individuals (n = 25) and MicroObes (n = 25), MetaHit (n = 25) or MetaCardis (n = 25) individuals (#=FDR < 0.05; *=p-value < 0.05; Wilcoxon rank-sum test). Positive values correspond to MGS significantly increased in the PWH group, whereas negative values correspond to MGS significantly depleted in the PWH group. Right panel represents the robustness of the associations to MSM status (FDR < 0.1; Wilcoxon rank-sum test in MSM vs. control and no-MSM vs. control comparisons; n = 13 MSM, n = 10 no-MSM). (B-E) Boxplots of log-transformed MGS abundances across study groups for the 4 MGS highlighted in green in panel A

Fig. 3

Gut Microbiome Modules (GMMs) with significant differences across study groups. (A) Heatmap of Cliff’s Delta effect sizes (right panel) of 77 GMMs with significant differences in abundance between PWH individuals (n = 25) and MetaCardis (n = 25), MetaHit (n = 25) and MicroObes (n = 25) individuals (#=FDR < 0.05; *=p-value < 0.05; Wilcoxon rank-sum test). Positive values correspond to GMMs significantly increased in PWH, whereas negative values correspond to GMMs significantly depleted in PWH. Left panel represents the robustness of the associations to MSM status (FDR < 0.1; Wilcoxon rank-sum test in MSM vs. control and no-MSM vs. control comparisons; n = 13 MSM, n = 10 no-MSM) (B-C) Boxplots of GMMs abundances across study groups for Butyrate production modules (significant decrease in PWH individuals vs. all three control groups and robust to MSM status; highlighted in green in panel A)

Fig. 4

Associations between metagenomic features and clinical variables in ROCnRAL sub-cohorts. (A) Heatmap of spearman correlations between 27 core MGS (y-axis) from Fig. 2A (significant differences in abundance between PWH (n = 25) and all three control groups) and clinical variables (y-axis) in PWH and MetaCardis individuals (left panel) and PWH and MicroObes individuals (right panel). #=FDR < 0.05, *=p-value < 0.05, Spearman correlation tests. (B) Same as panel A for 29 core Gut Metabolic Modules (GMM) in Fig. 3A

Fig. 5

Composition of Predomics Family of Best Models (FBM) in PWH classification vs. control groups. (A) Summary plot of the feature presence/absence (left panel), mean feature importance (center panel) and feature prevalence across study groups (right panel) for the 39 MGS (y-axis) retained in 718 bininter models included in the FBM in each prediction task (PWH vs. different control groups; n = 511 models in PWH vs. MetaCardis, 132 models in PWH vs. Metahit; 75 models in PWH vs. MicroObes). (B) Same as panel B for the 37 MGS retained in 669 terinter models included in the FBM in each prediction task (PWH vs. different control groups; n = 14 models in PWH vs. MetaCardis, 155 models in PWH vs. Metahit; 500 models in PWH vs. MicroObes). Blue and red MGS in model presence/absence and feature importance panels represent MGS with high mean abundances in control or PWH groups respectively. Core facet corresponds to MGS retained at least once in the FBM of each prediction task

Fig. 6

Performance of best prediction models (binary and ternary Predomics models and random forest models) of PWH vs. control groups on hold-out datasets. (A) Best Predomics bininter models for the classification of PWH vs. the 3 control groups. Left panel represents the MGS (x-axis) included in each model (y-axis) with colors representing the feature importance (mean decrease accuracy) and labels representing the sign of the association between groups (PWH = increased in PWH group; C = increased in control group). Right panel represents the AUC of each best model vs. different hold-out datasets (x-axis). Internal holdouts correspond to 5 PWH and 5 control samples from each of the three control groups unseen during the training process. External holdouts correspond to PWH and control samples coming from two external quantitative metagenomics studies (PRJNA692830: 13 PWH ART-controlled and 12 healthy controls; PRJNA391226: 61 PWH and 10 healthy controls) for which MGS abundances in the IGC reference space has been generated (see methods for details). (B) and (C) represent the same results as (A) for the best Predomics terinter models and random forest models respectively. In panel C, the left panel is limited to the top 20 MGS with the highest GINI score in each model