Research ArticleHIV

Dysbiosis of the Gut Microbiota Is Associated with HIV Disease Progression and Tryptophan Catabolism

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Science Translational Medicine  10 Jul 2013:
Vol. 5, Issue 193, pp. 193ra91
DOI: 10.1126/scitranslmed.3006438

Gut Reaction to HIV

Despite the value placed on individuality in the western world, no human is an island. Indeed, every person carries trillions of microorganisms in their intestine, and it’s becoming increasingly clear that the composition and interaction of these microorganisms can directly affect human health. Vujkovic-Cvijin et al. now demonstrate that this is the case for individuals infected with HIV, even those where viral load is controlled by therapy.

The authors screened the intestinal microbiome for microbial drivers of HIV-associated immunopathology. They found that a dysbiotic mucosal microbial community associated with mucosal immune disruption, T cell activation, and chronic inflammation in HIV-infected subjects, even in patients controlled with antiviral therapy. They then looked further into the mechanism of this association and found that the extent of dysbiosis correlated with tryptophan catabolism and plasma concentrations of the inflammatory cytokine interleukin-6 (IL-6), two established markers of disease progression. Indeed, gut-resident bacteria with the capacity to metabolize tryptophan were found to be enriched in HIV-infected subjects. These data link gut-resident microbial populations with the immunopathogenesis of HIV and suggest that modulating this community could provide a new therapeutic strategy for managing HIV disease progression.

Abstract

Progressive HIV infection is characterized by dysregulation of the intestinal immune barrier, translocation of immunostimulatory microbial products, and chronic systemic inflammation that is thought to drive progression of disease to AIDS. Elements of this pathologic process persist despite viral suppression during highly active antiretroviral therapy (HAART), and drivers of these phenomena remain poorly understood. Disrupted intestinal immunity can precipitate dysbiosis that induces chronic inflammation in the mucosa and periphery of mice. However, putative microbial drivers of HIV-associated immunopathology versus recovery have not been identified in humans. Using high-resolution bacterial community profiling, we identified a dysbiotic mucosal-adherent community enriched in Proteobacteria and depleted of Bacteroidia members that was associated with markers of mucosal immune disruption, T cell activation, and chronic inflammation in HIV-infected subjects. Furthermore, this dysbiosis was evident among HIV-infected subjects undergoing HAART, and the extent of dysbiosis correlated with activity of the kynurenine pathway of tryptophan catabolism and plasma concentrations of the inflammatory cytokine interleukin-6 (IL-6), two established markers of disease progression. Gut-resident bacteria with capacity to catabolize tryptophan through the kynurenine pathway were found to be enriched in HIV-infected subjects, strongly correlated with kynurenine levels in HIV-infected subjects, and capable of kynurenine production in vitro. These observations demonstrate a link between mucosal-adherent colonic bacteria and immunopathogenesis during progressive HIV infection that is apparent even in the setting of viral suppression during HAART. This link suggests that gut-resident microbial populations may influence intestinal homeostasis during HIV disease.

Introduction

Accumulating evidence from human and non-human primate studies supports the broad hypothesis that progression to AIDS during HIV infection is driven by chronically elevated T cell activation and systemic inflammation (14). Although the etiology of such persistent immune activation is incompletely understood, the gastrointestinal mucosal immune disruption that follows progressive HIV and SIV (simian immunodeficiency virus) infection is postulated to play a role. Specifically, compromised mucosal barrier function and increased translocation of immunostimulatory microbial products from the gut lumen into systemic circulation have been implicated in this process (5). Indeed, the presence of microbial products in the peripheral blood of HIV-infected subjects has been linked to immune activation and increased morbidity and mortality (6, 7). Among the reported defects in the mucosal barrier, a pronounced reduction in cell populations that are characterized by secretion of the enterocyte homeostasis-promoting factors, interleukin-17 (IL-17) and IL-22, has been associated with decreased gastrointestinal epithelial barrier integrity and accelerated disease (8, 9). Accompanying and apparently precipitating these changes are reported increases in activation of the kynurenine pathway of tryptophan catabolism through the interferon-inducible enzyme indoleamine 2,3-dioxygenase 1 (IDO1) (912), which produces tryptophan catabolites that can inhibit the differentiation of IL-17–secreting CD4+ T cells (13, 14). IDO1 activity is induced during the course of pathologic HIV and SIV infection, fails to normalize during progressive disease, and is associated with impaired mucosal immunity and microbial translocation, the postulated drivers of chronic inflammation that precipitate AIDS (15). Although highly active antiretroviral therapy (HAART) results in a partial diminution in inflammatory markers, including IDO1 activity, residual markers of T cell activation and inflammation [for example, IL-6 and interferon-inducible protein 10 (IP-10)] persist, and the extent of their elevation correlates with incomplete immune recovery and an increased risk for non–AIDS-related morbidities (1618). Nonetheless, the molecular and cellular mechanisms contributing to this inflammatory process during early infection and antiretroviral-treated infection remain poorly understood.

Gut-resident bacteria can modulate the mucosal immune system in ways that overlap with the salient features of HIV pathogenesis. For instance, the absence of a Clostridia class commensal bacterium, segmented filamentous bacteria, in the murine gut results in a decrease in IL-17–secreting CD4+ T cell subpopulations (19), whereas other members of the class Clostridia (Clostridium clusters IV and XIVa) have been found to induce mucosal expression of IDO1 (20). Indeed, Enterobacteriaceae family members belonging to the genera Klebsiella (21), Citrobacter (22), and Salmonella (23, 24) can actively induce an inflammatory microenvironment in the gastrointestinal tract that supports their proliferation and persistence. Additionally, disruptions in the mucosal innate immune system can result in the outgrowth of a “dysbiotic” proinflammatory community that can be sufficient to sustain pathologic, chronic inflammation in the mucosa and the periphery (21, 2527). These findings suggest not only that gut bacterial species may modulate mucosal immunity in humans but also that inflammatory imbalance can promote outgrowth of pathobionts that further exacerbate mucosal immune disequilibrium. Because HIV disease is characterized by pronounced disruptions to mucosal immunity as well as a sustained chronic inflammatory state, we sought to understand whether the gut microbiota is altered during HIV infection, and to assess relationships between these assemblages and characteristic immunologic states of HIV disease progression.

Results

Gut bacterial microbiota composition differs between HIV and viremic untreated (VU) subjects

To capture a wide range of stages of HIV disease progression and treatment, we assembled a cohort of 32 male subjects including 6 viremic, untreated (VU) HIV-infected subjects, 18 virally suppressed subjects on HAART with varying degrees of CD4+ T cell recovery (HAART), 1 HIV-infected long-term non-progressor, and 9 uninfected risk-matched controls (HIV); see table S1 for defining characteristics of the cohort. Rectosigmoid biopsy specimens and peripheral blood were collected for immunophenotyping of immune cell subpopulations and for profiling of microbial communities. Biopsy tissue was chosen over stool sampling on the basis of evidence that immunomodulatory bacterial species adhere closely to the gastrointestinal mucosal epithelium in the mouse (19, 20), comprising a community that is different from that within the gut lumen (28). Microbial profiling was performed with a standardized, high-density microarray containing oligonucleotide probes targeted against hypervariable regions of the bacterial 16S ribosomal RNA (rRNA) gene [G3 PhyloChip (29)]. This approach is ideally suited for high-resolution microbiome comparative analyses and detection of lower-abundance species that may not be identified using sequencing approaches of relatively shallow read depth (30). This is particularly relevant given recent evidence demonstrating the presence of low-abundance “keystone” species that, despite their low numbers, are pivotal in modulating commensal community composition and host-microbe interactions in the oral cavity (31).

Analysis of rectosigmoid biopsies showed that total bacterial load was similar across all subjects, regardless of infection status (fig. S1A), and that total bacterial load measures were concordant across different biopsies from a given individual as well as across different DNA extraction methods (fig. S1B). Additionally, comparative analysis of gross microbial community metrics showed no significant differences between HIV-infected and uninfected subjects in community evenness or richness (fig. S1C). By contrast, analysis of between-subject microbiota composition using a Canberra community dissimilarity matrix revealed distinct clustering of the subject groups representing extremes of health and disease: the HIV and the VU groups, respectively (Fig. 1). Permutational multivariate analysis of variance confirmed that the observed variability in community composition was significantly related to subjects belonging to the VU or HIV group (Adonis; P = 0.002), indicating that untreated HIV infection is associated with a distinct mucosal microbiota composition. HAART-treated patients, although uniformly exhibiting viral suppression (that is, plasma viral load <40 copies/ml), exhibited highly dissimilar community compositions: the microbiota of some patients clustered closely with VU subjects, whereas others were more similar to HIV subjects.

Fig. 1 Gut bacterial microbiota composition in HIV-infected VU subjects differs from that of HIV-uninfected risk-matched controls.

Community composition dissimilarity was analyzed using microbiota profiles generated using PhyloChip. Nonmetric dimensional scaling (NMDS) was used to plot each sample community on the basis of a Canberra community dissimilarity matrix. Each dot represents the microbiome of a single subject, and color indicates subject group as denoted in the legend. Ellipses represent 95% confidence intervals for SE of weighted NMDS score means of respective groups, and are colored on the basis of subject group (red denotes HIV+ VU subjects, whereas blue denotes HIV). Community differences were verified using Adonis, P = 0.002.

To identify differences in the adherent bacterial communities of HIV and VU subjects, we used a permutational analysis algorithm with integrated false discovery correction [Statistical Analysis of Microarrays (SAM) (32)] to detect specific bacterial taxa in differential fold abundance across these two subject groups. Of the 33,951 taxa (defined as a group of bacteria having at least 99% 16S rRNA sequence identity) that were detected in at least one of the samples studied, 625 exhibited significantly different relative abundance between VU and HIV after stringent data filtering. This list comprised 579 taxa that were enriched and 45 taxa that were depleted in VU subjects compared with HIV subject samples (Fig. 2 and table S2 for list of taxa). Among the taxa enriched in VU subjects, the most enriched was Erysipelotrichaceae in the class Mollicutes, a class that has been associated with obesity and heightened cardiovascular morbidity (3335). Most of the taxa enriched in VU subjects included members of the phylum Proteobacteria, with a notably high representation of the Enterobacteriaceae family, a finding verified by quantitative polymerase chain reaction (qPCR) (fig. S2) and supported by a previous report (36). Enriched genera from within this family included known proinflammatory pathobionts such as Salmonella, Escherichia, Serratia, Shigella, and Klebsiella species. Additionally, Staphylococcus, Pseudomonas, and Campylobacter spp., known opportunistic pathogens and sources of bacteremia in HIV-infected subjects (3739), were highly enriched in the mucosae of VU subjects. Significant reductions in the relative abundance of specific members of Clostridia and Bacteroidia were observed in VU subjects, with the greatest degree of depletion among members of the Bacteroides and Alistipes genera, which have been previously found to be depleted in inflammatory bowel disease (40). We refer to this community of bacterial taxa (identified as differing significantly in abundance between VU subjects and HIV-uninfected subjects) as the “disease-associated microbial community” (DMC).

Fig. 2 Phylogenetic distribution of the DMC, defined as significantly depleted or enriched in VU subjects compared with HIV subjects.

Phylogenetic tree representing 16S rRNA gene sequence relatedness of taxa identified as significantly enriched or depleted in relative abundance between VU and HIV subjects (see Materials and Methods for statistical analyses). The 625 taxa identified belonged to 172 individual genera, and representative taxa for each individual genus are depicted. Tree branches are colored-coded by phyla as indicated in the legend. Red and blue bars depict bacterial genera in greater (red bars) and lesser (blue bars) abundance in VU subjects. Dashed lines indicate magnitude of relative abundance change [inner dashed circle denotes mean fluorescence intensity (MFI) of 500, outer denotes 2000 MFI difference between comparative groups; 1000 MFI change represents a log2 difference in relative abundance].

Intestinal dysbiosis correlates with markers of disease progression among HIV-infected subjects

Although levels of T cell activation [as measured by co-expression of CD38 and human leukocyte antigen (HLA)–DR] and viral load are well-accepted markers of HIV disease progression (3), it is now also appreciated that other immune parameters are linked to morbidity and mortality, including plasma markers of inflammation, IDO1 activity, and markers of microbial translocation (7, 11, 17). Notably, specific murine gut commensal species have been shown to adhere to mucosal epithelia and induce altered inflammatory cytokine secretion patterns (19, 20), some in association with IDO1 up-regulation (20). To determine whether associations exist between the mucosa-associated DMC and HIV disease progression, peripheral blood was drawn at the time of mucosal sampling and additional biopsies were obtained for immunophenotyping (see table S3 for a complete list of immune parameters measured). Correlations between the relative abundance of taxa within the DMC and immunologic parameters among all HIV-infected subjects (HAART and VU) were examined (Fig. 3A). Strong associations were not found between taxon abundances and peripheral blood CD4+ T cell count or gut HIV RNA or DNA levels. However, taxa that were enriched in the DMC correlated with elevated levels of T cell activation, of catabolism of tryptophan through the kynurenine pathway [a marker of IDO1 activity measured by the ratio of plasma concentrations of the downstream product, kynurenine, to the parent compound, tryptophan (Kyn/Trp)], of soluble plasma markers of inflammation [such as IP-10, soluble tumor necrosis factor (TNF) receptor II, and IL-6], and diminished levels of mucosal T cells secreting IL-17 and IL-22. Although relative abundances of many taxa were found to correlate with these immune parameters (Fig. 3A), phylogenetically distinct members of the Enterobacteriaceae family were among the strongest correlates of elevated gut CD4+ and CD8+ T cell activation (unadjusted P = 0.00004, RS = 0.82; unadjusted P = 0.00006, RS = 0.80; table S3; RS, Spearman rho correlation coefficient), whereas members of Staphylococcaceae were among the strongest correlates of higher Kyn/Trp and of elevated plasma IP-10 levels (unadjusted P = 0.00007, RS = 0.76; unadjusted P = 0.0014, RS = 0.64). Taxa within the families Micrococcaceae, Rhodobacteraceae, Halomonadaceae, and Pasteurellaceae also exhibited strong correlations with numerous markers of disease progression. Conversely, those taxa depleted in the DMC were found to correlate with lower levels of T cell activation and higher levels of mucosal T cells secreting IL-17, with the strongest correlates of lower T cell activation belonging to Bacteroidaceae (unadjusted P = 0.0017, RS = −0.68) and the strongest correlates of higher gut IL-17 production belonging to taxa within the Rikenellaceae family (unadjusted P = 0.0004, RS = 0.72; see fig. S3 and table S3 for full list of all correlation coefficients). Collectively, these findings indicate that higher abundance of multiple, diverse microbial taxa within the DMC is linked to immune activation and to poor gut IL-17 responses, and that this state is concomitantly characterized by an apparent depletion of taxa linked to immune homeostasis and conditions that promote gastrointestinal integrity, that is, lower levels of T cell activation and higher levels of IL-17 secretion.

Fig. 3 Bacterial community enriched in untreated infection associates with immunopathologic markers of HIV disease progression within HIV-infected subjects.

(A) Spearman correlations between individual taxa of the DMC and immunologic parameters were calculated among all HIV-infected subjects (treated and untreated), collated, and represented by color. Correlation coefficients for taxa with representative species belonging to the same genus and displaying similar FI trends were averaged for visualization purposes. Intersections of each genus within the DMC (rows) and immunologic parameters (columns) represent the strength of the positive (red) or negative (blue) correlation as determined by Spearman correlation coefficient (RS). Bacteria depleted in VU subjects correlate positively with protective immune markers (low T cell activation in the blood and gut, higher gut IL-17 secretion), whereas bacteria enriched in VU subjects correlate with markers of disease progression [converse of above, as well as lower T cell–mediated IL-22 secretion in the gut, higher Kyn/Trp, and higher concentrations of plasma markers of inflammation (IP-10, IL-6, and soluble TNF receptor II)]. Families and genera of taxa that exhibited numerous strong correlations with immunologic parameters are indicated. Intersections enclosed by a black box represent associations with unadjusted P < 0.02. (B) First principal component (PC1) representing trends in DMC bacterial community composition accounts for 52.8% of bacterial community variance and separates VU subjects from HIV, whereas subjects undergoing HAART have PC1 values distributed among those for VU and HIV subjects (PC1, principal component 1; LTNP, long-term nonprogressor). (C) First principal component correlates with markers of disease progression: plasma concentration of inflammatory marker IP-10, IDO1 activity (Kyn/Trp), gut T cell activation, and peripheral blood CD8 T cell activation (RS, Spearman rho correlation coefficient; dotted lines represent 95% confidence interval bands for linear regression coefficients).

Gut-resident microbial communities function as discrete ecosystems bound together by interdependent metabolic exchange and collective protective mechanisms. Accordingly, many taxa of the DMC exhibited similar patterns of correlation with immune parameters (Fig. 3A) and appeared to trend together in abundance across individuals. Therefore, a holistic approach was taken to understand such trends in gross community abundance as they may relate to inflammatory states. Principal components analysis (PCA) was performed to consolidate patterns among covarying microbial abundances into a handful of summary variables and to compare such variables to immune parameters. The first principal component (PC1) of this analysis comprised 52.8% of DMC variance (Fig. 3B and fig. S4A), was able to differentiate HIV from VU (Fig. 3C), and was chiefly characterized by opposing trends in the relative abundance of Proteobacteria (including Pasteurellaceae, Mycobacteriaceae, Pseudomonadaceae, and Enterobacteriaceae members) and Bacteroidaceae (fig. S4B). We used PC1 as a measure of the degree to which a subject’s gut microbiota was similar to that of VU or HIV (and therefore a surrogate marker for extent of dysbiosis), and related it to markers of immunopathology and disease progression among all HIV-infected subjects. PC1 was found to associate strongly with plasma IP-10 (adjusted P = 0.044), a marker of systemic inflammation shown to correlate with accelerated HIV disease progression (18, 41), with Kyn/Trp (adjusted P = 0.007), and with T cell activation in the gut and peripheral blood (respective significance values shown in figure) (Fig. 3D and table S4).

Heterogeneity in microbiota of subjects undergoing HAART correlates with markers of disease progression

Although HAART is effective in diminishing plasma viremia to nearly undetectable levels, subjects receiving long-term treatment can exhibit persistently elevated levels of chronic immune activation (16), of which the etiology is unknown. We hypothesized that persistence of even a subset of the DMC could continue to sustain pathologic chronic immune activation in this population despite suppression of viral replication. To address this hypothesis, the relative abundance of DMC members was averaged across each of the VU, HAART, and HIV subject groups and compared between these groups. The DMC in HAART subjects was found to represent an intermediate state between that observed in VU subjects and uninfected controls (Fig. 4A). Additionally, longitudinal sampling of two HIV-infected subjects before and after 9 months of effective HAART demonstrated a relative shift in the DMC to more closely resemble that of the HIV group (fig. S5). However, individual subjects within the HAART group exhibited a spectrum of relative abundance for members of the DMC, such that the microbial community composition of some subjects was more similar to that of HIV subjects, whereas the composition of others was more similar to that of VU subjects (Fig. 4B).

Fig. 4 Relative abundance of DMC members is diminished in HAART subjects compared to VU and falls along a spectrum of VU- or uninfected-like bacterial communities.

(A) Relative abundances of genera within the DMC averaged within subject groups. Each row represents an individual genus. Yellow denotes higher relative abundance, whereas blue denotes lower relative abundance. After assessment of data distribution, a two-tailed, unpaired t test assuming unequal variance was used to test significance of differences (***P < 5 × 10−10). (B) Heat map of DMC member relative abundances, ordered first by subject grouping, then by subject PC1 values (relative values denoted by color below each subject column). Rows represent genera of the DMC, whereas columns represent individual subject samples. In the treated subject grouping, subjects were ranked for each variable (that is, Kyn/Trp and IL-6 concentration), and these rankings are represented by colored squares below each subject column (red indicates high Kyn/Trp and high plasma IL-6 in the first and second rows, respectively). (C) Correlations between PC1 and Kyn/Trp or plasma IL-6 concentration. P values displayed represent the contribution of PC1 to the best-fit linear regression model that best predicts each immunologic variable, adjusted to account for multiple comparisons (see Materials and Methods for details).

We considered a number of possibilities to account for the presence of this spectrum in the DMC of subjects on HAART. Because prophylactic antibiotics are commonly used by subjects on HAART and may influence microbial community composition, relationships between PC1 and antibiotic administration history (for which data were available for 13 of 18 subjects) were analyzed. No significant trends were observed, as was the case for comparisons to age and time on HAART (fig. S6). Furthermore, gut inflammation itself has been found to stimulate the outgrowth of facultative anaerobes, including members of the Enterobacteriaceae family (24, 42, 43), by providing growth substrates in the form of anaerobic respiration terminal electron acceptors. Expression of genes involved in oxygen and nitrogen radical production [reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (CYBB subunit), arginase 1, nitric oxide synthase 2 (NOS2), type I interferons (IFN-α and IFN-β), type II interferon (IFN-γ), and TNF-α] was accordingly quantified in whole biopsies and compared to PC1, and no significant associations were found (table S6). Rather, associations were found between PC1 and numerous markers of disease progression, including levels of IL-6 in plasma, Kyn/Trp, soluble TNF-α receptor II levels in plasma, blood coagulation marker d-dimer levels in plasma, gut IDO1 transcript, blood memory CD8 T cell activation, plasma IP-10 levels, and gut CD4+ T cell activation (P < 0.05, |RS| > 0.5; table S5). After adjusting for multiple comparisons, significant relationships remained between PC1 and two disease markers: plasma Kyn/Trp and plasma IL-6 concentration (Fig. 4C). To understand whether these associations were influenced by potentially confounding variables and to incorporate a greater proportion of microbial community abundance trends, multiple linear regression analysis was performed. This approach allows for simultaneous consideration of multiple adjustment variables (antibiotics usage history, days on HAART, age, and race) and the five most representative principal components of DMC variance (PC1, PC2, PC3, PC4, and PC5) toward understanding which variables are most predictive of the immunologic parameters of interest (Kyn/Trp and plasma IL-6). Out of all possible models involving these variables, the model with the best leave-one-out cross-validated (44) (LOO-CV) predictive accuracy was chosen and P values were obtained by permutation. PC1 and ethnicity were significantly correlated with Kyn/Trp in the best-scoring model (P = 0.0006 and P = 0.0036, respectively). In the case of plasma IL-6 levels, the variable lending greatest predictive power to the most predictive multivariate model was PC1 (P = 0.018), whereas four other variables contributed less significantly: PC4 (P = 0.032), ethnicity (P = 0.048), age (P = 0.027), and duration of HAART (P = 0.020). Comparisons of Kyn/Trp and plasma IL-6 to PC1 alone are shown in Fig. 4C.

Members of the DMC participate in immunoactive tryptophan catabolism

IDO1 is induced during inflammation and is thought to actively regulate immune activation by its numerous effects on T cells, including inhibition of IL-17 secretion and induction of FoxP3-expressing regulatory T cells (Tregs) (45). These effects are mediated by its production of kynurenine pathway compounds from its enzymatic substrate tryptophan, a reaction for which IDO1 is thought to govern the rate-limiting step (46). Consistent with previous findings (47), we found that IDO1 was strongly associated with type II interferon expression in colonic mucosal tissue, and the ratio of plasma concentrations of kynurenine and tryptophan (Kyn/Trp) was indeed associated with reduced IL-17 secretion in CD4+ T cells and higher abundance of Tregs in the colon (fig. S7, A and B). However, although there was a significant positive correlation between IDO1 expression and plasma Kyn/Trp in VU subjects, there was no such association in the HAART HIV-infected subject group (fig. S7C).

Given that a significant association was found between our marker of dysbiosis (PC1) and Kyn/Trp within the HAART subject group, we postulated that the DMC may directly contribute to immunomodulatory tryptophan catabolism through the kynurenine pathway. Comparing taxon abundances to plasma Kyn/Trp, we found that taxa within 140 genera positively correlated significantly with Kyn/Trp (table S7). Although bacteria are among the primary producers of the essential amino acid tryptophan, relatively few are known to encode the enzymatic machinery for tryptophan catabolism (4851). Indeed, analysis of genomic and proteomic annotations from the UniProt Consortium (52) revealed several bacterial genera within Proteobacteria that encode enzymes homologous to those necessary for catabolism of tryptophan to the most potent immunomodulatory product of the kynurenine pathway (9, 13), 3-hydroxyanthranilic acid (3-HAA; Fig. 5A and table S7). Comparing this list to that of genera enriched in the DMC, we found that genera that bore genetic homologs to tryptophan catabolism enzymes of the kynurenine pathway were preferentially enriched in the DMC (Fig. 5B), including Pseudomonas, Xanthomonas, Burkholderia, Stenotrophomonas, Shewanella, and Bacillus, as well as members of the families Rhodobacteraceae, Micrococcaceae, and Halomonadaceae (see table S7 for full list). Furthermore, genera for which at least one member bore genetic homologs to three to four enzymes within the immunoactive kynurenine pathway correlated more strongly with Kyn/Trp ratios than those that bore one to two tryptophan catabolism enzyme homologs, and even more so than those that had no genetic evidence for kynurenine pathway metabolic machinery (Fig. 5C and fig. S8).

Fig. 5 Bacterial tryptophan catabolism machinery is genetically and functionally homologous to IDO1 enzymatic activity and is enriched in the DMC.

(A) Homologs to tryptophan catabolism pathway enzymes are genetically encoded in numerous bacterial genera as denoted by “Totals.” Phylogenetic distribution of annotated enzymes in the kynurenine pathway was collated and represented using pie charts as defined in the legend. (B) A Fisher’s exact test for enrichment found that a greater proportion of genera overrepresented in VU encoded three to four genes involved in the tryptophan catabolism pathway compared to genera not differing in abundance between VU and HIV (P = 0.037). (C) All genera that were both detected in our cohort and contained members for which genome sequence data were available were ranked on the basis of their strength of nonparametric Spearman correlation to Kyn/Trp ratios in HIV-infected subject plasma. Genera with three or four genetically encoded homologs to kynurenine pathway enzymes exhibited significantly stronger correlations to plasma Kyn/Trp ratios than those with only one or two homologs, and even more so than those with no annotated homologous enzymes. (D) Pseudomonas fluorescens strain A506 and a laboratory strain of Escherichia coli (DH5α) were grown in King’s B Medium for 20 hours in the presence of varying concentrations of tryptophan. Supernatants from these cultures were assayed for the presence of kynurenine. Error bars indicate SDs calculated from three independent experiments. Nonparametric Mann-Whitney tests were used for group comparisons (*P < 0.05, **P < 0.005).

To confirm the capacity for production of kynurenine pathway catabolites among members of the DMC, we assayed a representative species of the strongest bacterial correlate to plasma Kyn/Trp ratios (genus Pseudomonas) for kynurenine production in pure culture. Pseudomonas fluorescens strain A506 was found to produce significant amounts of kynurenine from tryptophan (Fig. 5D) in vitro, further supporting the hypothesis that gut mucosal-adherent bacteria associated with HIV infection might directly contribute to immunoactive tryptophan catabolism during HIV disease.

Discussion

Here, we report observations of differences in the composition of the colonic mucosal-adherent bacterial microbiota of untreated HIV-infected subjects compared to uninfected risk-matched controls. This altered community prominently includes known pathobionts, such as Staphylococcus spp., Pseudomonas spp., and Enterobacteriaceae family members with proinflammatory potential. Although these bacteria are documented sources of bacteremia in HIV-infected subjects with advanced disease (37, 39), it is possible that they assume an important but overlooked pathologic role outside the setting of overt bacteremia. Within all HIV-infected subjects that we studied (n = 24), the composition of this community associated with markers of disease progression and previously described correlates of morbidity and mortality in HIV-infected populations (1, 11, 1618), namely, higher levels of T cell activation in the blood and gut as well as higher levels of tryptophan catabolism and elevated plasma markers of inflammation. When considering aviremic subjects on effective HAART (n = 18), correlations remained evident between dysbiosis and two immunologic correlates of disease, production of kynurenine and plasma levels of the inflammatory cytokine IL-6, suggesting a relationship between the composition of the gut microbial community and markers of disease outcomes even in treated subjects. Finally, numerous bacterial taxa of the DMC that were enriched in untreated HIV infection were found to encode the enzymatic machinery that performs the same catabolic function as human IDO1. Subsequent in vitro experiments confirmed the capacity of a model member of this community in the genus Pseudomonas to transform tryptophan to kynurenine.

Although the mechanistic relationships defining the observed associations are not fully understood, a complex cross talk between gut immune states and the microbiome in the context of HIV disease progression and treatment is tenable. Previous studies in the mouse support the idea that HIV-associated gut inflammation may contribute to the observed dysbiotic states (2224, 42, 53). Gut inflammation may induce changes in nutrient microenvironments via production of oxygen and nitrogen radicals that allow outgrowth of facultative anaerobes such as members of the Enterobacteriaceae family (24, 42). Although our current investigation did not find correlations between enzymes responsible for reactive oxygen and nitrogen species production and dysbiosis, we were limited by measurements of enzyme expression in whole tissue as opposed to direct measurement of enzymatic end products or measurement of enzyme expression in specific cell lineages (for example, tissue macrophages). Perturbations to gut immune barrier function such as disruptions in microbial pattern recognition sensors have also been found to result in the outgrowth of dysbiotic communities in mice (26, 27). Numerous cells that participate in microbial recognition and response are dysregulated during progressive HIV/SIV disease, including dendritic cells (54), macrophages (55), T helper 17 (TH17) cells (56), enterocytes (57), and Paneth cells (58). Such cells are critical regulators of gut microbial community composition, and their disruption may allow for outgrowth of the dysbiotic community described herein.

Although gut inflammation may well engender dysbiosis, murine studies also suggest that the dysbiotic communities that result from inflammation and immune barrier defects can reciprocally act to stimulate chronic inflammation in the gut and systemic circulation (21, 2527). Although bacterial species that can directly alter or augment IDO1 activity, T cell differentiation, or gut inflammation in humans have not yet been identified, analogous immunomodulatory relationships are likely to exist between gut bacteria and human hosts. If so, the capacity for the mucosal immune system to regulate the abundance of such immunomodulatory species may be impaired during HIV disease. Indeed, we find that HIV-infected subjects are likely to harbor a community of enteropathogenic bacteria that can catabolize tryptophan into immunomodulatory kynurenine derivatives known to correlate with disease progression and thought to contribute to mucosal immune disruption (9, 15). Although our 16S rRNA–based profiling cannot directly confirm the functional capacity of this community, existing bacterial genome data suggest that members of the phylogenetic groups described herein indeed have the functional capacity to produce kynurenine. We show here that a model member of this community, a strain from within the genus bearing strongest correlation to Kyn/Trp catabolism (Pseudomonas), is indeed able to catabolize tryptophan to kynurenine. Furthermore, members of this disease-associated community (including Pseudomonas, Xanthomonas, Bacillus, and Burkholderia spp.) have been shown to use kynurenine pathway catabolites as growth substrates (50, 5963). In aggregate, these observations suggest that the outgrowth of this community may be driven by the persistent host IDO1 activity found during chronic HIV infection and may in turn exacerbate IDO1-mediated immune disruption by accelerating tryptophan catabolism along the kynurenine pathway. If so, the potential exists for a self-amplifying cycle of dysbiosis and inflammation. A reenvisioning of the pathogenic mechanisms associated with HIV disease progression may thus include dysregulation of the balance between homeostasis-promoting versus gut barrier–disrupting microbes, contributing to weakened mucosal immune function, microbial translocation, and subsequent chronic inflammation. This process may further select for functionally distinct, enteropathogen-enriched communities in the gut mucosa that aggravate these immunologic phenomena. Intriguingly, the one HIV-infected long-term non-progressor assayed in this study (classified as such by a stable peripheral blood CD4+ count despite more than 21 years of untreated infection) revealed a gut community similar to that of uninfected subjects, presenting the interesting possibility that non-progressive disease may be linked to host-microbiome homeostasis.

Notably, a potentially detrimental host-microbiome interaction in the setting of HIV disease is not always reversed by effective antiretroviral treatment. Indeed, HAART subjects exhibited higher relative abundances of the identified DMC than did uninfected controls, and those with the greatest degrees of dysbiosis were characterized by elevated markers of chronic inflammation and disease progression, that is, high levels of kynurenine production and of the plasma inflammatory marker IL-6 (17). Previous findings support the possibility that the pathobiont community of Enterobacteriaceae enriched in untreated infection can indeed potentiate immunologic disruptions similar to those found in HIV disease (21, 2325). Bacteroides genus members, which can protect mice from colitis (64) and that are notably depleted in sufferers of inflammatory bowel disease (40) and asthma (65), were similarly found in relative depletion among our cohort of HIV-infected untreated subjects.

Finally, we present data suggesting that a diverse community of gut-resident bacteria may augment IDO1-mediated mucosal disruption by direct catabolism of tryptophan through the kynurenine pathway. Targeting these microbial communities may present new avenues for therapeutic interventions aimed at preventing or reversing HIV-associated immunopathology. Indeed, recent reports of trials using agents that modulate the composition of the microbial community in HIV-infected populations or SIV-infected macaques [for example, with antibiotics (66), probiotics (6769), and prebiotics (70)] have shown improvements in clinical outcomes as measured by CD4+ T cell recovery, reduced mortality, and decreased markers of microbial translocation. However, microbial population dynamics during HIV disease progression remain poorly understood, and efforts at developing therapeutics targeting the microbiome will certainly be augmented by a greater understanding of the host-microbiome relationship in HIV-infected subjects. Further efforts to determine the mechanistic processes underlying the complex relationships between the gut microbiome and its host will be critical toward developing the potential of gut microbial community modulation as a therapeutic strategy, and may lead to new approaches for the management of HIV disease as well as of other chronic inflammatory conditions.

Materials and Methods

Study design

The primary aims of the present study were to understand whether differences in mucosal-adherent bacterial communities exist between HIV-infected untreated, HIV-infected on HAART, and HIV-uninfected subjects, and whether such differences are associated with immunological perturbations characteristic of HIV disease. To accomplish these aims, microarray-based microbial community profiling was used and assessment of immunologic status of subjects was performed with clinical data, flow cytometry, enzyme-linked immunosorbent assay (ELISA), liquid chromatography–tandem mass spectrometry, and qPCR. The investigators were not blinded to serostatus or to viral load measurements, medication history, or peripheral blood CD4+ T cell counts. The control subjects were seronegative risk-matched men who had a history of having sex with men and/or with histories of exposure to HIV from interactions with seropositive partners. The initial cohort of HIV-infected subjects on HAART comprised HIV-infected patients who had maintained undetectable viral loads on stable antiretroviral regimens for at least 1 year, with equal numbers of individuals with peripheral blood CD4+ T cell counts >500 cells/mm3 and with CD4+ T cell counts <350 cells/mm3, although CD4+ T cell counts at times of sample collection spanned the range between these benchmark CD4+ T cell counts. All subjects were included that met the criterion of having had no recorded antibiotics usage within 2 months of mucosal sampling, regardless of peripheral blood CD4+ T cell count, and the final list of subjects included nearly equivalent numbers of subjects with <350 CD4+ T cells/mm3 and >500 CD4+ T cells/mm3. Triplicate 16S amplicon hybridizations using separate G3 PhyloChip microarrays (Second Genome Inc.) were performed with mucosal biopsy samples from two different study subjects. All triplicates were found to cluster closest together in a global hierarchical clustering analysis based on a Canberra community dissimilarity matrix.

Subject sample collection

All participants gave written informed consent for research sigmoidoscopy and biopsies, a protocol approved by the Committee on Human Research, University of California, San Francisco (UCSF). Study participants underwent a blood draw and received a Fleet enema before the procedure, and rectosigmoid biopsies (each ~3 mm in diameter) were obtained between 10 and 20 cm from the anus using jumbo forceps. Four biopsies were formalin-fixed and paraffin-embedded for immunohistochemistry; 12 biopsies were placed immediately in 15 ml of RPMI 1640 with 15% fetal calf serum and processed for flow cytometric analysis within 4 hours of collection; two biopsies were placed in RNAlater (Life Technologies) and frozen at −80°C for quantitation of inflammatory gene expression; finally, another two allocations of two biopsies each were snap-frozen in liquid N2 and stored at −80°C for HIV viral nucleic acid quantitation and bacterial community profiling by G3 PhyloChip microarray.

Nucleic acid extraction and qPCR

For quantitation of inflammatory and interferon-stimulated gene (ISG) expression, RNA was extracted from snap-frozen biopsies frozen in RNAlater (Life Technologies) after homogenization in TRIzol (Life Technologies) and purification with the RNeasy Mini Kit (Qiagen). RNA was also prepared from whole blood via collection with the PAXgene Blood RNA Kit (Qiagen). Complementary DNA was prepared from RNA with Omniscript RT Kit (Qiagen) using random hexamer primers. qPCR was carried out with TaqMan Gene Expression MasterMix (Applied Biosystems) in a StepOne Plus Real Time PCR system (Applied Biosystems). IDO1 transcript was measured with custom primers and probe (forward, 5′-GCCAAATCCACAGGAAAATCT; reverse, 5′-GCTGTGACTTGTGGTCTGTGA; probe, 5′-AAACATCTGCCTGATCTCATAGAGT). ISGs were measured with commercially available primer-probe sets from Applied Biosystems: GBP1 Hs00977005_m1, IFI27 HSs00271467_m1, MX1 Hs00895601_m1, and OAS1 Hs00973637_m1. ISG expression was consolidated with the geometric mean of relative expression values for all four aforementioned genes. Quantitation of expression of inflammatory genes [NADPH oxidase (CYBB subunit), arginase 1, NOS2, type I interferons (IFN-α and IFN-β), type II interferon (IFN-γ), ISG expression, and TNF-α] was performed with the Fluidigm platform, with gene-specific preamplification using proprietary computationally designed primers (DELTAgene from Fluidigm). Relative expression was calculated with the ΔΔCT method, using HPRT (hypoxanthine-guanine phosphoribosyltransferase) as housekeeping gene.

Total bacterial burden quantification was carried out with DNA extracted from separate snap-frozen rectosigmoid biopsies that were collected at the same time as those above. Biopsies were resuspended in RNAlater-ICE (Life Technologies), drained, and transferred to Lysis Matrix B (MP Biomedicals) tubes containing 600 μl of RLT buffer and subjected to 30 s of bead beating at 5.5 m/s. Samples were briefly spun down, and DNA was extracted with the AllPrep Kit (Qiagen). TaqMan qPCR was performed, as above, with the following primers: P891F, 5′-TGGAGCATGTGGTTTAATTCGA; P1033R, 5′-TGCGGGACTTAACCCAACA; and UniProbe, 5′-CACGAGCTGACGACARCCATGCA. A standard curve of known 16S rRNA copy numbers was included with each run and was used to calculate total bacterial burden normalized to total 16S rRNA genes. Enterobacteriaceae were quantitated with Brilliant II SYBR Green (Agilent Technologies) and the following primers: forward, 5′-ATGGCTGTCGTCAGCTCGT; reverse, 5′-CCTACTTCTTTTGCAACCCACTC (71). Bacteroides fragilis was also quantified with Brilliant II SYBR Green and the following primers: forward, 5′-GAAAGCATTAAGTATTCCACCTG; reverse, 5′-CGGTGATTGCTCACTGACA (72).

T cell phenotyping analysis by flow cytometry

Mononuclear cells were prepared from peripheral blood by density gradient centrifugation and isolation with Ficoll-Paque (STEMCELL Technologies). Rectosigmoid biopsy mononuclear cells were isolated from biopsy specimens with a protocol optimized for lymphocyte viability and yield (73). Biopsy pieces were digested in three rounds of collagenase type II (0.5 mg/ml) (Sigma-Aldrich), after which tissue was disrupted with a syringe bearing a 16-gauge blunt-end needle and passaged through a 70-mm cell strainer.

Cells were then stained directly or after phorbol 12-myristate 13-acetate/ionomycin stimulation in the presence of brefeldin and monensin for surface marker expression, and then permeabilized and stained intracellularly for FoxP3 or cytokine production with the FoxP3 Fixation/Permeabilization Staining Set (eBioscience) or Cytofix/Cytoperm (BD Biosciences), respectively. Acquisition was performed on a Becton Dickinson LSRII (BD Biosciences) and analyzed with FlowJo software, version 9 (TreeStar). Naïve T cells were identified as CD27+ cells lacking CD45RO expression and were excluded to yield total memory T cells. Proportions of activated CD4+ and CD8+ T cell were measured by the fraction of these cell populations staining positive for HLA-DR and CD38. Proportions of Tregs from total CD4+ cells were determined by costaining of CD25 and FoxP3.

Immunohistochemistry and viral load quantification in rectosigmoid tissues

Four rectosigmoid biopsies were collected into 4% paraformaldehyde, embedded in paraffin, cut into 5-μm sections, and mounted on glass slides. Slides were deparaffinized in Clear-Rite 3 and rehydrated in an ethanol series, and antigenic epitopes were then retrieved by placing slides in a pressure cooker for 20 min at 121°C in pH 6 citrate buffer. Slides were blocked and stained with polyclonal rabbit anti-CD3 (DAKO), and stain was developed with DAB chromogen (anti-rabbit Polink-2 Plus HRP Detection Kit, GBI Labs). Whole-slide composite images were acquired on a ScanScope (Aperio) and analyzed with the accompanying software to quantify CD3-stained cells per area of tissue. To determine the number of CD4+ cells per area, the CD3+ cells/mm2 value was multiplied by the fraction of CD3+ cells that stained with CD4 as determined by flow cytometry. Two rectosigmoid biopsies were snap-frozen in liquid N2 for quantification of HIV DNA and RNA as previously described (74).

Plasma inflammatory marker measurements

Plasma was collected from peripheral blood by centrifugation and stored at −80°C. The following soluble plasma proteins were measured by ELISA with commercially available kits and according to the manufacturer’s instructions: IP-10, soluble TNF receptor II, IL-6, FABP2, soluble CD14 (all from R&D Systems), zonulin (Immundiagnostik), and d-dimer (Diagnostica Stago). Concentrations of kynurenine, tryptophan, and 3-HAA were quantitated in plasma and in bacterial growth cultures by liquid chromatography–tandem mass spectrometry, as previously described (9).

16S rRNA amplicon preparation, PhyloChip hybridization, scanning, and data normalization

Nucleic acid extraction was performed on snap-frozen rectosigmoid biopsies by resuspending in RNAlater-ICE (Life Technologies), according to the manufacturer’s protocol and as previously described (75). G3 PhyloChip microarrays were obtained from Second Genome Inc. PCR amplification of the bacterial 16S rRNA gene, PhyloChip hybridization, PhyloChip scanning, and fluorescence intensity normalization and initial probe set filtering of PhyloChip data were performed as previously described (29), with the exception of performing 25 cycles of 16S PCR amplification rather than 30 and the usage of customized quartile “r score” cutoff values. For each 16S rRNA gene probe set, an r score was independently calculated that incorporated mismatch probe fluorescence intensity as well as distribution of total chip fluorescence to account for per-chip background fluorescence. Quartile r score cutoffs were chosen on the basis of r scores retrieved for control probes spiked-in at known concentrations, as recorded from all PhyloChip microarrays of the current study, and were as follows: rQ1 > 0.30, rQ2 > 0.65, rQ3 > 0.88.

PhyloChip analysis

After probe intensity normalization and to reduce the data set for analyses, probe sets belonging to phylogenetically related (based on species-level identity) 16S rRNA sequences and bearing similar probe set response patterns across all samples were represented by a single taxon, whereas others were eliminated from the data set. This process was performed by calculating the variance in fluorescence intensity for each probe set and its two most phylogenetically related neighbors, and was repeated for each taxon across multiple different samples; the sum of all variances was used to define groups of taxa whose probe sets responded similarly. The resulting measures were ranked to select for removal of the top ~15% of probe sets exhibiting similar response trends across all samples, allowing reduction of redundant probe sets. Next, the “vegan” package within the R programming environment was used to perform NMDS based on community dissimilarity using the Canberra dissimilarity metric. The “Adonis” package was used to estimate strength of relationship between community dissimilarity and classification of subjects into either VU or HIV-uninfected. To identify bacterial taxa in differential abundance among VU and HIV subjects, the software package SAM (32), which incorporates the Storey (76) method of false discovery rate estimation in the form of adjusted q values, was used. Taxa were filtered down to a final list of 547 taxa by the following criteria: q < 0.05, raw MFI fold change > 2, and Mann-Whitney U test P < 0.03. Genera to which belonged taxa within the final list were visualized with the Interactive Tree of Life (77, 78) (iTOL, http://itol.embl.de/).

Comparisons of disease-associated microbial genera to immunologic parameters

The R software package “multtest” was used to calculate Spearman rho correlation coefficient (RS) values for comparisons of all 562 bacterial taxa that comprise the DMC to each of the 24 immunologic parameters measured. Measures of correlations between immunologic variables and taxa were aggregated and condensed to genus-level classifications for visualization purposes in Fig. 3A, using the following methodology: For bacterial genera that were represented by more than one taxon in the DMC, RS values representing correlations between taxa belonging to the same genus and an immunologic parameter were averaged to yield an RSave that represents the correlation between relative abundance of that genus and each immune parameter. This was performed on the remaining immunologic parameters and all genera represented by more than one taxon, and was visualized with Cluster3 and Java TreeView (version 1.1.6r2). To represent unadjusted significance values for each correlation shown in Fig. 3A, boxes were made to surround each correlation for which unadjusted P < 0.02. For genera that were represented by more than one taxon in the DMC, the median unadjusted P value for comparisons among immunologic parameters and taxa within that genus was used. Choice of mean for computing aggregated RS and median for aggregated unadjusted P values for each genus was informed by assessment of global RS and P value distributions (normal and nonnormal, respectively; fig. S9).

Principal components analysis

To quantify trends in covariance among taxa that have similar patterns in relative abundance across all subject samples, a PCA was performed within the R programming environment (package “prcomp”). This allowed the condensation of trends in covarying microbial abundances into discrete variables, thereby yielding single measures that represented broad trends in abundance of multiple taxa. To reduce the number of relationships tested, Spearman rank correlations with immunologic parameters were calculated only for the principal component that represented the greatest portion of covariance (PC1), and the resultant P values were adjusted for multiple comparisons by the Benjamini-Hochberg technique (79).

Multiple linear regression model selection analysis

Among treated subjects, associations were confirmed with linear regression, allowing for the inclusion of a variety of adjustment variables comprising age, race, antibiotic use, and duration of HAART, as well as the first five principal components representing the greatest proportion of microbial abundance covariance (PC1, PC2, PC3, PC4, and PC5). All possible models were fit and compared according to their predictive accuracy. Predictive accuracy was defined as the mean squared error (MSE) calculated from LOO-CV. Under this approach, the model is fit repeatedly, leaving out one observation each time to assess a given model’s ability to predict new observations. For each outcome, the model with the lowest LOO-CV MSE was chosen. Given the small number of observations available, P values were then obtained by permutation of residuals (80).

Analysis of kynurenine pathway enzyme annotations and associations with plasma Kyn/Trp ratios

The UniProt Consortium database (52) (http://www.uniprot.org) was queried for all annotated bacterial homologs of enzymes within the pathway to catabolize tryptophan to 3-HAA (E.C. numbers 1.13.11.11, 3.5.1.9, 3.7.1.3, and 1.14.13.9). Phylum classifications for each annotation were combined, counted, and represented as pie charts with colors representing phyla as denoted in the legend of Fig. 5A. Totals represent numbers of individual genera within the domain Bacteria that bear annotated homologs in the highly curated and comprehensive UniProt database for each respective enzyme at the time of publication.

Spearman correlation P values were calculated for each taxon in relation to subject plasma Kyn/Trp ratios. For Fig. 5C, each genus was ranked on the basis of P values of the taxon with the strongest correlation (as defined by the taxon with the lowest P value within a given genus). Annotations for tryptophan catabolism enzymes for all sequenced species and strains within each genus were considered when counting numbers of tryptophan catabolism enzymes for each genus.

Pseudomonas fluorescens kynurenine production in bacterial cultures

Pseudomonas fluorescens strain A506 (American Type Culture Collection 31948) was grown in King’s B Medium (81) within U-bottomed 96-well polystyrene culture plates at an initial inoculum of 2.85 × 107 colony-forming units per well. King’s B Medium was prepared with Bacto Proteose Peptone No. 3, which, according to manufacturer-provided information, would contain 0.3 mM in the King’s B Medium preparation. Thus, bacterial cultures contained the free tryptophan already present plus 0, 0.5, 1.0, and 5.0 mM tryptophan added exogenously. Cultures were grown at room temperature and, at 20 hours after inoculation, were spun at 12,000 rpm for 10 min in a tabletop microcentrifuge. Supernatants were filtered through a 0.22-μm filter, and kanamycin was added to a final concentration of 50 μg/ml. Liquid chromatography–tandem mass spectrometry was used to assay concentrations of kynurenine and tryptophan in growth culture supernatants as described previously (9). Three temporally independent biological replicates were assessed for each condition.

Supplementary Materials

www.sciencetranslationalmedicine.org/cgi/content/full/5/193/193ra91/DC1

Fig. S1. Gross bacterial community metrics do not differ significantly across subject groups or intrasubject samples.

Fig. S2. Comparisons of qPCR measurements and PhyloChip relative abundance measurements (MFI) for selected taxa of interest.

Fig. S3. Genus and immunologic parameter identities for genera bearing one or more correlation with unadjusted P < 0.005, from analyses including all HIV-infected subjects studied.

Fig. S4. PCA characteristics.

Fig. S5. HAART causes a shift in DMC composition toward the healthy, uninfected state in two subjects.

Fig. S6. Comparison of adjustment variables (antibiotics usage, days on HAART, age) to PC1.

Fig. S7. Relationships between IDO1 expression, IDO1 activity, T cell differentiation, and type II interferon.

Fig. S8. Bacterial taxa that predict plasma Kyn/Trp ratios in a multivariate machine-learning analysis preferentially encode genetic homologs of tryptophan catabolism enzymes involved in the kynurenine pathway.

Fig. S9. Data distribution of RS and P values for all taxa compared to all immunologic variables in Spearman correlation tests.

Table S1. Patient cohort data.

Table S2. DMC taxon phylogenetic classifications and statistical analyses.

Table S3. Spearman correlations between individual taxa of the DMC and markers of disease progression, as calculated among all HIV-infected subjects.

Table S4. Spearman correlations between PC1 and immunologic variables among all HIV-infected subjects.

Table S5. Spearman correlations between PC1 and immunologic variables among HIV+ subjects on HAART.

Table S6. Comparisons of PC1 marker of dysbiosis to gut inflammatory gene expression in whole rectosigmoid biopsy specimens.

Table S7. Spearman correlation to Kyn/Trp genus ranks, and analysis of kynurenine pathway metabolic enzymes (those involved in metabolism of tryptophan to 3-HAA) in bacteria.

References and Notes

  1. Acknowledgments: We would like to acknowledge S. Yukl for HIV viral load measurements, M. Krone for cohort characteristics data, M. Segal and S. Glantz for statistical analysis consultation, and J. Robinson, B. Clagett, S. Sirdeshmukh, and D. Dorazio for assistance in plasma inflammatory marker analyses. We would like to additionally thank P. Baum, W. Yonemoto, J. Mar, E. Krow-Lucal, M. Lowe, A. Chitre, C. Baker, B. Kanwar, T. Burt, N. Nagalingam, K. Fujimura, M. Rauch, and S. Galang for helpful discussion. Funding: This study was funded by NIH NRSA 5T32AI007334-23, NIH U19 AI96109, NSF 1144247, NIH UCSF-GIVI Center for AIDS Research P30-AI027763, and the Harvey V. Berneking Living Trust. J.M.M. is a recipient of the NIH Director’s Pioneer Award Program, part of the NIH Roadmap for Medical Research, through grant number DPI OD00329. Additional support was provided by the American Foundation for AIDS Research (107854-48-RGRL to J.M.M. and S.G.D.), the Hurlbut-Johnson Fund administered by the AIDS Research Institute at UCSF (to R.M.D.), R37 AI040312 (to J.M.M.), K23 CA157929 (to M.S.), F32 AI091534 (to R.M.D.), the California HIV/AIDS Research Program (ID09-SF-067 to P.W.H.), and the UCSF Clinical and Translational Science Institute (RR024131). Author contributions: I.V.-C., R.M.D., M.J.B., S.V.L., and J.M.M. designed the experiments. I.V.-C. and S.I. performed PhyloChip experiments. I.V.-C., M.C.M., R.D.H., and S.I. performed PhyloChip analysis. R.M.D. and R.G.A. performed flow cytometry and analysis. M.S., P.W.H., and S.G.D. recruited subjects and obtained human samples. Y.H. and M.M.L. provided plasma inflammatory marker measurements. I.V.-C., R.M.D., J.M.M., and S.V.L. wrote the manuscript. Competing interests: S.V.L. is a member of the Scientific Advisory Board of Second Genome Inc. The remaining authors declare that they have no competing interests. Data and materials availability: PhyloChip data used in this study are available at the Gene Expression Omnibus Web site (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE43746.
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