Characterization of the gut microbiota in different immunological responses among PLWH

Characterization of the gut microbiota in different immunological responses among PLWH | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characterization of the gut microbiota in different immunological responses among PLWH Yanyan Guo, Gan Tang, Ziwei Wang, Qinshu Chu, Xinhong Zhang, Xuewei Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4591403/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objectives Despite gut microbial dysbiosis has been demonstrated in HIV-infected patients, the association between gut microbial and inflammatory cytokines in HIV-infected with different immunoreaction to antiretroviral therapy (ART) is poorly understood. The purpose of this study is to explore between gut microbial and inflammatory cytokines in HIV-infected with different immunoreaction. Method 68 HIV-infected patients and 27 healthy controls in Anhui Province were recruited from December 2021 to March 2022, including 35 immunological responders (IRs) (CD4 + T-cell count ≥ 350 cells/µL) and 33 immunological non-responders (INRs) (CD4 + T-cell count < 350 cells/µL) without comorbidities. Blood and stool samples were collected from all participants. Blood was used to detect microbial translocation biomarkers and inflammatory cytokines. Luminex Multifactor Detection Technology were performed to quantify plasma microbial translocation biomarkers and inflammation cytokines. Bacterial 16S rDNA sequencing was performed on stool samples. Result Microbiome sequencing revealed that the relative abundances of Fusobacteria, Actinobacteria, Verrucomicrobiaceae Acidaminococcaceae , Fusobacteriaceae and Megasphaera were greater, whereas Verrucomicrobia, Ruminococcaceae, Megamonas, Faecalibacterium, Roseburia and Dialister were more depleted in the HIV groups than those in the HCs (all P < 0.05). In the INRs group, the relative abundances of Actinomycetales , Micrococcaceae , Actinomyces , I ntestinibacter , Rothia were greater (all P < 0.05), whereas Sutterellaceae , Parabacteroides , Veillonella , Butyricimonas resulted less abundant than in the IRs (all P < 0.05). TNF-ɑ are negatively correlated with the abundances of Dialiste ( P = 0.022). CD54 are negatively correlated with Dialister and Subdoligranulum ( P = 0.011). Recent and baseline CD4 + T cells counts are directly proportional to Butyricimonas and Parabacteroides , while are inversely proportional with Veillonella and Rothia (all P < 0.05). Conclusion Dysbiosis of the gut microbial might be one of the factors leading to the different immunoreaction and therapeutic effects of ART. Biological sciences/Microbiology/Communities Biological sciences/Microbiology/Communities/Microbiome HIV immunological responders immunological non-responders gut microbiota inflammatory cytokines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION In 1981, the US reported that five HIV-infected patients and officially acknowledged AIDS as a new disease 1 . Since then, AIDS has spread worldwide. Despite major scientific advances in HIV research, a curative antiretroviral therapy (ART) and a preventive vaccine are still out of reach. There were 39 million people living with HIV (PLWH) around the world at the end of 2022, with 1.3 million of them being new infections 2 . While ART is not a cure for HIV, its advent has transformed the outcome of HIV infection from life-threatening diagnosis to a controllable and treatable chronic disease 3,4 . ART could diminish HIV viral load (VL) to a nondetectable level and significantly improve CD4 + T-cell count 4,5 . Hence, the AIDS-related mortality among PLWH have decreased dramatically and their life expectancy has been effectively extended. Nevertheless, the results of ART vary greatly from individual. CD4 + T-cell do not recover well in some PLWH. Optimal treatment fails to restore CD4 + T-cell despite suppression of VL 6 who were called immunological non-responders (INRs). Study found that INRs mortality was nearly three times higher than that of immunologic responders (IRs) 7 . INRs’ morbidity and mortality from AIDS and non-AIDS (liver disease, nephropathy, cardiovascular disease and so on) events were significantly increased compared with IRs 8 . There is differently definition about INRs in various studies. However, at the core of all definitions of INRs is the inability to achieve the defined CD4 + T-cell count threshold (> 200, > 250, >350 cells/µL) or a specified percentage above baseline (< 50, < 100, <400 cells/µL) 6,9,10 . Currently, < 350 cells/µL was common criterion for INRs after 48 weeks of VL suppression 6 . An estimated 20% of patients were still not successful in achieving this goal in spite of long-term ART and complete VL suppression 11–13 . The underlying mechanism of the phenomenon of INRs is very complicated and may be under the influence of many factors. Mainly including weakened hematopoiesis in the bone marrow, abnormal immune activation, insufficient thymic output, perturbations of cytokine secretion, residual virus replication, gut microbial disorder, age, sex, and so on 14–18 . Current research focuses on immune cells, along with elucidating INRs from a molecular and immune perspective 14,19,20 . However, biomarkers of microbial translocation and intestinal injury, immune activation, and inflammation are also worth to explore. Xie Y et al. 21 and Ponte R et al. 22 found that poor CD4 + T-cell recovery was related to increased abundance of Enterobacteriaceae bacteria. Although there was a growing body of the gut microbiota research of PLWH, there have been fewer reports on the gut microbiota of with different immunoreaction to ART 23,24 . Hence, this study explored the gut microbiota and inflammatory cytokines in PLWH with different immunoreaction after ART. METHODS 2.1 Study population A total of 68 HIV-infected patients were recruited form the first Affiliated Hospital of USTC (University of Science and Technology of China, USTC) from December 2021 to March 2022. 35 INRs were characterized by CD4 + T cell count < 350 cells/µl and VL < 20 copies/mL after ≥ 48 weeks of ART, while 33 IRs were those whose CD4 + T-cell count ≥ 350 cells/µl and VL < 20 copies/mL after the same ART duration. Meanwhile, 27 healthy controls (HCs) were also enrolled: 15 were MSM from the Anhui Qingwei Public Health Service Center, and 12 were relatives or friends of HIV-infected patients who accompanied them to the clinic. All subjects provided written informed consent before participating in this study. For the two groups, inclusion criteria for INRs were: ( 1 ) 18–60 years of age; ( 2 ) ART duration ≥ 48 weeks; ( 3 ) CD4 + T-cell count < 350 cells/µL; ( 4 ) VL < 20 copies/ml. Inclusion criteria for IRs were: ( 1 ) 18–60 years of age; ( 2 ) ART duration ≥ 48 weeks; ( 3 ) CD4 + T-cell count ≥ 350 cells/µl; ( 4 ) VL < 20 copies/ml. Inclusion criteria for HCs were: ( 1 ) 18–60 years of age; ( 2 ) HIV test negative. The participants were excluded based on the following criteria: ( 1 ) serious opportunistic infections prior to enrollment; ( 2 ) received immunomodulatory therapy within 3 months prior to enrollment; ( 3 ) co-infection with other serious diseases; ( 4 ) used antibiotics, probiotics, or prebiotics within the last 24 weeks; ( 5 ) pregnancy women; ( 6 ) did not sign the informed consent; ( 7 ) renal and liver insufficiency. 2.2 Specimen collection Blood and stool samples were obtained and temporarily preserved through ice packs and incubators and transported to the laboratory for processing within 3 hours. Plasma was separated by centrifugation from a collected 5 ml blood sample. Plasma and stool samples were stored frozen at -80℃ environment before to testing experiments. Plasma was used to detect biomarkers of microbial translocation and inflammatory cytokines. These indicators include soluble CD14 (sCD14), lipopolysaccharide-binding protein (LBP), C-reactive protein (CRP), tumor necrosis factor (TNF-α), interleukin 6 (IL-6), CD106, CD54, D-dimer. Fecal samples are used to observe the composition and changes in gut microbiota. 2.3 Plasma biomarkers of microbial translocation and inflammation cytokines Luminex Multifactor Detection Technology were performed to quantify plasma microbial translocation biomarkers and inflammation cytokines, including sCD14, LBP, CRP (Human Luminex Discovery Assay (3-plex), R&D Systems, USA), TNF-α, IL-6, D-dimer, CD106, CD54 (Human Luminex Discovery Assay (5-plex), R&D Systems, USA). 2.4 DNA extraction and 16S rRNA gene sequencing DNA extraction and 16S rDNA sequencing followed cited protocols 25 . 2.5 Data analysis Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using USEARCH (v7.0.1090). α-diversity indices included (AEC, Chao 1, Shannon, Simpson) and was calculated using MOTHUR software (v1.31.2). Permutational multivariate analysis of variance (PERMANOVA) and principal coordinate analysis (PCoA) were used to visual assessment of overall difference and similarity of bacterial communities. The linear discriminant analysis (LDA) effect size (LEfSe) method was used to identify the differential abundant bacterial taxa. Screening of the intestinal flora as a diagnostic marker by means of Random Forest (RF) using a 70 − 30 split for training and testing. To evaluate the effectiveness of the classification model, receiver operating characteristic curve (ROC) was chosen. Statistical analyses used R software (v 3.4.1). Group comparisons: t-tests for normal, Wilcoxon for non-normal, and Chi-squared/Fisher’s exact for categorical. Spearman's rank correlation explored the correlation between variables. P-value < 0.05 was considered statistically significant. RESULT 3.1 demographic characteristics of the participants There were 98 participants enrolled in this study, including 33 INRs, 35 IRs and 27 HCs. The demographic characteristics of PLWH and HCs were shown in Table 1. There was no significant difference between HIV group and HCs in BMI, age, gender, and so on. Table 2 demonstrates the demographic characteristics of the INRs and IRs. There were statistically significant differences between INRs and IRs in recent CD4 + T-cell count ( P < 0.001), baseline CD4 + T-cell count ( P < 0.001), and nadir CD4+ T-cell counts ( P 0.05）. Table 1 Demographic characteristics of PLWH and HCs Variables HC ( N =27) HIV ( N =68) χ 2 / Z P -value Age (years) 29.0(25.0,54.0) 39.0(34.0,50.8) -1.379 0.168 a BMI (kg/m 2 ) 22.6(21.5,23.8) 22.0(20.6,25.2) -0.900 0.368 a Gender - 0.347 c Female 3(11.11) 3(4.41) Male 24(88.89) 65(95.59) Education level 2.441 0.295 b Junior high school and bleow 9(33.33) 13(19.11) Senior high school 5(18.52) 12(17.65) College or above 13(48.15) 43(63.24) Marital status 1.685 0.243 b Unmarried/divorced/widowed 14(51.85) 45(66.18) Married/remarried 13(48.15) 23(33.82) a Wilcoxon rank sum test; b chi-square test; c Fisher's exact test Abbreviations: HCs, healthy controls Table 2 Demographic characteristics of INR and IR Variables INR ( N =33) IR ( N =35) χ 2 / Z P value Age (years) 41.0(35.0,55.0) 39.0(33.0,48.0) -1.173 0.241 a BMI (kg/m 2 ) 22.1(19.9,24.0) 22.0(21.0,25.4) -0.841 0.401 a ART duration (years) 4.3(3.0,6.8) 4.7(2.7,6.8) -0.202 0.840 a Recent CD4+T-cell count (cells/μL) 253.0(202.0,297.0) 548.0(474.0,626.0) -7.087 <0.001 a Baseline CD4+T-cell count (cells/μL) 140.0(40.5,196.0) 344.5(238.5,425.8) -5.502 <0.001 a Nadir CD4+ T-cell count 111.0(40.5,162.0) 333.0(181.3,398.2) -4.921 <0.001 a ART regimen - 0.146 b PIs-based 5(15.15) 12(34.29) INSTIs-based 6(18.18) 3(8.57) NNRTIs-based 22(66.67) 20(57.14) Gender - 1.000 c Male 32(96.97) 33(94.29) Female 1(3.03) 2(5.71) Education levels 0.041 0.980 b Junior high school and below 6(18.18) 7(20.00) Senior high school 6(18.18) 6(17.14) College or above 21(63.64) 22(62.86) Marital status 0.185 0.667 b Unmarried/divorced/widowed 21(63.63) 24(68.57) Married/remarried 12(36.36) 11(31.43) a Wilcoxon rank sum test; b chi-square test; c Fisher's exact test Abbreviations: INRs, immunological non-responders; IRs, immunological responders; PIs, protease inhibitor; INSTIs, integrase strand transfer inhibitors; NNRTIs, non-nucleoside reverse transcriptase inhibitors. 3.2 Gut Microbiome Diversity and Composition 3.2.1 α-diversity α-diversity of the gut microbiota was indicated by the species richness indices (ACE, Chao1) and species evenness index (Shannon, Simpson). The analysis revealed that lower in species richness indices (ACE, Chao1) were observed in HIV group compared to HCs ( P 0.05）were found in species evenness index (Shannon, Simpson) (Fig. 1). Significantly difference of gut microbial α-diversity between INRs and IRs groups was not observed ( P > 0.05) (Fig. S1). Fig 1 α-diversity of bacterial between PLWH and HCs. ***P<0.001.(A) ACE; (B) Chao 1; (C) Shannon; (D) Simpson. 3.2.2 β-diversity The β-diversity in the HIV group and the HCs group as well as the INRs group and the IRs group were compared using PCoA of the weighted UniFrac distance. For β-diversity, the findings indicated that the composition of bacterial communities varied markedly between PLWH and HCs ( R 2 = 0.033, P = 0.036) (Fig. 2A), while no significant differences were observed between IRs and INRs ( R 2 = 0.006, P = 0.777) (Fig. 2B). Fig 2 β-diversity of bacterial between PLWH and HCs. (A) PLWH and HCs; (B) IRs and INRs. 3.2.3 Compositional analysis of fecal microbiota Average relative abundances data for each bacterial at phylum and genus between HIV-infected patients and HCs as well as INRs and IRs are showed respectively in Fig.3-Fig.6. Fig. 3 Community structure of HIV-infected patients and HCs. (A) phylum level; (B) family level; (C) genus level. At the phylum level, the top five abundant bacterial phyla in HCs were Bacteroidetes, Firmicutes , Proteobacteria, Actinobacteria, Verrucomicrobia (Fig. 3A). The top five abundant bacterial phyla in HIV-infected patients (both INRs and IRs) were Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, Actinobacteria (Fig. 3A, Fig. 5A). The top 10 phylum of abundance were selected for comparative analysis. Fusobacteria (3.341% vs . 0.048%, P < 0.001) and Actinobacteria (1.257% vs. 1.069%, P = 0.023) resulted more abundant in PLWH than HCs, Verrucomicrobia (0.023% vs. 0.282%, P = 0.004) resulted less abundant in PLWH than HCs (Fig. 4A). At the phylum level, no statistically difference was observed between IRs and INRs (Fig. 6A). Fig. 4 Comparison of key species differences between HCs and HIV-infected patients. (A) phylum level; (B) family level; (C) genus level. *P < 0.05, **P < 0.01, *** P <0.001. At the family level, the top five abundant bacterial family in HCs, INRs, and IRs were Prevotellaceae , Ruminococcaceae , Bacteroidaceae , Lachnospiracea , Verrucomicrobiaceae (Fig. 3B. Fig. 5B). At the family level, Verrucomicrobiaceae (21.715% vs. 7.994%, P = 0.037), Acidaminococcaceae (4.776% vs. 2.175%, P = 0.028), Fusobacteriaceae (3.341% vs. 0.048%, P < 0.001) are more abundant in PLWH than those in HCs, while Fusobacteriaceae (3.341% vs. 0.048%, P < 0.001) are more abundant in HCs than those in HIV-infected patients (Fig. 4B). Only Sutterellaceae (0.852% vs. 0.558%, P = 0.022) were more depleted in INRs than those in IRs (Fig. 6B). Fig. 5 Community structure of IRs and INRs. (A) phylum level; (B) family level. (C) genus level. At the genus level, the top five abundant bacterial genera in HCs were Prevotella , Bacteroides , Faecalibacterium , Megamonas , Roseburia (Fig.3C). The five most prevalent bacterial genus in INRs were prevotella , Megamonas , Bacteroides , Megasphaera , phascolarctobacterium (Fig. 5C), as well as which in IRs were prevotella , Bacteroides , Megamonas , Fusobacterium , Faecalibacterium (Fig. 5C). At the genus level, Megamonas (15.291% vs. 4.356%, P = 0.004) and Megasphaera (3.414% vs. 0.344%, P = 0.027) are higher in PLWH than in HCs. The relative abundances of Faecalibacterium (4.129% v s. 14.478%, P < 0.001), Roseburia (2.235% vs. 3.803%, P = 0.032) and Dialister (2.198% vs. 2.601%, P = 0.002) are higher in HCs than in HIV-infected patients (Fig. 4C). At the genus level, the difference in bacterial abundance between IRs and INRs was not statistically (Fig. 6C). Fig. 6 Comparison of key species differences between IRs group and INRs group. (A) phylum level; (B) family level; (C) genus level. *P < 0.05. In order to identify specific microbiota taxa between PLWH and INRs, the bacterial composition between PLWH and HCs as well as INRs and IRs were compared using the LDA effect size (LEfSe) algorithm. The threshold of two on effect size was used. Twenty-five microbiota taxa were identified to distinguish HIV patients and thirty-one were identified to distinguish HCs. Figures 7A, 7B show taxonomic cladograms representing the microbiota structure and predominant bacteria in PLWH and HCs. The predominant bacteria of PLWH and HCs in phylum, class, order, family, and genus are presented in Table S1. Six bacterial taxa were identified to distinguish INRs and four were identified to distinguish IRs. Figures 7C, 7D show taxonomic cladograms representing the microbiota structure and predominant bacteria in INRs and IRs. The predominant bacteria of INRs and IRs in phylum, class, order, family, and genus are presented in Table S2. Fig. 7 Taxonomic differences between gut microbiota of PLWH and HCs as well as IRs and INRs. Differentially abundant bacterial taxa quantified as LEfSe and only taxa with an LDA score >2.0 are shown. (A) Taxonomic differences between PLWH and HCs; (B) Taxonomic cladogram of the data shown in panel A; (C) Taxonomic differences between IRs and INRs; (D) Taxonomic cladogram of the data shown in panel C. 3.4 Exploring the potential of the gut microbiota as a diagnostic biomarker for INRs Screening of the intestinal flora as a diagnostic marker of INRs by means of Random Forest (RF) model. In the study constructed ROC based on the intestinal flora signature that screened five OTUs of Clostridium_XlVa , Streptococcus , Roseburia , parabacteroides for inclusion in the model as optimal diagnostic biomarkers of INRs. The area under the ROC curve was 69.78% (95% CI : 54.23%-86.33%) (Fig. 8A). Diagnostic efficacy of microbial markers for INRs were validated using this data set, and the area under the ROC curve as 68.52% (95% CI : 44.87%-92.17%) (Fig. 8B). Fig. 8 ROC curves for the OTU-based diagnostic biomarker of INRs. Diagonal lines represent random classification (AUC=0.5). AUC, area under the curve. (A) training set RF model. (B) test set RF model. 3.5 Correlation between gut microbiota and translocation biomarkers, inflammation cytokines, clinical index To investigate the correlation between differential gut microbiota and translocation biomarkers, inflammation cytokines, clinical index by spearman correlations. The overall correlation between cytokine and taxa enriched or reduced in PLWH is shown in (Fig. 9). TNF-ɑ is negatively correlated with the abundances of Dialister ( r s = -0.278, P = 0.022). IL-6 is negatively correlated with the abundances of Dialister ( r s = -0.457, P < 0.001), Parabacteroides ( r s = -0.262, P = 0.031) and Actinomyces ( r s = -0.251, P = 0.039). CD54 is negatively correlated with the abundances of Dialister ( r s = -0.306, P = 0.011) and Subdoligranulum ( r s = -0.265, P = 0.029). LBP is negatively correlated with the abundances of Dialister ( r s = -0.290, P = 0.016). CRP is negatively correlated with the abundances of Dialister ( r s = -0.274, P = 0.024), Sporobacter ( r s = -0.264, P = 0.030) and Veillonella ( r s = -0.246, P = 0.043). A positively correlated between the abundance of Butyricimonas and Parabacteroides with recent CD4+ T-cell count and baseline CD4+ T-count. A negatively correlated between the abundances of Veillonella ( r s = -0.247, P = 0.043) and Rothia ( r s = -0.309, P = 0.010) with recent CD4+ T-cell count. Baseline CD4 + T-cell count is positively correlated with the abundances of Odoribacter ( r s = 0.250, P = 0.039) (Fig. 9). DISCUSSION The goal of ART is the suppression of the plasma VL below detectable levels and the achievement of immune reconstitution in PLWH. However, even if the CD4 + T-cell count returns to normal, the microbial translocation and inflammation caused by HIV infection may be continue. Research has demonstrated that gut microbiota plays an important role in the biology and pathophysiology of human, and it is generally accepted by scholars at present that gut microbes are key elements in the process of immune homeostasis 23,26,27 . Changes in gut microbiota composition and function suggest another relationship between intestinal microbiota and immune function in PLWH of receiving ART 26,28 . However, it is not known that how the intestinal microbiota elicits different immunoreaction in PLWH on ART. Hence, this study was to explored relationship between the gut microbiota and immune responses in PLWH with different immunoreaction after ART. α diversity has been widely measured to assess the biodiversity of gut microbiota in HIV-infected patients and HCs. Previous studies have observed a reduced diversity of gut microbiota in HIV-infection 21,29,30 . This reduction in α-diversity was not statistically significant in some studies 31,32 . Our study used weighted UniFrac distances to measure β-diversity in HIV group and HCs group. Earlier research has revealed that the gut microbial of PLWH and healthy people is significantly different 33 . Our findings confirm previously observed changes in microbial composition following HIV infection in Western countries. We observed that the α-diversity index of HIV-infected patients treated with ART was lower than that of HCs. This difference was caused by HIV infection and antiretroviral drugs. Antiretroviral drugs have certain antimicrobial properties, and it is possible that different antiretroviral drugs may have different effects on the gut microbial 34,35 . Our study found that PLWH have unique microbiota characteristics at the phylum level compared to HCs. There is more abundant Akkermansia in HCs than those in PLWH. Akkermansia is a normal human intestinal microorganism that plays an important role in improving fat and sugar metabolism 36 . Thus, the reduction of Akkermansia may lead to HIV-associated metabolic disorders and diminished CD8 + T-cell function. Akkermansia is thought to have potential "probiotic" effects, and increasing the abundance of Akkermansia may improve HIV-associated metabolic diseases 37 . LEfSe analysis reveals depletion of Faecalibacterium , Butyricimonas , and Ruminococcus in the intestines of PLWH, which aligns with the findings of Ishizaka et al. 24 and Wang.et al 38 . In addition, the cohort study by Ji et al. 34 also reported the same results. Microbial metabolites, which are by-products produced by gut bacteria, also have a very important impact on the immune response. In vitro experiments have shown that elevated butyrate levels can inhibit pathogen-induced T-cell activation and cytokine production 39 . This study indicated that butyrate-producing bacteria are vital for upholding maintaining intestinal homeostasis. Faecalibacterium , Butyricimonas , and Ruminococcus are butyrate-producing bacteria. Reduction of these bacteria may lead to disruption of the intestinal barrier and disturbance of the gut microbiota in PLWH. Just like Akkermansia, Parabacteroides is normal microbiota in the human gut. Parabacteroides drives T-cell differentiation toward anti-inflammatory CD25+ T cells, while generating a large number of CD25+IL-10+FoxP3-Tr1 cells that are closely associated with immunomodulatory phenotypes 40 . A decrease of Parabacteroides in INRs was observed. This suggests that a reduction of Parabacteroides may be one of the reasons for increased levels of inflammation in INRs vivo. Actinomycetes is Gram-positive bacteria and facultative pathogenic bacteria 41 . Actinomyces can enter the bloodstream through damaged tissues and cause systemic infections, including central nervous system, cardiovascular, and gastrointestinal diseases 42 . Actinomyces is enriched in INRs. After HIV-infection, CD4+ T lymphocytes in GALT are destroyed in large numbers, which leads to alter in the architecture and activity of mucosal immunity and causes increased gut permeability 43 . Actinomycetes eventually enter the circulation through the damaged intestinal mucosa, leading to elevated levels of inflammation in INRs, which further contributes to the progression of their non-HIV-related disease. However, there is still a need for further research to reach this conclusion. A combination of CD4+ T count, VL, and duration of ART is commonly used in the clinical setting to identify INRs. However, CD4+ T count and VL testing are not only expensive but also invasive methods. In recent years, intestinal flora has been widely used to explore biomarkers for some diseases due to its easy accessibility and little damage to body. In this study, five OTUs were screened as biomarkers of INR using RF model, including Clostridium_XlVa , Streptococcus , alloprevotella , Roseburia and Parabacteroides . Normal levels of TNF-α effectively regulate the body's immune levels, but TNF-α levels are elevated in HIV patients 44 , which may lead to destruction of secretory feedback in PLWH. Elevated levels of TNF-α also further promote viral replication and induce the production of other cytokines, for instance interleukins. Thereby increasing the level of immunity in HIV-infected individuals. TNF-ɑ are negatively correlated with the abundances of Dialister . An elevation of Dialister could lead to a reduction in immune capacity in PLWH. CD54 is a glycoprotein that is involved in inflammation and the immune response. CD54 levels significantly increased in HIV-infected patients. CD54 plays a key role in pro-inflammatory responses as well as in enhancing HIV infectivity 45 . This research discovered that CD54 are negatively correlated with the abundances of Dialister and Subdoligranulum . Subdoligranulum may be a protective factor against HIV infection and HIV-associated inflammation. Previous studies have shown a negatively correlated between the abundance of Fusobacterium, Prevotella as well as Lactobacillus with CD4 + T-cell count 46,47 . We found a positively correlated between Parabacteroides and Butyricimona with CD4+ T-cell count. This difference may be due to both ethnic and regional differences. In summary, there was no difference in gut microbiota structure between IRs and INRs, but there were differences in composition of the microbiota. Higher abundances of pathogenic bacteria, opportunistic pathogen, and pro-inflammatory bacteria in INRs. The results of the RF model suggest that need to further explore the potential of gut microbiota as biomarker for HIV-infected individuals. In addition, ART treatment does not completely restore the intestinal microbiota disturbances caused by HIV infection. There were differences in composition of the microbiota and in gut microbiota structure between PLWH and HCs. The exploration of adjuvant treatments beyond ART (such as supplement with intestinal probiotics) is important for disease progression in PLWH. This study has the following limitations: (1) this is a cross-sectional study based on a small sample size of the Chinese population, and the generalizability of these results is limited; (2) this study utilized 16S, allowing taxonomic classification only at the genus level; (3) because of the limitations of our participants, the differences between ART regimens still need to be extrapolated by increasing the sample size. Abbreviations INRs immunological non-responders IRs immunological responders PIs protease inhibitor INSTIs integrase strand transfer inhibitors NNRTIs non-nucleoside reverse transcriptase inhibitors. Declarations Ethics approval and consent to participate The research protocol, endorsed by Anhui Medical University Ethics Committee (Approval number: 20200594), was executed in compliance with the Helsinki Declaration guidelines. All subjects gave informed consent for sample collection and analysis. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Inflammation and Immune Mediated Diseases Laboratory of Anhui Province Open Project (IMMDL20220001), Research Funds of Center for Big Data and Population Health of IHM(JKS2022003) and Project of Chuzhou Health Commission༈CZWJ2022B002༉. Author Contribution The main manuscript was written by and revised by Yanyan Guo and Gan Tang, and the data preprocessing and analysis were performed by Ziwei Wang, Qinshu Chu, and Xinhong Zhang, under the supervision of Xuewei Xu and Yinguang Fan. The final text has been reviewed and approved by all authors. Acknowledgements Not applicable. Data Availability The datasets generated and/or analysed during the current study are available in the Sequence Read Archive (SRA) repository, [https://www.ncbi.nlm.nih.gov/sra/PRJNA957577] References Kaposi's sarcoma and Pneumocystis pneumonia among homosexual men–New York City and California. MMWR. Morbidity and mortality weekly report. Jul 3 1981;30(25):305–308. UNAIDS. AIDS BY THE NUMBERS. 2024; https://www.unaids.org/en . Lu DY, Wu HY, Yarla NS, Xu B, Ding J, Lu TR. HAART in HIV/AIDS Treatments: Future Trends. Infectious disorders drug targets. 2018;18(1):15–22. Zhao H, Feng A, Luo D, et al. Altered gut microbiota is associated with different immunologic responses to antiretroviral therapy in HIV-infected men who have sex with men. Journal of medical virology. Mar 2023;95(3):e28674. Prabhu S, Harwell JI, Kumarasamy N. Advanced HIV: diagnosis, treatment, and prevention. The lancet. HIV. 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Low abundance of colonic butyrate-producing bacteria in HIV infection is associated with microbial translocation and immune activation. AIDS (London, England). Feb 20 2017;31(4):511–521. Faith JJ, Ahern PP, Ridaura VK, Cheng J, Gordon JI. Identifying gut microbe-host phenotype relationships using combinatorial communities in gnotobiotic mice. Science translational medicine. Jan 22 2014;6(220):220ra211. Cunha F, Sousa DL, Trindade L, Duque V. Disseminated cutaneous Actinomyces bovis infection in an immunocompromised host: case report and review of the literature. BMC infectious diseases. Mar 29 2022;22(1):310. Li J, Li Y, Zhou Y, Wang C, Wu B, Wan J. Actinomyces and Alimentary Tract Diseases: A Review of Its Biological Functions and Pathology. BioMed research international. 2018;2018:3820215. Asowata OE, Singh A, Ngoepe A, et al. Irreversible depletion of intestinal CD4 + T cells is associated with T cell activation during chronic HIV infection. JCI insight. Nov 22 2021;6(22). 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Supplementary Files TableS1.docx TableS2.docx SupplementaryInformation.doc Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Nov, 2024 Reviews received at journal 13 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers invited by journal 25 Jun, 2024 Editor assigned by journal 25 Jun, 2024 Editor invited by journal 21 Jun, 2024 Submission checks completed at journal 19 Jun, 2024 First submitted to journal 16 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4591403","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320712210,"identity":"e87e508c-b057-4809-9c1a-cd0c418b7df7","order_by":0,"name":"Yanyan Guo","email":"","orcid":"","institution":"Chuzhou Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Guo","suffix":""},{"id":320712211,"identity":"782b341e-7596-4363-bf69-7ed820eb6670","order_by":1,"name":"Gan Tang","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gan","middleName":"","lastName":"Tang","suffix":""},{"id":320712213,"identity":"b1acbaf6-5594-44dc-a5b5-21f1d8f8a75a","order_by":2,"name":"Ziwei Wang","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziwei","middleName":"","lastName":"Wang","suffix":""},{"id":320712217,"identity":"de66e105-8f7f-4228-a97a-affb6f1e8fc7","order_by":3,"name":"Qinshu Chu","email":"","orcid":"","institution":"Anhui Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qinshu","middleName":"","lastName":"Chu","suffix":""},{"id":320712218,"identity":"2c603cca-9123-4669-b76a-fb14365bfbcd","order_by":4,"name":"Xinhong Zhang","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinhong","middleName":"","lastName":"Zhang","suffix":""},{"id":320712220,"identity":"73c50541-5774-44b4-9a9b-2298f5bcb8b9","order_by":5,"name":"Xuewei Xu","email":"","orcid":"","institution":"Chuzhou Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xuewei","middleName":"","lastName":"Xu","suffix":""},{"id":320712223,"identity":"adad5291-3a1a-4eec-ba0e-78cf24c30424","order_by":6,"name":"Yinguang Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBAC9gYgkQBhMz5IqKghrIXnAEILs8GDM8eI1AIFbJIPW5iJ0MJ+9pjEwx21if2z269VJDawMfC3dyfg18KTlyaReOZ44ow7Z8puJO6QYZA4c3YDXi32DDlmEoltxxIbbuSk3Ug8w8ZgIJGLXwsP/xuIlvlALQWJbcxEaJEA21KTuOFG+jEGIrW8MbZIbDtgvPFGDrNEwpljPAT9wsOfY3jzZ1ud7Lwb6Q8//qiokeNv78WvBQhYJBgYDoN0G4DNIKQcBJg/MDDUAWn2B8SoHgWjYBSMghEIAMqLToZbzBpsAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yinguang","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2024-06-17 02:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4591403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4591403/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98379-0","type":"published","date":"2025-04-24T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60598165,"identity":"506d3805-b02d-4f72-b6ee-5416919e0acc","added_by":"auto","created_at":"2024-07-18 15:53:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164833,"visible":true,"origin":"","legend":"\u003cp\u003eα-diversity of bacterial between PLWH and HCs. ***P\u0026lt;0.001.(A) ACE; (B) Chao 1; (C) Shannon; (D) Simpson.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/893c24cf559df0fd4a8e6481.jpg"},{"id":60598167,"identity":"60edc57f-697b-4034-864e-d4fa399837d3","added_by":"auto","created_at":"2024-07-18 15:53:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126885,"visible":true,"origin":"","legend":"\u003cp\u003eβ-diversity of bacterial between PLWH and HCs. (A) PLWH and HCs; (B) IRs and INRs.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/be249079ac3d4aafaca2546a.jpg"},{"id":60600227,"identity":"2d29624b-76af-4676-926b-8701bf1d484c","added_by":"auto","created_at":"2024-07-18 16:01:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184869,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity structure of HIV-infected patients and HCs. (A) phylum level; (B) family level; (C) genus level.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/916efc29d9a924c9feafd2f2.jpg"},{"id":60598169,"identity":"501af62e-c8d9-442c-9877-e9c5b155ac2e","added_by":"auto","created_at":"2024-07-18 15:53:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198890,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of key species differences between HCs and HIV-infected patients. (A) phylum level; (B) family level; (C) genus level. *P \u0026lt; 0.05, **P \u0026lt; 0.01, *** P \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/ee36f1daa183c62459ab4867.jpg"},{"id":60598176,"identity":"22452c4c-0b6e-4b0d-b4fb-631c7f1cc46d","added_by":"auto","created_at":"2024-07-18 15:53:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":190943,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity structure of IRs and INRs. (A) phylum level; (B) family level. (C) genus level.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/e6cef73b2e4da02b7423b44c.jpg"},{"id":60598173,"identity":"bbd122b4-8fa6-4af2-a870-ee1da0cf8040","added_by":"auto","created_at":"2024-07-18 15:53:09","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":120951,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of key species differences between IRs group and INRs group. (A) phylum level; (B) family level; (C) genus level. *P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/725d1009990e0e7e61140639.jpg"},{"id":60598171,"identity":"38342855-e0f4-4c86-b85f-f8f0130c47af","added_by":"auto","created_at":"2024-07-18 15:53:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":494611,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic differences between gut microbiota of PLWH and HCs as well as IRs and INRs. Differentially abundant bacterial taxa quantified as LEfSe and only taxa with an LDA score \u0026gt;2.0 are shown. (A) Taxonomic differences between PLWH and HCs; (B) Taxonomic cladogram of the data shown in panel A; (C) Taxonomic differences between IRs and INRs; (D) Taxonomic cladogram of the data shown in panel C.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/a546b2748a549717f8561963.jpg"},{"id":60600215,"identity":"e2938833-ddce-489a-bd35-d37adc1eb759","added_by":"auto","created_at":"2024-07-18 16:01:09","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38862,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the OTU-based diagnostic biomarker of INRs. Diagonal lines represent random classification (AUC=0.5). AUC, area under the curve. (A) training set RF model. (B) test set RF model.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/694a6b4b40f54659ae4fcdf0.jpg"},{"id":60598178,"identity":"1bd5e05f-25f9-44a7-9530-2107e0f7902f","added_by":"auto","created_at":"2024-07-18 15:53:12","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":251987,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of the correlation between differential genera and clinical indicators, microbial translocation markers and inflammatory factors. Spearman rank correlation analysis was performed.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/d43a52352ade9d17971ea304.jpg"},{"id":81569868,"identity":"3769d8e8-ff61-4395-aadc-3e0478b5b118","added_by":"auto","created_at":"2025-04-28 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2723510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/e47947e9-90ce-40d0-ad31-79e869390cbd.pdf"},{"id":60600249,"identity":"56475ed8-c297-46e8-977c-a17d3a6a690c","added_by":"auto","created_at":"2024-07-18 16:01:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24287,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/bfd90d540064c1f06c9792b6.docx"},{"id":60598174,"identity":"4a1a98e2-b3b7-4d89-940d-915cab7c817e","added_by":"auto","created_at":"2024-07-18 15:53:09","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17856,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/4574acaec87d0f12275ddf68.docx"},{"id":60598175,"identity":"44fb1b57-432d-4e03-aa13-06fb6f13b310","added_by":"auto","created_at":"2024-07-18 15:53:10","extension":"doc","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":225280,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.doc","url":"https://assets-eu.researchsquare.com/files/rs-4591403/v1/b7c779d9563c8f735d5245c3.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of the gut microbiota in different immunological responses among PLWH","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn 1981, the US reported that five HIV-infected patients and officially acknowledged AIDS as a new disease\u003csup\u003e1\u003c/sup\u003e. Since then, AIDS has spread worldwide. Despite major scientific advances in HIV research, a curative antiretroviral therapy (ART) and a preventive vaccine are still out of reach. There were 39\u0026nbsp;million people living with HIV (PLWH) around the world at the end of 2022, with 1.3\u0026nbsp;million of them being new infections\u003csup\u003e2\u003c/sup\u003e. While ART is not a cure for HIV, its advent has transformed the outcome of HIV infection from life-threatening diagnosis to a controllable and treatable chronic disease\u003csup\u003e3,4\u003c/sup\u003e. ART could diminish HIV viral load (VL) to a nondetectable level and significantly improve CD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u003csup\u003e4,5\u003c/sup\u003e. Hence, the AIDS-related mortality among PLWH have decreased dramatically and their life expectancy has been effectively extended.\u003c/p\u003e \u003cp\u003eNevertheless, the results of ART vary greatly from individual. CD4\u003csup\u003e+\u003c/sup\u003e T-cell do not recover well in some PLWH. Optimal treatment fails to restore CD4\u003csup\u003e+\u003c/sup\u003e T-cell despite suppression of VL\u003csup\u003e6\u003c/sup\u003e who were called immunological non-responders (INRs). Study found that INRs mortality was nearly three times higher than that of immunologic responders (IRs)\u003csup\u003e7\u003c/sup\u003e. INRs\u0026rsquo; morbidity and mortality from AIDS and non-AIDS (liver disease, nephropathy, cardiovascular disease and so on) events were significantly increased compared with IRs\u003csup\u003e8\u003c/sup\u003e. There is differently definition about INRs in various studies. However, at the core of all definitions of INRs is the inability to achieve the defined CD4\u003csup\u003e+\u003c/sup\u003e T-cell count threshold (\u0026gt;\u0026thinsp;200, \u0026gt;\u0026thinsp;250, \u0026gt;350 cells/\u0026micro;L) or a specified percentage above baseline (\u0026lt;\u0026thinsp;50, \u0026lt;\u0026thinsp;100, \u0026lt;400 cells/\u0026micro;L)\u003csup\u003e6,9,10\u003c/sup\u003e. Currently, \u0026lt;\u0026thinsp;350 cells/\u0026micro;L was common criterion for INRs after 48 weeks of VL suppression\u003csup\u003e6\u003c/sup\u003e. An estimated 20% of patients were still not successful in achieving this goal in spite of long-term ART and complete VL suppression\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe underlying mechanism of the phenomenon of INRs is very complicated and may be under the influence of many factors. Mainly including weakened hematopoiesis in the bone marrow, abnormal immune activation, insufficient thymic output, perturbations of cytokine secretion, residual virus replication, gut microbial disorder, age, sex, and so on\u003csup\u003e14\u0026ndash;18\u003c/sup\u003e. Current research focuses on immune cells, along with elucidating INRs from a molecular and immune perspective\u003csup\u003e14,19,20\u003c/sup\u003e. However, biomarkers of microbial translocation and intestinal injury, immune activation, and inflammation are also worth to explore. Xie Y et al.\u003csup\u003e21\u003c/sup\u003e and Ponte R et al.\u003csup\u003e22\u003c/sup\u003e found that poor CD4\u003csup\u003e+\u003c/sup\u003e T-cell recovery was related to increased abundance of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e bacteria. Although there was a growing body of the gut microbiota research of PLWH, there have been fewer reports on the gut microbiota of with different immunoreaction to ART\u003csup\u003e23,24\u003c/sup\u003e. Hence, this study explored the gut microbiota and inflammatory cytokines in PLWH with different immunoreaction after ART.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eA total of 68 HIV-infected patients were recruited form the first Affiliated Hospital of USTC (University of Science and Technology of China, USTC) from December 2021 to March 2022. 35 INRs were characterized by CD4\u0026thinsp;+\u0026thinsp;T cell count\u0026thinsp;\u0026lt;\u0026thinsp;350 cells/\u0026micro;l and VL\u0026thinsp;\u0026lt;\u0026thinsp;20 copies/mL after \u0026ge;\u0026thinsp;48 weeks of ART, while 33 IRs were those whose CD4 \u003csup\u003e+\u003c/sup\u003e T-cell count\u0026thinsp;\u0026ge;\u0026thinsp;350 cells/\u0026micro;l and VL\u0026thinsp;\u0026lt;\u0026thinsp;20 copies/mL after the same ART duration. Meanwhile, 27 healthy controls (HCs) were also enrolled: 15 were MSM from the Anhui Qingwei Public Health Service Center, and 12 were relatives or friends of HIV-infected patients who accompanied them to the clinic. All subjects provided written informed consent before participating in this study.\u003c/p\u003e \u003cp\u003eFor the two groups, inclusion criteria for INRs were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 18\u0026ndash;60 years of age; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ART duration\u0026thinsp;\u0026ge;\u0026thinsp;48 weeks; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) CD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u0026thinsp;\u0026lt;\u0026thinsp;350 cells/\u0026micro;L; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) VL\u0026thinsp;\u0026lt;\u0026thinsp;20 copies/ml. Inclusion criteria for IRs were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 18\u0026ndash;60 years of age; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ART duration\u0026thinsp;\u0026ge;\u0026thinsp;48 weeks; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) CD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u0026thinsp;\u0026ge;\u0026thinsp;350 cells/\u0026micro;l; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) VL\u0026thinsp;\u0026lt;\u0026thinsp;20 copies/ml. Inclusion criteria for HCs were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 18\u0026ndash;60 years of age; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) HIV test negative.\u003c/p\u003e \u003cp\u003eThe participants were excluded based on the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) serious opportunistic infections prior to enrollment; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) received immunomodulatory therapy within 3 months prior to enrollment; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) co-infection with other serious diseases; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) used antibiotics, probiotics, or prebiotics within the last 24 weeks; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) pregnancy women; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) did not sign the informed consent; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) renal and liver insufficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Specimen collection\u003c/h2\u003e \u003cp\u003eBlood and stool samples were obtained and temporarily preserved through ice packs and incubators and transported to the laboratory for processing within 3 hours. Plasma was separated by centrifugation from a collected 5 ml blood sample. Plasma and stool samples were stored frozen at -80℃ environment before to testing experiments. Plasma was used to detect biomarkers of microbial translocation and inflammatory cytokines. These indicators include soluble CD14 (sCD14), lipopolysaccharide-binding protein (LBP), C-reactive protein (CRP), tumor necrosis factor (TNF-α), interleukin 6 (IL-6), CD106, CD54, D-dimer. Fecal samples are used to observe the composition and changes in gut microbiota.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Plasma biomarkers of microbial translocation and inflammation cytokines\u003c/h2\u003e \u003cp\u003eLuminex Multifactor Detection Technology were performed to quantify plasma microbial translocation biomarkers and inflammation cytokines, including sCD14, LBP, CRP (Human Luminex Discovery Assay (3-plex), R\u0026amp;D Systems, USA), TNF-α, IL-6, D-dimer, CD106, CD54 (Human Luminex Discovery Assay (5-plex), R\u0026amp;D Systems, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DNA extraction and 16S rRNA gene sequencing\u003c/h2\u003e \u003cp\u003eDNA extraction and 16S rDNA sequencing followed cited protocols\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cp\u003eOperational taxonomic units (OTUs) were clustered with 97% similarity cutoff using USEARCH (v7.0.1090). α-diversity indices included (AEC, Chao 1, Shannon, Simpson) and was calculated using MOTHUR software (v1.31.2). Permutational multivariate analysis of variance (PERMANOVA) and principal coordinate analysis (PCoA) were used to visual assessment of overall difference and similarity of bacterial communities. The linear discriminant analysis (LDA) effect size (LEfSe) method was used to identify the differential abundant bacterial taxa. Screening of the intestinal flora as a diagnostic marker by means of Random Forest (RF) using a 70\u0026thinsp;\u0026minus;\u0026thinsp;30 split for training and testing. To evaluate the effectiveness of the classification model, receiver operating characteristic curve (ROC) was chosen.\u003c/p\u003e \u003cp\u003eStatistical analyses used R software (v 3.4.1). Group comparisons: t-tests for normal, Wilcoxon for non-normal, and Chi-squared/Fisher\u0026rsquo;s exact for categorical. Spearman's rank correlation explored the correlation between variables. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULT","content":"\u003cp\u003e\u003cstrong\u003e3.1 demographic characteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 98 participants enrolled in this study, including 33 INRs, 35 IRs and 27 HCs. The demographic characteristics of PLWH and HCs were shown in Table 1. There was no significant difference between HIV group and HCs in BMI, age, gender, and so on. Table 2 demonstrates the demographic characteristics of the INRs and IRs. There were statistically significant differences between INRs and IRs in recent CD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), baseline CD4\u003csup\u003e+\u003c/sup\u003e T-cell count (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and nadir CD4+ T-cell counts (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). There were no significant differences in gender, ART duration, ART regimen, and so on between the two groups(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05）.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eTable 1 Demographic characteristics of PLWH and HCs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003eHC (\u003cem\u003eN\u003c/em\u003e=27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eHIV (\u003cem\u003eN\u003c/em\u003e=68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e29.0(25.0,54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e39.0(34.0,50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e-1.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e0.168\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eBMI\u0026nbsp;(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e22.6(21.5,23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e22.0(20.6,25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e-0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e0.368\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e0.347\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e3(11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e3(4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e24(88.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e65(95.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eEducation level\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e0.295\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eJunior high school and bleow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e9(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e13(19.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e5(18.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e12(17.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e13(48.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e43(63.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eMarital status\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e0.243\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eUnmarried/divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e14(51.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e45(66.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.37373737373738%\"\u003e\n \u003cp\u003eMarried/remarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e13(48.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e23(33.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eWilcoxon rank sum test;\u0026nbsp;\u003csup\u003eb\u003c/sup\u003e chi-square test; \u003csup\u003ec\u003c/sup\u003e Fisher\u0026apos;s exact test\u003c/p\u003e\n\u003cp\u003eAbbreviations: HCs, healthy controls\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eTable 2 Demographic characteristics of INR and IR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eINR (\u003cem\u003eN\u003c/em\u003e=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eIR (\u003cem\u003eN\u003c/em\u003e=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e41.0(35.0,55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e39.0(33.0,48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.241\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e22.1(19.9,24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e22.0(21.0,25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.401\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eART duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e4.3(3.0,6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e4.7(2.7,6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.840\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eRecent CD4+T-cell count (cells/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e253.0(202.0,297.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e548.0(474.0,626.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-7.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eBaseline\u0026nbsp;CD4+T-cell count (cells/\u0026mu;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e140.0(40.5,196.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e344.5(238.5,425.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-5.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eNadir CD4+ T-cell count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e111.0(40.5,162.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e333.0(181.3,398.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-4.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eART regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003ePIs-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e5(15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e12(34.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eINSTIs-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6(18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3(8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eNNRTIs-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e22(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e20(57.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e32(96.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e33(94.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1(3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2(5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eEducation levels\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.980\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eJunior high school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6(18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e7(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6(18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6(17.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e21(63.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e22(62.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eMarital status\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e0.667\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eUnmarried/divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e21(63.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e24(68.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.20618556701031%\"\u003e\n \u003cp\u003eMarried/remarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e12(36.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e11(31.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eWilcoxon rank sum test;\u0026nbsp;\u003csup\u003eb\u0026nbsp;\u003c/sup\u003echi-square test; \u003csup\u003ec\u003c/sup\u003e Fisher\u0026apos;s exact test\u003c/p\u003e\n\u003cp\u003eAbbreviations: INRs, immunological non-responders; IRs, immunological responders; PIs, protease inhibitor;\u0026nbsp;INSTIs, integrase strand transfer inhibitors; NNRTIs, non-nucleoside reverse transcriptase inhibitors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Gut\u0026nbsp;Microbiome Diversity and Composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 \u0026alpha;-diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026alpha;-diversity\u0026nbsp;of the gut microbiota\u0026nbsp;was indicated by the species richness indices (ACE, Chao1) and species evenness index (Shannon, Simpson). The analysis revealed that lower in species richness indices (ACE, Chao1) were observed in HIV group compared to HCs\u0026nbsp;(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), but no statistically significant differences (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05）were found in species evenness index (Shannon, Simpson)\u0026nbsp;(Fig. 1). Significantly difference of gut microbial \u0026alpha;-diversity between INRs and IRs groups was not observed (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05) (Fig. S1).\u003c/p\u003e\n\u003cp\u003eFig 1 \u0026alpha;-diversity of bacterial between PLWH and HCs. ***P\u0026lt;0.001.(A) ACE; (B) Chao 1; (C) Shannon; (D) Simpson.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 \u0026beta;-diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;\u0026beta;-diversity in the HIV group and the HCs group as well as the INRs group and the IRs group were compared using\u0026nbsp;PCoA of the weighted UniFrac distance. For \u0026beta;-diversity, the findings indicated that the composition of bacterial communities varied markedly between PLWH and HCs (\u003cem\u003eR\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u003c/em\u003e= 0.033,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e= 0.036) (Fig. 2A), while no significant differences were observed between IRs and INRs (\u003cem\u003eR\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u003c/em\u003e= 0.006, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.777) (Fig. 2B).\u003c/p\u003e\n\u003cp\u003eFig 2 \u0026beta;-diversity of bacterial between PLWH and HCs. (A) PLWH and HCs; (B) IRs and INRs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 Compositional analysis of fecal microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAverage relative abundances data for each bacterial at phylum and genus between HIV-infected patients and HCs as well as INRs and IRs are showed respectively in Fig.3-Fig.6.\u003c/p\u003e\n\u003cp\u003eFig. 3 Community structure of HIV-infected patients and HCs. (A) phylum level; (B) family level; (C) genus level.\u003c/p\u003e\n\u003cp\u003eAt the phylum level, the top five abundant bacterial phyla in HCs\u0026nbsp;were\u0026nbsp;Bacteroidetes, Firmicutes\u003cem\u003e,\u003c/em\u003e Proteobacteria, Actinobacteria, Verrucomicrobia (Fig. 3A). The top five abundant bacterial phyla in HIV-infected patients (both INRs and IRs) were Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, Actinobacteria (Fig. 3A, Fig. 5A). The top 10 phylum of abundance were selected for comparative analysis. Fusobacteria (3.341% \u003cem\u003evs\u003c/em\u003e. 0.048%, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001) and Actinobacteria\u003cem\u003e\u0026nbsp;\u003c/em\u003e(1.257% \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e1.069%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.023) resulted more abundant in PLWH than HCs, Verrucomicrobia (0.023% \u003cem\u003evs.\u003c/em\u003e 0.282%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.004) resulted less abundant in PLWH than HCs (Fig. 4A). At the phylum level, no statistically difference was observed between IRs and INRs (Fig. 6A).\u003c/p\u003e\n\u003cp\u003eFig. 4 Comparison of key species differences between HCs and HIV-infected patients. (A) phylum level; (B) family level; (C) genus level. *P \u0026lt; 0.05, **P \u0026lt; 0.01, *** P \u0026lt;0.001.\u003c/p\u003e\n\u003cp\u003eAt the family level, the top five abundant bacterial family in HCs, INRs, and IRs were \u003cem\u003ePrevotellaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eLachnospiracea\u003c/em\u003e, \u003cem\u003eVerrucomicrobiaceae\u0026nbsp;\u003c/em\u003e(Fig. 3B. Fig. 5B). At the family level, \u003cem\u003eVerrucomicrobiaceae\u003c/em\u003e (21.715% \u003cem\u003evs.\u003c/em\u003e 7.994%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.037), \u003cem\u003eAcidaminococcaceae\u003c/em\u003e (4.776% \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e2.175%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.028), \u003cem\u003eFusobacteriaceae\u0026nbsp;\u003c/em\u003e(3.341% vs. 0.048%, P \u0026lt; 0.001) are more abundant in PLWH than those in HCs, while \u003cem\u003eFusobacteriaceae\u003c/em\u003e (3.341% \u003cem\u003evs.\u003c/em\u003e 0.048%, \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001) are more abundant in HCs than those in HIV-infected patients (Fig. 4B). Only \u003cem\u003eSutterellaceae\u003c/em\u003e (0.852% \u003cem\u003evs.\u003c/em\u003e 0.558%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.022) were more depleted in INRs than those in IRs (Fig. 6B).\u003c/p\u003e\n\u003cp\u003eFig. 5 Community structure of IRs and INRs. (A) phylum level; (B) family level. (C) genus level.\u003c/p\u003e\n\u003cp\u003eAt the genus level, the top five abundant bacterial genera in HCs were \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003eRoseburia\u0026nbsp;\u003c/em\u003e(Fig.3C). The five most prevalent bacterial genus in INRs were \u003cem\u003eprevotella\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eMegasphaera\u003c/em\u003e, \u003cem\u003ephascolarctobacterium\u0026nbsp;\u003c/em\u003e(Fig. 5C), as well as which in IRs were \u003cem\u003eprevotella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u0026nbsp;\u003c/em\u003e(Fig. 5C). At the genus level, \u003cem\u003eMegamonas\u003c/em\u003e (15.291% \u003cem\u003evs.\u003c/em\u003e 4.356%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.004) and \u003cem\u003eMegasphaera\u003c/em\u003e (3.414% \u003cem\u003evs.\u003c/em\u003e 0.344%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.027) are higher in PLWH than in HCs. The relative abundances of \u003cem\u003eFaecalibacterium\u003c/em\u003e (4.129% v\u003cem\u003es.\u003c/em\u003e 14.478%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), \u003cem\u003eRoseburia\u003c/em\u003e (2.235% \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e3.803%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.032) and \u003cem\u003eDialister\u003c/em\u003e (2.198% \u003cem\u003evs.\u003c/em\u003e 2.601%, \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.002) are higher in HCs than in HIV-infected patients (Fig. 4C). At the genus level, the difference in bacterial abundance between IRs and INRs was not statistically (Fig. 6C).\u003c/p\u003e\n\u003cp\u003eFig. 6 Comparison of key species differences between IRs group and INRs group. (A) phylum level; (B) family level; (C) genus level. *P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eIn order to identify specific microbiota taxa between PLWH and INRs, the bacterial composition between PLWH and HCs as well as INRs and IRs were compared using the LDA effect size (LEfSe) algorithm. The threshold of two on effect size was used. Twenty-five microbiota taxa were identified to distinguish HIV patients and thirty-one were identified to distinguish HCs. Figures 7A, 7B show taxonomic cladograms representing the microbiota structure and predominant bacteria in PLWH and HCs. The predominant bacteria of PLWH and HCs in phylum, class, order, family, and genus are presented in Table S1. Six bacterial taxa were identified to distinguish INRs and four were identified to distinguish IRs. Figures 7C, 7D show taxonomic cladograms representing the microbiota structure and predominant bacteria in INRs and IRs. The predominant bacteria of INRs and IRs in phylum, class, order, family, and genus are presented in Table S2.\u003c/p\u003e\n\u003cp\u003eFig. 7 Taxonomic differences between gut microbiota of PLWH and HCs as well as IRs and INRs. Differentially abundant bacterial taxa quantified as LEfSe and only taxa with an LDA score \u0026gt;2.0 are shown. (A) Taxonomic differences between PLWH and HCs; (B) Taxonomic cladogram of the data shown in panel A; (C) Taxonomic differences between IRs and INRs; (D) Taxonomic cladogram of the data shown in panel C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Exploring the potential of the gut microbiota as a diagnostic biomarker for INRs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScreening of the intestinal flora as a diagnostic marker of INRs by means of Random Forest (RF) model. In the study constructed ROC based on the intestinal flora signature that screened five OTUs of \u003cem\u003eClostridium_XlVa\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eparabacteroides\u003c/em\u003e for inclusion in the model as optimal diagnostic biomarkers of INRs. The area under the ROC curve was 69.78% (95% \u003cem\u003eCI\u003c/em\u003e: 54.23%-86.33%) (Fig. 8A). Diagnostic efficacy of microbial markers for INRs were validated using this data set, and the area under the ROC curve as 68.52% (95% \u003cem\u003eCI\u003c/em\u003e: 44.87%-92.17%) (Fig. 8B).\u003c/p\u003e\n\u003cp\u003eFig. 8 ROC curves for the OTU-based diagnostic biomarker of INRs. Diagonal lines represent random classification (AUC=0.5). AUC, area under the curve. (A) training set RF model. (B) test set RF model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Correlation between gut microbiota and translocation biomarkers, inflammation cytokines, clinical index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the correlation between differential gut microbiota and translocation biomarkers, inflammation cytokines, clinical index by spearman correlations. The overall correlation between cytokine and taxa enriched or reduced in PLWH is shown in (Fig. 9). TNF-ɑ is negatively correlated with the abundances of\u003cem\u003e\u0026nbsp;Dialister\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.278, \u003cem\u003eP =\u003c/em\u003e 0.022). IL-6 is negatively correlated with the abundances of \u003cem\u003eDialister\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.457, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), \u003cem\u003eParabacteroides\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.262, \u003cem\u003eP =\u003c/em\u003e 0.031) and \u003cem\u003eActinomyces\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.251, \u003cem\u003eP =\u003c/em\u003e 0.039).\u0026nbsp;CD54 is negatively correlated with the abundances of \u003cem\u003eDialister\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.306, \u003cem\u003eP =\u003c/em\u003e 0.011) and \u003cem\u003eSubdoligranulum\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.265, \u003cem\u003eP =\u003c/em\u003e 0.029). LBP is negatively correlated with the abundances of \u003cem\u003eDialister\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.290, \u003cem\u003eP =\u003c/em\u003e 0.016). CRP is negatively correlated with the abundances of \u003cem\u003eDialister\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.274, \u003cem\u003eP =\u003c/em\u003e 0.024),\u003cem\u003e\u0026nbsp;Sporobacter\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.264, \u003cem\u003eP =\u003c/em\u003e 0.030) and \u003cem\u003eVeillonella\u0026nbsp;\u003c/em\u003e(\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.246, \u003cem\u003eP =\u003c/em\u003e 0.043). A positively correlated between the abundance of \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e with recent CD4+ T-cell count and baseline CD4+ T-count. A negatively correlated between the abundances of \u003cem\u003eVeillonella\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.247, \u003cem\u003eP =\u003c/em\u003e 0.043) and \u003cem\u003eRothia\u0026nbsp;\u003c/em\u003e(\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = -0.309, \u003cem\u003eP =\u003c/em\u003e 0.010) with recent CD4+ T-cell count. Baseline CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT-cell count is positively correlated with the abundances of \u003cem\u003eOdoribacter\u003c/em\u003e (\u003cem\u003er\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e = 0.250, \u003cem\u003eP =\u003c/em\u003e 0.039) (Fig. 9).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe goal of ART is the suppression of the plasma VL below detectable levels and the achievement of immune reconstitution in PLWH. However, even if the CD4\u003csup\u003e+\u003c/sup\u003e T-cell count returns to normal, the microbial translocation and inflammation caused by HIV infection may be continue. Research has demonstrated that gut microbiota plays an important role in the biology and pathophysiology of human, and it is generally accepted by scholars at present that gut microbes are key elements in the process of immune homeostasis\u003csup\u003e23,26,27\u003c/sup\u003e. Changes in gut microbiota composition and function suggest another relationship between intestinal microbiota and immune function in PLWH of receiving ART\u003csup\u003e26,28\u003c/sup\u003e. However, it is not known that how the intestinal microbiota elicits different immunoreaction in PLWH on ART. Hence, this study was to explored relationship between the gut microbiota and immune responses in PLWH with different immunoreaction after ART.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026alpha; diversity has been widely measured to assess the biodiversity of gut microbiota in HIV-infected patients and HCs. Previous studies have observed a reduced diversity of gut microbiota in HIV-infection\u003csup\u003e21,29,30\u003c/sup\u003e. This reduction in \u0026alpha;-diversity was not statistically significant in some studies\u003csup\u003e31,32\u003c/sup\u003e. Our study used weighted UniFrac distances to measure \u0026beta;-diversity in HIV group and HCs group. Earlier research has revealed that the gut microbial of PLWH and healthy people is significantly different\u003csup\u003e33\u003c/sup\u003e. Our findings confirm previously observed changes in microbial composition following HIV infection in Western countries. We observed that the \u0026alpha;-diversity index of HIV-infected patients treated with ART was lower than that of HCs. This difference was caused by HIV infection and antiretroviral drugs. Antiretroviral drugs have certain antimicrobial properties, and it is possible that different antiretroviral drugs may have different effects on the gut microbial\u003csup\u003e34,35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study found that PLWH have unique microbiota characteristics at the phylum level compared to HCs. There is\u0026nbsp;more abundant Akkermansia in HCs than those in PLWH. Akkermansia is a normal human intestinal microorganism that plays an important role in improving fat and sugar metabolism\u003csup\u003e36\u003c/sup\u003e. Thus, the reduction of Akkermansia may lead to HIV-associated metabolic disorders and diminished CD8\u003csup\u003e+\u003c/sup\u003e T-cell function. Akkermansia is thought to have potential \u0026quot;probiotic\u0026quot; effects, and increasing the abundance of Akkermansia may improve HIV-associated metabolic diseases\u003csup\u003e37\u003c/sup\u003e. LEfSe analysis reveals depletion of \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e in the intestines of PLWH, which aligns with the findings of Ishizaka et al.\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e and Wang.et al\u003csup\u003e38\u003c/sup\u003e. In addition, the cohort study by Ji et al.\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e also reported the same results. Microbial metabolites, which are by-products produced by gut bacteria, also have a very important impact on the immune response. In vitro experiments have shown that elevated butyrate levels can inhibit pathogen-induced T-cell activation and cytokine production\u003csup\u003e39\u003c/sup\u003e. This study indicated that butyrate-producing bacteria\u0026nbsp;are vital for upholding maintaining intestinal homeostasis. \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e are butyrate-producing bacteria. Reduction of these bacteria may lead to disruption of the intestinal barrier and disturbance of the gut microbiota in PLWH.\u003c/p\u003e\n\u003cp\u003eJust like Akkermansia, \u003cem\u003eParabacteroides\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eis normal microbiota in the human gut. \u003cem\u003eParabacteroides\u003c/em\u003e drives T-cell differentiation toward anti-inflammatory CD25+ T cells, while generating a large number of CD25+IL-10+FoxP3-Tr1 cells that are closely associated with immunomodulatory phenotypes\u003csup\u003e40\u003c/sup\u003e. A decrease of \u003cem\u003eParabacteroides\u003c/em\u003e in INRs was observed.\u0026nbsp;This suggests that a reduction of \u003cem\u003eParabacteroides\u003c/em\u003e may be one of the reasons for increased levels of inflammation in INRs vivo. \u003cem\u003eActinomycetes\u003c/em\u003e is Gram-positive bacteria and facultative pathogenic bacteria\u003csup\u003e41\u003c/sup\u003e. \u003cem\u003eActinomyces\u003c/em\u003e can enter the bloodstream through damaged tissues and cause systemic infections, including central nervous system, cardiovascular, and gastrointestinal diseases\u003csup\u003e42\u003c/sup\u003e. \u003cem\u003eActinomyces\u003c/em\u003e is enriched in INRs. After HIV-infection, CD4+ T lymphocytes in GALT are destroyed in large numbers, which leads to alter in the architecture and activity of mucosal immunity and causes increased gut permeability\u003csup\u003e43\u003c/sup\u003e. \u003cem\u003eActinomycetes\u003c/em\u003e eventually enter the circulation through the damaged intestinal mucosa, leading to elevated levels of inflammation in INRs, which further contributes to the progression of their non-HIV-related disease. However, there is still a need for further research to reach this conclusion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA combination of CD4+ T count, VL, and duration of ART is commonly used in the clinical setting to identify INRs. However, CD4+ T count and VL testing are not only expensive but also invasive methods. In recent years, intestinal flora has been widely used to explore biomarkers for some diseases due to its easy accessibility and little damage to body. In this study, five OTUs were screened as biomarkers of INR using RF model, including \u003cem\u003eClostridium_XlVa\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Streptococcus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;alloprevotella\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Roseburia\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormal levels of TNF-\u0026alpha; effectively regulate the body\u0026apos;s immune levels, but TNF-\u0026alpha; levels are elevated in HIV patients\u003csup\u003e44\u003c/sup\u003e, which may lead to destruction of secretory feedback in PLWH. Elevated levels of TNF-\u0026alpha; also further promote viral replication and induce the production of other cytokines, for instance interleukins. Thereby increasing the level of immunity in HIV-infected individuals. TNF-ɑ are negatively correlated with the abundances of\u003cem\u003e\u0026nbsp;Dialister\u003c/em\u003e. An elevation of \u003cem\u003eDialister\u003c/em\u003e could lead to a reduction in immune capacity in PLWH. CD54 is a glycoprotein that is involved in inflammation and the immune response. CD54 levels significantly increased in HIV-infected patients. CD54 plays a key role in pro-inflammatory responses as well as in enhancing HIV infectivity\u003csup\u003e45\u003c/sup\u003e. This research discovered that CD54 are negatively correlated with the abundances of \u003cem\u003eDialister\u003c/em\u003e and \u003cem\u003eSubdoligranulum\u003c/em\u003e. \u003cem\u003eSubdoligranulum\u003c/em\u003e may be a protective factor against HIV infection and HIV-associated inflammation. Previous studies have shown a negatively correlated between the abundance of \u003cem\u003eFusobacterium,\u003c/em\u003e \u003cem\u003ePrevotella\u003c/em\u003e as well as \u003cem\u003eLactobacillus\u003c/em\u003e with CD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u003csup\u003e46,47\u003c/sup\u003e. We found a positively correlated between \u003cem\u003eParabacteroides and Butyricimona\u0026nbsp;\u003c/em\u003ewith\u003cem\u003e\u0026nbsp;\u003c/em\u003eCD4+ T-cell count. This difference may be due to both ethnic and regional differences.\u003c/p\u003e\n\u003cp\u003eIn summary, there was no difference in gut microbiota structure\u0026nbsp;between IRs and INRs, but there were differences in composition of the microbiota.\u0026nbsp;Higher abundances of pathogenic bacteria, opportunistic pathogen, and pro-inflammatory bacteria in INRs. The results of the RF model suggest that need to further explore the potential of gut microbiota as biomarker for HIV-infected individuals. In addition, ART treatment does not completely restore the intestinal microbiota disturbances caused by HIV infection. There were differences in composition of the microbiota and in gut microbiota structure between PLWH and HCs.\u0026nbsp;The exploration of adjuvant treatments beyond ART (such as supplement with intestinal probiotics) is important for disease progression in PLWH.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study has the following limitations: (1) this is a cross-sectional study based on a small sample size of the Chinese population, and the generalizability of these results is limited; (2) this study utilized 16S, allowing taxonomic classification only at the genus level; (3) because of the limitations of our participants, the differences between ART regimens still need to be extrapolated by increasing the sample size.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunological non-responders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunological responders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotease inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINSTIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintegrase strand transfer inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNNRTIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-nucleoside reverse transcriptase inhibitors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The research protocol, endorsed by Anhui Medical University Ethics Committee (Approval number: 20200594), was executed in compliance with the Helsinki Declaration guidelines. All subjects gave informed consent for sample collection and analysis.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Inflammation and Immune Mediated Diseases Laboratory of Anhui Province Open Project (IMMDL20220001), Research Funds of Center for Big Data and Population Health of IHM(JKS2022003) and Project of Chuzhou Health Commission༈CZWJ2022B002༉.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe main manuscript was written by and revised by Yanyan Guo and Gan Tang, and the data preprocessing and analysis were performed by Ziwei Wang, Qinshu Chu, and Xinhong Zhang, under the supervision of Xuewei Xu and Yinguang Fan. The final text has been reviewed and approved by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Sequence Read Archive (SRA) repository, [https://www.ncbi.nlm.nih.gov/sra/PRJNA957577]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKaposi's sarcoma and Pneumocystis pneumonia among homosexual men\u0026ndash;New York City and California. MMWR. Morbidity and mortality weekly report. Jul 3 1981;30(25):305\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNAIDS. 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BioMed research international. 2018;2018:3820215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsowata OE, Singh A, Ngoepe A, et al. Irreversible depletion of intestinal CD4\u0026thinsp;+\u0026thinsp;T cells is associated with T cell activation during chronic HIV infection. JCI insight. Nov 22 2021;6(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMen\u0026eacute;ndez-Arias L, Mart\u0026iacute;n-Alonso S, Frutos-Beltr\u0026aacute;n E. An Update on Antiretroviral Therapy. Advances in experimental medicine and biology. 2021;1322:31\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Shang H, Jiang Y. ICAM-1 in HIV infection and underlying mechanisms. Cytokine. Jan 2020;125:154830.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDillon SM, Lee EJ, Kotter CV, et al. Gut dendritic cell activation links an altered colonic microbiome to mucosal and systemic T-cell activation in untreated HIV-1 infection. Mucosal immunology. Jan 2016;9(1):24\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Santiago J, Gianella S, Massanella M, et al. Gut Lactobacillales are associated with higher CD4 and less microbial translocation during HIV infection. AIDS (London, England). Jul 31 2013;27(12):1921\u0026ndash;1931.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, immunological responders, immunological non-responders, gut microbiota, inflammatory cytokines","lastPublishedDoi":"10.21203/rs.3.rs-4591403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4591403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eDespite gut microbial dysbiosis has been demonstrated in HIV-infected patients, the association between gut microbial and inflammatory cytokines in HIV-infected with different immunoreaction to antiretroviral therapy (ART) is poorly understood. The purpose of this study is to explore between gut microbial and inflammatory cytokines in HIV-infected with different immunoreaction.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003e68 HIV-infected patients and 27 healthy controls in Anhui Province were recruited from December 2021 to March 2022, including 35 immunological responders (IRs) (CD4\u003csup\u003e+\u003c/sup\u003eT-cell count\u0026thinsp;\u0026ge;\u0026thinsp;350 cells/\u0026micro;L) and 33 immunological non-responders (INRs) (CD4\u003csup\u003e+\u003c/sup\u003eT-cell count\u0026thinsp;\u0026lt;\u0026thinsp;350 cells/\u0026micro;L) without comorbidities. Blood and stool samples were collected from all participants. Blood was used to detect microbial translocation biomarkers and inflammatory cytokines. Luminex Multifactor Detection Technology were performed to quantify plasma microbial translocation biomarkers and inflammation cytokines. Bacterial 16S rDNA sequencing was performed on stool samples.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eMicrobiome sequencing revealed that the relative abundances of \u003cem\u003eFusobacteria, Actinobacteria, Verrucomicrobiaceae Acidaminococcaceae\u003c/em\u003e, \u003cem\u003eFusobacteriaceae\u003c/em\u003e and \u003cem\u003eMegasphaera\u003c/em\u003e were greater, whereas \u003cem\u003eVerrucomicrobia, Ruminococcaceae, Megamonas, Faecalibacterium, Roseburia and Dialister\u003c/em\u003e were more depleted in the HIV groups than those in the HCs (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the INRs group, the relative abundances of \u003cem\u003eActinomycetales\u003c/em\u003e, \u003cem\u003eMicrococcaceae\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e, I\u003cem\u003entestinibacter\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e were greater (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas \u003cem\u003eSutterellaceae\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e resulted less abundant than in the IRs (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TNF-ɑ are negatively correlated with the abundances of \u003cem\u003eDialiste\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). CD54 are negatively correlated with \u003cem\u003eDialister\u003c/em\u003e and \u003cem\u003eSubdoligranulum\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). Recent and baseline CD4\u003csup\u003e+\u003c/sup\u003eT cells counts are directly proportional to \u003cem\u003eButyricimonas\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e, while are inversely proportional with \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003eRothia\u003c/em\u003e (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDysbiosis of the gut microbial might be one of the factors leading to the different immunoreaction and therapeutic effects of ART.\u003c/p\u003e","manuscriptTitle":"Characterization of the gut microbiota in different immunological responses among PLWH","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 15:53:03","doi":"10.21203/rs.3.rs-4591403/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-14T18:42:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-13T13:21:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T10:03:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72051481027552031805480856901920437089","date":"2024-10-22T10:21:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236380776898507004498552003276973276571","date":"2024-10-21T11:55:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-25T06:12:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-25T06:07:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-21T20:31:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-19T09:27:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-17T02:49:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df2c4ede-ea39-4b0e-8f46-970dfc27a7cf","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33906682,"name":"Biological sciences/Microbiology/Communities"},{"id":33906683,"name":"Biological sciences/Microbiology/Communities/Microbiome"}],"tags":[],"updatedAt":"2025-04-28T16:06:14+00:00","versionOfRecord":{"articleIdentity":"rs-4591403","link":"https://doi.org/10.1038/s41598-025-98379-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-24 15:57:51","publishedOnDateReadable":"April 24th, 2025"},"versionCreatedAt":"2024-07-18 15:53:03","video":"","vorDoi":"10.1038/s41598-025-98379-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98379-0","workflowStages":[]},"version":"v1","identity":"rs-4591403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4591403","identity":"rs-4591403","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}