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. 2019 May 11;7:73. doi: 10.1186/s40168-019-0687-5

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© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 3 — Dynamics of bacterial clusters following HIV-1 infection. a Within group co-abundant bacterial genus clusters (SP1 to 7) obtained using 16S rRNA gene sequencing. The color gradient is proportional to the mean of scaled individual relative abundance values (mean = 0, sd = 1) per bacterial genera and study group. Dots show statistically significant differences in genus abundance relative to HIV-negative subjects (NEG). b Linear mixed models of the longitudinal evolution of bacterial clusters in subjects with recent HIV-1 infection (RHI) and HIV-negative (NEG) individuals. Horizontal axes show months after study enrollment. Each dot corresponds to a sample, and samples from the same individual through follow-up are line-connected. Single dots correspond to individuals with no longitudinal follow-up. Thick black lines correspond to the modeled slope of each bacterial cluster. Statistically significant differences from 0 (flat slope) are shown with asterisks. p values *< 0.1 and **< 0.05. c Spearman’s correlation between bacterial clusters and immune markers measured in blood. The color gradient is proportional to the Spearman’s rho value. Only unadjusted statistically significant correlations (p value < 0.05) are shown. CHI_ART, CHI_noART, and first available samples from individuals in any of the RHI < 6, RHI > 6, and NEG groups were used to compute correlation values. Other immune markers measured in blood include IgA, IgM, IgG2, and IgG4 for serological makers; EndoCab IgG and IgA ASCA for gut permeability; FABP2 for bacterial translocation; IL7, IL13, GCSF, RANTES, MIP1 alpha, and beta for T cell function; IFN gamma, TNF alpha, and IL8 for Th1 pro-inflammatory responses; TGF beta for anti-inflammatory responses; CD40 ligand and IL21 for B cell function, Eotaxin, IL5, sCD163, and IL15 for innate cells; CXCL16 and IL1 beta for inflammation; B7H1, PDL2, and IL2R for immune activation; and EGF and VEGF for angiogenesis. Several markers were also measured in feces although none of them showed significant correlations with bacterial clusters: sIgA, ANCA, and ASCA for serological markers; EDNEPX, calprotectin, PMNE, lactoferrin, and S100A12 for neutrophil and eosinophil activation; and HBD2, zonulin, and alpha 1 antitrypsin for enterocyte damage and gut permeability. No correlations were found between bacterial clusters and levels of CD4⁺ and CD8⁺ T cell activation, exhaustion, and senescence in blood.