Effects of HIV, antiretroviral therapy and prebiotics on the active fraction of the gut microbiota : AIDS

Secondary Logo

Journal Logo


Effects of HIV, antiretroviral therapy and prebiotics on the active fraction of the gut microbiota

Deusch, Simona,*; Serrano-Villar, Sergiob,*; Rojo, Davidc,*; Martínez-Martínez, Mónicad; Bargiela, Rafaeld,†; Vázquez-Castellanos, Jorge F.e,f; Sainz, Talíag; Barbas, Coralc; Moya, Andrése,f; Moreno, Santiagob; Gosalbes, María J.e,f; Estrada, Vicenteh; Seifert, Janaa; Ferrer, Manueld

Author Information
doi: 10.1097/QAD.0000000000001831



Recent works have underlined that HIV-infected individuals harbor a distinct microbiota [1–12]. The HIV-associated microbiota is thought to be a consequence of a combination of factors, including HIV infection, its treatment or sexual orientation, yet ongoing hypothesis that some bacteria might evolve in response to HIV infection or in response to interventions to help controlling disease progression begins to take shape [7,8,13,14]. In agreement with this, probiotics and/or prebiotic supplementation [SLV#3, Culturelle; prebiotic inulin; short-chain galactooligosaccharides/long-chain fructooligosaccharides/pectin hydrolysate-derived acidic oligosaccharides (scGOS/lcFOS/pAOS)] in simian immunodeficiency virus (SIV)-infected macaques [15] and HIV-infected persons [16,17] have been found to improve the frequency and functionality of gastrointestinal antigen-presenting cells, to enhance reconstitution and functionality of CD4+ T cells, to reduce fibrosis of lymphoid follicles in the colon and to cause a significant reduction in the inflammatory predictors of mortality IL-6 and D-dimers and a moderate but significant increase of butyrate production and amelioration of inflammatory biomarkers soluble CD14 and high-sensitivity C reactive protein [15–17].

Most HIV-microbiota studies have largely relied on the sequencing of 16S ribosomal gene (16S rRNA) to infer the composition of the total bacterial community and its relation with disease markers [1,4,5,7,18]. By applying this method, increased abundance of bacteria assigned to the genera Bifidobacterium, Lactobacillus, Faecalibacterium and Lachnospira strongly correlated with markers of clinical progression during HIV disease [9,19]. However, it is increasingly recognized that a large proportion of the bacteria colonizing a person's colon are dead, dormant or inactive and that some microbes only become active under certain conditions but not others [20]. Therefore, it is important not only to decipher which bacteria are altered during interventions in the context of HIV infection, but also to assess which of them are activated, as the activated fraction may more profoundly affect immune recovery. The identities and abundances of active community members can be estimated by monitoring the 16S ribosomal genes generated from cDNA (so-called RNA-seq), and by the taxonomic binning of sequences from genes found to be expressed in the metatranscriptome and proteins found to be synthesized in the metaproteome, as they are transcriptionally active [7,20]. In this study, we used the term ‘active fraction of the microbiota’ to refer to bacteria that are actively synthesizing proteins when examining the metaproteome of bacteria from feces.

We have recently performed a blinded randomized study in which the composition of total gut bacteria in HIV-infected and healthy individuals after a short supplementation with prebiotics (scGOS/lcFOS/glutamine) was evaluated. Viremic untreated (VU) patients (n = 12) and antiretroviral-therapy-treated virally suppressed patients that are either immunological responders [≥350 CD4+ T-cell counts/μl after >2 years of viral suppression; immunological responders (IR); n = 15] or immunological nonresponders (<350 CD4+ T-cell counts/μl; INR; n = 8) and nine healthy controls were included. We found that the intervention ameliorates dysbiosis of total gut bacteria, particularly among viremic untreated patients. Here, we have extended this analysis with a characterization of the fecal metaproteome, to detect differences at a level of active bacteria. As this work is an exploratory study and given the highly resource-consuming nature of the proteomic analyses, we decided to perform this study evaluating the effects of the dietary prebiotic supplementation in a subset of patients with best adherence to the intervention (>90%). Particularly, from the total number of 44 individuals for which total bacterial composition was evaluated [9], a subset of six HIV-uninfected individuals, and five VU, seven IR and four INR patients were selected for proteomic analysis. The population structure of active gut bacteria of each individual was obtained by applying a meta-proteomic approach, and further compared with that of total gut bacteria of the same subset of individuals previously obtained using deep 16S rRNA gene sequencing [9]. We found that the positive influence of the prebiotic therapy in the microbiome activity, by meaning of the activation of key beneficial bacteria, was most evident in the viremic untreated subgroup of patients. This, together with the fact that the intervention ameliorates total gut dysbiosis in those patients as compared with ART-treated patients who were found more resilient to the intervention [9], suggests that the potential positive effects of prebiotics become particularly apparent in patients not receiving ART. Altogether, this work expands previous knowledge in the field of HIV infection by characterizing for the first time the effects of a prebiotic intervention on the active fraction of the microbiota in HIV-infected individuals.

Materials and methods

The study design, participants, setting and eligibility and description of prebiotic intervention (including placebo) was described recently [9]. Protein extraction, mass spectrometry, data analysis and taxonomic assignations were performed as reported previously [8]. Briefly, we used an optimized protocol [8,20] to isolate proteins from fecal material of 22 individuals at baseline and post/placebo-intervention (44 samples in total). It involves a pretreatment step to enrich for microbial cells that are then disrupted to recover the proteins. This sample processing method has been shown to be critical in allowing a proper representation of microbial proteins in fecal material [8,20]. In total, 10 635 quality-filtered proteins were obtained from the 44 samples (1390 ± 485 proteins per sample). The composition of active bacteria was inferred by binning proteins into family-level as described previously [8]. Shotgun proteomics does not allow deeper taxonomic characterization [8].

The linear discriminant analysis effect size (LEfSe) algorithm [21] was used to identify the bacterial groups that were over-represented or down-represented in each of the four groups of the cohort before and after the intervention. The P value cutoff was set to be below to 0.001 for the Kruskal–Wallis test and 0.01 for each of the Wilcoxon test pairwise comparisons between the groups. The minimum abundance level of 1% in at least one condition (before or after the intervention) was also setup. The resulting groups with significant differences between samples were used to build the linear discriminant analysis (LDA) model and to estimate its effect as a discriminant feature among them. The threshold used to consider a discriminative feature for the logarithmic LDA score was set to more than 2. The association (Spearman-correlation P value <0.05) between the active microbiota and innate immune activation and bacterial translocation markers (Supplementary Table S1A, https://links.lww.com/QAD/B266 and Ref. [9]) was determined by applying a generalized linear regression model, using glmnet package (library ‘glment’ function ‘cv.glmnet’) as described previously [8,9].

Functional analysis of proteins was performed by assigning k numbers using GhostKOALA, kyoto encyclopedia of genes and genomes (KEGG)'s internal annotation tool for KEGG orthologies to analyze the varying abundance of specific proteins in certain pathways as defined by KEGG [22]. Paired two-sample t tests were used for pairwise comparisons of the abundance means of proteins (IBM SPSS Statistics, Version 20.0; IBM Corp., Armonk, New York, USA).

The current study was approved by the Ethics Committees of University Hospital Clinico San Carlos and University Hospital Ramón y Cajal (approval number 11/284). All the participants gave informed consent before initiation of the study procedures. The Clinical Trials Registry Identification Number Identifier (ClinicalTrials.gov) for this study is NCT01838915.


General characteristics of the study population and shotgun proteomics

A total of 22 individuals completed the 6-week course of treatment, with 16 receiving prebiotics (scGOS/lcFOS/glutamine) and six receiving a placebo (Supplementary Table S1A, https://links.lww.com/QAD/B266). These included five VU, seven IR and four INR patients, and six HIV-uninfected controls. HIV-infected individuals on ART were representative of a medium-aged population undergoing long-term treatment without comorbidities [9]. No statistically significant differences were observed among the groups in dietary habits and the intervention was well tolerated [9].

The fecal microbiota composition in potentially active state was herein evaluated by using shotgun proteomics (refer to Materials and methods section). Briefly, the activated fraction of the microbiota was defined on the basis of the phylogeny of bacteria actively synthesizing proteins, evaluated as follows. First, total proteins from bacterial cells, previously separated from fecal material, were subjected to shotgun proteomics. Second, the phylogeny of all peptides assigned to quality-filtered proteins was further obtained at family level as described previously [8]. The phylogeny of peptides corresponds to bacteria which are metabolically active, and thus, this information can be used to infer diversity parameters of presumptive active bacteria [8]. The active population was further compared with that of the total community, previously reported by 16S rRNA gene sequencing [9]. In both cases, the observed alterations that will be described below were attributable to the prebiotic intervention as the patients receiving the placebo do not show statistically significant differences with baseline samples at the level of total [9] and active (Supplementary Tables S1B and C, https://links.lww.com/QAD/B266) bacteria, and the short duration of the intervention (6 weeks) is insufficient to expect changes related to ART-mediated immune reconstitution.

Effects of the nutritional prebiotic intervention on active alpha diversity

Alpha diversity of bacteria is used to measure the richness and evenness of bacterial taxa within a community. The nutritional intervention did not result in a significant variation of the alpha diversity parameters when examining the total population structure in any of the four groups using deep 16S rRNA gene sequencing [9]; this was found at the level of total Margalef species richness, Pielou's evenness and Shannon index (Fig. 1a).

Fig. 1:
Alpha diversity parameters of potentially total (a) and active (b) microbiota before (baseline) and after (postintervention) prebiotic intervention.Alpha diversity parameters were based on the analysis of proteins identified and quantified using MaxQuant (Max Planck Institute of Biochemistry, Martinsried, Bavaria, Germany) and the 16S rRNA dataset (for raw data see Supplementary Table S1B, https://links.lww.com/QAD/B266), in the same order. Richness, Pielou's evenness and Shannon index are indicated. P values are given per each of the comparisons. The taxonomy of proteins at the family level was obtained from peptide information as described previously [8]. Analyzing the phylogeny of all peptides (after quality check) already enabled the affiliation down to family level of only approximately 50% of the peptides in each sample; the others could be either assigned to levels of phyla or order. The 16S rRNA taxonomic data for the same set of patients for which proteomic analysis was made were extracted from Ref. [9]). P values for each of the group comparisons are shown on the right.

In the healthy controls, the prebiotic intervention did not result in a significant variation of any of the three alpha diversity parameters of the bacteria being active or potentially active (Fig. 1b; P value >0.28). The microbiota of healthy controls remained the least diverse when compared with the other groups of HIV-infected individuals (P value <6.2 × 10−6). Opposite the dietary intervention elicited major changes on the active fraction of the microbiota in the HIV-infected group, particularly among VU patients. Thus, although we observed no significant differences (P value >0.28) for the richness of active species in INR and IR groups, they were evident in VU patients whose richness was the highest and slightly increased after the intervention (P value = 4.59 × 10−2). The number of species (by meaning of evenness and Shannon index); however, increased significantly in all HIV-patients after the dietary intervention (P value <2.29 × 10−2). This suggests that the prebiotic intervention provokes an enrichment of the functional bacteria among the viremic HIV patients, whose richness increased only among the untreated patients.

The proportion of active bacteria compared with total bacteria before and after the intervention was further evaluated (Fig. 2). Bacterial richness of the total community is commonly based on operational taxonomic units level, whereas protein data are only family-based data [8]. To perform comparisons between the total and active fraction of the microbiota, we considered the richness of the total fraction based on the family level. Analysis of the ratio between active-to-total alpha diversity parameters revealed that HIV infection activates an important fraction of the gut microbiota as compared with healthy controls. Thus, the richness ratio of active bacteria in all HIV-infected individuals was on the order of two-fold higher than in healthy controls (P value ≤0.05). This ratio was maintained after the intervention for the VU and INR (P value ≤0.03), but not IR (P value = 0.1), individuals.

Fig. 2:
Ratio active-to-total richness before and after the intervention.The ratio was calculated from richness data in Fig. 1. P values for each of the group comparisons are shown on the right.

Effects of the nutritional prebiotic intervention on active beta diversity

Analysis of beta-diversity revealed that proteins being actively synthesized belong to at least 21 bacterial families (Fig. 3a), which were also identified when examining total community compositions (Fig. 3b), albeit at different relative abundance. These active bacteria belong to six phyla, namely Actinobacteria, Firmicutes, Bacteroidetes, Proteobacteria, Spirochaetes and Verrucomicrobia.

Fig. 3:
Beta diversity of potentially active (a) and total (b) microbiota.The data are based on the analysis of proteins identified and quantified using MaxQuant and the 16S rRNA dataset, respectively (for raw data see Supplementary Table S1C, https://links.lww.com/QAD/B266). Abundance levels of each of the 21 families were calculated as described previously [8]. The abundance levels of families at the level of 16S rRNA were extracted, for the same set of patients for which proteomic analysis was made, from Ref. [9]. P values are given per each of the comparisons before and after the prebiotic intervention.

The taxonomic distribution of the identified expressed proteins at the family level was first analyzed by a nonmetric multidimensional scaling ordination plot (using Bray–Curtis similarity [23]). A clear separation was observed not only between the uninfected and infected groups of patients but also within the three HIV-infected subgroups before and after the intervention (Supplementary Fig. S1, https://links.lww.com/QAD/B266). As shown, the prebiotic intervention did not affect the active fraction of the microbiota in control study participants, whereas exerting detectable effects in HIV-infected individuals. To quantify this effect, we evaluated which active specific families’ abundance differed after the prebiotic treatment, and a similar analysis was performed at the level of total families’ abundance. Variations were not apparent (P value >0.05) in any of the 21 active families in healthy study participants (Fig. 3a), as it was also observed at the level of total families’ abundance (Fig. 3b). However, the active microbiota of HIV-infected patients was found more influenced after the intervention (Fig. 3a). Indeed, the abundance of active bacteria assigned to 14 (in VU), 16 (in INR) and 16 (in IR) out of 21 families were statistically altered (P value <0.05). None of the altered active taxa was common to all three groups of HIV-infected patients (Fig. 3a), suggesting that the prebiotic influenced the microbiota dormancy yet without clear directionality across all three groups of HIV-infected patients.

We applied LEfSe biomarker discovery tool, with restrictive criteria of abundance of more than 1% in at least one condition (before or after the intervention), to detect active bacterial groups most affected by the nutritional intervention in HIV-infected patients. Within the bacterial families being activated by the prebiotic intervention according to LEfSe tool, we found a major and selective effect in the Bifidobacteriaceae family (Fig. 4). In the VU group, Bifidobacteriaceae (LEfSe of 4.56 and P value = 0.0102) was the most enriched active bacterial family after the intervention (99-fold). This family also increased, to much lower extend, in IR patient (6.9-fold) with a LEfSe score of 4.35 (P value = 0.0047). However, the prebiotic intervention did not alter the abundance of active Bifidobacteriaceae in the INR patients. Noticeably, bacteria assigned to Bifidobacteriaceae were among the less abundant but most active, before or after the prebiotic intervention in VU (P value = 1 × 10−4) and IR (P value = 0.02) patients. Their abundance (5.92 and 3.93% of total active bacteria, respectively) approximated that of healthy individuals (3.5% of total active bacteria) after the intervention (P value = 0.03).

Fig. 4:
Prebiotic intervention drives selective enrichment of active bacteria assigned to Bifidobacteria.We applied linear discriminant analysis effect size algorithm with restrictive criteria of abundance of more than 1% in at least one condition. Linear discriminant analysis (LDA) scores (log10) for the most prevalent taxa among study participants after prebiotic intervention are represented in the positive scale, whereas LDA-negative scores indicate those taxa enriched in the basal (before intervention) samples.

We used the generalized linear model to explore the interactions between the abundance level of active Bifidobacteriaceae in all groups of subjects and the immunological and virological markers involved in HIV immune-pathogenesis (data available in Ref. [9]). We found a correlation (r2 = 0.82) between the degree of the thymic function and the percentage of active Bifidobacteriaceae when considering data from all four groups of individuals (Supplementary Fig. S2, https://links.lww.com/QAD/B266).

Functional significance of the changes in active Bifidobacteria

We performed a functional analysis by assigning KEGG orthologies to the identified proteins to check for effects of the prebiotic intervention in different metabolic pathways as defined by KEGG. Furthermore, the contribution of proteins synthesized by Bifidobacteria was compared with all the residual proteins of other species in each of the considered pathways.

Among the analyzed KEGG pathways exhibiting differences, we found that Bifidobacteria were the major contributors to biosynthesis of amino acids, particularly in VU but as well in IR patients with a significant increase in abundance (P value <0.05) after the prebiotic treatment (Fig. 5). Regarding VU patients, the 35 proteins from Bifidobacteria assigned to this pathway (Fig. 5a) constituted even about 52% of the total abundance of all the 601 proteins that were found to be involved in the KEGG pathway for biosynthesis of amino acids, while the remaining 566 proteins of other species accounted for only about 48% (Fig. 5b). Therefore, Bifidobacteria may be considered as the major active contributor to the biosynthesis of amino acids in the colonic space in this group of patients after the prebiotic intervention.

Fig. 5:
The functional significance of active Bifidobacteria before and after the prebiotic therapy.The total label-free quantification (LFQ) abundance of proteins involved in the biosynthesis of amino acids pathway for the 35 proteins of Bifidobacteria on the left and the residual 566 proteins of other species on the right. Error bars show the SEM. *Indicates significant difference for P < 0.05. **Indicates significant difference for P < 0.01.

The current contribution was particularly noticeable in the KEGG orthologies group K01915 comprising eight glutamine synthetases (EC: of Bifidobacteria that are responsible for the conversion of glutamate to glutamine and exhibited a 74-fold increase in abundance in VU patients after the prebiotic treatment (P value <0.05).

Amino acids play crucial roles in metabolism, cell function, body composition and immunity, and therefore, the contribution of Bifidobacteria to amino acid metabolism may not be ruled out regarding health promotion after the prebiotic intervention.


In a recent study, a short dietary supplementation with prebiotics (scGOS/lcFOS/glutamine) was found to attenuate HIV-associated dysbiosis (in terms of total bacterial composition in feces), yet the microbiota of ART-treated patients was found more resilient than that of untreated patients [9]. It is therefore likely that more energetic interventions are required to significantly affect the gut microbiota of ART-experienced individuals. To deepen into this relationship, we herein analyzed the effects of the nutritional intervention on the active bacterial composition in feces using shotgun proteomics. The composition of the active bacterial population was compared with that of the total bacteria based on 16S rRNA next-generation sequencing [9]. This study helps to understand, for first time to the best of our knowledge, how the activity of gut bacteria is affected by the prebiotic in the context of HIV infection and ART.

We found that the dietary supplementation with prebiotics in healthy individuals resulted in little or no beneficial effect at the level of active community structure, as it was found at the level of total community composition. We further demonstrated that HIV infection decreases dormancy and increases alpha diversity of active bacteria, which was not further influenced by the prebiotic intervention. We conjecture that HIV infection most likely represents a stress condition to which a high diversity of gut bacteria actively reacts, and that the prebiotic does not influence the activation or dormancy level of gut bacteria. This may suggest that a low energetic prebiotic intervention does not alter the metabolic state of gut bacteria neither in healthy individuals in whom no pressure exists, nor among the HIV patients in whom the disease seems exert a strong pressure in the gut environment, regardless the higher level of active bacteria in HIV patients. However, by meaning of the alpha-diversity of the active population we observed that the prebiotic intervention provokes an enrichment of the richness of functional bacteria among VU patients (Fig. 1b), which was further more visible at the beta-diversity of the active population. Particularly, we observed that VU patients and, to a lesser extent, IR patients experienced a compositional shift toward the control group in the abundance of active bacteria assigned to Bifidobacteriaceae family, a situation that was not observed in INR patients. This taxon encompasses a large number of species associated with health benefits and commonly used as probiotics [24]. This, together with the fact that the abundance of active Bifidobacteriaceae was highly correlated with thymic output and that it was most abundant in IR study participants compared with INR patients, suggests that this family might play a crucial role in HIV immunopathogenesis by contributing to ART-mediated immune recovery [8]. Very strikingly, using 16S rRNA next-generation sequencing [9], Bifidobacterium spp. was not revealed as a biomarker of HIV infection or prebiotic treatment, highlighting the importance of the active microbiota when investigating the effects of therapies aimed at manipulating the microbiome.

The results of this exploratory trial suggest that dietary supplementation with a prebiotic (scGOS/lcFOS/glutamine) had major functional consequences in viremic untreated patients as compared with healthy individuals and HIV-infected patients undergoing ART, as it produced an overall amelioration of total gut dysbiosis [9] and an activation of bacteria which are known to exert immunomodulatory properties, particularly Bifidobacteria. Recently, the increase in total Bifidobacteria (and also Lactobacilli) was also associated with a significant reduction in the inflammatory predictors of mortality IL-6, and D-dimers in pre/probiotic-treated ART-naive HIV-infected adults [16,17]. It is now important to provide insights into why this occurred, and the functional metabolic consequences of the prebiotic therapy. Our results indicated that one of the metabolic pathways being most altered is the biosynthesis of amino acids in which Bifidobacteria are major contributors. The functional significance of Bifidobacteria in this pathway is particularly noticeably among VU and IR patients, after a short supplementation with prebiotics.

Although we do not observe any major beneficial effect of the prebiotic intervention in patients undergoing antiretroviral treatment, we anticipate a major role of antiretroviral drugs to hinder reconstitution of functional bacteria following prebiotics. The fact that the prebiotic intervention provokes an enrichment of the functional bacteria, particularly those being beneficial, among the HIV patients in the absence of antiretroviral drugs, agree with this hypothesis. It is plausible that the prebiotic intervention ameliorates dysbiosis and increases the abundance of active bacteria with strong immunomodulatory properties, conditions under which a later therapy with antiretroviral drugs may promote beneficial effects. Further studies are needed to clarify this issue.

Our study suggests that the antiretroviral drugs affect the active fraction of the microbiota in HIV patients in an extent that cannot be significantly altered by a short nutritional intervention. This situation was not encountered in the case of VU patients, in whom the richness of active bacteria increased. However, the reasons why viremic untreated patients and healthy controls do react differentially to the prebiotic intervention if in both group of individuals there is no treatment with antiretroviral drugs needs to be clarified.

The current study is limited by the relatively short follow-up duration (6 weeks) of the nutritional intervention, the low number of individuals investigated (22 in total) and the absence of placebo controls in the INR group of patients. Increasing the number of patients and continuing the follow-up may help clarifying the role of persistent activation of beneficial bacteria during treatment, as well as the possible impact in immune recovery. Whatever the case, this study represents the first investigation in which the effects of a prebiotic intervention have been investigated at the functional level in an infectious disease.


Contributors: Conceived and designed the experiments: S.S.-V., J.S. and M.F. Coordinated the samples collection: S.S.-V., T.S., S.M., V.E. Performed the experiments: S.S., D.R., M.M.-M., M.F. Analyzed the data: S.S., S.S.-V., R.B., J.F.V.-C., J.S. and M.F. Wrote the article: S.S.-V., J.S. and M.F. Review and edited the article: all authors. All authors have seen and approved the article; the corresponding author has full access to the data and had final responsibility for the decision to submit for publication.

The authors wish to acknowledge the participation of all of the study participants who contributed to this work as well as the clinical research staff of the participating institutions who made this research possible.

The research reported in the publication was supported by the Health Research Institute of University Hospital Clínico San Carlos, ‘Generalitat Valenciana’ (PROMETEOII/2014/065), by the Spanish Ministry of Economy and Competitiveness (SAF2012-31187, SAF2013-49788-EXP, SAF2015-65878 from MINECO), Instituto de Salud Carlos III (Plan Estatal de I+D+i 2013–2016, projects PIE14/00045, AC15/00022, PI15/00345 and the SPANISH AIDS Research Network RD16/0025/0001) and cofinanced by the European Development Regional Fund ‘A way to achieve Europe’ (ERDF). The present investigation was also funded by the Instituto de Salud Carlos III and the Fundación Agencia Española contra el Cáncer within the ERA NET TRANSCAN-2 program, grant number AC17/00022. C.B and D.R would like to acknowledge funding from the Spanish Ministry of Economy and Competitiveness (CTQ2014-55279-R). J.F.V.-C. was supported by a fellowship ‘Ayudas Predoctorales de Formación en Investigación en Salud’ from the Instituto de Salud Carlos III (Spain) and the CONACYT-SECITI (México). S.S.-V. and T.S. are supported by a grant from the Spanish Ministry of Science and Innovation (Contratos Juan Rodés, ECC/1051/2013). The funding bodies did not have a role in the design or conduct of the study, the analysis and interpretation of the results, the writing of the report or the decision to publish.

Conflicts of interest

There are no conflicts of interest.


1. Dillon SM, Lee EJ, Kotter CV, Austin GL, Dong Z, Hecht DK, et al. An altered intestinal mucosal microbiome in HIV-1 infection is associated with mucosal and systemic immune activation and endotoxemia. Mucosal Immunol 2014; 7:983–994.
2. Dinh DM, Volpe GE, Duffalo C, Bhalchandra S, Tai AK, Kane AV, et al. Intestinal microbiota, microbial translocation, and systemic inflammation in chronic HIV infection. J Infect Dis 2015; 211:19–27.
3. Li SX, Armstrong A, Neff CP, Shaffer M, Lozupone CA, Palmer BE. Complexities of gut microbiome dysbiosis in the context of HIV infection and antiretroviral therapy. Clin Pharmacol Ther 2016; 99:600–611.
4. Ling Z, Jin C, Xie T, Cheng Y, Li L, Wu N. Alterations in the fecal microbiota of patients with HIV-1 infection: an observational study in a Chinese population. Sci Rep 2016; 6:30673.
5. Mutlu EA, Keshavarzian A, Losurdo J, Swanson G, Siewe B, Forsyth C, et al. A compositional look at the human gastrointestinal microbiome and immune activation parameters in HIV infected subjects. PLoS Pathog 2014; 10:e1003829.
6. Noguera-Julian M, Rocafort M, Guillén Y, Rivera J, Casadellà M, Nowak P, et al. Gut microbiota linked to sexual preference and HIV infection. EbioMedicine 2016; 5:135–146.
7. Serrano-Villar S, Ferrer M, Gosalbes MJ, Moreno S. How can the gut microbiota affect immune recovery in HIV-infected individuals?. Future Microbiol 2017; 12:195–199.
8. Serrano-Villar S, Rojo D, Martínez-Martínez M, Deusch S, Vázquez-Castellanos JF, Bargiela R, et al. Gut bacteria metabolism impacts immune recovery in HIV-infected individuals. EBioMedicine 2016; 8:203–216.
9. Serrano-Villar S, Vázquez-Castellanos JF, Vallejo A, Latorre A, Sainz T, Ferrando-Martínez S, et al. The effects of prebiotics on microbial dysbiosis, butyrate production and immunity in HIV-infected subjects. Mucosal Immunol 2017; 10:1279–1293.
10. Vázquez-Castellanos JF, Serrano-Villar S, Latorre A, Artacho A, Ferrús ML, Madrid N, et al. Altered metabolism of gut microbiota contributes to chronic immune activation in HIV-infected individuals. Mucosal Immunol 2015; 8:760–772.
11. Vujkovic-Cvijin I, Dunham RM, Iwai S, Maher MC, Albright RG, Broadhurst MJ, et al. Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism. Sci Transl Med 2013; 5: 193ra91.
12. Nowak RG, Bentzen SM, Ravel J, Crowell TA, Dauda W, Ma B, et al. Rectal microbiota among HIV-uninfected, untreated HIV, and treated HIV-infected in Nigeria. AIDS 2017; 31:857–862.
13. Dillon SM1, Frank DN, Wilson CC. The gut microbiome and HIV-1 pathogenesis: a two-way street. AIDS 2016; 30:2737–2751.
14. Nowak P, Troseid M, Avershina E, Barqasho B, Neogi U, Holm K, et al. Gut microbiota diversity predicts immune status in HIV-1 infection. AIDS 2015; 29:2409–2418.
15. Klatt NR, Canary LA, Sun X, Vinton CL, Funderburg NT, Morcock DR, et al. Probiotic/prebiotic supplementation of antiretrovirals improves gastrointestinal immunity in SIV-infected macaques. J Clin Invest 2013; 123:903–907.
16. Gori A, Rizzardini G, Van’t Land B, Amor KB, van Schaik J, Torti C, et al. Specific prebiotics modulate gut microbiota and immune activation in HAART-naive HIV-infected adults: results of the ‘COPA’ pilot randomized trial. Mucosal Immunol 2011; 4:554–563.
17. Cahn P, Ruxrungtham K, Gazzard B, Diaz RS, Gori A, Kotler DP, et al. The immunomodulatory nutritional intervention NR100157 reduced CD4+ T-cell decline and immune activation: a 1-year multicenter randomized controlled double-blind trial in HIV-infected persons not receiving antiretroviral therapy (The BITE Study). Clin Infect Dis 2013; 57:139–146.
18. Dillon SM, Frank DN, Wilson CC. The gut microbiome and HIV-1 pathogenesis: a two-way street. AIDS 2016; 30:2737–2751.
19. Pérez-Santiago J, Gianella S, Massanella M, Spina CA, Karris MY, Var SR, et al. Gut Lactobacillales are associated with higher CD4 and less microbial translocation during HIV infection. AIDS 2013; 27:1921–1931.
20. Rojo D, Méndez-García C, Raczkowska BA, Bargiela R, Moya A, Ferrer M, Barbas C. Exploring the human microbiome from multiple perspectives: factors altering its composition and function. FEMS Microbiol Rev 2017; 41:453–478.
21. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol 2011; 12:R60.
22. Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 2016; 428:726–731.
23. Bray JR, Curtis JT. An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 1957; 27:325–349.
24. Davis C. Enumeration of probiotic strains: review of culture-dependent and alternative techniques to quantify viable bacteria. J Microbiol Methods 2014; 103:9–17.

* Simon Deusch, Sergio Serrano-Villar and David Rojo contributed equally to the article.

† Present address: School of Chemistry, Bangor University, LL57 2UW Bangor, UK.


antiretroviral therapy; HIV; microbiome; microbiota; prebiotic; proteomic

Supplemental Digital Content

Copyright © 2018 Wolters Kluwer Health, Inc.