HIV-1 activates oxidative phosphorylation in infected CD4 T cells in a human tonsil explant model

Introduction Human immunodeficiency virus type 1 (HIV-1) causes a chronic, incurable infection leading to immune activation and chronic inflammation in people with HIV-1 (PWH), even with virologic suppression on antiretroviral therapy (ART). The role of lymphoid structures as reservoirs for viral latency and immune activation has been implicated in chronic inflammation mechanisms. Still, the specific transcriptomic changes induced by HIV-1 infection in different cell types within lymphoid tissue remain unexplored. Methods In this study, we utilized human tonsil explants from healthy human donors and infected them with HIV-1 ex vivo. We performed single-cell RNA sequencing (scRNA-seq) to analyze the cell types represented in the tissue and to investigate the impact of infection on gene expression profiles and inflammatory signaling pathways. Results Our analysis revealed that infected CD4+ T cells exhibited upregulation of genes associated with oxidative phosphorylation. Furthermore, macrophages exposed to the virus but uninfected showed increased expression of genes associated with the NLRP3 inflammasome pathway. Discussion These findings provide valuable insights into the specific transcriptomic changes induced by HIV-1 infection in different cell types within lymphoid tissue. The activation of oxidative phosphorylation in infected CD4+ T cells and the proinflammatory response in macrophages may contribute to the chronic inflammation observed in PWH despite ART. Understanding these mechanisms is crucial for developing targeted therapeutic strategies to eradicate HIV-1 infection in PWH.


Introduction
Human immunodeficiency virus type 1 (HIV-1) leads to a chronic, incurable infection that progresses to acquired immunodeficiency syndrome (AIDS) when unsuppressed by lifelong antiretroviral therapy (ART). Despite the efficacy of ART in suppressing viral load, people with HIV (PWH) experience significantly higher rates of age-associated comorbidities, including cardiovascular disease, neurocognitive decline, malignancies, and decreased life expectancy (1)(2)(3)(4). Many of these comorbidities have been attributed to the chronic, dysfunctional, hyperactive inflammatory state associated with HIV-1 infection (1-7).
The molecular mechanisms underlying this abnormal inflammatory state are multifactorial and largely unknown. HIV-1 infection leads to plasmacytoid dendritic cell (pDC) decline, and overexpression of proinflammatory cytokines, such as interferon  (IFN), may skew T-cell differentiation in favor of activated effector T cells (8,9). HIV-1-induced intestinal barrier defects allow microbial translocation in amounts that exceed the host immune defense mechanisms.
This leads to persistently high extracellular lipopolysaccharide (LPS) and bacterial DNA that likely drive chronic immune activation (10,11). Impaired suppression of co-infections such as cytomegalovirus (CMV) contributes significantly to persistent T cell activation (12). Elevated circulating levels of LPS, as well as extracellular nucleotides released by inflamed or apoptotic cells, may lead to activation of purinergic receptors, Toll-like receptors (TLRs), and the downstream NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) inflammasome signaling axis (13). An emerging body of literature has implicated the critical role of the NLRP3 inflammasome in mediating inflammatory signaling in HIV-1 infection and inflammation and inflammatory cell death, known as pyroptosis .
The tonsils, a form of mucosa-associated lymphoid tissue (MALT), are a primary location of the latent HIV-1 reservoir and are likely mediators of HIV-associated acute and chronic inflammation (38,39). Unlike the peripheral blood, tonsils act as sequestered viral reservoirs during the clinical latency period with active, progressive viral replication, storage, and persistence, even in the presence of ART (40)(41)(42). A defining feature of the tonsils is an intricate and well-defined spatial arrangement of diverse immune cell types. Follicular regions rich in B cells are surrounded by extrafollicular regions composed primarily of CD4+ T cells. There are extensive epithelial, endothelial, and myeloid cell networks (43). This precise architecture facilitates the propagation and amplification of immune cascades between individual cells and cell types at border zones. The prominent role of cytoarchitecture in mediating tonsillar immune responses highlights the importance of studying these pathways in their native spatial arrangements (43)(44)(45)(46)(47)(48).
Human tonsil explant models preserve the native cellular repertoire of lymphoid tissue and crucial cell-cell interactions (49,50). HIV-1 predominantly infects activated CD4 + T cells in single-cell culture, leading to direct killing via apoptosis (6,51,52). Tonsil tissue explants can support productive HIV-1 infection without exogenous activation, as PBMCs require. When challenged with recall antigens, tonsil tissue blocks can produce a specific antibody response modulated by HIV-1 infection in a pattern similar to that observed in vivo (53). In tonsil explant cultures, HIV-1 has been shown to induce the secretion of proinflammatory cytokines. These effects, however, are not seen in PBMCs (39,42,54,55) or single cell culture of dissociated tonsil tissue (unpublished data). CD4+ T cells in the tonsils have been implicated in triggering inflammatory cascades in neighboring immune cells, rendering them more susceptible to abortive infection and pyroptosis, a form of inflammatory cell death (54,56). These phenotypic

Infection, processing, and RNA sequencing of tonsillar cells
A broad overview of human tonsil preparation, exposure to HIV-1NL-CI, a CXCR4-tropic virus including a fluorescent mCherry reporter, sequencing, and analysis is shown in Figure 1A. The viability and infection rate of the cells were quantified by flow cytometry through LIVE/DEAD stain and mCherry expression, respectively. Eight days after HIV exposure, 95% of cells were determined to be alive, and 2.45% of live cells were productively HIV-infected ( Figures 1B and   1C).
Following scRNA-seq of exposed and unexposed tonsil samples from seven donors, the integrated data ( Figure 2A

Establishing infection based on HIV-1 alignment and characterization of infected cell clusters
To understand the association between the detection of HIV-1 transcripts and HIV productive infection, HIV-exposed cells were sorted by mCherry fluorescence to detect productively infected cells ( Figure 3A). Sorted cells were analyzed by scRNA-seq, including alignment to the HIV-1 NLCI reference (61) to characterize the read alignment profile of productively infected cells, as shown in Figure 3B. HIV expression in mCherry-sorted cells showed a clear separation of infected and uninfected cells ( Figure 3C). This distribution corresponded well with previous results indicating that 2.45 % of exposed cells exhibited productive infection ( Figure 1B, C).
Based on this, a cutoff of the top 2.45 % of HIV-exposed cells was established, such that in subsequent analyses, those with levels of HIV transcript above that threshold were considered infected. The total cells, unexposed and exposed to HIV, are indicated in Figures 3D and 3E, showing 2.45 % of infected cells in the HIV-exposed cells. Figure 3F shows the distribution of unexposed, exposed uninfected, and infected cells after dimensionality reduction (UMAP plots).
Supplemental Figure 2 shows the UMAP of unexposed, exposed uninfected, and infected cells (SI Fig 2A), each tonsil donor by the distribution of cell exposure and infection (SI Fig 2B), and ridge plots of HIV transcripts by tonsil donor (SI Fig 2C).

Profiling the immune cell landscape during HIV-1 infection
The impact of HIV-1 exposure on the distribution of cell category ( Figure 4A) and cell type (Supplemental Figure 3A) was determined within each tonsil to evaluate the immune cell landscape. While considerable heterogeneity was noted among the seven samples, no notable differences in cell category or cell type distribution were noted between exposed and unexposed donors. Samples of exposed and unexposed tonsils were available for Donor 1, 3, 4, 5, and 7, unexposed tonsil samples were available for Donor 1, 2, 3, 4, 5, and 7, and exposed tonsil samples were available for Donor 1, 3, 4, 5, 6, and 7. Ridge plots of HIV-1 transcript expression level are shown by cell category in Figure 4B and cell types in Supplemental Figure 3B. High levels of HIV-1 transcript were noted in the B cell, T cell, ILC, and DC compartments ( Figure   4B). In contrast, no infected cells were observed in macrophages or epithelial populations. For the subsequent analysis, groups were compared based on three categories, unexposed, exposed uninfected, and infected. The normalized relative abundance of infected cells in cell type categories is shown in stacked box plots in Figure 4C (and cell category and type in Supplemental Table 1). T cells and DCs comprised the largest relative groups of infected cells, while a lesser part of ILCs and a negligible percentage of B cells exhibited infection. Finally, as above, no macrophages or epithelial cells were productively infected. HIV infection frequency is shown in each cell category ( Figure 4D) and cell type (Supplemental Figure 3C), with the highest frequency of infection in T cells. While HIV expression was noted in B cells, Figure 4C and D show a significantly lower number of B cells among the infected cells compared to uninfected cells. Figure 4C also indicates that each cell category had relatively comparable viability between unexposed and exposed cells in B cells, T cells (CD4 and CD8), ILCs, and DCs. In contrast, epithelial cells showed a modest reduction, and macrophages had the greatest reduction in representation in exposed compared to unexposed cells.

Differential gene expression in infected cells reveals activated oxidative phosphorylation.
Differential gene expression was performed by comparing all infected cells to unexposed cells ( Figure 5A, 5C, Supplemental Table 2) and all exposed but uninfected cells to unexposed cells ( Figure 5B, 5D, Supplemental Table 3). Volcano plots ( Figure 5A and 5B) show the fold change of gene expression between infected cells (5A) as well as exposed uninfected cells (5B) compared to unexposed cells, with the y-axis indicating statistical significance. The corresponding bar plots show the top 15 Reactome (reactome.org) gene sets enriched in the infected (Figure 5C) or exposed uninfected ( Figure 5D) compared to unexposed cells, ranked by Normalized Enrichment Score (NES), with positive values indicating enrichment and negative values depletion. Among the top gene sets enriched in infected cells were several related to cellular respiration, including 1) respiratory electron transport, ATP synthesis by chemiosmotic coupling and heat production by uncoupling proteins (R-HSA-163200), 2) Respiratory electron transport (R-HSA-6111050), and 3) The citric acid (TCA) cycle and respiratory electron transport (R-HSA-1428517). R-HSA-16320 is related explicitly to uncoupling proteins during oxidative phosphorylation. Uncoupling proteins are mitochondrial transporters that dissipate the proton gradient across the inner mitochondrial membrane. R-HSA-6111050 is more broadly associated with respiratory electron transport, the process by which electrons are transferred along the electron transport chain in the inner mitochondrial membrane to generate a proton gradient and ATP. R-HSA-1428517 is related to both the citric acid cycle and respiratory electron transport. The citric acid cycle occurs in the mitochondrial matrix and generates highenergy electron carriers such as NADH and FADH2, which are used in the electron transport chain to generate ATP.
These gene sets were not prominently enriched in the exposed uninfected cells, where more disparate pathways were noted ( Figure 5D). These included cytokine signaling in the immune system and Signaling by ROBO receptors gene sets related to cellular processes involved in responding to environmental stimuli and maintaining cellular homeostasis. Another major group of gene sets represented included several protein translation and interferon signaling gene sets.
To further probe the impact of HIV infection on differential gene expression, cell categories were explored individually. Figure 6 (and Supplemental Table 4) demonstrates the differential gene expression in infected T cells compared to unexposed T cells. A volcano plot ( Figure 6A) and bar plot ( Figure 6B) demonstrate the same cellular respiration gene sets mentioned above 1) Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins (R-HSA-163200), 2) Respiratory electron transport (R-HSA-6111050), and 3) The citric acid (TCA) cycle and respiratory electron transport (R-HSA-1428517). This similarity to the parallel comparison in all cells likely stems from the high abundance of T cells among infected cells. This result further supports the notion that HIV-1 infected T cells display increased activity in oxidative phosphorylation pathways relative to uninfected T cells and exposed uninfected T cells. Of note, this trend was not observed in exposed and uninfected T cells relative to unexposed T cells (Supplemental Figure 4A, B, Supplemental Table 5), suggesting that the upregulation of oxidative phosphorylation pathways is specific to the infected state of T cells.
Five representative oxidative phosphorylation genes (NDUFA13, NDUFA4, COX4I1, COX6C, UQCR11) were noted to be expressed most highly in infected T cells among all cell categories with intermediate expression in exposed uninfected T cells and the lowest expression in unexposed T cells ( Figure 6B). Subsets of T cells exhibited increased expression of each of these genes in infected cells compared to uninfected, as shown in the violin plots in Figure 6C and Supplemental Figure 4C. This more highly resolved cell type analysis of the genes listed demonstrates that infected naïve or memory T cells, cytotoxic CD4 T cells (CD4 CTL), and T follicular helper cells (Tfh) exhibit the highest comparative expression of this gene group, followed by regulatory T cells (Tregs).
Further analysis revealed that a gene signature comprising pannexin-1 and pannexin-2 ("Pannexins") was highly upregulated in infected T cells, following a similar trend as oxidative phosphorylation gene signatures ("OxPhos"), as shown in Figure 6D. These findings suggest that pannexins may act as a conduit to mediate ATP flux from infected T cells. This may represent communication by ATP release between infected cells and nearby cells.

Macrophages exposed to HIV-1 upregulate the NLRP3 inflammasome.
Macrophages represented a small percentage of the total cells in this analysis, which is expected from tonsil explant-derived cells. No infected macrophages were present; however, exposed uninfected macrophages were compared to unexposed macrophages and were analyzed for differential gene expression ( Figure 7A). The following enrichment analysis represented several pathways related to viral infection and immune signaling ( Figure 7B, Supplemental Table 6), including Leishmania infection (R-HSA-5653656). This gene set includes NOD-like receptors (NLRs), cytosolic pattern recognition receptors upstream of inflammasomes that are activated by microbial ligands or danger signals, NLRP3, NLRP1, and NLRC4; ASC (apoptosis-associated speck-like protein containing a CARD), an adaptor protein that links activated NLRs to pro-caspase-1, the protease that cleaves and activates the proinflammatory cytokines IL-1β and IL-18; Caspase-1, the protease that is activated by inflammasomes and cleaves pro-IL-1β and pro-IL-18 to produce the mature cytokines; IL-1β and IL-18, pro-inflammatory cytokines that are produced in response to inflammasome activation. Similarly, when visualized via violin plot, each gene was increased in exposed uninfected macrophages compared to unexposed macrophages ( Figure 7D). These findings suggest that macrophages play a crucial role in developing HIV-related inflammation, indicating that exposure to HIV leads to significant changes in inflammatory signaling, even without productive infection in that cell type.
Other cell categories were separately analyzed for differential gene expression, including B cells  Table 13). The expression pattern of various B cell stage markers in infected B cells compared to uninfected B cells, such as the almost absent expression of CD19, also seems to indicate that the very few B cells that were identified as infected may be a different type of B cell than uninfected ones or may even have been misannotated (Supplemental Figure 6A). Interestingly, B cells showed a less pronounced difference between the infected and exposed uninfected populations, as shown in Supplemental Figure 6B, C, where both differential gene expression analyses resulted in elevated oxidative phosphorylation genes. This may reflect a higher baseline metabolic state of B cells and confirm that productive infection is unlikely, as there is a lack of significant metabolic shift between the intermediate and high viral transcript levels.
ILCs showed enrichment in gene sets primarily associated with protein synthesis and folding (Supplemental Figure 7), and DCs exhibited gene sets related to cellular homeostasis (Supplemental Figure 8). There were no clear patterns, similarly consistent with a lack of significant difference between cells containing intermediate and high viral transcript levels.
Finally, as shown in Supplemental Figure 9, epithelial cells expressed various gene sets related to transcription regulation and signaling. Only exposed uninfected and unexposed populations were compared here, as no infected epithelial cells were present.

Differential gene expression demonstrates profound distinctions between infected T cells and macrophages.
Thus far, our observations have distinguished infected T cells as having high levels of oxidative phosphorylation specific to cells containing high levels of HIV-1 transcript, indicative of infection.
By contrast, macrophages that are not infected showed high levels of NLRP3 inflammasome genes, suggesting that infected cells can activate related inflammatory signaling. were most significant in infected B cells, T cells, and ILCs. While those pathways were also elevated in B cells in the exposed uninfected group, this distinguished infected T cells as uniquely impacted by HIV-1 productive infection. By contrast, the inflammasome signaling pathways, such as Purinergic signaling in leishmaniasis infection (R-HSA-9660826) and Cell recruitment (pro-inflammatory response) (R-HSA-9664424), were most significant in the exposed uninfected macrophages. Supplemental Figure 10 expands the included cell types of this dot plot and similarly shows that oxidative phosphorylation pathways were generally increased in infected B cells and CD4 T cells. In contrast, the inflammasome signaling pathways were enriched in macrophages in the exposed uninfected group. A complete list of DEGs for infected vs. unexposed and exposed uninfected vs. unexposed total cells and by cell categories can be found in Supplemental Tables 2-11.

Discussion
Here we demonstrate how single-cell RNA sequencing analysis of a human tonsil explant model can characterize key immune cell subsets involved in HIV-1 infection and induced transcriptomic changes not accessible via bulk RNA analysis. The most identified immune cell types in the tonsil explants were lymphocytes, including B cells, CD4 + T cells, and CD8 + T cells.
HIV-1 exposure resulted in approximately 2.5 % infection as measured by flow cytometry mCherry expression, consistent with reported rates in similar ex vivo tonsil explant models (57,58). Infected cells were found most prominently in the CD4 + T cell compartment, consistent with the understanding that CD4 + T cells are most permissive to HIV-1 infection. Among the infected cells, a small population included B cells which are not expected to be productively infected.
There are several possible explanations for this finding. The first is that it is possible that those cells are not, in fact, B cells. They represent a minor population of the total B cell population identified by cluster analysis based on the expression of CD19, CD27, CD24, CD38, MME, CR2, and MS4A1. These seven genes were the most distinctive in characterizing the common

features of those cells identified as naive B cells, memory B cells, GC B cells, and plasma cells.
It is important to note that these markers are not exclusive to B cells and that HIV-1 infection of CD4 T cells might shift expression such that these infected cells might be T cells. The following markers have been described to various extents in CD4 T cells that might impact the characterization: CD19, CD27, CD24, CD38, CR2, and MS41 (59)(60)(61)(62)(63)(64)(65). It is important to note that much of the literature on these markers is based on protein expression and not transcriptomics. Together with how transcriptional changes are impacted by HIV-infected, there may be confounding variables (66). Other potential explanations for these markers include the presence of cells that express alternative HIV receptors or engage in antibody-dependent cellular phagocytosis while harboring HIV RNA. It is worth noting that the macrophage and epithelial cell compartments did not contain any infected cells.
The upregulation of both oxidative phosphorylation genes and pannexin-1 in infected T cells suggests an important role for these processes in the immune response to viral infections. The high levels of oxidative phosphorylation in infected T cells and the upregulation of pannexin-1 may allow these cells to communicate with nearby cells and mediate inflammatory signaling.
The fact that pannexin-1 is more ubiquitously expressed than pannexin-2, which is more prominently expressed in the central nervous system (67), may explain why the effect of transcriptional upregulation in infected cells is more pronounced in pannexin-1.
A possible model to integrate and interpret our findings is shown in Figure 8B. A productively HIV-1-infected CD4 + T cell may undergo transcriptional upregulation of metabolism, notably the TCA cycle and oxidative phosphorylation. This results in increased ATP production, which travels out through pannexin-1 in the infected CD4 + T cell and is recognized by the P2RX7 receptor on the surface of nearby macrophages. In concert with TLR signaling to activate pro-IL- been reported but with lower efficiency than in T cells (68)(69)(70). Similarly, ILCs are not thought capable of HIV-1 productive infection; however, some reports demonstrate that NK cells can be infected (71)(72)(73), particularly with X4-tropic virus, whose clone was used in this study. It is also possible that B cells and ILCs harbor HIV-1 transcripts due to endocytosis or have virus particles bound to their cell surface. In both cases, HIV transcripts may be detected in ILCs even if they are not producing new viruses.
Single-cell RNA-sequencing identified 71% of exposed cells to exhibit low and widely heterogeneous levels of HIV-1 transcript. The biological significance of these transcripts, if any, and the discordance between viral transcript and protein expression remains unclear. One potential explanation for the low-level HIV-1 transcript expression in exposed uninfected cells is as an artifact, such as detecting virions adherent to cells' surfaces despite single-cell processing. As the low transcript levels are specific to the exposed uninfected cells, this may reflect a "bystander" statusclose physical proximity to an infected cell producing large amounts of HIV-1 transcript. A second possibility is that the exposed uninfected cells harboring

Materials availability
This study did not generate new unique reagents.
Data and code availability will be made available upon publication.

Study participants, sampling protocols, and experimental approach
Human tonsil explants were obtained from healthy tonsillectomy patients and dissected into tissue blocks cultivated on collagen rafts, as previously described (120,121). Briefly, palatine tonsil tissue was dissected into nine 1 mm explants which were subsequently cultured and exposed to HIV-1NL-CI, a CXCR4-tropic fluorescent reporter virus with mCherry in place of nef where nef is expressed on an IRES, as previously described (122). Media was removed on days 2, 5, and 8 following HIV-1 NL-CI exposure. Cells were collected and washed, as shown in Figure 1A, which describes the flow of sample collection through single cell isolation and cDNA generation, single-cell sequencing, data pre-processing, and analysis, including cell identification and cluster annotation, compositional analysis, network analysis, and differential expression. Viability and infection were quantified by flow cytometry with LIVE/DEAD stain and mCherry expression, respectively ( Figures 1B and 1C). Infection was observed over eight days with stable viability and demonstrated productive infection of 2.45 % of exposed cells by day 8.  Downstream analyses, such as graph-based clustering and differential expression analysis/visualization, were performed using the Loupe Cell Browser (10x Genomics) and Seurat (123)(124)(125), as illustrated in Figure 1A.
scRNA-seq analysis. The R-based package Seurat (version 4.0.5) (126) was used to process the scRNA-seq data. Genes detected in less than 0.5 % of cells were excluded from the analysis. Cells with an expression of < 1000 or > 40000 total molecules, < 100 or > 6000 unique genes, and > 15 % mitochondrial genes were removed for quality control before analysis. The normalized datasets of each treatment group (HIV unexposed and HIV exposed) were integrated through the Seurat RPCA integration method, using unexposed samples as The 2.45 % of HIV-exposed cells with the highest HIV transcript levels were presumed to be productively infected, as determined by FACS analysis (Figure 1). Overall HIV transcript distribution in mCherry-sorted cells ( Figure 3C) verified this estimate, and these cells were classified as "Infected." The remaining HIV-exposed cells were identified as "Exposed uninfected," and cells stemming from samples with no prior virus contact as "Unexposed." Differential expression analysis was performed using the Seurat function FindMarkers(), specifying the tonsil donor as a latent variable to correct for inter-donor differences and reduce the impact of confounding variables or batch effects. Gene set enrichment of the resulting differentially expressed genes in the Reactome pathways (128) was performed using the ReactomePA R package (129), which aggregates the per-gene statistics across genes within a gene set, therefore making it possible to detect situations where all genes in a predefined set change in a small but coordinated way.   Human tonsil explants exposed to HIV-1NL-CI were subject to flow sorting to separate mCherry negative and mCherry positive cells (A). Sequencing reads were aligned to the genome of HIV-1NL-CI with exposed and infected by positive mCherry sort in red and exposed and uninfected by mCherry sort in yellow. The map of the HIV-1NL-CI genome (122) shows where sequencing reads were aligned. Using a joint GRCh38-2020-A Human reference and a custom HIV reference whose regions with known features are marked as exonic (in blue), the pipeline  . "exp uninf" = exposed uninfected, "un"/ "uninf" = uninfected, "inf" = infected.  "exp uninf" = exposed uninfected, "un"/ "uninf" = uninfected, "inf" = infected. . "exp uninf" = exposed uninfected, "un"/ "uninf" = uninfected, "inf" = infected.  Figure 1 Infection of human tonsil explants by X4-tropic HIV-1NL-CI. The scheme shows the collection of human tonsil explants, infection with HIV-1NL-CI, single-cell isolation, and cDNA generation using 10X Chromium, single-cell RNA sequencing, data pre-processing, cell identi cation and cluster annotation using Seurat, and compositional, network, and differential analysis using Seurat and ReactomePA (Created with Biorender.com) (A). Human tonsil explants were cultured on collagen rafts and infected with HIV-1NL-CI or vehicle media. Supernatants were collected on days 2, 5, and 8 post-infection. Sloughed-off cells and media were separated by centrifugation, and a full media change was performed on each indicated time cells were collected. Cells were subjected to LIVE/DEAD staining and ow cytometry to evaluate viability. Suspension cells in supernatants were collected on day 8 for single-cell dissociation and processing for single-cell sequencing (B). Flow cytometry results are quanti ed as percent infection and percent viability in tonsils unexposed or exposed to HIV-1. Mean values ± standard errors of the means from four donors (C). *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001.  HIV-1NL-CI transcript expression aligns with HIV-1 productive infection in human tonsils. Human tonsil explants exposed to HIV-1NL-CI were subject to ow sorting to separate mCherry negative and mCherry positive cells (A). Sequencing reads were aligned to the genome of HIV-1NL-CI with exposed and infected by positive mCherry sort in red and exposed and uninfected by mCherry sort in yellow. The map of the HIV-1NL-CI genome (122) shows where sequencing reads were aligned. Using a joint GRCh38-2020-A Human reference and a custom HIV reference whose regions with known features are marked as exonic (in blue), the pipeline grouped and de-duplicated reads mapped to the transcriptome using 10X cellular barcodes and UMIs (Unique Molecular Identi ers) (B). Ridge plots illustrate the distribution of detected HIV transcript in mCherry-positive and negative sorted cells (C) and in all cells, split by cell groups de ned as infected and uninfected based on a 2.45 % cutoff (D). Exposed tonsils demonstrate 2.45% infection (E). Split UMAP showing total cells by HIV groups, unexposed, exposed uninfected, and infected (F).

Figure 4
Impact of HIV-1NL-CI exposure on cell category. Stacked bar plots illustrate relative frequencies of cell categories in the integrated dataset, strati ed by tonsil donor and HIV-1 exposure (A). Ridge plots show the distribution of HIV-1 transcript expression in each cell type by HIV-1 exposure group: unexposed, exposed uninfected, and infected (B). Stacked bar plots show each cell type's relative abundance of infected cells (C). Bar plots show the frequency of HIV-1 infection by cell type. Error bars denote the standard error of the mean (D). "exp uninf" = exposed uninfected, "un"/ "uninf" = uninfected, "inf" = infected.

Figure 5
Differential expression by HIV-1NL-CI infection and exposure. Volcano plots of genes differentially expressed in infected cells compared to unexposed cells (A) and in exposed uninfected cells compared to unexposed cells (B). Colors denote signi cance and fold change cutoffs: red = absolute log2(fold change) ≥ 0.6 and adjusted p-value < 0.05, pink = absolute log2(fold change) < 0.6 and adjusted p-value <0.05, orange = absolute log2(fold change) ≥ 0.6 and adjusted p-value >0.05, grey = absolute log2(fold change) < 0.6 and adjusted p-value >0.05. Bar plots illustrate the top 15 Reactome database gene sets enriched in infected cells vs. unexposed cells (C) and exposed uninfected cells vs. unexposed cells (D). Gene sets with adjusted p-value <0.05 were considered signi cant. A positive Normalized Enrichment Score (NES) value indicates enrichment. Colors denote signi cance and fold change cutoffs: red = absolute log2(fold change) ≥ 0.6 and adjusted p-value < 0.05, pink = absolute log2(fold change) < 0.6 and adjusted p-value <0.05, orange = absolute log2(fold change) ≥ 0.6 and adjusted p-value >0.05, grey = absolute log2(fold change) < 0.6 and adjusted p-value >0.05. Gene sets with adjusted p-value <0.05 were considered signi cant. A positive Normalized Enrichment Score (NES) value indicates enrichment (A). Dot plots demonstrate the expression of 5 oxidative phosphorylation genes in each of the ve cell categories. Dot size is proportional to the percentage of cells within a group. The dot color indicates the average expression across the group (red = high, blue = low) (B). Violin plots of the ve oxidative phosphorylation genes are shown by expression in ten T cell populations (C). A heatmap demonstrates the relative expression of gene signatures of pannexin genes and genes involved in oxidative phosphorylation in each of the cell types (D). "exp uninf" = exposed uninfected, "un"/ "uninf" = uninfected, "inf" = infected. Macrophages exposed to HIV but not infected demonstrate in ammatory signaling increases, including the NLRP3 in ammasome. Volcano plots of genes differentially expressed (A), and bar plot illustrating 15 representative top Reactome database gene sets enriched in exposed uninfected macrophages vs. unexposed macrophages (B).
Colors denote signi cance and fold change cutoffs: red = absolute log2(fold change) ≥ 0.6 and adjusted p-value < 0.05, pink = absolute log2(fold change) < 0.6 and adjusted p-value <0.05, orange = absolute log2(fold change) ≥ 0.6 and adjusted p-value >0.05, grey = absolute log2(fold change) < 0.6 and adjusted p-value >0.05 (A). Gene sets with adjusted p-value <0.05 were considered signi cant. A positive Normalized Enrichment Score (NES) value indicates enrichment (B). Dot plots demonstrate the expression of ve NLRP3 in ammasome genes in ve cell categories. Dot size is proportional to the percentage of cells within a group. The dot color indicates the average expression across the group (red = high, blue = low) (C). Violin plots of the NLRP3 in ammasome gene expression are shown in macrophages (D). "exp uninf" = exposed uninfected, "un"/ "uninf" = uninfected, "inf" = infected. HIV-1 infection promotes oxidative phosphorylation in infected CD4+ T cells and NLRP3 in ammasome activation in macrophages. Dot plots illustrating enrichment of key oxidative phosphorylation and in ammasome signaling gene sets by cell type in infected cells vs. unexposed cells (left) and in exposed uninfected cells vs. unexposed cells (right). Oxidative phosphorylation gene sets include Complex I biogenesis (R-HSA-6799198) and Respiratory electron transport (R-HSA-61105). In ammasome signaling