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Gut microbiota diversity predicts immune status in HIV-1 infection

Nowak, Piotra; Troseid, Mariusb,c; Avershina, Ekatarinad; Barqasho, Babiloniae; Neogi, Ujjwale; Holm, Kristianc,f,g; Hov, Johannes R.c,f,g; Noyan, Kajsae; Vesterbacka, Jana; Svärd, Jennye; Rudi, Knutd; Sönnerborg, Andersa,e

Author Information
doi: 10.1097/QAD.0000000000000869



The gut-associated lymphoid tissue (GALT) is an essential part of the immunological network that maintains integrity of the gastrointestinal tract against microbes in the gut lumen, denoted as the gut microbiota [1]. HIV type 1 (HIV-1) infection starts with a massive destruction of CD4+ T cells residing in the GALT, leading to structural and functional changes in the mucosal immunity [2,3][2,3]. Thus, CD4+ T cells, especially T-helper 17 (Th17) cells, which have a crucial role in maintaining the gut immunological barrier, are depleted early during HIV-1 infection [4].

The depletion of Th17 T cells creates a pathogenic milieu where the supervision of microbial species is less effective, leading to increased permeability of the gut–blood barrier [5]. Hence, parts of the bacteria enter the systemic circulation in a process termed as microbial translocation, contributing to inflammation, immune activation, and ultimately exhaustion of the immune system [6,7][6,7]. Additionally, the alterations in the mucosal immune system could affect the gut microbiota composition [8,9][8,9]. Thus, changes in the gut microbiota during chronic HIV-1 infection are likely to contribute to low-grade inflammation in the gut and consequently also in the circulation, thereby fueling and maintaining the HIV-associated immune dysfunction [10]. Antiretroviral therapy (ART) appears to at least partially restore the gut integrity, although microbial translocation and inflammation are not reduced to the levels seen in uninfected persons [11–13][11–13][11–13]. Also, the impact of ART on the gut microbiota is not well described.

In the past year, cross-sectional studies have demonstrated changed gut microbiota in HIV-1-infected patients, but there are no published longitudinal studies reporting the effect of ART on microbiota composition (reviewed in [14]). We hypothesized that alterations of the gut microbiota would predict immune dysfunction and HIV-1 progression, and that introduction of ART would partially restore the gut microbiota composition.


Study participants

An observational cohort of 31 HIV-1-infected individuals was recruited from the HIV Outpatient Clinic at Karolinska University Hospital, Stockholm, Sweden (Table 1 and Supplementary Table ST1, Additionally, a sex and age-matched control group of nine healthy HIV-1-seronegative individuals healthy controls (CTR) was included, consisting of household members and partners of the patients, to avoid major differences in food intake, ethnicity, and social background. Stool and peripheral blood samples were collected from all study participants at baseline and for 19 patients at follow-up (median 10 months; interquartile range 4–15) after ART introduction. ART consisted of two nucleoside reverse transcriptase inhibitors (NRTIs) in combination with a non-nucleoside reverse transcriptase inhibitor (NNRTI; n = 8) or a ritonavir-boosted protease inhibitor (n = 11). Neither patients nor controls had been prescribed antibiotics or consumed probiotics during the preceding 2 months, or had infectious diarrhea. The Regional Ethical Committee (Stockholm, Sweden, Dnr 2009-1485-31-3) had approved the study. All participants provided written informed consent in accordance with the Declaration of Helsinki.

Table 1:
Cohort characteristics at baseline.

T-cell populations and viral load

Analyses of CD4+ and CD8+ T-cell counts and plasma HIV-1 RNA load were performed as part of the clinical routine with flow cytometry and Cobas Amplicor (Roche Molecular Systems Inc., Branchburg, New Jersey, USA), respectively.

DNA extraction from stool samples and 16s rRNA sequencing of gut microbiota

Stool samples were collected and stored at −80°C until further use. The samples were processed and DNA was extracted as previously described [15]. Extracted DNA was PCR-amplified, targeting the 16S rRNA gene using universal primers (for all prokaryotic organisms) flanking V3–V4 region of 16S gene modified with addition of TruSeq Illumina adapters [15]. PCR amplification consisted of 30 cycles of 95°C for 30 s, 50°C for 30 s, 75°C for 45 s, and followed by a final elongation at 72°C for 7 min. Each PCR-amplicon was quantified using Quant-iTTM PicoGreen dsDNA kit (Life Technologies, Grand Island, New York, USA), then normalized and pooled in equal concentrations. The pooled amplicon was purified with E.N.Z.A. Cycle Pure Kit (Omega Bio-Tek, Norcross, Georgia, USA) and eluted in 0.1 mmol/l Tris buffer (pH 8.5). Sequencing was performed on the Illumina MiSeq platform using the 150 bp and then replicated with 300 bp paired end protocol (Illumina, San Diego, California, USA) at Norwegian Sequencing Centre (Oslo, Norway). The two analyses showed good correlation (r = 0.81, P < 0.001), and the results are presented based on the 300-bp sequencing.

Sequence analysis

Reads were demultiplexed, preprocessed, and subsequently sequences were analyzed with QIIME ver.1.8.0 (Quantitative Insights into Microbial Ecology) software package. To ensure even amount of information, 10 000 sequences per sample were randomly selected from the full dataset. Taxonomy classification was performed using Uclust classifier and Greengenes ver. 13.8 Operational taxonomic units (OTUs) database with 97% cluster identity [16]. Alpha diversity was calculated using number of observed species, Shannon, and reciprocal Simpson's diversity indexes, respectively. To assess interindividual diversity (beta diversity), we performed a principal coordinate analysis (PCoA). Additionally, comparisons were performed with the weighted Unifrac and Bray–Curtis indices, using MATLAB R2013a software (MathWorks, Natick, Massachusetts, USA). The sequence data are deposited at the EMBL Nucleotide Sequence Database (accession number pending).

Microbial translocation markers

Limulus Amebocyte Lysate (LAL) (Lonza; Basel, Switzerland) and Human sCD14 Quantikine ELISA (R&D, Minneapolis, Minnesota, USA) were used to measure plasma lipopolysaccharide (LPS) and soluble CD14 (sCD14), respectively, as described [12]. Additionally, soluble sCD163, IL-6 (R&D), LPS binding protein (LBP) (Hycult Biotech, Uden, The Netherlands), and D-Dimer (Technoclone, Vienna, Austria) were determined in plasma samples by ELISA according to the manufacturers’ instructions.

Statistical analysis

Experimental variables between the two groups of individuals were analyzed using the Mann–Whitney U test in QIIME and Wilcoxon matched-pairs rank test in R statistical software (http:// Alpha-diversity indexes were compared in QIIME using a nonparametric t test based on Monte Carlo permutations, whereas Kruskal–Wallis tests were used for beta-diversity comparisons. For comparisons between baseline and follow-up samples, Wilcoxon's signed-rank test was used. Correlations were assessed using nonparametric Spearman's rank tests. Given the small sample size, multivariate linear regression models included only age and sex as covariates. Models including a third covariate were created, adding variables one at a time, on the basis of significant associations in the univariate analyses or potential impact on gut microbiota and immune status. Statistical assumptions for the use of the linear regression model were fulfilled. A two-tailed significance level of 0.05 was used. The statistical analyses were performed with SPSS software, version 19.0 (SPSS Inc, Chicago, Illinois, USA). P values were corrected for multiple testing using false discovery rate (FDR) as indicated in the text.


Study cohort

The cohort consisted of 31 patients of whom 28 had detectable plasma viral load (>20 copies/ml) at baseline and are further referred to as viremic patients. The remaining three participants were classified as elite controllers [undetectable HIV-1 RNA since diagnosis of HIV-1 infection (median 20 years)]. Six patients (among viremic patients) had a highly deteriorated immune status at baseline (median CD4+ T-cell count 135 cells/μl; range 120–150) and they are referred to as immune defect patients. Fifteen of the 19 patients who received ART had undetectable viral load at follow-up, and the remaining four had low levels (median 60 copies/ml; range 29–224). Median CD4+ T-cell recovery for the whole treated group at follow-up was 140 cells/μl (range 0–740). The analyzed dataset contained sequences from 31 patients and nine control individuals at baseline. Sixteen (of 19) patients who initiated ART had follow-up data (≥10 000 sequences per sample).

Decreased alpha diversity in untreated HIV-1-infected patients

At baseline, the number of observed bacterial species was lower in the HIV-1-infected patients as compared to the controls, independent of the number of bacterial sequences analyzed per sample (Fig. 1a) and independent of whether the three elite controllers were included or not. The lowest number of bacterial species was present among the patients with the lowest CD4+ T-cell counts (immune defect patients) (Fig. 1b).

Fig. 1:
Differences of alpha diversity indexes in untreated HIV-1-infected patients as compared to controls at baseline.(a) Number of observed bacterial species in relation to number of analyzed sequences. (b) Distribution of number of observed bacterial species. (c) Shannon alpha-diversity index. (d) The distribution of CD4+ T cells in viremic patients after stratification into those with low or high diversity (on the basis of median Shannon diversity). P values are generated by a nonparametric test based on Monte Carlo permutations (b, c) and by Mann–Whitney U test (d). VP*, viremic patients without ID patients (n = 22); ID, patients with CD4+ T-cell counts below 200 cells/μl (n = 6); EC, elite controllers (n = 3); CTR, healthy controls (n = 9).

At baseline, the number of observed species correlated positively with CD4+ T-cell count (r = 0.59, P = 0.009), CD4+% (r = 0.54, P = 0.02), CD4+/CD8+ ratio (r = 0.53, P = 0.003), and negatively with LPS (r = −0.51, P = 0.007), LBP (r = −0.45, P = 0.01), sCD14 (r = −0.42, P = 0.02), and sCD163 (P = −0.48, P = 0.02) (Supplementary Table ST2,

To further characterize the alpha diversity of the bacterial population in each participant, we calculated Shannon and Simpson index, respectively. At baseline, the Shannon index (P = 0.04) and the Simpson index (P = 0.02) were significantly lower in HIV-1-infected patients compared to controls (Fig. 1c). Similarly to the observed species results, the individuals with the lowest CD4+ T-cell counts had the lowest alpha diversity (P = 0.01). Furthermore, stratifying the patients at median Shannon index (5.68) revealed that the patients with lower alpha diversity had significantly lower CD4+ T-cell count (P = 0.01) and CD4+% (P = 0.03) compared to patients with higher alpha diversity (Fig. 1d). In line with the observed species results, the Shannon alpha-diversity index correlated significantly with several parameters of HIV-1 progression and microbial translocation, although the correlations were slightly weaker (Supplementary Table ST2, There was no correlation between the number of observed bacterial species or alpha diversity and levels of IL-6, D-dimer, age, sex, BMI, country of origin, or ethnicity.

Number of observed species and Shannon index are independent predictors of CD4+ T-cell count in viremic patients

Given the strong associations in the univariate analyses, we performed multivariate linear regression analyses with CD4+ T-cell count as dependent variable and observed species as well as Shannon index as predictors (Table 2). For every age and sex-adjusted increase in the number of bacterial species, the CD4+ T-cell count increased with 0.88 [95% confidence interval (CI) 0.35–1.41] units (P = 0.002). Adding a third covariate (ethnicity, HIV-1 RNA load, BMI, LPS, LBP, sCD14, sCD163) to the model one at a time did not change the independent association between the observed bacterial species and the CD4+ T-cell count. LPS was the only covariate that was independently associated with CD4+ T-cell counts (adjusted beta = −0.77, 95% CI −1.44, −0.11, P = 0.025). In a model containing all covariates, LPS levels (P = 0.037) and number of bacterial species (P = 0.034) were the only factors independently associated with CD4+ T-cell counts. Similar results were seen for Shannon alpha diversity as a predictor of CD4+ T-cell counts (Table 2).

Table 2:
Number of observed species and Shannon alpha diversity as independent predictors of CD4+ T-cell counts in viremic patients (n = 28).

Elite controllers have a lower interindividual variation in gut microbiota composition

To compare the interindividual taxa differences among the groups, we calculated the beta diversity, using the weighted Unifrac and Bray–Curtis diversity indexes. The weighted Unifrac analysis revealed increased beta diversity in the HIV-1-infected patients at baseline as compared to the controls (P < 0.001), whereas the three elite controllers had the lowest beta diversity and hence the lowest interindividual variation (Fig. 2a). The Bray–Curtis diversity index analysis yielded similar results (data not shown). In principal coordinate analysis based on beta-diversity results, the three elite controllers clustered along PC1 and PC2, whereas the gut microbiota of viremic patients and controls were overlapping (Fig. 2b).

Fig. 2:
Characterization of beta diversity and microbiota composition at baseline.Differentiation in beta diversity in viremic patients (VP), elite controllers (ECs), and healthy controls (CTR) presented as weighted Unifrac diversity index (a) and principal coordinate analysis (b). P value was generated by Kruskal–Wallis test. Distribution of five most abundant bacterial phyla in patients and controls at baseline (c). Heat map illustrating the baseline differences in bacterial genera between three categories of patients (in separate columns): controls, HIV-1 (viremic) and ECs (d). The 45 bacterial genera with more than 0.01% abundance, present at least in 25% of the patients in HIV-1-positive group, are included (rows). Arrows indicate increase (red) or decrease (blue) of bacterial genera between viremic patients and controls. The asterisk mark ‘*’ by the bacterial genus indicates statistically significant difference between viremic patients and controls (P < 0.05 derived from Mann–Whitney analysis).

Differences in microbiota composition between elite controllers and viremic patients

Because of the distinct clustering of elite controllers in the beta diversity and PCoA analyses, we analyzed the microbiota composition in viremic patients (n = 28) compared to both control individuals (n = 9) and elite controllers (n = 3). The most common phyla were Firmicutes, Bacteroidetes, and Actinobacteria, accounting for 94–95% of the overall microbes in all the groups (Fig. 2c). The most evident difference on phylum level was a higher relative abundance of Bacteroidetes in the elite controllers compared to the viremic patients (P = 0.02), whereas Actinobacteria were enriched in viremic patients compared to the elite controllers (P = 0.02). Among the less abundant phyla, there was an increased relative abundance of Proteobacteria in viremic patients compared to elite controllers (P = 0.02). No significant differences were observed between viremic patients and control individuals on phylum level.

On genus level, we could observe increased relative abundance of Lactobacillus in viremic patients compared to uninfected control individuals (P = 0.002). Additionally, the genera of Lachnobacterium, Faecalibacterium, and Hemophilus were significantly reduced in viremic patients compared to control individuals (P = 0.018, 0.008, and 0.04, respectively; Fig. 2d).

Reduction in alpha diversity after introduction of antiretroviral therapy

After the introduction of ART, we found a moderate, yet significant decrease in the number of observed species (P = 0.001) and Shannon index (P = 0.006) compared to baseline (Fig. 3a). The changes of the bacterial taxa within the individuals were reflected by the overall significant (P < 0.001) increase in beta diversity (Fig. 3b). Additionally, the microbiota composition at follow-up changed for several taxa on phylum and genus level (Fig. 3c, d). Hence, genera of Lachnospira (P = 0.002), Oribacterium (P = 0.007), and Oscillospira (P = 0.04), belonging to phylum Firmicutes, were significantly reduced. Also, the abundance of Sutturella genus (phylum Proteobacteria) significantly decreased (P = 0.03). Finally, Prevotella genus (phylum Bacteroidetes) was significantly depleted after ART (P = 0.0007). The latter difference remained significant after adjustment for multiple testing (P = 0.007). Prevotella levels were not different after ART as compared to healthy controls (P = 0.08, data not included). We did not observe any influence of different ART regimes (NNRTI vs. protease inhibitor) on richness or intra/interdiversity at follow-up.

Fig. 3:
Changes of Shannon alpha diversity (intrapatients) (a) and beta diversity (interpatients) indexes (b), as well as microbiota composition (c) at phylum level after introduction of ART.P values are generated by Wilcoxon signed-rank test. Heat map illustrating changes of genera after antiretroviral therapy (ART) introduction (d). The data from individual patients (n = 16) are presented in separate columns. The 45 bacterial genera with more than 0.01% abundance, present at least in 25% of the patients in HIV-1-positive group are included (rows). Arrows indicate increase (red) or decrease (blue) of bacterial genera after ART. The asterisk mark ‘*’ by the genus indicates statistically significant difference (P < 0.05 derived by Wilcoxon signed-rank test). Patients who received non-nucleotide reverse inhibitors (NNRTIs; n = 8) or protease inhibitors (PIs; n = 8) are included in appropriate column.


The main finding of our study was that HIV-1-infected viremic individuals had an altered gut microbiota compared to controls, and that the alpha diversity was independently associated with CD4+ T-cell counts. This association was strong in magnitude, with approximately 0.9 CD4+ T cells/μl gained for each bacterial taxon added to the gut microbiota. Furthermore, patients with immunodeficiency had the lowest alpha diversity. Our study is the first that closely links a reduced diversity of the gut microbiota to immune dysfunction and reduced CD4+ T-cell counts, suggesting an important role of microbiota diversity in maintaining the balance of the host immune system.

Previously, a low alpha diversity has been reported in conditions with chronic gut inflammation [17] and metabolic disorders including obesity [18]. Also, our results are in line with recent reports of reduced alpha diversity in rectal mucosa-associated microbiota during HIV-1 infection [19,20][19,20]. Interestingly, the number of bacterial species and Shannon index were also significantly associated with markers of microbial translocation and monocyte activation. Thus, in addition to alpha diversity, LPS levels were independently and inversely associated with CD4+ T-cell counts. Hence, our results suggest that alterations in microbiota composition are closely associated with both microbial translocation and immune dysfunction in progressive HIV-1 infection.

Whether an altered gut microbiota contributes to or is caused by immune dysfunction cannot be determined in a cross-sectional cohort. We therefore followed a subset of patients after the introduction of ART, as one of the first longitudinal studies in this area. To our surprise, the alpha diversity did not increase after effective ART with viral suppression. On the contrary, we found that individual microbiota compositions became less diverse and with a larger interindividual variation in HIV-1-infected individuals during the 10-month period of follow-up. Inflammation fueling mucosal immune reconstitution, remaining viral replication throughout the gut mucosa, as well as direct drug effects on the bacterial population, may have contributed to the increased alterations in the gut microbiota during ART [10]. Antimicrobial effects of ART have previously been discussed, and have been demonstrated for protease inhibitors against Candida species and cryptococcal infection [21]. However, we could not observe any differences between classes of ART. In line with our results, cross-sectional studies could not show a complete restoration of microbiota in patients on ART as compared to controls [19,22][19,22]. Furthermore, results from simian immunodeficiency virus-infected macaques show early microbiome changes after introduction of ART [23]. Hence, future studies should aim for longer follow-up to assess the long-term effects of ART on the gut microbiota.

The microbiota of three elite controllers was distinctly different from that of viremic patients, and more similar to the composition found in healthy individuals. This was confirmed by analysis of the beta diversity, which showed less interindividual variation in both elite controllers and healthy controls as compared to the individuals with uncontrolled HIV-1 infection. Although the clustering between three elite controllers could have occurred by chance, our data are in agreement with a recent study showing that the microbiota of one long-term progressor was more similar to healthy controls than to viremic patients [22]. Hence, although no firm conclusion can be drawn on the basis of only three individuals, our results could imply a mucosal factor that potentially controls viremia and counteract changes in the composition of the gut microbiota. In-depth studies of the gut microbiota in larger cohorts of elite controllers could answer this hypothesis.

As a consequence of the structural defect in the gut mucosa and the associated changes of the microbial flora [10], clinical manifestations such as diarrhea, weight loss, and malnutrition are frequently observed in untreated HIV-1-infected individuals [24]. However, systemic symptoms can also be related to the change in the gut microbiota. Thus, a study investigating the bacterial population in recto-sigmoid biopsies showed associations between a dysbiotic microbiota and a number of disease progression markers including systemic inflammation and tryptophan catabolism [22]. Additionally, analysis of colon biopsies and fecal samples have shown enrichment of Proteobacteria and decreased abundance of Firmicutes [25]. The results from our study are generally in line with those reported in the literature, such as enrichment of Proteobacteria and reduced alpha diversity in viremic patients, although the findings reported on the genus level differ from the earlier studies [14,22,25][14,22,25][14,22,25]. Interestingly, our patients had significantly increased relative abundance of genus Lactobacillus – a finding that has not been reported earlier. In HIV microbiome studies, Perez-Santiago et al.[26] observed associations between higher proportions of order Lactobacillales from anal swabs of recently HIV-1-infected individuals with a favorably immunological profile. However, the results from clinical trials in which strains of Lactobacillus were used as probiotics in HIV-1 patients have been conflicting [27–30][27–30][27–30][27–30]. Although Lactobacillus is mostly considered an anti-inflammatory commensal, the genus contains more than 100 species, and recent data showed increased abundance of Lactobacillus in patients with type II diabetes [31,32][31,32]. Hence, further studies to delineate the potential role of various Lactobacillus strains in disease and as probiotics should preferably apply methodology allowing species or strain-level resolution as suggested recently [33].

Whereas the genus Prevotella has been found to be enriched in HIV individuals in other studies [20,25,34,35][20,25,34,35][20,25,34,35][20,25,34,35], this was not observed in our cohort. However, the abundance of Prevotella was significantly decreased after ART, suggesting an involvement in HIV-driven inflammation. Finally, genera Lachnobacterium, Faecalibacterium, and Hemophilus were significantly reduced in viremic patients compared to controls. The depletion of genus Faelicobacterium has been reported earlier in HIV-1 infection and in Crohn's disease [20,35,36][20,35,36][20,35,36]. It is considered to be a major butyrate producer, which has also anti-inflammatory properties apart from being an important energy source [37].

The differences between our results and previous microbiota data may have several explanations apart from methodological differences (different sampling techniques and bacterial classification methods). Thus, our HIV-positive cohort had a wider range of CD4+ T-cell counts and included severely immune-deficient patients compared to those earlier published, which may uniquely affect microbiota composition [14]. Moreover, an influence of a geographical difference in the ‘background’ microbiota cannot be dismissed [38].

We acknowledge some limitations of our work. First, the influence of diet was not controlled for. Second, the blood samples were not collected fasting which could influence the plasma levels of LPS [39]. However, the indirect markers of microbial translocation and monocyte activation such as LBP, sCD14, and sCD163 are less affected by nonfasting conditions, and it is reassuring that their associations with the observed number of bacterial species and alpha diversity had a similar strength. Third, analysis of stool samples is most commonly used in microbiota studies, although analysis of bacterial populations from both stool and intestinal biopsies would have been optimal [40]. Thus, the analysis of luminal microbiome could underestimate the Proteobacteria which are increased in mucosal samples [14,41][14,41]. Additionally, the group of healthy controls was not matched for ethnical background, which could affect the patient–control evaluations. Moreover, the evaluation of ‘immune dysfunction’ markers in the cohort was only restricted to CD4+/CD8+ T-cell counts and plasma analytes.

Our study, however, also has obvious strengths, in particular, the extensive characterization of the patients, as well as the longitudinal design. Collectively, our study shows that the diversity and composition of the gut microbiota are changed in viremic patients, and are associated with microbial translocation, monocyte activation, and immune dysfunction. Most importantly, the reduced diversity of the gut microbiota independently predicts a lower CD4+ T-cell count in viremic patients, and this dysbiosis is not reversed by the introduction of ART, at least not during the 10 months of follow-up. Hence, our study gives support to the rationale of gut microbiota as a potential therapeutic target in HIV-1 infection [10,42][10,42].


The study was supported by Stockholm's County Council (SLL-KI), Swedish Physician Against AIDS Research Fund, Swedish Research Council, and Swedish Society for Medical Research (SSMF for Piotr Nowak).

Author contributions: Conceived and designed the experiments: P.N., M.T., and A.S. Coordinated the samples collection: B.B. and J.V. Performed the experiments: E.A., B.B., J.S., and K.N. Analyzed the data: E.A., K.H., J.S., J.R.H., K.R., U.N., M.T., and P.N. Wrote the paper: P.N. and M.T. Reviewed and/or edited the manuscript: all authors.

Conflicts of interest

All authors declare no conflict of interest.


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elite controllers; microbial translocation; microbiota; Shannon alpha-diversity index

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