Hierarchical Clustering and Trajectory Analyses Reveal Viremia-Independent B-Cell Perturbations in HIV-2 Infection

Time to AIDS in HIV-2 infection is approximately twice as long compared to in HIV-1 infection. Despite reduced viremia, HIV-2-infected individuals display signs of chronic immune activation. In HIV-1-infected individuals, B-cell hyperactivation is driven by continuous antigen exposure. However, the contribution of viremia to B-cell perturbations in HIV-2-infected individuals remains largely unexplored. Here, we used polychromatic flow cytometry, consensus hierarchical clustering and pseudotime trajectory inference to characterize B-cells in HIV-1- or HIV-2-infected and in HIV seronegative individuals. We observed increased frequencies of clusters containing hyperactivated T-bethighCD95highCD27int and proliferating T-bet+CD95highCD27+CD71+ memory B-cells in viremic HIV-1 (p < 0.001 and p < 0.001, respectively), viremic HIV-2 (p < 0.001 and p = 0.014, respectively) and in treatment-naïve aviremic HIV-2 (p = 0.004 and p = 0.020, respectively)-infected individuals, compared to seronegative individuals. In contrast, these expansions were not observed in successfully treated HIV-1-infected individuals. Finally, pseudotime trajectory inference showed that T-bet-expressing hyperactivated and proliferating memory B-cell populations were located at the terminal end of two trajectories, in both HIV-1 and HIV-2 infections. As the treatment-naïve aviremic HIV-2-infected individuals, but not the successfully ART-treated HIV-1-infected individuals, showed B-cell perturbations, our data suggest that aviremic HIV-2-infected individuals would also benefit from antiretroviral treatment.


Introduction
Human immunodeficiency virus type 1 (HIV-1) and type 2 (HIV-2) are causative agents of acquired immunodeficiency syndrome (AIDS), but HIV-2 has been shown to be less pathogenic and less transmissible than HIV-1 [1][2][3]. It was previously suggested that HIV-2 infection had limited impact on the survival for many of the infected individuals [4][5][6]. However, we recently reported that HIV-1-and HIV-2-infected individuals not receiving antiretroviral therapy (ART) in fact display similar disease trajectories, albeit with approximately twice as long time to AIDS and HIV-related death in HIV-2-infected

Study Participants
The study participants were enrolled from an occupational police cohort of police officers in Guinea-Bissau [31,32], and included HIV seronegative, and treatment-naïve (or sub-optimally ART-treated with VL > 1000 RNA copies/mL plasma) HIV-1-or HIV-2-infected individuals [33]. The HIV-2-infected individuals were further divided into viremic and aviremic individuals, based on the HIV-2 plasma VL quantification limit of 75 RNA copies/mL [12]. In addition, HIV-1-infected individuals on successful ART and with VL < 1000 RNA copies/mL were included from the same cohort.

Sample Collection, HIV Status, and CD4 + T-Cell Level Determinations
All blood samples were collected in Guinea-Bissau and shipped to Sweden, with an intact cold chain. Plasma samples were collected using EDTA vacutainer tubes (BD Biosciences, San Jose, CA, USA). Aliquoted plasma was stored at −80 • C until use. Whole blood was collected using Cyto-Chex BCT tubes (Streck, Omaha, NE, USA), where the immunophenotype of lymphocytes is preserved but rendered inadequate for functional analysis [34], and analyzed within 14 days of collection. HIV infection status, absolute CD4 + T-cell counts per µL, and percentage of CD4 + T-cells of lymphocytes (CD4%) were determined by serology and flow cytometry, as previously described [11,24]. CD4% was Cells 2022, 11, 3142 3 of 18 used as a marker of disease progression based on previous reports suggesting that CD4% is less variable than absolute CD4 + T-cell counts, particularly in settings with high pathogenic burden and comorbidities [35][36][37][38]. Moreover, CD4% has been shown to correlate with markers of T-cell exhaustion in HIV-1 infection [39], and CD4% has been the primary marker for disease progression in previous studies from the Guinea-Bissau police cohort [40].

Plasma HIV-1 and HIV-2 Viral Load Determinations
With minor modifications, HIV-1 and HIV-2 VL was determined using in-house TaqMan-based quantitative reverse transcriptase PCR (qRT-PCR) protocols, as previously described [12]. In brief, RNA was extracted using the miRNeasy micro Kit (Qiagen, Hilden, Germany), and RNA was quantified by qRT-PCR using the Superscript III Platinum One Step qRT-PCR kit (ThermoFisher Scientific, Waltham, MA, USA). The VL quantification limit was 75 RNA copies/mL plasma for HIV-1 and HIV-2, respectively [12].

Plasma IgG1 and IgG3 Quantification
Plasma IgG1 and IgG3 concentrations were determined using the IgG Subclass Human ELISA Kit (ThermoFisher Scientific, Waltham, MA, USA), according to the manufacturer's instructions. All plasma samples were diluted 1:2500 for both IgG1 and IgG3 quantifications.

Flow Cytometry
Whole blood samples, stabilized in Cyto-Chex BCT tubes, were stained with a B-cell phenotyping antibody panel, including antibodies specific for 10 extracellular markers (CD19, CD20, CD24, CD27, CD38, CD71, CD95, HLA-DR, CD3 and CD14) and the intracellular T-bet antigen. Clones, fluorochromes and suppliers are listed in Table S1. In brief, prior to staining the cells with the antibody panel, the whole blood samples were incubated in red blood cell lysis buffer (BD Biosciences, San Jose, CA, USA) at a ratio of 1:6 for 15 min before washing with PBS/FCS (2%). After DNase (6 U/mL) treatment, extracellular antigens were labeled in PBS/EDTA (2 mM) for 30 min. Cells were thereafter permeabilized using the FoxP3 kit (eBiosciences, San Diego, CA, USA) prior to intracellular staining for 30 min. The cells were washed and resuspended in Cytofix Buffer (BD Biosciences, San Jose, CA, USA) prior to analysis on a BD Fortessa instrument (BD Biosciences, San Jose, CA, USA).
The Uniform Manifold Approximation and Projection (UMAP) was based on the expression of the above-mentioned markers from 50,000 cells per HIV status group, with equal sampling from each sample within each infection group, using the uwot package (v0.1.11) [47]. The same subsampled cells were used for subsequent pseudotime trajectory inference analysis using the Slingshot package (v2.0.0), as previously described [48,49]. We specified approx_points = 100 to reduce the computational time due to the large size of our data set [49]. The ggplot2 R package (v3.3.5) [50] was used to produce both UMAP and density plots, to display marker expression in each FlowSOM cluster, density of PC1 values of individuals within each HIV status group, and density of cells from each HIV status group by pseudotime in each pseudotime trajectory.
Differences in frequencies in manually gated B-cell subpopulations and FlowSOM generated clusters, as well as in principal component one (PC1) values, were calculated using the non-parametric Kruskal-Wallis test with Dunn's post hoc test comparing all HIV status groups, and Mann-Whitney U test for comparisons of two groups, using GraphPad Prism version 9.2.0 (GraphPad Software, Inc., La Jolla, CA, USA). Spearman rank correlation analysis was performed to test for correlations between cluster frequencies and CD4%, VL and plasma proteins concentrations.

Both HIV-1 and HIV-2 Infection Induce an Expansion of T-Bet and CD95-Expressing B-Cells
To study the impact of HIV-1 and HIV-2 infection on B-cell activation, we investigated the expression of the activation marker CD95 and the transcription factor T-bet, by CD19 + CD20 + B-cells in HIV-1-or HIV-2-infected, and HIV seronegative individuals. A clear skewing of the T-bet and CD95 expression patterns was observed in all treatment-naïve or sub-optimally treated HIV-1-or HIV-2-infected individuals, as compared to the HIV seronegative individuals ( Figure 1A,B). In contrast, studied B-cell populations were not found to be altered in HIV-1-infected individuals on ART. The majority of the T-bet + CD95 + B-cells were CD27 − ( Figure 1C). Increased frequencies of T-bet + CD95 + CD27 − B-cells were observed in viremic HIV-1 (p < 0.001), viremic HIV-2 (p < 0.001), and treatment-naïve aviremic HIV-2 (p = 0.030)-infected individuals, compared with HIV seronegative individuals ( Figure 1D). In addition, a significant expansion of T-bet + CD95 + CD27 + B-cells was observed in viremic HIV-1 (p < 0.001), and viremic HIV-2 (p = 0.002)-infected individuals, but not in aviremic HIV-2-infected individuals, compared to HIV seronegative individuals ( Figure 1D).
Taken together, these findings show that both HIV-1 and HIV-2 infection induces an expansion of activated T-bet-expressing B-cells.

Hierarchical Clustering Analysis Shows That HIV-1 and HIV-2 Infections Induce Phenotypic Perturbations in the B-Cell Compartment
To investigate the impact of HIV-1 and HIV-2 viremia on B-cell perturbations, we performed unsupervised consensus hierarchical clustering using the FlowSOM algorithm [43]. The analysis suggested 16 distinct clusters of B-cells, based on the expression of eight assessed markers ( Figure S2A). Following quality control of each cluster, where the expression pattern of analyzed markers was compared to previously reported B-cell populations [51], clusters 13-16 were excluded from further analyses on the rationale of minor size and surface marker expression patterns that do not correspond to previously described B-cell populations ( Figure S2). The remaining 12 clusters could be divided into four cluster groups; transitional/naïve-like B-cells (cluster 1-2), memory-like B-cells (Cluster 3-7), T-bet +/high memory-like B-cells (cluster 8-11), and PB/PCs (cluster 12) ( Figure 2A). The transitional/naïve-like B-cell cluster group was characterized by clusters of cells expressing high to intermediate CD38 levels and no CD27 ( Figure 2B,C). CD24 expression was highest in cluster 1 (transitional B-cells) and low in cluster 2 (naïve-like B-cells), in accordance with previous B-cell characterization [51]. The memory-like B-cell cluster group (cluster 3-7) contained resting and activated memory B-cells expressing CD27 and intermediate to high levels of CD95. Cluster 3 contained activated naïve-like/early memory B-cells with intermediate CD27, CD24 and CD95 expression, but with CD38 expression at levels comparable to naïve-like B-cells in cluster 2. In contrast, the B-cells in cluster 4 had downregulated CD38, a sign of differentiation into memory cells [52], despite lower CD27 expression levels compared to cluster 3 B-cells. Cluster 5 represented resting memory cells with high CD27 and low CD38 expression, while cluster 6 and 7 were activated memory cells expressing CD27, CD38 and CD95. The T-bet +/high memory-like B-cell cluster group (clusters 8-11) contained activated memory B-cells expressing low to high levels of CD27, intermediate to high levels of CD95 and T-bet. The PB/PC cluster group (clusters 12) included B-cells negative for CD20, with high CD27 and CD38, and low to intermediate HLA-DR, expression levels.   viremic HIV-1-infected individuals (p = 0.026). The PC1 value correlated with increasing CD4% (r = 0.695; p < 0.001) and decreasing VL (r = −0.560; p = 0.002) when all ART-naïve or sub-optimally treated HIV-1-and HIV-2-infected individuals were analyzed together. The significant correlation with CD4% remained when only HIV-2-infected individuals were analyzed (r = 0.477; p = 0.034). In summary, the unsupervised consensus hierarchical clustering identified B-cell perturbations in individuals with HIV-1 or HIV-2 infection.   Figure 3A). The frequency of cluster 8, 9 and 11, representing T-bet high CD95 high CD27 int hyperactivated memory cells, T-bet + CD95 high CD71 + proliferating T-bet + memory cells and T-bet high CD95 int CD27 − hyperactivated memory cells, were significantly higher in viremic HIV-1 (p < 0.001, p < 0.001, and p = 0.009, respectively) and viremic HIV-2 (p < 0.001, p = 0.014, and p < 0.001, respectively)-infected individuals compared to HIV seronegative individuals. Moreover, the frequency of cluster 2, containing resting naïve-like cells, was lower in viremic HIV-1 (p < 0.001), and viremic HIV-2 (p = 0.004)-infected individuals, compared to HIV seronegative individuals ( Figure 3B and Figure S2B). Successful ART treatment of HIV-1-infected individuals reversed the depletion of cluster 2 (p = 0.017). Treatment-naïve aviremic HIV-2-infected individuals separated from HIV seronegative individuals by higher frequencies of cluster 6, consisting of T-bet int CD95 + activated memory cells, (p = 0.010), cluster 8 (p = 0.004) and cluster 9 (p = 0.020), as well as lower frequencies of cluster 2 (p = 0.019). Of note, cluster 8 and 11 frequency distinguished viremic from aviremic HIV-2-infected individuals (p = 0.001 and p = 0.004, respectively) in direct comparisons, but this difference was not significant in the multiple comparison containing all HIV status groups. Since T-bet promotes class-switching to IgG1 and IgG3 [30], we next investigated the correlation between the frequency of the T-bet high -expressing clusters, 8 and 11, and IgG1 or IgG3 plasma titers. The results showed that both IgG1 and IgG3 titers correlated with the T-bet high -expressing B-cells, i.e., cluster 8 (r = 0.588; p = 0.023, and r = 0.625; p = 0.015, respectively) and cluster 11 (r = 0.781; p < 0.001, and r = 0.618; p = 0.016, respectively) frequencies among the HIV-1-infected individuals ( Figure S3A). IgG1 titers also correlated with the cluster 8 (r = 0.446; p = 0.048) frequency among the HIV-2-infected individuals ( Figure S3B). Furthermore, the frequency of cluster 8 in HIV-2-infected individuals was associated with plasma VL (r = 0.704; p < 0.001), and CD4% (r = 0.723; p < 0.001), in addition to plasma concentrations of proinflammatory Th1-associated cytokines: IL-12 (r = 0.549; p = 0.015), IL-18 (r = 0.468; p = 0.043), and TNF-α (r = 0.686; p = 0.001), IFN-γ (r = 0.530; p = 0.020); and chemokines CXCL9 (r = 0.619; p = 0.005), and CXCL10 (r = 674; p = 0.002) ( Figure S3C).
Intriguingly, the frequency of cluster 3, containing CD95 int CD27 int activated naïvelike/early memory B-cells, was found to be significantly higher in viremic and aviremic HIV-2-infected individuals compared to HIV seronegative individuals (p = 0.002, p = 0.009, respectively, Figure S4). However, since the frequency of naïve-like B-cells (i.e., cluster 2) was shown to be reduced in viremic HIV-1-infected individuals, we hypothesized that the reason for not observing any increase in activated naïve-like/early memory B-cells in this group was due to the low abundance of naïve-like B-cells. We therefore determined the ratio of activated naïve-like/early memory B-cells (cluster 3) to CD95 -CD27resting naïvelike (cluster 2) B-cells. The analysis showed that the ratio of activated vs. resting naïve-like B-cells was significantly higher in viremic HIV-1 (p < 0.001), viremic HIV-2 (p < 0.001), and aviremic HIV-2 (p = 0.003)-infected individuals compared with HIV seronegative individuals ( Figure S4).
Unsupervised hierarchical clustering analysis showed that, independent of viremia, especially hyperactivated T-bet-expressing memory B-cells were elevated, whereas naïvelike B-cells were reduced, in treatment-naïve or sub-optimally treated HIV-1-and HIV-2infected individuals.

Both HIV-1 and HIV-2 Infection Promotes B-Cell Differentiation Accompanied by T-Bet Expression
As an increase in frequencies of B-cell clusters containing T-bet high activated memory cells was observed, we used the Slingshot method [48] to infer pseudotime trajectories for the purpose of investigating the impact of HIV-1 and HIV-2 infections on B-cell differentiation. Cluster 1, containing CD27 − CD24 high CD38 high transitional B-cells, was designated as the starting population since this was the least differentiated B-cell subtype detected [52]. The analysis indicated four distinct trajectories, where clusters 1-6 represented the root of all lineages (Figure 4A,B). The trajectory analysis subsequently supported branching of trajectory 1 into CD20 -HLA-DR int/− PB/PCs (cluster 12), trajectory 2 into T-bet high CD38 low activated memory B cells (cluster 10), trajectory 3 into T-bet high CD27 int hyperactivated memory B-cells (cluster 8), and trajectory 4 into T-bet + CD71 + proliferating memory B-cells (cluster 9). To identify the impact of HIV-1 and HIV-2 infections and viremia on B-cell differentiation, each pseudotime trajectory was stratified by respective HIV status group ( Figure 4C and Figure S5A

Discussion
In this study, we show that both HIV-1 and HIV-2 infection induce an expansion of hyperactivated T-bet-expressing B-cells. In contrast to successfully treated HIV-1-infected individuals, treatment-naïve aviremic HIV-2-infected individuals could be distinguished from seronegative individuals by increased frequency of T-bet-expressing B-cells, supported by both a manual gating strategy and a consensus hierarchical clustering approach. This suggests that HIV-2 infection perturbs the phenotype of B-cells despite unquantifiable viremia.
T-bet has previously been described to be upregulated in several cell types following viral infection as part of a multi-cell type Th1-associated antiviral response [28,53]. Knox et al. reported that T-bet expression was highest in activated memory cells [29], defined as CD27 + CD21 − memory and CD27 − CD21 − tissue-like memory (TLM) cells. However,

Discussion
In this study, we show that both HIV-1 and HIV-2 infection induce an expansion of hyperactivated T-bet-expressing B-cells. In contrast to successfully treated HIV-1-infected individuals, treatment-naïve aviremic HIV-2-infected individuals could be distinguished from seronegative individuals by increased frequency of T-bet-expressing B-cells, supported by both a manual gating strategy and a consensus hierarchical clustering approach. This suggests that HIV-2 infection perturbs the phenotype of B-cells despite unquantifiable viremia.
T-bet has previously been described to be upregulated in several cell types following viral infection as part of a multi-cell type Th1-associated antiviral response [28,53]. Knox et al. reported that T-bet expression was highest in activated memory cells [29], defined as CD27 + CD21 − memory and CD27 − CD21 − tissue-like memory (TLM) cells. However, despite being heterogenous populations, in depth characterization of T-bet expression in distinct subpopulations was not attempted as in the current study. The association between T-bet + memory B-cell populations and pathogenic outcome, for example following rhinovirus exposure and kidney transplantation, has previously been studied using unsupervised cluster analysis [54,55]. We therefore choose to apply hierarchical cluster analysis to characterize distinct T-bet-expressing B-cell subpopulations in HIV-1 and HIV-2 infections. Of note, cluster 8 and 9 (which distinguished both viremic and aviremic HIV-2-infected individuals from seronegative controls) contained T-bet high CD27 int/+ hyperactivated memory B-cells and T-bet + CD95 high CD71 + proliferating T-bet + memory cells, respectively. This likely represent a mixture of the two activated memory populations described by Knox et al. [29]. Moreover, HIV-1 infection has previously been shown to induce the expansion of a CD20 high CD27 int hyperactivated memory B-cell population [56] at similar frequencies compared to the frequency of cluster 8 observed in HIV-1-infected individuals in our study. Cluster 9, which contained CD71 + proliferating memory Bcells, most likely instead resembles the B-cell population found to be expanded following Ebola and Influenza infections, as observed by Ellebedy et al. [57]. Thus, based on the immunophenotypic similarity, cluster 9 could represent either re-activated memory B-cells, or a novel lineage of memory B-cell arising from activated naïve B-cells, as a part of an ongoing anti-viral response. However, and as previously suggested, sequential B-cell receptor (BCR) clonal analysis would be required to delineate the origin of these cells and to determine if they further differentiate into antibody secreting cells or re-enter the pool of resting memory B-cells [57]. In addition, our pseudotime trajectory inference analyses showed that cluster 9 was located at the terminal end of lineage 4, which supports the hypothesis that these cells would not further differentiate into antibody secreting cells, but rather become resting memory B-cells.
Although aviremic HIV-2-infected participants in our study tended to harbor lower frequencies of cluster 8 compared to viremic HIV-2-infected individuals, the aviremic HIV-2-infected individuals still had significantly higher frequencies of cluster 8 compared to HIV seronegative individuals. In contrast, this was not observed among successfully treated HIV-1-infected individuals. In accordance with this, HIV-1-induced expansion of T-bet high memory B-cells has previously not been observed at higher frequencies in successfully treated HIV-1-infected individuals [29]. This suggests that, in contrast to treatment suppressed HIV-1 infection, HIV-2 infection promotes the expansion and activation of T-bet-expressing memory cells, even in the absence of quantifiable plasma viremia. These observations are also in line with our previous findings showing that the frequency of T-bet + CD4 + T-cells, expressing activation and exhaustion markers, are higher in aviremic HIV-2-infected individuals compared to HIV seronegative individuals [20]. Moreover, a recent phenotypic characterization of CD8 + T-cells, in study participants also included in the current study, showed that increased frequencies of activated and exhausted CD8 + T-cells distinguish aviremic HIV-2-infected individuals from HIV seronegative individuals [24]. In line with the findings of the current study on hyperactivated B-cells, the frequency of activated and exhausted CD8 + T-cells in HIV-2-infected individuals correlated with plasma levels of the inflammation marker CXCL10, and also soluble CD14 and beta-2 microglobulin [24]. These plasma inflammation markers have also recently been shown by us to correlate with expression levels of interferon alpha-inducible protein 27 (IFI27) in HIV-2-infected individuals [58]. Furthermore, the expression of IFI27 was shown to distinguish aviremic HIV-2-infected from HIV seronegative individuals [58]. Taken together, this suggests that inflammation and immune activation, including responses within Type I and II IFN signaling pathways, are elevated among aviremic HIV-2-infected compared to HIV seronegative individuals.
We also observed an increased proportion of activated naïve-like/early memory Bcells. HIV-1 infection-induced expansion of activated naïve B-cells has been suggested to be viremia-dependent, as elevated frequencies of CD95 + activated naïve B-cells were observed in viremic HIV-1-infected individuals, but not in HIV-1-infected individuals receiving successful ART treatment [59]. However, the significantly higher cluster ratio of activated naïve-like/early memory B-cells, identified in cluster 3, to resting naïve-like B-cells, in cluster 2, in aviremic HIV-2-infected individuals compared to HIV seronegative individuals, suggest increased stimulation of naïve B-cells despite unquantifiable HIV-2 viremia. Possible explanations for such immune activation, despite low or no HIV-2 viremia, could be virus replication in other compartments or long duration of the HIV-2 infection. Indeed, the median time from estimated infection or diagnosis was more than 17 years for our study participants (data not shown). It is still not known whether active HIV-2 replication occurs in aviremic HIV-2-infected individuals, or not. However, intracellular viral mRNA has been detected in peripheral blood mononuclear cells of aviremic HIV-2-infected individuals [60,61]. Similarly, both Gag and viral mRNA has been detected in sigmoid and ileum biopsies from aviremic HIV-2-infected individuals [62]. Moreover, Fumarola et al. recently reported that CD4% recovered in HIV-2-infected individuals receiving ART, while untreated HIV-2-infected individuals displayed declining CD4% [63], further supporting ongoing virus replication in aviremic HIV-2-infected individuals.
The effect of HIV-2 infection on B-cells is much less studied than the effect of HIV-1 infection, but both HIV-1 and HIV-2 infections have been reported to induce a depletion of isotype switched and unswitched memory B-cells [21,23,25]. In HIV-1-infected individuals, this depletion has been shown to occur with a concomitant expansion of activated TLM B-cells [56]. Due to limited availability of clinical samples from the study participants, we were not able to differentiate phenotypes of HIV-specific and non-HIV-specific B-cells. However, Knox et al. has previously reported that HIV-1 gp140-specific B-cells represents approximately 1% of all class-switched memory B-cells [29]. As the frequency of the T-bet high clusters far exceeds this, the perturbations observed in the current study should involve non-HIV-specific activation of B-cells. In line with this, we observed that the frequency of cluster 8 correlated with hypergammaglobulinemia and increasing plasma concentrations of Th1-associated proinflammatory cytokines and chemokines in HIV-2-infected individuals, with the strongest correlations to TNF-α and CXCL10. Interestingly, Zumaquero et al. has previously reported that the expansion of T-bet high B-cells in patients with active SLE, i.e., chronic autoantigen stimulation, correlated with CXCL10 serum concentration [64].
In this study, we utilized both unsupervised consensus hierarchical clustering and pseudotime trajectory inference analysis to study the impact of HIV infection on the Bcell phenotypes. As most pseudotime trajectory inference analysis tools often are used to analyze a small number of cells, usually in the range of tens of thousands of cells, these tools have so far mainly been used to analyze single-cell RNA sequencing data rather than flow cytometry data [65]. However, recent developments have shown the capacity of analyzing hundreds of thousands of cells [49,66]. In a comparison of 45 different trajectory inference methods, with regard to accuracy, stability, scalability, and usability of each method, Slingshot was found to perform well across all four criteria [65]. Moreover, Slingshot was previously shown to produce pseudotime trajectories resembling known CD8 + T-cell differentiation trajectories using flow cytometry data acquired with the same number of markers used here. Thus, we chose to perform the pseudotime trajectory inference analysis using the Slingshot algorithm [48,49], which supported the definition of four separate B-cell differentiation lineages. The observed trajectories were in line with previously described B-cell differentiation patterns [52]. Lineage 1 was the only lineage where cells from HIV seronegative donors were detected throughout the full spectrum of the pseudotime trajectory. The remaining lineages branched away from the PB/PC trajectory and instead differentiated into T-bet high /T-bet + memory B-cells. As described above, lineage 4 contained B-cells progressing from transitional B-cells to proliferating memory B-cells, which according to Ellebedy et al. could represent clonally expanded memory B-cells that would eventually become resting memory B-cells [57]. However, since Slingshot can only detect linear trajectories such circular differentiation pathways would not be detected. Furthermore, sequential BCR sequencing of the memory B-cell population would be required to prove if these cells indeed become resting memory Bcells. Within lineage 3, B-cells differentiated from transitional B-cells into hyperactivated T-bet high memory B-cells (cluster 9). Interestingly, cluster 9 cells were also part of lineage 4 where they eventually differentiated into proliferating B-cells represented within cluster 10. Longitudinal studies would be required to improve our understanding of T-bet + memory B-cell fate, since they have been suggested to be able to give rise to PB/PCs and/or selfrenew [30].
In conclusion, independent of plasma viremia, HIV-2-infected participants had increased frequencies of both activated naïve, and hyperactivated memory B-cells. These observations display, for the first time, the maintenance of T-bet high B-cell populations despite unquantifiable plasma VL among HIV-infected individuals. Together with our previous findings that the majority of HIV-2-infected individuals will develop AIDS [2], it is likely that aviremic HIV-2-infected individuals would also benefit from early ART initiation, for the purpose of inhibiting B-cell perturbations.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cells11193142/s1, Figure S1. Gating strategies.; Figure S2: Marker expression and UMAP location of FlowSOM clusters; Figure S3: Frequency of T-bet high clusters correlate with markers of disease progression and inflammation; Figure S4: HIV-1 and HIV-2 infection induces activation of naïve-like B-cells; Figure S5: Slingshot-defined lineage faceted by HIV status group; Table S1: Description of antibodies used for flow cytometry.