Abstract

   Gastrointestinal (GI) B cells and plasma cells (PCs) are critical to
   mucosal homeostasis and the host response to HIV-1 infection. Here,
   high-resolution mapping of human B cells and PCs sampled from the colon
   and ileum during both viremic and suppressed HIV-1 infection identified
   a reduction in germinal center (GC) B cells and follicular dendritic
   cells (FDCs) during HIV-1 viremia. Immunoglobulin A–positive (IgA^+)
   PCs are the major cellular output of intestinal GCs and were
   significantly reduced during viremic HIV-1 infection. PC-associated
   transcriptional perturbations, including type I interferon signaling,
   persisted in antiretroviral therapy (ART)–treated individuals,
   suggesting ongoing disruption of the intestinal immune milieu during
   ART. GI humoral immune perturbations were associated with changes in
   the intestinal microbiome composition and systemic inflammation. These
   findings highlight a key immune defect in the GI mucosa due to HIV-1
   viremia.

INTRODUCTION

   Neutralizing antibodies (NAbs) play a critical role in host protection
   against invading pathogens such as HIV-1 ([93]1). There is a relative
   lack of understanding of the humoral immune dynamics in gut-associated
   lymphoid tissue (GALT), a site where viral replication is concentrated
   during HIV-1 infection ([94]2–[95]4) concomitant with a profound CD4^+
   T cell depletion within this site ([96]5–[97]8). GALT-associated HIV-1
   replication is associated with seeding of HIV-1 reservoirs
   ([98]9–[99]11), disruption of mucosal barrier integrity, and systemic
   inflammation ([100]12–[101]17). Therefore, a detailed examination of
   the humoral immune system of the GALT is important to understand HIV-1
   pathogenesis and to inform HIV-1 cure efforts.

   The cellular output of GALT is dominated by secretory immunoglobulin A
   (SIgA) that regulates barrier integrity ([102]18–[103]21) and mediates
   mucosal homeostasis ([104]22). The generation of IgA requires B cell–T
   cell interactions within the highly organized microenvironment of
   germinal centers (GCs) ([105]22–[106]24). Within the GCs, specialized
   stromal cells called follicular dendritic cells (FDCs) are crucial to
   the maturation of the B cell response through affinity-based selection
   of B cells that drive the production of highly specific antibodies
   ([107]25). B cells egress from the GC as memory B cells or as plasma
   cells (PCs), which then migrate to effector sites such as the
   intestinal lamina propria or to the bone marrow for long-term survival
   ([108]25–[109]28).

   Intimate bidirectional cross-talk exists between SIgA and intestinal
   bacteria. Intestinal dysbiosis is recognized in HIV-1 infection
   ([110]29–[111]36); however, the impact of humoral perturbations on
   microbiota alterations is not well elucidated in persons with HIV-1
   infection (PWH). In addition, although a number of gut
   barrier–associated homeostatic mechanisms have been shown to be
   disrupted by HIV-1 infection ([112]17, [113]37–[114]39), B cells and
   PCs, key regulators of the gut barrier, remain understudied in PWH.

RESULTS

Viremic HIV-1 infection is associated with a major depletion of GC B cells in
the GI tract

   To dissect the dynamics of intestinal B cells and PCs, we performed
   single-cell RNA sequencing (scRNA-seq) in nine PWH [four people with
   viremic HIV (PWVH) with plasma HIV-RNA > 20 copies/ml off ART and five
   people with suppressed HIV (PWSH) on ART with plasma HIV-RNA < 20
   copies/ml for at least 12 months] and nine HIV-negative controls (NCs)
   ([115]tables S1 and [116]S2). Immune cell analyses were conducted
   independently for the colon and ileum to reflect potential differences
   in the immune effector (colon) and inductive (ileum) sites,
   respectively ([117]40). Unsupervised cell clustering of 62,365 cells
   with an average of 9016 unique molecular identifiers (UMIs) per cell
   from the colon generated 23 cell clusters ([118]Fig. 1, [119]A and
   [120]B; [121]fig. S1A; and [122]table S3), whereas 43,291 cells with an
   average of 6292 UMIs per cell from the ileum mapped to 26 cell clusters
   ([123]Fig. 1, [124]C and [125]D; [126]fig. S1B; and [127]table S3). On
   the basis of canonical gene expression, we identified clusters
   representing epithelial (KRT8, SLC25A5, and CA2), endothelial (PLVAP
   and PECAM), stromal (PDGFRB, CXCL14, CALD1, and PMP22), and immune cell
   lineages that included PCs (JCHAIN, MZB1, and XBP1), B cells (CD79A and
   BANK1), CD4^+ T cells (CD3D, CD4, and TRAC), CD8^+ T cells (CD3D, CD8,
   and TRAC), myeloid cells (CD14, CD68, and MNDA), innate lymphoid cells
   (IL7R, KIT, and IL23R), and mast cells (TPS and KIT) both in the colon
   and in the ileum ([128]Fig. 1, [129]E and [130]F, and [131]tables S4 to
   [132]S7). Cell-type frequencies across groups are quantified in
   [133]Fig. 1 ([134]G and [135]H), [136]fig. S1 ([137]C and [138]D), and
   [139]table S8.

Fig. 1. scRNA sequencing–based identification of major immune cell types in
the intestinal lamina propria during HIV infection.

   Fig. 1.
   [140]Open in a new tab

   (A) UMAP of scRNA-seq data from colonic lamina propria with
   representation of sample distribution across the groups (NCs, n = 9,
   green; PWSH, n = 5, orange; PWVH, n = 4, purple). (B) UMAP representing
   cell-type annotated clusters derived from the colonic lamina propria.
   (C) UMAP of ileal lamina propria–derived samples with representation of
   sample distribution across the groups (NCs, n = 5, green; PWSH, n = 5,
   orange; PWVH, n = 4, purple). (D) UMAP representing cell-type annotated
   clusters derived from the ileal lamina propria. (E and F) Heatmap
   showing the average z score–normalized log expression of canonical
   cell-type markers across different colonic (E) and ileum-derived (F)
   cell clusters. (G and H) Bar plots showing cell-type distribution
   within each individual for colon-derived (G) and ileum-derived (H)
   samples. (I) UMAPs showing reclustered, colon-derived B cell clusters
   using scRNA-seq. (J) Heatmap showing the average normalized log
   expression z score of canonical B cell markers across different
   colon-derived B cell subclusters. (K) UMAPs showing reclustered,
   ileum-derived B cell clusters using scRNA-seq. (L) Heatmap showing the
   average z score normalized log expression of canonical B cell markers
   across different ileum-derived B cell subclusters. (M and N) scRNA-seq
   data demonstrating the frequency of B cell subclusters derived from the
   colon (M) and ileum (N) across the three groups (NCs, n = 9 for colon
   and n = 5 for ileum; PWSH, n = 5 for both colon and ileum; PWVH, n = 4
   for both colon and ileum). Bars represent median values. P values from
   Wilcoxon signed-rank sum test are reported.

   Next, we reclustered B cells using an unsupervised clustering algorithm
   ([141]41). In the colon, 3382 B cells yielded five clusters, whereas in
   the ileum, 5095 B cells yielded six clusters ([142]Fig. 1, [143]I and
   [144]K, and [145]table S9). We annotated naïve B cells (IGHM, IGHD,
   FCER2, no CD27; colon cluster no. 1; ileum cluster no. 2), memory B
   cells (TNFRSF13B, CD27, no IGHM, IGHD; colon cluster nos. 0 and 2;
   ileum cluster nos. 0, 1, 3, and 5), and GC B cells (AICDA, BCL6, CD38,
   MME; colon cluster no. 3; ileum cluster no. 4) ([146]Fig. 1, [147]J and
   [148]L, and [149]tables S10 to [150]S13). Notably, Ig genes and gene
   fragments were considered and retained in the subclustering algorithm,
   because they are critical for cell state definition (specifically IGHM
   and IGHD for naïve versus memory B cells) and otherwise did not
   contribute to the determination of the clusters ([151]Materials and
   Methods). We examined the frequency of total, naïve, memory, and GC B
   cells across groups. GC B cells showed a −4.255 log[2] fold change in
   the colon and a −2.622 log[2] fold change in the ileum in PWVH compared
   with NCs and a −3.517 log[2] fold change in the colon and a −2.380
   log[2] fold change in the ileum compared with PWSH ([152]Fig. 1, [153]M
   and [154]N, and [155]table S14).

   To validate scRNA-seq data in a larger cohort, we examined B cell
   subsets using multiparameter flow cytometry (FC) in 18 PWVH, 49 PWSH,
   and 87 NCs ([156]tables S1, [157]S2, [158]S15, and [159]S17).
   Consistent with the scRNA-seq data, GC B cells
   (CD45^+CD3^−CD19^+CD38^+IgD^−; [160]fig. S2A) showed a −1.6 log[2] fold
   change in the colon (P = 0.0027, Cohen’s d effect size: 0.829) and a
   −2.4 log[2] fold change in the ileum (P = 0.0058, Cohen’s d effect
   size: 0.698) in PWVH compared with both PWSH and NCs ([161]Fig. 2,
   [162]A and [163]B, and [164]tables S17 and [165]S18).

Fig. 2. Depletion of intestinal GC B cells during viremic HIV-1 infection.

   Fig. 2.
   [166]Open in a new tab

   (A and B) Flow cytometric data quantifying colon-derived (A) and
   ileum-derived (B) total B cells and B cell subsets. Comparisons between
   groups performed with one-way ANOVA with Dunn’s correction for multiple
   comparisons, and P values are as indicated. (C) Representative IHC
   images of intestinal lymphoid follicles for each of the three groups
   showing staining for CD4 (top), BCL6 (middle), and CD23 (bottom). Scale
   bar represents 100 μm. (D) Summary data for IHC (NCs, n = 6; PWSH, n =
   5; PWVH, n = 4), quantifying lymphoid follicle size, GC size, the ratio
   between GC and lymphoid follicle size, the number of BCL6^+ cells in
   each GC, and the % area positive for CD23 staining within each GC. (E)
   Correlation analysis between the area with positive CD23 staining and
   the number of BCL6^+ cells within each GC. Comparisons between groups
   were performed using Kruskal-Wallis test with Dunn’s correction for
   multiple comparisons. Median and interquartile range were used to plot
   summary data, and P values <0.1 are shown as indicated (P values >0.1
   are not shown). Correlation was obtained using Spearman’s r correlation
   coefficient, and P values are as indicated.

   To further confirm our findings, we performed immunohistochemistry
   (IHC) on intestinal biopsy tissues where a lymphoid follicle was
   identified ([167]tables S2 and [168]S16 and [169]figs. S3 to [170]S5).
   The size of GCs was on average 16 times smaller (P = 0.0538) in PWVH
   compared with NCs despite overall comparable lymphoid follicle size
   ([171]Fig. 2, [172]C and [173]D). We used BCL6 as a marker of putative
   GC B cells and found a 31-fold reduction of CD4^−BCL6^+ cells within
   the GC in PWVH when compared with NCs ([174]Fig. 2, [175]C and [176]D,
   and [177]table S16). In addition, we examined FDCs, given their
   importance in maintaining the GC response ([178]25), using CD23
   staining ([179]42). CD23 expression was ninefold lower in PWVH compared
   with NCs ([180]Fig. 2, [181]C and [182]D; [183]fig. S6; and [184]table
   S16) and correlated with BCL6 expression ([185]Fig. 2E and [186]fig.
   S7). Together, on the basis of scRNA-seq, FC, and IHC, we identified a
   significant loss of GC B cells in the gastrointestinal (GI) tracts of
   PWVH that is accompanied by disruption of the FDC network.

Total PCs and IgA^+ PCs are decreased in the colon during viremic HIV-1
infection and demonstrate an altered transcriptional profile

   We next examined whether PC changes accompany the loss of GC B cells in
   PWVH. Using multiparametric FC, we defined intestinal PCs ([187]fig.
   S2A) and observed a −0.9 and −1.7 log[2] fold change in the frequency
   of colonic PCs (live CD27^+CD38^hicells) in PWVH when compared with
   PWSH and NCs, respectively ([188]Fig. 3, [189]A and [190]B, and
   [191]tables S17 and [192]S18). Among the PC isotypes, the frequency of
   IgA^+ PCs was 1.1 times lower in PWVH [median (interquartile range,
   IQR): 82 (61 to 91)%] compared with NCs [92 (87 to 95)%, P = 0.005,
   Cohen’s d effect size: 1.251] ([193]Fig. 3, [194]C and [195]D, and
   [196]tables S17 and [197]S18). Concomitantly, the frequencies of
   immunoglobulin G–positive (IgG^+) [6.2 (2.9 to 13)%] and IgM^+ [4.7
   (2.5 to 7.9)%] PCs were higher in PWVH when compared with NCs [3.8 (2.2
   to 7.4)%, P = 0.289, Cohen’s d effect size: 0.857 for IgG^+ and 2.3
   (1.4 to 3.9)%, P = 0.0132, Cohen’s d effect size: 0.958 for IgM^+]
   ([198]Fig. 3, [199]C and [200]D, and [201]tables S17 and [202]S18).
   PC-related changes in the ileum were less notable than those observed
   in the colon ([203]fig. S8, [204]A and [205]B, and [206]table S17).

Fig. 3. Colonic PCs are decreased during untreated HIV infection and show a
switch in isotype expression.

   Fig. 3.
   [207]Open in a new tab

   (A) Representative flow cytometry plots of PCs (live
   CD27^+CD38^hicells) derived from NCs, n = 51; PWSH, n = 35; PWVH, n =
   16. (B) Summary data quantifying colonic PCs for each of the three
   groups. (C) Representative flow cytometry plots demonstrating PC
   isotype expression. (D) Summary data quantifying colonic IgA^+, IgG^+,
   and IgM^+ PCs. (E) Representative immunofluorescence (IF) images
   showing the expression of CD138 (red), IgA (blue), and DAPI
   (4′,6-diamidino-2-phenylindole) (gray). Top panels (10× magnification)
   with yellow inset are shown in the bottom panels (20× magnification).
   Red cells indicate IgA^− PCs, blue cells indicate IgA^+ cells, and
   purple cells (merge) indicate IgA^+ PCs. (F) Summary IF data showing
   total IgA^− PCs per square millimeter area (left) and ratio of
   IgA^−:IgA^+ PCs per square millimeter area (right). (G) Representative
   flow cytometry plots of PB-derived PCs (live
   CD45^+CD27^+IgD^−CD38^hicells) for each of the three groups. (H)
   Summary data quantifying PB PCs. (I) Summary data quantifying
   PB-derived IgA^+, IgM^+, and IgG^+ PCs. Comparisons between groups were
   performed using Kruskal-Wallis test with Dunn’s correction for multiple
   comparisons. Median and interquartile range was used to plot summary
   data, and P values <0.1 are shown as indicated (P values >0.1 are not
   shown).

   Next, we quantified the absolute numbers of IgA^+ and IgA^− PCs and
   noted that PWVH showed a twofold increase in IgA^− PCs (P = 0.0333) and
   IgA^−:IgA^+ PC ratio (P = 0.0106) compared with NCs ([208]Fig. 3,
   [209]E and [210]F, and [211]table S19). Using the criteria defined by
   Landsverk et al. ([212]43), we characterized changes in short-lived
   (SL; live, CD27^+ CD38^hiCD45^+CD19^+ cells), long-lived (LL; live,
   CD27^+CD38^hi CD45^+CD19^− cells), and very long-lived (VLL; live,
   CD27^+CD38^hi CD45^−CD19^− cells) ([213]fig. S2A and [214]table S17)
   PCs and noted trends similar to those observed in the overall PCs in
   the colon ([215]fig. S8C and [216]table S17). In the ileum, a higher
   frequency of LL PCs was noted for PWVH compared with NCs ([217]fig. S8D
   and [218]table S17). Again, isotype expression across PC subclasses
   showed trends similar to those observed for the overall PCs ([219]fig.
   S8, [220]E and [221]F, and [222]table S17).

   In contrast with the intestinal mucosa and similar to prior reports
   ([223]44, [224]45), an increased frequency of PCs was observed in the
   peripheral blood (PB) in PWVH compared with the other two groups
   ([225]Fig. 3, [226]G and [227]H, and [228]table S17). Similar to what
   was observed in the colonic LP, a reduced frequency of IgA^+ PCs was
   noted in the PB of PWVH compared with NCs ([229]Fig. 3I and [230]table
   S17).

   Together, these data show a correlation between the perturbed GC output
   and the reduction in mucosal antibody-secreting PCs. Further, there is
   a switch in the PC isotype expression from the homeostatic IgA^+ PCs to
   potentially proinflammatory non-IgA^+ PC subsets. Functional studies
   will be needed to establish a causal relationship between reduced GC B
   cells and changes in PC frequency and subsets.

   To further define intestinal PCs in PWH, we performed scRNA-seq on
   isolated mononuclear cells from the intestinal LP and bulk RNA
   sequencing (RNA-seq) of fluorescence-activated cell sorting
   (FACS)–isolated colonic PCs. scRNA-seq–based subclustering of the PCs
   ([231]Fig. 4A) revealed 12 (colon) and 8 (ileum) PC subclusters
   ([232]table S20). On the basis of immunoglobulin gene expression, PCs
   were grouped into IgA (colon cluster nos. 0, 1, 2, 3, 4, 5, 7, 8, and 9
   and ileum cluster nos. 1, 2, 5, and 6), IgM [colon cluster no. 11 and
   ileum cluster nos. 0 and 3 (mixed IgM and IgA PCs clusters)], and IgG
   (colon cluster nos. 6 and 10 and ileum cluster nos. 4 and 7) ([233]Fig.
   4A; [234]fig. S9, [235]A and [236]B; and [237]tables S21 to [238]24).
   We investigated differentially expressed pathways for each PC isotype
   (FDR < 0.1; [239]Fig. 4B and [240]tables S25 to [241]S27). In colonic
   PCs, enrichments in interferon (IFN) response (IRF7, ISG15, IFI6,
   STAT1, IRF9, IRF2, OAS2, IFNAR1, and STAT2), cell cycle regulation
   (NUMA1, TUBGCP2, and RAD21), apoptosis and cell fate (CFLAR, CASP8,
   FADD, and TNFRSF10), antigen presentation (HLA-DR, TAPBP, and AP1G1),
   and antiviral response (PSMA7, PSMC1, and APOBEC3G) pathways were
   noted, especially for IgA^+ PCs, in PWVH. In PWSH, differences in IFN
   response, cell cycle, apoptosis, and cell metabolism persisted,
   especially in IgA^+ and IgG^+ PCs. Furthermore, respiratory chain
   pathways (SDHA, HADHB, IDH2, and FH) were specifically up-regulated in
   PWSH, suggesting an altered metabolic state. Pathway dysregulation was
   less notable in ileum PCs ([242]table S28).

Fig. 4. Plasma cell transcriptional profile is altered during HIV-1
infection.

   Fig. 4.
   [243]Open in a new tab

   (A) UMAPs (left) showing reclustered and PC clusters using scRNA-seq
   data and heatmaps (right) showing the average normalized log expression
   z score of immunoglobulin genes across different colon (top) and ileum
   (bottom) PC subclusters. (B) Heatmaps showing the mean pathway z score
   for each colon-derived PC isotype, across the three groups (NCs, n = 9
   for colon and n = 5 for ileum; PWSH, n = 5 for both colon and ileum;
   PWVH, n = 4 for both colon and ileum). (C) Bubble plot showing the
   Normalized Enrichment Score (NES) of selected pathways of interest
   derived from the top five pathways identified with Sumer analysis
   ([244]Materials and Methods) in FACS-sorted colonic PCs of each of the
   three groups (NCs, n = 4; PWSH, n = 5; PWVH, n = 3). The bubble size
   represents the NES, and the color represents the direction of the
   change (purple = increased, yellow = decreased). Color intensity
   indicates statistical significance levels, with lighter colors
   indicating P < 0.05, darker colors denoting P < 0.01, and the darkest
   colors indicating P < 0.001. Pathway names ending with “K” are from the
   KEGG database, “B” represents Bioplanet, and “R” stands for the
   Reactome database. (D) Heatmap showing the expression of selected
   leading edge genes (LEGs) from pathways in (C) for each participant
   across the three groups.

   To further validate these findings, we FACS-isolated colonic PCs and
   examined their transcriptional profile using bulk RNA-seq from 12 study
   participants (PWVH, n = 3; PWSH, n = 5; NCs, n = 4, [245]table S29).
   Because of the small sample size, the analysis was underpowered for
   detection of differentially expressed genes (at adjusted P < 0.05).
   However, gene set enrichment analysis (GSEA) leveraging different
   databases [i.e., Kyoto Encyclopedia of Genes and Genomes (KEGG),
   Reactome, and Bioplanet] indicated dysregulation of multiple pathways
   ([246]table S30). To reduce redundancy of pathway information, we
   performed a Sumer analysis ([247]46) that uses a weighted set cover
   algorithm to select the fewest number of gene sets that cover all genes
   associated with the enriched sets ([248]tables S31 to [249]S36). In
   alignment with the scRNA-seq dataset, PCs from PWVH were enriched in
   cell cycle (CHAF1A, SETD1A, and CREBBP), IFN signaling (IFI6, IRF7,
   STAT1, ISG15, OAS2, and OAS3), and innate and adaptive immune pathways
   (HLA-DRA, HLA-DQB1, CD226, and KIF11) ([250]Fig. 4, [251]C and [252]D,
   and [253]tables S31 to [254]S36), indicating a proinflammatory response
   in PWVH. On the other hand, PCs from PWSH showed up-regulation in
   protein degradation (PSMB and PSMD) and mitochondrial metabolism
   pathways (ATP6, COX5A, and SDHD) ([255]Fig. 4, [256]C and [257]D, and
   [258]tables S31 to [259]S36), again demonstrating an altered metabolic
   state of PCs in PWSH during effective ART. Given the intrinsic
   methodological differences in the analysis performed via scRNA-seq and
   bulk RNA-seq, we performed a comparative analysis of the differentially
   expressed pathways for each of the groups and observed an enrichment in
   pathways that were shared between the two methodologies across
   comparisons, especially in the comparisons between PWSH and NCs and
   PWVH and PWSH ([260]fig. S9C and [261]table S37).

   Together, these data demonstrate major perturbations in the number and
   transcriptional profile of intestinal PCs that accompany the disrupted
   GC dynamics during HIV-1 viremia. In addition, the transcriptional
   profile of intestinal PCs remains abnormal during ART, with persistent
   IFN signaling and an altered metabolic profile, further indicating that
   the mucosal milieu remains dysfunctional despite suppression of HIV-1
   viremia.

HIV-1–induced intestinal T cell disruption associates with PC and B cell
abnormalities

   Next, we studied the association of HIV-1–induced T cell changes with
   alterations in intestinal B cells and PCs. Using scRNA-seq, we
   annotated 18,204 T cells in colon and 17,676 T cells in ileum and
   identified nine colonic T cell clusters and 10 ileal T cell clusters
   ([262]Fig. 5, [263]A and [264]B, and [265]table S38). T cell clusters
   mapped to CD8^+ cytotoxic T lymphocytes (CTLs; CD8, GZMM GZMA, GZMB,
   and IFNG; colon cluster nos. 0, 2, and 7; and ileum cluster nos. 0 and
   7), naïve and central memory T cells (CCR7 and CD27, colon cluster no.
   3, and ileum cluster no. 4), and effector memory T cells (CD27 and
   ICOS, colon cluster nos. 1, 4, and 8; and ileum cluster nos. 1, 2, 3,
   6, and 9) ([266]Fig. 5, [267]C and [268]D, and [269]tables S39 to
   [270]S42). In addition, we identified regulatory T cells (T[regs];
   FOXP3 and MAF) in the colon (cluster no. 5) and ileum (cluster no. 8)
   as well as a T follicular helper cell (T[FH])–like cluster (CD40L and
   BCL6) in the colon (cluster no. 6) and ileum (cluster no. 5). In PWH,
   the fraction of CD4^+ T cells was decreased and the fraction of CD8^+ T
   cells was increased compared with that in NCs ([271]Fig. 5, [272]E and
   [273]F, and [274]table S43). We observed a decrease in naïve and
   central memory T cells and an increase in effector memory T cells in
   PWVH in both colon and ileum as well as a nonsignificant decrease in
   the fraction of T[regs] in the colon ([275]Fig. 5, [276]E and [277]F,
   and [278]table S43).

Fig. 5. Viremic HIV-1 infection is associated with perturbations in T cell
subsets including T[FH]-like cells.

   Fig. 5.
   [279]Open in a new tab

   (A and B) UMAPs showing reclustered, colon (A)– and ileum (B)–derived T
   cell clusters using scRNA-seq. (C and D) Heatmaps showing the average z
   score–normalized log expression of T cell–related genes across
   different colon-derived (C) and ileum-derived (D) T cell subclusters.
   (E and F) Boxplots showing the scRNA-seq–derived frequencies of
   colon-derived (E) and ileum-derived (F) CD4^+ and CD8^+ T cells as a
   fraction of all T cells (top), across the three groups (NCs, n = 9 for
   colon and n = 5 for ileum; PWSH, n = 5 for both colon and ileum; PWVH,
   n = 4 for both colon and ileum). In (E) and (F), bottom boxplots show
   the scRNA-seq–derived frequencies of colon-derived (E) and
   ileum-derived (F) subtypes of CD4^+ T cells, across the three groups.
   In (E) and (F), comparisons were performed with Wilcoxon test; P values
   are as indicated. (G) Correlation matrix showing relationships between
   frequencies of colonic PCs and PC isotypes and frequency of colonic T
   cell subsets. (H) Correlation matrix showing relationships between
   frequencies of ileum-derived B cells and frequency of ileal T cell
   subsets. (G) and (H) represent data derived from the flow cytometric
   dataset (NCs, n = 51; PWSH, n = 35; PWVH, n = 16). (I) Heatmap
   representing the mean z score enrichment for selected pathways within
   the ileum-derived T[FH]-like T cell subcluster.

   We confirmed a significant depletion of CD4^+ T cells in the intestinal
   mucosa in PWVH compared with NCs by FC, which did not normalize in PWSH
   ([280]fig. S10, [281]A and [282]B, and [283]table S17). We also
   detected increased frequencies of activated (CD38^+) and cycling
   (Ki67^+) CD4^+ and CD8^+ T cells in PWVH compared with NCs ([284]figs.
   S2C and [285]S10, [286]C to [287]F, and [288]table S17). Next, we
   assessed the association between intestinal T cells and intestinal PCs.
   We found an inverse correlation between the frequency of colonic PCs
   and colonic IgA^+ PCs and activated and cycling T cells ([289]Fig. 5G;
   [290]fig. S10, [291]H and [292]I; [293]table S17). Conversely, a direct
   correlation was found between the frequency of colonic IgM^+ (and to a
   lesser extent IgG^+) PCs and activated and cycling T cell subsets
   ([294]Fig. 5G, [295]fig. S10J, and [296]table S17), suggesting an
   association between the local inflammatory milieu and decreased PC
   frequency with a shift of PC isotype to non-IgA^+ PCs. In the ileum, a
   direct correlation between total B cells, naïve B cells, switched
   memory (SM) B cells, and GC B cells was observed with CD4^+ T cells. An
   inverse correlation was detected between CD8^+ T cells and total B
   cells, naïve B cells, SM B cells, and GC B cells ([297]Fig. 5H,
   [298]fig. S10K, and [299]table S17).

   Next, we examined the contribution of T[FH] cells in the perturbed GC
   dynamics. In the scRNA-seq dataset, a higher proportion of colonic
   CD4^+ T cells mapping to the T[FH]-like cluster was noted in PWSH
   compared with NCs. A similar, although nonsignificant, trend was also
   noted for PWVH in the colon as well as for PWH in the ileum ([300]Fig.
   5, [301]E and [302]F). Because functional abnormalities within T[FH]
   cells are reported in PWH ([303]47, [304]48), we examined the
   transcriptional profile of T[FH] cells using scRNA-seq and observed
   increased activation of type I IFN, IFN response, cell division, and
   apoptosis pathways in PWVH in both the ileum and the colon ([305]Fig.
   5I, [306]fig. S10L, and [307]tables S44 and [308]S45). Similar changes
   were seen in the T[FH]-like cell clusters of PWSH, suggesting that
   despite no detectable change in the relative frequency of T[FH]-like
   cells, the transcriptional profile of this population is altered during
   HIV-1 infection.

Changes in intestinal PCs are associated with systemic inflammation

   To determine whether the observed perturbations in intestinal PCs
   affected the systemic immune milieu, we examined soluble and
   cell-associated inflammatory parameters in the circulation of the study
   participants. As reported previously ([309]49–[310]51), we confirmed a
   higher frequency of PB-activated T cells (CD38^+CD4^+ and CD38^+CD8^+ T
   cells) in PWVH and, to a lesser extent in PWSH, compared with NCs
   ([311]Fig. 6, [312]A and [313]B, and [314]table S17). PWVH also had an
   increased frequency of cycling (Ki67^+CD4^+ and Ki67^+CD8^+) T cells
   compared with NCs ([315]Fig. 6, [316]C and [317]D, and [318]table S17).
   Colonic IgA^+ PCs inversely correlated with the frequency of activated
   and cycling CD4^+ and CD8^+ T cells in circulation. Conversely, colonic
   IgM^+ PCs were directly correlated with both activated and cycling
   CD4^+ and CD8^+ T cells in circulation ([319]Fig. 6E and [320]table
   S17).

Fig. 6. Altered frequency and isotype expression of colonic PCs contribute to
persistent systemic inflammation and immune activation during HIV infection.

   Fig. 6.
   [321]Open in a new tab

   (A and B) Frequency of PB-derived, activated (CD38^+) CD4 T cells (A)
   and CD8^+ T cells (B) obtained via flow cytometry, across the three
   groups of study participants (NCs, n = 33; PWSH, n = 32; PWVH, n = 12).
   (C and D) Frequency of PB-derived cycling (Ki67^+) CD4^+ T cells (C)
   and CD8^+ T cells (D) across the three groups of study participants.
   For (A) to (D), comparisons between groups were performed using
   Kruskal-Wallis test with Dunn’s correction for multiple comparisons.
   Median and interquartile range were used to plot summary data; P values
   <0.1 are shown as indicated (P values >0.1 are not shown). (E)
   Correlation matrix showing the correlation between colonic PCs and PC
   isotypes and PB-derived activated and cycling T cell subsets using flow
   cytometry data, and correlation was obtained using Spearman’s r
   correlation coefficient; P values are as indicated. (F) Bar plot
   showing systemic inflammatory markers in PWVH compared with NCs (dark
   red) and in PWSH versus NCs (light red). Dark gray bars represent
   systemic inflammatory markers found to be significantly higher in NCs
   compared with PWVH, and light gray bars indicate systemic inflammatory
   markers found to be significantly higher in NCs compared with PWSH.
   Comparisons were performed by Student’s t test with Benjamini-Hochberg
   correction for multiple comparisons; P values are as indicated. (G)
   Correlation matrix showing the relationship between flow
   cytometry–derived colonic PCs as well as PC isotypes and systemic
   inflammatory markers noted to be elevated in PWH compared with NCs.
   Correlation was obtained using Spearman’s r correlation coefficient; P
   values are as indicated.

   We then examined a panel of soluble circulating inflammatory parameters
   using a multiplexed proteomic assay in all study participants [n = 86;
   PWVH, n = 13; PWSH, n = 39; NCs, n = 34 ([322]table S46)] for whom
   plasma samples were available. After correcting for multiple
   comparisons, 34 analytes were found to be significantly increased
   during HIV-1 infection. PWVH showed higher concentration of analytes
   relating to T cell activation (IL-17C, SLAMF1, IL10, IFN-γ, CD6,
   IL-15RA, IL6, EN-RAGE, IL-13, CCL20, CD8A, PD-L1, IL-20, CD244, TNFB,
   CCL11, and FLT3L), myeloid cell activation (SLAMF1, TNF, CXCL9, IL6,
   EN-RAGE, CXCL10, TNFRSF9, CXCL11, CCL20, PD-L1, CCL19, IL-20, CD244,
   and IL-12B), B cell activation (SLAMF1, CD6, CD5, PD-L1, and IL-20),
   and NK cell activation (IFN-γ, IL-20, IL-18, CD244, TRAIL, and FLT3L).
   Of these analytes, 17 remained increased in PWSH, including markers of
   T cell activation (IL-17C, SLMAF1, CD6, IL-15RA, IL6, CCL20, and CD8A),
   myeloid cell activation (SLAMF1, TNF, CXCL9, CCL20, and IL-20), B cell
   activation (SLAMF1), and NK cell activation (TRAIL) ([323]Fig. 6F and
   [324]table S46). NCs, on the other hand, had increased concentrations
   of analytes involved in stem cell activation (SCF), T cell and
   dendritic cell proliferation (TNFSF14), and cell apoptosis (CASP-8). We
   found an inverse association of colonic IgA^+ PCs and a direct
   association of IgG^+ and IgM^+ with most of the analytes that were
   increased during HIV infection ([325]Fig. 6G). Together, these results
   demonstrate perturbed T cell dynamics in the GI tracts of PWH, with T
   cell activation associated with a decrease in colonic PCs, a shift
   toward nonhomeostatic PC isotypes, and an inverse correlation between
   IgA^+ PCs and markers of systemic inflammation during HIV-1 infection.

Discrete intestinal microbiome changes associate with distinct colonic PCs
isotypes

   Given the intimate, bidirectional communication of the intestinal
   microbiota with the mucosal immune system ([326]52–[327]56), we
   examined the relationship between reduced IgA^+ PCs and the microbiota
   by sorting IgA-bound and unbound bacteria from stool suspensions
   ([328]Materials and Methods) in a subset of study participants (7 PWVH,
   18 PWSH, and 16 NCs). The frequency of IgA-bound bacteria trended lower
   in PWVH compared with both PWSH and NCs ([329]Fig. 7, [330]A and
   [331]B, and [332]table S47). Similar findings were observed in a
   validation cohort [Consortium for the Evaluation and Performance of HIV
   Incidence Assays (CEPHIA) of the University of California, San
   Francisco; [333]Materials and Methods] where IgA binding of stool
   bacteria was analyzed for 5 PWVH, 13 PWSH, and 12 NCs ([334]fig. S11A
   and [335]table S47). To delineate bacterial species targeted by
   IgA-mediated host immune response, we performed IgA-seq analysis of
   sorted IgA-bound versus unbound bacteria in each of the three groups
   and calculated the log ratio of bound to unbound bacteria at the genus
   level ([336]57). The IgA log Palm score for Actinomyces (R = 0.44, P =
   0.049) and Eubacterium (R = 0.52, P = 0.015) correlated with the log
   abundance of IgA^+ PCs within each individual, and the IgA log Palm
   score for Eubacterium inversely correlated with the log abundance of
   IgG^+ PCs ([337]Fig. 7C and [338]table S48), suggesting an additional
   homeostatic role for this genus, which has previously been shown in the
   context of healthy gut microbiome ([339]58–[340]60).

Fig. 7. Distinct intestinal microbiota changes and targeting by secretory
IgAs during HIV infection relate to altered colonic PC isotype expression.

   Fig. 7.
   [341]Open in a new tab

   (A) Representative flow plots showing IgA-bound and unbound stool
   bacteria across the three groups (NCs, PWSH, and PWVH). (B) Summary
   data representing the frequency of IgA-bound bacteria across the three
   groups where stool samples were available for stool bacteria sorting in
   the primary cohort (NCs, n = 16; PWSH, n = 18; PWVH, n = 7). Bar
   represents median and IQR values. Statistical comparisons were
   performed with Mann-Whitney test; P values are as indicated. (C)
   Correlation plots between log Palm score and log cell abundance of
   IgA^+ or IgG^+ colonic PCs for bacteria where a significant correlation
   was found. (D) PCoA plot showing beta diversity (using Bray-Curtis
   distances) between PWH and NCs. (E) PCoA plot showing beta diversity
   (using Bray-Curtis distances) across PWH who are MSM or not and NCs who
   are MSM or not. (F) Box plots showing the relative abundance of
   Prevotella (top) and Bacteroides (bottom) across PWH and NCs who are
   MSM or not. (G) Bar plot showing the distribution of bacterial phyla
   across the three groups. (H) LEfSe performed on all operational
   taxonomic units (OTUs) comparing PWVH and NCs. (I) Log IgA score for
   Roseburia in the validation cohort. Bar represents median and IQR
   values. Statistical comparisons performed with Mann-Whitney test; P
   values are as indicated. (J) Correlation plots between the relative
   abundance of selected bacterial genera and the frequency of colonic PCs
   or IgA PCs.

   To define the impact of the altered host immune landscape on the
   overall composition of the intestinal microbiota, we examined the stool
   metagenome of PWVH (n = 7), PWSH (n = 18), and NCs (n = 16). Among the
   25 PWH for whom stool microbiota was analyzed, 15 participants
   identified as men who have sex with men (MSM) ([342]table S1). Because
   sexual practices may affect the intestinal microbiome ([343]31,
   [344]61–[345]63), we specifically recruited HIV-negative study
   participants who identified as MSM (n = 8, 50% of NCs). Beta diversity
   clustering by principal coordinate analysis (PCoA) revealed similar
   compositions between PWH and NCs ([346]Fig. 7D). Notably, microbiota
   from MSM participants clustered together and were distinct from the
   microbiota from non-MSM participants regardless of HIV infection status
   and microbiome distribution of HIV-infected non-MSM participants,
   spanned across all other participant groups ([347]Fig. 7E). Consistent
   with previous reports ([348]61–[349]63), a higher relative abundance of
   Prevotella and lower relative abundance of Bacteroides was noted in MSM
   participants, regardless of their HIV status ([350]Fig. 7F).

   When examined across all three groups, the major bacterial phyla were
   represented by Firmicutes, Bacteroidetes, and Actinobacteria, with
   marginal differences in their relative abundance across the three
   groups ([351]Fig. 7G). No major differences across the three groups
   were noted in the relative abundance of different bacteria genera
   ([352]fig. S11B). Nonetheless, linear discriminant analysis effect size
   (LEfSe) revealed enrichment of Enterococci and Mitsuokella in PWH and
   enrichment of Roseburia and Alistipes in NCs ([353]Fig. 7H and
   [354]fig. S11C). In the validation cohort, Roseburia was found to be
   less IgA-coated in PWH compared with NCs as well ([355]Fig. 7I).

   Last, we explored the relationship between mucosal PC frequency and the
   intestinal microbiota at the genus level. Frequency of colonic PCs was
   directly related to several microbial genera ([356]fig. S11D and
   [357]table S48), including Roseburia ([358]Fig. 7J), which was found to
   be enriched in the NCs in the LEfSe analysis and more IgA-coated in our
   validation cohort. Similarly, IgA^+ PCs were directly associated with
   the relative abundance of Bilophila, Parabacteroides, Odoribacter, and
   Streptococcus ([359]fig. S11D and [360]table S49) and inversely
   associated with the relative abundance of Enterobacter and Mitsukella
   ([361]Fig. 7J), both enriched in PWVH. Together, these results
   demonstrate reduced bacterial targeting by IgAs during viremic HIV
   infection and alterations in the relative abundance of specific
   bacteria, despite the lack of major changes in the overall microbiome
   composition in PWH.

DISCUSSION

   NAb production is a highly coordinated process involving
   antigen-directed participation of T[FH] cells, GC B cells, and FDCs
   ([362]64–[363]67), and GC B cell frequency relates to the magnitude of
   NAb titers ([364]68). A majority of the identified broadly neutralizing
   anti–HIV-1 antibodies are highly mutated ([365]69), emphasizing a
   pivotal role of GC B cells in the NAb response. Trama et al. have
   demonstrated that the GALT-generated responses to HIV-1 are limited and
   predominantly comprise microbiome-directed B cells ([366]70). In
   addition, Planchais et al. ([367]71) have reported that intestinal B
   cell–derived antibodies are largely polyreactive, with low affinity to
   HIV-1 envelope glycoproteins. Our report highlights a fundamental
   injury to the high-affinity NAb-generating apparatus in the GI tract
   and provides mechanistic insights regarding an impaired GALT antibody
   response in HIV. We posit that the loss of intestinal GC response
   likely contributes to a failure of virologic control, resulting in the
   establishment of viral sanctuaries in the intestines. This represents
   an HIV-specific phenomenon, given that most infections and vaccinations
   induce a GC response ([368]72–[369]76).

   HIV-1–associated GC dysfunction is likely multifactorial, including
   Nef-mediated perturbations of B cell function ([370]77); disruption of
   cognate interactions at T cell–B cell immune synapses ([371]78); and
   Vpr- and Vpu-mediated reduced B cell class switching ([372]79) and
   antigen presentation, respectively ([373]80). Chronic antigen
   persistence within GCs is associated with IL-6–enriched,
   cytokine-driven expansion of T[FH] cells ([374]81). Yet, as shown by
   our data and from prior reports ([375]47, [376]48, [377]82), T[FH]
   cells remain functionally altered in PWH and likely provide poor
   support to the GC reaction. In addition, GC infiltration by CD8^+ T
   cells and NK cells ([378]83) and loss or fibrosis of the stromal
   network of the GALT likely contribute to altered GC dynamics in HIV-1
   ([379]84). Herein, we also identify a reduction of GALT FDCs,
   reminiscent of degenerative changes in the FDC network in peripheral
   lymph nodes in patients with advanced HIV-1 disease ([380]85, [381]86).
   Direct infection of FDCs by HIV-1 remains unproven ([382]87), whereas
   targeted killing by cytotoxic CD8^+ T cells and NK cells, given high
   HIV-1 loads within FDCs, is a favored hypothesis ([383]88).

   IgA is the predominant antibody to emerge from intestinal GCs ([384]89,
   [385]90), making it the most abundant immunoglobulin in the intestines
   ([386]91, [387]92). Inflammatory stimuli, in particular T[H1] cytokines
   such as IFN-γ, redirect immunoglobulin class switching toward IgG
   ([388]93). Here, we describe an increase in intestinal IgG and IgM
   response during viremic HIV infection that is somewhat in contrast with
   the response induced by most intestinal parasitic, bacterial, and viral
   pathogens given that IgA remains the mainstay of protection against
   Giardia ([389]94), Salmonella ([390]95), Enterococcus ([391]75), Vibrio
   cholerae ([392]76), as well as rotavirus infection ([393]96) and
   vaccination ([394]97, [395]98). Further, we observe that suppression of
   plasma HIV VL results in “normalization” of intestinal IgA responses,
   consistent with a prior report by Zaunders et al. ([396]99) but in
   contrast with Planchais et al.’s findings ([397]100). Notably, in the
   latter study, participants who had started treatment during acute HIV
   infection showed normalization of IgA^+ B cells and PCs, suggesting
   that timing of initiating of ART may influence immune reconstitution
   ([398]101).

   In addition to altered PC frequency and isotypes, we have also
   identified altered transcriptional states of intestinal PCs, including
   upregulation of IFN responses; antigen presentation; and altered
   intracellular and cytokine signaling in PWVH. The transcriptome of
   intestinal PCs remains perturbed in PWSH even after 5 years of fully
   suppressive ART, with an increase in mRNA transcripts encoding for
   oxidative phosphorylation and mitochondrial metabolism. These data
   emphasize that mucosal dynamics remain perturbed even after years of
   apparently “well-controlled” HIV-1 infection.

   In our data, consistent with previous reports ([399]61–[400]63), sexual
   practices appeared to be a driver of dysbiosis. In addition, we have
   observed that the microbiome in PWVH lacked bacterial genera and
   species known to be producers of butyrate (Lachnospiraceae and
   Ruminococcaceae), a short-chain fatty acid important for mucosal
   integrity and health ([401]102–[402]104), while enriched for
   Mitsoukella ([403]30), a bacterium associated with increased
   concentration of proinflammatory metabolites ([404]105–[405]107).
   Furthermore, we observed Mitsoukella to be inversely associated with
   colonic PC frequency. Conversely, other butyrate-producing bacteria of
   the Odoribacter genus were positively associated with the frequency of
   the homeostatic IgA^+ PCs and inversely with the frequency of
   potentially proinflammatory IgG^+ PCs, suggesting a contribution by
   Odoribacter to mucosal homeostasis in patients with HIV-1 infection, as
   in other inflammatory diseases ([406]108–[407]110). Roseburia, a genus
   that is positively associated with general health ([408]111, [409]112),
   was depleted in individuals with HIV-1 viremia in our cohort,
   consistent with prior reports ([410]33, [411]34, [412]113, [413]114),
   and was directly related to the frequency of colonic PCs. In validation
   of our findings, samples from the CEPHIA cohort ([414]115) demonstrated
   significantly lower IgA binding of Roseburia in PWH as compared with
   NCs. In addition, we observed a trend toward lower IgA-bound bacteria
   in PWVH and a higher abundance of IgA-bound Actinomyces and
   Eubacterium. IgA-bound Eubacteria were inversely correlated with the
   abundance of IgG^+ PCs, highlighting the homeostatic properties of this
   genus ([415]58–[416]60). These data in aggregate demonstrate a
   bidirectional relationship between mucosal PCs and homeostatic
   microbiota in health that is disrupted in PWH.

   Last, we examined the associations between mucosal PCs and measures of
   systemic inflammation during HIV-1 infection. Consistent with prior
   reports ([417]51), we confirmed heightened systemic inflammation and
   its persistence despite effective ART. The frequency of mucosal IgA^+
   PCs was inversely associated with most proinflammatory parameters
   analyzed. The opposite was true for IgG^+ and IgM^+ PCs, demonstrating
   an association between the mucosal humoral compartment and control of
   systemic inflammation in HIV-1 infection during ART.

   As potential caveats and limitations, it is important to highlight the
   heterogeneity of the human GALT and PC frequencies across its length
   and across age ([418]116–[419]118), which could potentially affect the
   interpretation of our findings. The frequency of IgM^+ PCs is lower in
   the present study when compared with a previous report by Magri et al.
   ([420]119), which could be related to a combination of factors that
   include differences in biological specimens, processing protocols,
   variable distribution of IgM^+ PCs across the GI tract, and
   interindividual variability. Lastly, functional studies will be
   necessary to prove a causal effect of the perturbed GC dynamics and the
   observed alterations in PC frequency and isotype composition.

   In summary, the present report details previously underappreciated
   disruptions in intestinal B cell and PC compartments during HIV-1
   infection. Significant reduction in intestinal GC B cells during
   viremic infection may underlie the key changes in PC frequency.
   Further, alterations in the frequency, isotype expression, and
   transcriptional profile of PCs and their association with the local and
   systemic inflammatory environment emphasize potential contributions of
   the intestinal humoral response to chronic morbidity associated with
   HIV even during effective ART.

MATERIALS AND METHODS

Study design and ethical considerations

   This is a prospective observational study with the objective of
   studying the changes in the humoral immune responses in the GALT in
   PWH. Participants were recruited from the Icahn School of Medicine at
   Mount Sinai. Informed consent was obtained from all study participants.
   The study protocol was approved by the Mount Sinai institutional review
   board (HS no. 16–00512/GCO no. 16–0583). We enrolled PWVH who were ART
   naive or within 10 days of starting antiretroviral treatment or who
   self-discontinued ART and had HIV RNA > 1000 copies/ml at the time of
   enrollment. We also enrolled PWSH who had well-suppressed HIV-1 RNA
   (<20 copies/ml) for >3 months while receiving ART. Isolated viral
   “blips” (one-time HIV RNA > 20 copies/ml but <1000 copies/ml) were
   allowed. In addition, we enrolled HIV-negative controls ([421]table
   S1). We excluded participants who were pregnant or had a concomitant
   intestinal infection. All data points obtained were used for analysis.

Ileocolonoscopy, biopsy processing, and lamina propria isolation

   Mucosal biopsies were obtained using standard large cup biopsy forceps
   (n = 30, limiting the effect of variability in sampling), transported
   in Roswell Park Memorial Institute medium (RPMI, GIBCO catalog no.
   11875085) on ice, and processed within 2 hours of collection.
   Similarly, PB samples were obtained at the time of ileocolonoscopic
   procedure and processed within 2 hours of collection to isolate
   peripheral blood mononuclear cells (PBMCs) via Ficoll-based separation
   as previously described ([422]120). All samples (including the PB) were
   processed fresh (to avoid the effect of cryopreservation on PC
   viability).

   Intestinal mononuclear cells were obtained as previously described
   ([423]120). Briefly, two 20-min rounds of dissociation to remove the
   epithelial layer [dissociation medium: 10 ml of Hanks’ balanced salt
   solution (free of calcium and magnesium), EDTA (0.5 M, pH 8,
   Invitrogen), and Hepes (1 M, Lonza)] were followed by a 40-min round of
   digestion at 37°C using gentle agitation (180 rpm) [digestion solution
   (10 ml per sample) containing RPMI, fetal bovine serum (FBS) (Corning
   35–010-CV), 0.005 g of type IV collagenase (Sigma-Aldrich, catalog no.
   C5138–1G), and DNase I (Sigma-Aldrich Fine Chemicals Biosciences
   catalog no. 10104159001)]. The digested tissue was mechanically
   disrupted by syringe aspiration and sequential filtering through a
   100-μm and a 40-μm cell strainer and washed with RPMI twice. All
   experiments were performed once per study participant.

Multiparameter FC

   Intestinal lamina propria cells and PBMCs were stained with cell
   type–specific antibody panels ([424]table S15). The cell suspension was
   fixed using either 2% formaldehyde (for extracellular markers staining
   only) or fixation/permeabilization buffer (Invitrogen no. 00–5523-00)
   for intracellular staining. Samples were acquired using an LSR Fortessa
   (BD). Each intestinal lamina propria cell suspension was acquired in
   its entirety, and a minimum of 500,000 PBMCs were acquired [median
   (IQR) of PBMCs acquired: 927,000 (513,000 to 1,070,000) for the entire
   cohort] given the low frequencies of PCs in circulation during health.
   The gating strategies for annotating B cell subsets, PC subsets, and
   activated T cell subsets are shown in [425]fig. S2. Data were analyzed
   using FlowJo v10 (Tree Star). Dead cells and doublets were excluded
   from all analyses. Statistical analysis was performed using Prism
   GraphPad software by Dotmatics. Comparison across groups was done with
   one-way analysis of variance (ANOVA) for nonparametric variables
   (Kruskal-Wallis tests) with Dunn’s correction for multiple comparisons.
   Median and IQR were used to plot summary data.

Immunofluorescent microscopy

   Immunofluorescence microscopy staining was performed on biopsies from
   NCs (n = 7), PWSH (n = 7), and PWVH (n = 10). Formalin-fixed,
   paraffin-embedded intestinal biopsy tissue was cut in 5-μm sections.
   Tissue was dewaxed in xylene and rehydrated in graded alcohol and
   phosphate-buffered saline (PBS). Heat-induced epitope retrieval (HIER)
   was performed on sections submerged in target retrieval solution (Dako,
   S1699) in a pressure cooker. Non-specific binding was blocked with 10%
   goat serum for 1 hour at room temperature. Tissue was then incubated in
   primary antibodies ([426]table S15) diluted in 10% goat serum overnight
   at 4°C. Slides were then washed and incubated in secondary antibody
   ([427]table S16) and 4′,6-diamidino-2-phenylindole (1 μg/ml) for 1 hour
   at room temperature. Sections were mounted with Fluoromount-G (Electron
   microscopy sciences, no. 1798425). Controls included no primary
   controls and isotype controls. Sections were imaged using a Nikon
   Eclipse Ni microscope and digital SLR camera (Nikon, DS-Qi2).

   Image analysis was done in ImageJ/Fiji. IgA^+ PCs (IgA^+CD138^+) and
   IgA^− PCs (CD138^+IgA^−) were quantified per area of lamina propria
   (median area = 0.62 mm^2, range = 0.21 to 1.48 mm^2). CD138^+ cells
   were only counted in the lamina propria. Statistical analysis was
   performed using Prism GraphPad software by Dotmatics. The frequencies
   of cells per unit area were compared using one-way ANOVA for
   nonparametric variables (Kruskal-Wallis test) with Dunn’s correction
   for multiple comparisons. Median and IQR were used to plot summary
   data.

IHC staining

   Of 154 participant samples, 152 were available for analysis. All
   samples were reviewed by a clinical GI pathologist, and in 15 samples,
   a lymphoid follicle with a GC was identified. This included 4 of 18
   (22%) PWVH, 5 of 49 (10%) PWSH, and 6 of 87 (6.9%) NCs. All of the
   samples where a lymphoid follicle with GC was detected were used for
   the analysis. Slides were baked at 70°C for 60 min and then loaded onto
   an automated stainer (Leica Bond III) for CD4 staining and Dako Omnis
   (Dako /Agilent) for BCL6 staining. HIER was achieved with Epitope
   Retrieval Solution 2 for 30 min at 100°C (CD4 staining) Envision Flex
   TRS High Solution for 30 min at 97°C (BCL6 staining). Ready to Use
   Antibody reagent was added as marker and incubated for 15 min (CD4:
   Leica Biosystems, clone 4B12; BCL6: Dako/Agilent, Clone PG-B6p).
   Detection was achieved using a Leica DAB Detection Kit (Bond Polymer
   Refine Detection) for CD4 and Envision FLEX DAB^+ (Diaminobenzidine)
   (Dako Omnis) for BCL6. Slides were dehydrated and mounted. Positive and
   internal negative controls were used. Lymphoid aggregates and GCs were
   demarcated by a pathologist on hematoxylin and eosin (H&E)–stained
   sections, and BCL6^+ cells were counted manually within the GCs on IHC
   slides. CD23 staining within the GC was measured via the thresholder
   function in QuPath. After color deconvolution, a threshold of 0.25 was
   set for positive staining. The area of positive staining was calculated
   as % of GC area. Statistical analysis was performed using Prism
   GraphPad software by Dotmatics. Comparison across groups was performed
   by Kruskal-Wallis test with Dunn’s correction for multiple comparisons.
   Median and IQR were used to plot summary data.

Lamina propria cell sorting and mRNA extraction

   Twelve study participants (PWVH, n = 3; PWSH, n = 5; NCs, n = 4,
   [428]table S29) had colonic samples analyzed via bulk RNA-seq of
   flow-sorted PCs. Lamina propria single-cell suspensions were obtained
   as described above, stained with a dedicated Ab cocktail for B cell and
   PC lineage (live CD45^+CD38^hiCD27^+CD19^+ cells), and sorted using the
   BD FACS Aria II system. Sorted cells were then pelleted, and RNA was
   isolated using the RNeasy Plus Micro Extraction Kit (Qiagen # 74034) as
   per the manufacturer’s instructions. RNA libraries were prepared using
   either SMART-Seq v4 Ultra Low Input Nextera XT or SureSelect XT RNA
   Direct v6 ([429]table S29).

Bulk RNA-seq analysis of flow-sorted PCs

   The mean (±SD) depth of sequencing was 76.2 ± 12.5 million, for a total
   of 990.6 million reads for the bulk RNA-seq dataset. Preprocessing was
   done as previously reported ([430]121). Features with fewer than four
   normalized counts across all samples were filtered out. The counts were
   normalized using voom ([431]122). Normalized log counts per million and
   associated precision weights were then entered into the limma analysis
   pipeline ([432]123–[433]125). RNA-seq data were adjusted for RIN score
   using a linear model ([434]124–[435]126). Expression profiles were
   modeled using linear mixed-effects models including age and group
   variable (NCs, PWSH, and PWVH) as fixed effects and a random intercept
   for each individual. GSEA was performed using the clusterProfiler R
   package to identify the enriched terms (adjusted P < 0.05) in KEGG,
   Reactome, and Bioplanet for the comparison of the three groups. GSEA
   resulted in numerous enrichments indicating dysregulation of a large
   number of pathways: 659 for suppressed versus control, 320 for PWVH
   versus NCs, and 640 for PWVH versus PWSH ([436]table S30). To summarize
   and consolidate GSEA results, pathways with adjusted P values <0.05
   were selected from each database into the Sumer R package ([437]46),
   which was used to select the most representative pathways. This process
   reduced the number of pathway terms organized to various clusters as
   follows: for PWVH versus NCs, five clusters of 87 pathways; for PWSH
   versus NCs, eight clusters of 143 pathways; and for PWVH versus PWSH,
   four clusters of 67 pathways ([438]tables S31 to [439]S36). The top
   five pathways (by adjusted P value) per cluster were subselected for
   further analysis. Gene set variance analysis (GSVA) (z score method)
   was performed on the HIV gene expression data (RIN adjusted) to
   generate a pathway score with the core enriched genes for each pathway
   selected by the Sumer package. A bubble plot of net expression score
   (NES) of selected pathway scores across groups was constructed. The
   expression (RIN adjusted) levels of selected leading edge genes (LEGs)
   across each sample of each group are displayed in a summary heatmap.
   The color of the heatmap in each row is based on the i score values
   calculated by centering and scaling the data by SD using the formula (X
   ~ m)/SD, where X is an individual value and m is the mean of the row.

Lamina propria processing for scRNA-seq

   Samples from 18 study participants (PWVH, n = 4; PWSH, n = 5; NCs, n =
   9; [440]table S2) underwent scRNA-seq analysis. LP mononuclear cell
   suspensions were obtained from freshly processed intestinal biopsies as
   detailed above, and dead cells were removed using dead cell removal by
   magnetic binding (Miltenyi Biotec no. 130–090-101) as per the
   manufacturer’s instructions. Cells were loaded in one lane of the 10X
   Genomics NextGem 3’v3.1 assay as per the manufacturer’s protocol with a
   targeted cell recovery of 8000 cells. Gene expression libraries were
   prepared as per the 10X Genomics protocol ([441]127). Libraries were
   quantified via an Agilent 2100 hsDNA bioanalyzer and a KAPA library
   quantification kit (Roche catalog no. 0796014001) and sequenced at a
   targeted depth of 25,000 reads per cell. Libraries were pooled in
   equimolar concentration and sequenced on the Illumina NovaSeq 100 cycle
   kit with run parameters set to 28×8×0×60.

Single-cell RNA data processing and analysis

Filtering, normalization, and batch correction

   Only cells with total UMI counts greater than 1000 and mitochondria
   gene fraction less than 10% were considered for the analysis. The
   analysis of scRNA-seq data, including data normalization and batch
   effect correction, was performed via the Seurat package ([442]41,
   [443]128). First, each cell was normalized independently using the
   function NormalizeData, which divides feature counts for each cell by
   the total counts for that cell. Data were then natural log–transformed.
   Next, variable features were found using the function
   FindVariableFeatures ([444]41, [445]128). This function first fits a
   line to the relationship of log(variance) and log(mean) using local
   polynomial regression, then standardizes the feature values using the
   observed mean and expected variance (given by the fitted line). Feature
   variance was then calculated on the basis of the standardized values.
   Last, features that were repeatedly variable across datasets were
   selected for integration via the function SelectIntegrationFeatures
   ([446]41, [447]128). Anchors were then identified using the
   FindIntegrationAnchors function, which takes a list of Seurat objects
   as input and uses these anchors to integrate different datasets
   together via the IntegrateData function ([448]41, [449]128).
   Dimensionality reduction was performed using reciprocal principal
   components analysis. For visualization, the dimensionality of each
   dataset was further reduced using Uniform Manifold Approximation and
   Projection (UMAP) implemented with Seurat functions RunUMAP. The PCs
   used to calculate the embedding were the same as those used for
   clustering.

Clustering

   Clustering was performed via the function Find.Cluster in Seurat, with
   resolution parameter r = 0.5. We identified cluster-specific markers
   using the function FindMarkers in Seurat ([450]41, [451]128). Markers
   were ranked on the basis of the area under the receiver operating
   characteristic curve. On the basis of the cluster-specific markers and
   leveraging existing signatures from the literature ([452]129–[453]134),
   clusters were annotated into nine cell types for the ileum and the
   colon. Because clustering of scRNA-seq data cells may be potentially
   influenced by the use of BCR and TCR gene segments, we verified the
   similarity of the subclustering structure when removing segments of BCR
   and TCR genes that undergo V(D)J recombination [specifically IG(HKL)V,
   IG(KL)J and IG(KL)C, and TR(ABDG)(VJC)]. We chose to retain the
   IGH(ADEGM) genes because they are relevant in determining cell identity
   and function (i.e., naïve and memory B cells). With this approach, 84
   to 97% of individual cell pairs retained the same coclassification
   status [quantified by the Rand index (RI)] ([454]135), corresponding to
   the adjusted Rand index (ARI) of 0.61 to 0.89 (ARI ranges between −1
   and 1, with 0 being the expected value for unrelated classifications).
   We thus elected to retain BCR and TCR gene segments in our
   subclustering of B cells, PCs, and T cells.

Cell-type signatures

   We considered the lineage cell-type categories to obtain gene
   signatures from the scRNA-seq data. Mitochondrial and ribosomal protein
   genes were removed in the final list of genes because they were found
   expressed in multiple cell types ([455]41).

Reclustering of B cells, PCs, and T cells

   B cells, PCs, and T cells were reclustered as smaller individual
   subsets using the same nearest neighborhood clustering algorithm
   ([456]41). The resolution parameters used in reclustering were 0.5 for
   PCs and T cells and 0.4 for B cells. After identifying markers for each
   of the subclusters, we could assign them to specific subtypes of each
   cell type: naïve B cells; memory B cells; GC B cells; and IgA, IgM, and
   IgG isotypes for PCs.

Quantification of cell-type fractions

   Cell types from primary clusters were quantified as a fraction of total
   cells in each sample. Cell types from subclusters were quantified as a
   fraction of the corresponding primary cell type in each sample unless
   otherwise specified. Comparisons across groups were performed using
   Mann-Whitney U test.

Pathway enrichment analysis

   For each cell-type compartment (i.e., B cells and PCs), the log fold
   change was computed between the average gene expression in cells
   derived from PWVH, PWSH, and NCs. Enrichment scores and adjusted P
   values (FDR) were derived via gene set enrichment analysis ([457]136)
   for PWVH versus NCs and PWSH versus NCs separately. Specifically, genes
   were ordered in decreasing order of fold change, and a
   Kolmogorov-Smirnov statistic was used to find pathways enriched at the
   top of the list. A permutation-based strategy was used to derive P
   values and FDR for different pathways ([458]136). Molecular pathway
   gene sets used in this analysis were from the Reactome database
   ([459]137). The transcriptome of PCs is enriched in Ig genes ([460]138,
   [461]139), and accordingly, in our dataset, Ig-encoding genes comprised
   58% ± 13% of all UMIs in PCs in colon and 57% ± 17% in ileum, with a
   mean (SD) UMI number per PC of 15,195 ± 11,807 in the colon and 17,264
   ± 12,189 in the ileum.

Stool metagenomics

   Stool metagenomics was performed on all participants for whom stools
   were available (7 PWVH, 18 PWSH, and 16 NCs; [462]table S2). Stool
   samples were collected from study participants before or at least 2
   weeks after colonoscopy. Fresh samples were frozen at −80°C and
   mechanically pulverized. Fecal DNA for metagenomic sequencing was
   extracted as previously described ([463]140). DNA was quantified using
   the Broad Range or High Sensitivity Quant-IT dsDNA Assay kit (Thermo
   Fisher Scientific, [464]Q32853 and [465]Q33130) with a BioTek Synergy
   HTX Multi-Mode Reader. Using the NEBNext Ultra II DNA Library Prep Kit,
   metagenomic libraries were prepared. DNA that was fragmented by a
   Diagenode Bioruptor Pico sonicator was subjected to end repair,
   Illumina adaptor ligation, and purification with Beckman Coulter SPRI
   beads. NEBNext Ultra Q5 Master Mix was used to amplify ligated product
   using custom i5 and i7 index primers, and Beckman Coulter AMPure XP
   beads quantified the final product. Samples were sequenced with an
   Illumina HiSeq (paired-end 150 bp).

Computational analysis of metagenomic profiles

   MetaPhlAn2 was used for whole-metagenome shotgun sequencing ([466]141).
   Any sample with fewer than 100,000 reads was filtered out.
   Uncharacterized phylae and taxa that did not meet the minimum
   prevalence threshold of 3% of all samples were also filtered out. LEfSe
   was implemented to estimate bacterial candidates associated with study
   participant groups with a logarithmic LDA cutoff ≥2.0 ([467]142), using
   the R package microbial. Beta diversity (using Bray-Curtis distances)
   was calculated and visualized with a PCoA plot. Distance and diversity
   index calculations were performed using phyloseq ([468]143). Spearman
   correlation was performed on bacterial genus relative abundances and
   immune cell abundances. Given the exploratory nature of this study,
   reported P values are unadjusted.

IgA-bound and unbound stool bacteria sorting

   Fifty milligrams of frozen stool samples was resuspended in PBS (10×
   stool weight) and pelleted. The supernatant was filtered through a
   40-nm cell strainer. One hundred microliters of suspension was blocked
   for nonspecific staining using anti-mouse serum, stained for IgA (PE
   Miltenyi Biotec, catalog no. 130–113-476), washed thrice, and stained
   for DNA (SYBRGreen Applied Biosciences). IgA-bound (SYBRGreen^+, IgA^+)
   and IgA-unbound (SYBRGreen^+, IgA^−) stool bacteria were sorted and
   pelleted, and DNA was extracted using the QIAamp UCP Pathogen Mini Kit
   (Qiagen catalog no. 50214). Staining was directed toward total IgAs and
   not specifically toward SIgAs, potentially capturing infiltration from
   monomeric serum IgAs in the stools. Comparison across groups was done
   with one-way ANOVA for nonparametric variables (Kruskal-Wallis tests)
   with Dunn’s correction for multiple comparisons. Median and IQR were
   used to plot summary data.

16S rRNA sequencing and IgA-seq

   For DNA extraction, we used a phenol:chloroform method on 50 to 200 mg
   of fecal specimen as previously described ([469]144). We adjusted DNA
   templates to a concentration of 2 ng/ml. After this, the V4 variable
   region of the 16S rRNA gene underwent PCR amplification using indexed
   primers, adopting the methodology outlined in ([470]145). The resultant
   16S rRNA V4 amplicons were merged and subsequently purified using
   AMPure XP beads (Beckman Coulter, A63880) at a bead-to-PCR ratio of
   1:1. Gel electrophoresis was used to confirm the accurate amplicon size
   and to ensure the nonpresence of primer dimers. The consolidated
   samples were then sequenced on an Illumina MiSeq platform, producing
   paired-end reads of 250 bp each. To process and denoise paired-end
   sequence data, the DADA2 algorithm available within the QIIME2 pipeline
   was used. The Greengenes sequences database was used as a reference for
   paired-end reads merge and alignment.

   We removed samples with fewer than 1000 reads. Ambiguously annotated
   phylum-level taxonomic features (e.g. “NA”) and low-prevalence ASVs
   were removed. In addition, ASVs with a relative abundance less than
   0.01% in both the IgA-bound and unbound dataset were filtered out. A
   pseudocount of minimum (relative abundance of genus)/2 was computed to
   entries with exact 0 relative abundance entries in ASV tables. Log Palm
   score was calculated ([471]57) to quantify IgA binding. Spearman
   correlation was calculated on immune cell abundances and log Palm score
   of bacterial genus.

   A validation cohort (CEPHIA of the University of California, San
   Francisco) was also included to confirm the IgA-seq results. Samples
   were processed as previously described ([472]115). To obtain
   genus-level IgA scores for all taxa, the DADA2 pipeline was applied,
   and taxonomy was assigned on resulting amplicon sequence variants
   (ASVs) using the SILVA database (v138.1, nr99). ASVs belonging to the
   same genus were summed, and a pseudocount of 1 was applied. The IgA
   score was computed as a natural log of the ratio of each genus in the
   IgA-positive fraction over the IgA-negative fraction.

Multiplexed proteomic assay (Olink)

   Circulating inflammatory cytokines were quantified in all the study
   participants for whom plasma was available (total = 86; PWVH, n = 13;
   PWSH, n = 39; NCs, n = 34; [473]table S46) using a multiplexed
   proteomic inflammation panel (Olink) that simultaneously quantifies 92
   inflammation-related proteins. The concentration of each biomarker was
   compared between PWVH and NCs as well as between PWSH and NCs using
   Student’s t test. The Benjamini-Hochberg procedure for multiple
   comparisons was used to adjust the resulting P value. Correlation with
   LP cell-type frequencies and circulating inflammation-related proteins
   was obtained by Spearman’s rho correlation coefficient.

Supplementary Material

   Supplementary Figures
   [474]NIHMS2032038-supplement-Supplementary_Figures.pdf^ (350.1MB, pdf)
   Reproducibility checklist
   [475]NIHMS2032038-supplement-Reproducibility_checklist.docx^ (68KB,
   docx)
   Supplementary Tables
   [476]NIHMS2032038-supplement-Supplementary_Tables.xlsx^ (14.5MB, xlsx)

Acknowledgments: