TGF-β associated senescence and impaired metabolism in central memory CD4 T cells promotes HIV persistence

doi:10.21203/rs.3.rs-923015/v1

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TGF-β associated senescence and impaired metabolism in central memory CD4 T cells promotes HIV persistence

https://doi.org/10.21203/rs.3.rs-923015/v1

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Current therapeutic interventions to eradicate latent HIV (“reservoir”) and restore immune function in ART-treated HIV infection have yet to show efficacy. To explore mechanisms of HIV persistence, we apply an integrated systems biology approach and identify a distinct group of individuals with poor CD4 T-cell reconstitution (Immunologic non-responders, “INRs”) and high frequencies of cells with inducible HIV. Contrary to the prevailing notion that immune activation drives HIV persistence and immune dysfunction, peripheral blood leukocytes from these subjects have enhanced expression of a network of genes regulated by cellular senescence. In these subjects, increased frequencies of regulatory T cells and expression of the TGF-β signaling cascade are concomitant with the downregulation of cell cycle and metabolism in CD4 central memory T (TCM) cells. These cascades, downstream of TGF-β, lead to the accumulation of PD-1 expressing CD4 TCM and are associated with an increase in frequencies of cells with inducible HIV ex vivo. In vitro validation confirmed that this cellular profile was driven by a β-hydroxybutyrates/bile acid rich metabolic milieu and resulted in TGF-β associated latency establishment. Our findings identify targets for PD-1 or TGF-β specific interventions that can overcome cellular senescence; these therapeutic approaches have shown safety and efficacy in cancer, and may prove to be crucial for HIV eradication.

Immunology

Infectious Diseases

HIV

CD4 T-cell reconstitution

cellular senescence

HIV infection remains a major global public health concern (WHO, https://www.who.int/news-room/fact-sheets/detail/hiv-aids). Although effective combined anti-retroviral therapy (cART) has altered the course of HIV disease by preventing viral replication, it is not curative and does not fully restore immune function¹. Depending on the population studied and outcome definitions², up to 20% of cART-treated subjects fail to reconstitute CD4 + T-cell numbers despite years of effective treatment with sustained HIV suppression, and are predisposed to excessive risk of non-HIV comorbidities³.

These immune non-responder subjects (INRs) show severe homeostatic alterations in CD4 T-cells, including lower frequencies of naïve CD4 T-cells, accumulation of highly differentiated T-cells, T-cell activation and apoptosis^4–6. Furthermore, these CD4 T cells express makers of replicative senescence⁷ and exhaustion⁸; and have increased frequencies of immunosuppressive CD4 + regulatory T-cells (Tregs)⁹. Indeed, we have previously reported that a multivariate model characterized by naive T-cell depletion, higher frequencies of cycling CD4 + central memory (CM) and effector memory (EM) T-cells, expression of the T-cell activation markers (CD38, HLA-DR, CCR5 and/or PD-1 (p < 0.0001)), and levels of soluble CD14 (sCD14) can distinguish INRs from immune responder subjects (IRs), even after adjustments for key clinical indices like CD4 + T-cell nadir and age at cART initiation^10–12. On the immunological front, studies have shown that impairment in IL-7/L-7R signal transduction axis¹³, chronic inflammation and persistent type I interferon production could mediate poor CD4 T cell homeostasis, reduced thymic function¹⁴. In addition, changes in metabolic milieu (i.e. increase in SCFAs or bile acids) that induce Treg differentiation/impair effector CD4 T cell function can also drive poor immune reconstitution in INRs^15–17.

The aforementioned alternations in CD4 T cell homeostasis are further complicated by the fact that pro-viral HIV DNA integrates into the human genome and persists within memory CD4 T-cells¹⁸. This cellular "reservoir" reignites rounds of virus replication if cART is interrupted¹⁹, stressing the need for long-term cART administration. While, reconstitution of CD4 counts during uninterrupted cART leads to a decrease in intact pro-viral DNA²⁰, elevated levels of inflammatory markers characteristic of INRs (IL6, TNFɑ, IL1β, IFNɑ/β, MIP1ɑ/β, RANTES) are associated with HIV persistence²¹. HIV replication triggered by this inflammation can alter homeostatic proliferation and sustain the HIV reservoir by occasional expansion and contraction of individual CD4 T-cell clones^22–24. Indeed, increased expression of co-inhibitory receptors (PD-1, LAG-3, and TIGIT) that can drive cellular quiescence upon receptor engagement and inhibit HIV replication, have been shown to contribute to the magnitude of the HIV reservoir^24–26. Here, we studied two independent cohorts of HIV-infected cART-treated individuals and identified INRs with a senescent CD4 T cell profile that was observed to be downstream of α-ketobutyrate induced differentiation of TGF-β producing Tregs. CD4 T cells from these INR subjects showed reduced homeostatic proliferation (leading to lack of CD4 recovery), and heightened frequencies of inducible HIV RNA.

We used an unbiased approach that integrates datasets from peripheral blood transcriptome, high density flow cytometry and plasma cytokine measures to identify cellular and molecular drivers of HIV persistence and lack of CD4 T cell recovery (Supplementary Fig. 1a), in two independent cohorts of HIV-infected subjects (see Study Participants in Material and Methods; Table S1, Table S2). All subjects were under cART for at least three years and maintained consistently low CD4 T cell counts up to four years prior to sample collection (Supplementary Fig. 1b). The Cleveland Immune Failure cohort (CLIF) was comprised of 61 subjects (17 immune responders - IRs - >500 CD4 T-cells/mm³ and 44 immune non-responders - INRs - <350 CD4 T-cells/mm³); whereas, the Study of the Consequences of the Protease Inhibitor Era cohort (SCOPE) included 41 subjects (20 IRs with >500 CD4 T-cells/mm³ and 21 INRs with <350 CD4 T-cells/mm³).

Transcriptional profiling reveals systemic senescence as a driver of poor immune reconstitution

Exploratory analyses of whole blood transcriptomic data (CLIF cohort subjects; using unsupervised clustering described in the methods) (Fig. 1a and Supplementary Fig. 1c) identified three groups of subjects with unique transcriptional profiles: IRs and two distinct INR groups, “INR-A” and “INR-B”. INR-As exhibited the highest transcriptomic variation from IRs with approximately 3000 differentially expressed genes (DEGs). In contrast, INR-Bs were proximal to IRs with <400 DEGs (Table S3-5). Age, years on cART, CD4 T cell counts, CD4 T cell nadir, markers of gut barrier dysfunction (sCD14), and inflammation (IL-6, IP10) - known predictors of morbidity and mortality in INRs²⁷ - failed to distinguish the two INR groups (Supplementary Fig. 1d-p). Interestingly, DEGs specific to INR-As were mostly down-regulated (72% and 66% when contrasted against IRs and INR-Bs, respectively), suggesting a quiescent transcriptional state in these subjects (Supplementary Fig. 2a). Comprehensive pathway analyses using MSigDb’s Hallmark module^28-30 (Fig. 1b, Table S6) revealed that a majority of the pathways, likely driven by significantly elevated CD4 T cell numbers, were upregulated in IRs when compared to both INR-As and INR-Bs. Notably, when contrasting INR-As against INR-Bs (where CD4 T cell numbers are comparable), INR-As were observed to have significantly decreased expression of inflammation, cell cycling, apoptosis, metabolism genesets^31,32. This transcriptional profile was in line with reduced expression of genes regulated by Myc (a primordial transcription factor (TF) that regulates proliferation and metabolism of activated T cells)³³ and was indicative of a senescent state^31,32.

Cell subset deconvolution (using gene signatures from Nakaya et al³⁴) showed that genesets specific to all major innate and adaptive immune cell subsets were reduced in INR-As (vs INR-Bs) (Fig. 1c, Table S7). Similarly, down-regulation of gene signatures for specific CD4 and CD8 T-cell subsets (naive, memory and effector cell, extracted from Novershtern et al³⁵ - see methods for detail) were also observed (Fig. 1d, Table S8). The down-regulated signatures in T-cells, myeloid dendritic cells (mDCs) and monocytes mapped to genes that drive apoptosis, transcription/cell migration, and cellular/lipid metabolism (lipid storage, GTP metabolic process), respectively (Supplementary Fig. 2b). These data provide evidence for decreased global transcription and systemic senescence in immune cells from INR-As, when compared to INR-Bs or IRs.

Transcriptional profiles downstream of FOXO3 and SMAD2/3 signaling are enriched in senescent INRs

To identify mechanisms underlying poor immune reconstitution observed in INR-As, we identified TFs (p < 0.05, Supplementary Fig. 2c, Table S9) and mapped the pathways that were associated with genes upregulated (Reactome pathway database, ClueGO plugin in Cytoscape, FDR <0.05)^36,37 in the INR-As (vs IRs and INR-Bs; FDR < 0.05, Table S10). The upregulated TFs included IRF3 (driver of type I IFN production³⁸), FOXO3 (transcriptional repressor³⁹), SMAD2 (TGF-β signaling⁴⁰) and CCNT2 (Negative regulator of HIV Tat protein; i.e. driver of HIV latency induction⁴¹) (Supplementary Fig. 2c). Interestingly, enrichment of cellular processes downstream of these TFs - including heme metabolism, TGF-β signaling, IRF3 activation, reactive oxygen species (ROS) production and inhibition of NF-kB activation (Fig. 1e) - were observed in these subjects. Specifically, INR-As showed increased expression of features of senescence that included FOXO3 regulated genes like SOD/CAT^42-44 (driver of ROS production) (Fig. 1e); FOXO4/TP53⁴⁵ driven anti-apoptotic genes (BCL2L1; FDR < 0.01)^46-48 (Table S5); and targets of SMAD2/3 including ‘Inducer of Promyelocytic Leukemia’ (PML; conductor of TGF-β signaling via SMAD2/3, and regulator of HIV latency^49,50), WEE1⁵¹ (cell cycle regulator) and GLUL^52,53 (metabolic regulator). The downregulation MYC target genes (known to control ribosomal biosynthesis and translation)⁵⁴, genes of the electron transport chain, and genes regulating major metabolic pathways (including glycolysis, oxidative phosphorylation and fatty acid metabolism) further confirmed the senescent nature of these subjects (Supplementary Fig. 2d, Fig. 1b). The senescent/anti-inflammatory profile of INR-As contrasted the pro-inflammatory profile of INR-Bs where the enrichment of pathways downstream of TLR/IL1, cell cycling, and apoptosis/pyroptosis was observed. Increased expression of several members of the pro-inflammatory NF-kB family of TFs (NFKB1, NFKB2 and RELB), upregulation of NF-kB target genes including chemokines (e.g. CCL2 and CCL17)⁵⁵, genes of the inflammasome complex (NLRP3, IL1B and IL18)⁵⁶ and effector genes driving apoptosis/pyroptosis (e.g. CASP4, DIABLO, CASP2 and CASP3)⁵⁷ were characteristics of this group (Supplementary Fig. 2d).

Overall, our data suggest that the INR-As have increased expression of a signaling cascade downstream of TGF-b (SMAD2/3; PML) which culminates in the upregulation of FOXO3/4 driven senescence - characterized by impaired cell metabolism and cell cycle arrest⁵⁸. INR-As will henceforth be referred to as “Senescent-INRs”.

A gene-based classifier segregates the two INR groups in whole blood and in sorted central memory CD4 T cells

Next, we built a gene-based nearest shrunken centroid classifier(see methods) to identify Senescent-INRs in other cohorts of HIV infected cART treated subjects. The 352 features (genes, Table S11) classifier was trained on the CLIF cohort and had a misclassification error rate of 0.26 (Fig. 1f). To validate the capacity of the classifier to differentiate immune cell subsets from Senescent INRs (vs INR-Bs), we tested the enrichment of the classifier geneset in sorted CD4 TCM, CD4 TEM (effector memory T cells) and innate immune (HLA-DR+CD19-) cells. We observed that unlike the CD4 TEM and innate subsets, the classifier geneset was enriched in the CD4 TCMs from the senescent INRs (Fig. 1g). In line with the whole blood signatures (Fig. 1b), metabolic, cell cycling and apoptosis pathways were significantly reduced in these CD4 TCMs. Whereas, pathways that define quiescence (WNT/b-catenin signaling pathway) and senescence (cellular/oxidative stress induced) were enriched in CD4 TCMs from senescent INRs (Fig. 1h). Overall, these observations suggest that CD4 TCMs from the senescent INRs are metabolically impaired, have poor cell cycling capacity and could serve as hubs that maintain high levels of HIV reservoir.

Classifier confirms the generalizability of the Senescent-INR phenotype in HIV-infected subjects

Using an unsupervised approach (initially applied in the CLIF cohort), we confirmed two distinct INR groups in the SCOPE cohort (Supplementary Fig. 3a, Supplementary Fig. 3b). The 352-gene classifier segregated these two INR groups in the SCOPE cohort with an accuracy of 81% (Fig. 2a) - validating the generalizability and reproducibility of two INR groups (Fig. 2b: PCA representation of the 352-gene classifier across the SCOPE cohort) and highlighting the potential use of this classifier to distinguish Senescent-INRs in clinical settings. Our classifier (Table S11) included genes that drive a senescent biology (FOXO3, FOXO4, TGFBR2, RIOK3, IRF3 and BCL2L1)^46,59^,^60,61 and regulators of mitochondrial activity (NDUFS3, CYB5R3, ATP5G2)⁶². Several transporters of macromolecules: SLC6A8 (Sodium- and chloride-dependent creatine transporter 1⁶³), SLC48A1 (Heme transporter⁶⁴), SLC4A1 (Band 3 anion transporter⁶⁵), SLC25A23 (Mitochondrial Calcium carrier⁶⁶) and SLC38A5 (glucose⁶⁷) upregulated in Senescent-INRs and were also features of the classifier (Table S11). Overall, we observed that molecular pathways specific to cellular senescence^68-71 discriminate the two INR groups and counter-intuitively suggest that failure to increase CD4 T-cell numbers in senescent could be due to senescence and not to previously described pro-inflammatory cascades¹⁰.

The classifier gene set highlights the role of senescence in driving the magnitude of the inducible HIV reservoir

Although poor immune reconstitution has been associated with HIV persistence⁷², the cellular effectors and molecular mechanisms driving this association remain unidentified. To assess the role of senescence in HIV persistence, we measured frequencies of CD4 T-cells with inducible multi-spliced HIV RNA (“inducible HIV”) - in all subjects from the SCOPE cohort - using the Tat/rev Induced Limiting Dilution Assay (TILDA)⁷³. Our data show that Senescent-INRs have significantly higher inducible HIV when compared to IRs (~4.3-fold increase in median values, p<0.021) and INR-Bs (~5.5-fold increase in median values, p = 0.046) (Fig. 2c). And, these Senescent-INR subjects with the highest levels of inducible HIV were significantly enrichmed in (i.e. could be predicted by) the classifier geneset (Fig. 2d). Interestingly, inducible HIV levels in Senescent-INRs were negatively correlated with CD4 counts (rho = -0.52; p < 0.05; while this correlation was not significant in inflammatory INRs and in IRs). Comprehensive pathway analyses of the whole transcriptome further confirmed that genesets characteristic of Senescent-INRs (i.e. cell cycle arrest and ROS production) were associated with higher inducible HIV (Table S12).

Increased frequencies of TGF-b producing Tregs and PD1+ TCMs with impaired metabolism drive lower CD4 counts and HIV persistence

Given that transcriptional profiling identified the TGF-β pathway (PML, SMAD2/3; Fig. 1e, Supplementary Fig. 2 c,d) as a driver of cellular senescence in our cohort and that Forkhead box P3 (FOXP3) expressing T-regulatory cells (Tregs) are a primary source of TGF-β^74,75, we sought to ascertain the role of Tregs in driving senescence and HIV persistence. To this end, we developed a high-dimensional flow cytometry panel (Table S13) to quantify master regulators of Treg function (SATB1 and FOXP3⁷⁶), to discriminate differentiated Tregs (CD45RA, CD49B and CD39⁷⁷), to assess proliferation (Ki67) and TGF-β activation potential of a CD4 T cell (expression of latent (LAP) or activated (GARP) forms of TGF-β^78,79). UMAP dimension reduction (https://arxiv.org/abs/1802.03426) (Fig. 3a). followed by unbiased clustering⁸⁰ (Fig. 3a) led to the identification of three clusters of CD4 T cells (Fig. 3b, Fig. 3c, Table S14) that were enriched in senescent INRs (vs IRs) and expressed markers characteristic of TGF-b producing Tregs (FOXP3, CD25, lowCD127, LAP and GARP) (Fig. 3c). The most abundant of these clusters (i.e. Cluster 7; 0.81-5.34% of CD4 T cells) also showed an effector Treg phenotype (high CD39, low CD45RA) and low levels of markers that define IL-10 production capacity (Tr1) (i.e. CD49B, LAG3⁸¹) (Fig. 3c).

We assessed the systemic impact of these Treg clusters by studying their association with clusters of cytokines that define the host plasma milieu. We applied an unsupervised clustering using independent methods (k-means, and hierarchical clustering⁸²) to identify four clusters of plasma cytokines (from 43 cytokines testes) across all subjects of the SCOPE cohort (Supplementary Fig. 4a,b,c). Of these, the overall centroid score of “Cytokine Cluster 3” was significantly associated with the 352-gene classifier in Senescent-INRs (NES = 2.9, FDR = 0, Supplementary Fig. 4d, Table S15). No significant correlation between “Cytokine Cluster 3” centroid score and the classifier genes was observed in INR-Bs or IRs (Table S15). This cytokine cluster included several anti-inflammatory cytokines: TGF-β1, TGF-β2 (triggers of the TGF-β pathway⁸³), IL13 (known to inhibit inflammatory cytokine production⁸⁴), KC/GRO (CXCL1) (another anti-inflammatory cytokine⁸⁵), VEGF (the inhibitor of apoptosis⁸⁶) and homeostatic cytokines (IL3, IL7)^87,88 (Supplementary Fig. 4c). Importantly, our data showed that frequencies of GARP+ Tregs (Treg cluster 7) were univariately correlated with members of “Cytokine Cluster 3” (Fig. 3d, including TGF-b2 and IL-7; Table S16), and were associated with an enrichment in the classifier genes (significantly overlapped leading edge genes represented in Fig. 3e, Table S17).

The downstream impact of heightened frequencies of TGF-b secreting Tregs on HIV latency was further confirmed by the observation that ChIP-Seq validated SMAD2/3 targets⁸⁹ and HDAC1/2 targets (induced after siRNA knockdown)⁹⁰ were more abundant in subjects with the highest inducible HIV and frequencies of GARP+ Tregs (Fig. 3e). These genesets included genes that activate latent TGF-β (i.e. FURIN⁹¹), restrict T cell differentiation (LGAL3; inducer of TGF-β driven activation of b-catenin^92,93), reduce cell cycling (i.e. GADD45A⁹⁴) and restrict chromatin accessibility (co-operation between HDAC1/2 targets and SMAD2/3) can to induce cell quiescence (Fig. 3e).

Increased frequencies of senescent PD1+ TCMs with impaired mitochondrial function drive lower CD4 counts in the senescent INRs

Given that signaling via TGF-β/FOXO3 axis drives surface PD1 expression⁹⁵, cell cycle arrest and impaired metabolism⁹⁶ - we used high-dimensional flow cytometry, transcriptomics and cytokine data to identify the cellular drivers of immune reconstitution or lack thereof; we hypothesized that the upregulation of surface PD-1 and reactive oxygen species (ROS; measured by intracellular CellROX staining) in CD4 T-cells could be associated with lower CD4 counts⁹⁷. Using the analytical strategy described in the section above, we identified a cluster of PD1^hi ROS^hi CD4 central memory T-cells (TCM, Cluster 9; Fig. 4a,b,c, Table S18) that was induced in Senescent-INRs when compared with IRs (but not in INR-Bs vs IRs). Conversely, a cluster of PD1^hiROS^lo CD4 effector memory T-cells (TEM, Cluster 17; Fig. 4a,b,c, Table S18) was uniquely upregulated INR-Bs. Frequencies of PD1+ CD4/CD8 TCM cells were directly correlated with the frequencies of GARP+ Tregs (Treg cluster 7) and with cytokine cluster 3 (Fig. 4d). In line with previous studies^24,98, these data confirm the negative impact of PD-1 expression on T-cell homeostasis and immune reconstitution.

Evidence of impaired metabolism and cellular senescence in these PD1^hi ROS^hi TCMs was obtained by identifying metabolic and cell cycling genesets that associate with this cell subset (in senescent INRs vs IRs). Individuals with higher levels of PD1^hiROS^hi TCMs had poor mitochondrial metabolism profiles (higher ROS and lower oxidative phosphorylation), reduced cMyc activity and increased expression of genes that drive cellular senescence (Fig. 4f, Table S19). Specifically, expression of catalase (CAT), peroxidins (PRDXs) and cell cycle inhibitory genes (i.e. CDKN2D) was increased in senescent-INRs; while genes that regulate oxidative phosphorylation (NDUFs and COXs) and MYC target genes were expressed at lower levels. Concurrently, frequencies of PD1^hiROS^hi TCMs were also associated with cellular senescence pathways downstream of impaired mitochondrial activity (i.e: oxidative stress induced senescence, SASP), and were observed to be the highest in subjects with higher inducible HIV (Fig. 4e, Table S19). Altogether, these results indicate that PD1 expressing TCMs with impaired mitochondrial metabolism are senescent cells lacking the capacity to cycle and to differentiate into effector cells, thereby driving low CD4 cellular counts.

Bile and short chain fatty acid profiles drives Treg differentiation and TGF-β production in Senescent-INRs

The role of Tregs and impaired T cell homeostasis has been well characterized in prominent metabolic diseases (from diabetes, cancer and cardio vascular diseases)^99-103. Subjects presenting such metabolic aberrations have impaired host/microbe driven metabolic profiles that impair T cell function. To understand the metabolic milieu that could regulate Treg function in senescent INRs, we performed mass spectrometry (MS) to identified plasma metabolites that would be associated (ex vivo) with or could trigger (in vitro) Treg differentiation, TGF-β production or senescence. An unbiased assessment of the plasma metabolite profile of SCOPE cohort participants revealed that one of the primary components of variation (Principal Component 1 and Principal Component 2; derived from PCA analyses of ~750 detectable metabolites) was associated with inducible HIV (PC2 vs TILDA p-value <0.05; Fig. 5a). Specifically, the metabolite that were univariately correlated (p-value<0.05) with inducible HIV levels included members of the liver-biliary axis (i.e. primary liver/bile metabolites like bilirubin, biliverdin, cholate and glycol-beta-muricholate; secondary liver/bile metabolites like ursodeoxycholate), carnitine derivatives and members of the hydroxybutyrate family (i.e. a-ketobutyrate and hydroxybutyryl carnitine) (Fig. 5b). Several, but not all, of these metabolites were also associated with SMAD2/3 and HDAC1/2 target genesets and frequencies of GARP+ Tregs (Fig. 5b). Importantly, this analysis showed that a-ketobutyrate - a correlate of inducible HIV - was also correlated with C7 GARP+ Treg frequencies (p = 0.089), emphasizing the association between Tregs, metabolome and inducible HIV. Together these data indicate that metabolites could constitute integral components of the mechanisms that fuel HIV persistence.

TGF-β production resulting from alpha-ketobutyrate driven Treg differentiation causes increased HIV latency in vitro

To establish a causal link between butyrate metabolites on GARP+ Treg differentiation, we used the approach described by Ohno and Rudensky^17,104. Briefly, increasing concentrations of a-ketobutyrate were added to healthy sorted naïve CD4 T-cells stimulated with IL-2, anti-CD3/28 and/or TGF-β (Fig. 5 c,d) changes in frequencies of TGF-b secreting FOXP3+ cells were monitored¹⁰⁵. We observed that, stimulation of naïve T cells with high concentrations of a-ketobutyrate (in the absence of TGF-β) preferentially led to the differentiation of naïve T-cells into GARP+FOXP3+ cells (Fig. 5 c,d) that secreted significant amounts of TGF-β1 (Fig. 5e). Of note, a further increase in GARP+ Treg differentiation when both TGF-β and alpha-ketobutyrate were added to the culture (Supplementary Fig. 5a). Aside from Tregs, Increasing concentrations of a-ketobutyrate also led to significantly increase in PD1 expressing CD4 T-cells (Fig. 5 c,d, Supplementary Fig. 5b) that were associated with loss of effector function - shown by reduced secretion of T-helper cytokines in the culture media (i.e. IL17A, IFN-γ, IL9) (Fig. 5f). Altogether, these data indicate that in addition to enhancing Treg differentiation, the abundance of b-hydroxybutyrates could drive the upregulation of TGF-β associated suppressive activity of Tregs. The latter, as shown in Fig. 3, are critical for the maintenance of the HIV reservoir.

To provide evidence for a direct role of TGF-β in the induction of HIV latency, we developed an in vitro culture model¹⁰⁶ where TGF-β was added to HIV-infected CD4 T-cells. Increased numbers of CD4 T-cells with integrated proviral DNA were observed after 14 days of culture with increasing concentrations (0 to 50 ng/mL) of TGF-β (r= 0.7; p = 0.009) (Fig. 6a), demonstrating that TGF-β contributes to heightened levels of non-replicative forms of HIV DNA and establishment of HIV latency. In addition, we observed (in line with ex vivo observations shown in Fig. 4b) an increase in surface PD-1 levels at the two highest doses of TGF-b in the CD4 TCM subset (Fig. 6b). We then reversed latency by stimulating these CD4 T cells with immobilized CD3 and soluble CD28 specific antibodies. Our data show that frequencies of HIV p24+ cells were significantly higher in conditions where the highest concentrations of TGF-β (0.2-20ng/mL) were added (Fig. 6c). These frequencies of HIV p24+ cells were correlated with surface PD-1 levels on CD4 TCM (Fig. 6d). Altogether, these in vitro/ex vivo observations validate the critical role of TGF-β producing Tregs in driving the mechanistic establishment and/or maintenance of the HIV reservoir.

Integrated multi-omic model highlights cellular and molecular effectors of senescence as drivers of HIV persistence and lack of immune reconstitution

Using unbiased and holistic approaches, we have highlighted gene expression profiles, cytokines and T-cell subsets that are independently associated with HIV persistence and lack of CD4 reconstitution in the Senescent INRs. To investigative the interplay of pathways driving these cellular phenotypes and HIV persistence, we integrated multi-omic signatures above (Figures 2, 3 and 4, Table S20) across all subjects (n=41). Our data show that the levels of inducible HIV were positively correlated to senescence - characterized by impaired metabolism⁹⁷ (increased ROS, decreased OXPHOS), reduced transcription/translation (RPSs and EIFs) downstream of poor cMYC activity and TGF-β signaling (SMAD2/3 target genes) (Fig. 6e, Supplementary Fig. 5c). The induction of these cascades was consistent with higher expression of the transcriptional repressors that induced senescence (i.e. FOXO3, FOXO4)⁴⁵, and with lower expression of master regulators of innate antiviral activity (i.e. IRF7 and restriction factors of viral replication) (Supplementary Fig. 6). Lack of CD4 reconstitution and HIV persistence was also driven by higher frequencies of GARP+ Tregs, increased expression of SMAD2/3 targets (including FURIN, SMOX) and heightened levels of IRF3 induced genes (i.e. AFF, DARC, GUK1) (Supplementary Fig. 6). Our integrated analyses indicate that most pathways that drive HIV persistence are negatively associated with the recovery of CD4 numbers upon cART initiation in senescent subjects; and provided further evidence for the direct interplay between Treg frequencies, TGF-β production, heightened FOXO3 expression, interferon signaling, establishment of cellular senescence, impaired T cell homeostasis, and quiescent cellular and metabolic state as conditions that favor the maintenance of HIV reservoir in the unique senescent INR population described here.

In this manuscript, we identified cellular senescence as a mechanism that underlies HIV persistence and failure to reconstitute CD4 T-cell numbers in two independent cohorts of HIV infected cART-treated subjects. Our results provide a mechanistic framework where anti-inflammatory cytokines, TGF-β, VGEF and IL-13, trigger the upregulation of a transcriptional network that drives Treg differentiation. Our integrated analysis shows that increased frequencies of TGF-β producing Tregs and metabolically impaired PD-1 + TCMs drives, senescence, HIV persistence and poor CD4 T cell reconstitution.

Senescent-INRs, in our study, show increased expression of several molecules which are features of senescent cells. These include: FOXO4, TF upstream of the induction of senescence⁴⁵; PML, triggers cellular quiescence, HIV latency and is also downstream of TGF-β; as well as molecules with anti-apoptotic activity (BCL-2, BCL-xL¹⁰⁷) that ensure the survival of senescent cells⁷¹. In addition, FOXO3 - known to transcriptionally regulate genes involved in redox balance and anti-oxidant defenses^108,109 – and its targets like SNCA (α-syneculin¹¹⁰), mitophagy related genes (PINK1 and PARKIN¹¹¹) and GABARAPL2 (the autophagy regulatory gene¹¹²) and SLCs were observed to be upregulated in Senescent-INRs as part of a mitochondrial damage response. The downregulated oxidative phosphorylation and glycolysis observed in these subjects could be driven by the upregulation of FOXO3 target genes CYB5R3 and peroxiredoxins (PRDX2, PRDX3, PRDX5) involved in maintaining redox balance and anti-oxidant defense¹¹³. PRDXs, specifically PRDX2, can get oxidized by ROS due to a lack of electron transport in the mitochondria¹¹³ and can activate kinases (like p38) to initiate stress responses and protect cells from ROS mediated cell death, thereby supporting the survival of senescent cells¹¹³. In our data, the expression of genes associated with ROS production (Catalase and superoxide dismutase) and oxidized lipid biogenesis (CD36) were positively correlated with HIV reservoir size and confirm the link between senescence and HIV reservoir establishment.

The aforementioned senescence associated impaired mitochondrial metabolism in the senescent-INRs was also found to be associated with increased frequencies of PD-1 + TCMs and is suggestive of an accumulation of these cells in an early memory differentiated state. Indeed, we and others have shown that PD-1 expression leads to the accumulation of cells in an early memory differentiated stage¹¹⁴ while the genetic ablation of PD-1 results in increased differentiation to the effector memory stage¹¹⁵. Additionally, T-cells from Senescent-INRs show downregulation of pro-apoptotic pathways and increased expression of anti-apoptotic molecules that potentially restrain cell turnover¹¹⁶. The blockade of T-cell differentiation from TCM to TEM as a consequence of PD-1 expression could explain the low CD4 T cell numbers observed in these subjects (in contrast to TEM cells which are known to expand and proliferate thereby increasing CD4 cell numbers¹⁰⁶). This limited T-cell turnover in Senescent-INRs could also explain the high frequencies of cells with the multiply spiced HIV RNA, as these cells would accumulate with time. In this manuscript, we provide ex vivo and in vitro evidence that suggests TGF-β producing Tregs drive impaired T-cell homeostasis and HIV persistence in Senescent-INRs.

The increase in frequencies of TGF-β producing Tregs and activation of signal transduction cascades that lead to upregulation of PML¹¹⁷ and CCNT2 – targets of SMAD2/SMAD3 downstream of TGF-β^40,49 establishes cellular quiescence by stabilizing FOXO3 and FOXO4¹¹⁸. This stabilized FOXO4 can bind to p53 to downregulate pro-apoptotic machinery^45,46, a major hallmark of senescent cells. In vitro validation experiments, in addition to molecular and cellular mechanisms described here implicate TGF-β as the driver of senescence and dysregulated type I IFN-driven pathways (via FOXO3 and IRF3) which impact T-cell homeostasis and promote HIV persistence. In addition, we observe that the senescent INRs have increased expression of the pTEFb complex (comprised of CCNT2 and AFF1) known to support HIV replication^119,120 and likely aid in further promoting HIV persistence in these subjects.

Finally, in addition to known impact α-ketobutyrate/β-hydroxybutyrates on FOXO3 upregulation¹²¹, we use ex vivo and in vitro experiments to demonstrate that naive CD4 T-cells differentiation into GARP + Tregs upon exposure to α-ketobutyrate. These novel findings are in line with previous in vivo studies that have shown increased Treg differentiation upon exposure to butyric acid^17,104. The elevated levels of β-hydroxybutyrates (in addition to increase in primary/secondary bile acids) can be produced by the liver during periods of poor dietary intake¹²² and may promote higher inducible HIV (in senescent INRs). Alternately, the increase in plasma butyrates could also be attributed to increased bacterial metabolism¹⁷. In either case, our data suggest that β-hydroxybutyrates drive differentiation of TGF-β producing Tregs - which subsequently leads to CD4 T-cell senescence, lack of CD4 reconstitution and HIV persistence.

Our findings provide a strong rationale for the evaluation of drugs that would target senescent cells^123,124 as promising therapeutic interventions to reduce the HIV reservoir size. Combination interventions targeting PD-1 and TGF-β, or Senolytics, may have an improved therapeutic impact in Senescent-INRs where frequencies of PD-1 + cells are a correlate of high HIV reservoir and poor CD4 T-cell reconstitution. Interestingly, senescence and TGF-β signaling have been highlighted as hallmarks of cancer where anti-PD-1 therapy has shown efficacy¹²⁵. Our gene classifier could help identify subjects who will not respond to checkpoint therapies in HIV and cancer¹²⁶, and could benefit from interventions that target both PD-1 and TGF-β. Senescent-INRs could also benefit from interventions that promote T-cell differentiation (e.g. IL-15) or rescue the activity of deficient metabolic pathways allowing T-cells to overcome the senescent state and help restore both CD4 T-cell numbers and cell-mediated immunity, which could decrease reservoir size¹²⁷. The multi-omics senescent features and the classifier gene-set described here thus represent critical tools to identify Senescent-INRs. Additionally, the integrated approach used herein holds potential for the identification of novel targets and the design of optimal trials that combine different drugs/biologics targeting senescence and exhaustion to restore immune homeostasis and eradicate HIV.

Study Participants: These studies were approved by the Institutional Review Boards at University Hospitals/Case Medical Center and the Cleveland Clinic Foundation, the Vaccine and Gene Therapy Institute of Florida and University of California San Francisco; all patients provided written informed consent in accordance with the Declaration of Helsinki. Cohort 1 (CLIF): A total of 45 immunologic non-responders (INRs) and 17 immune responders (IRs) were identified from the Cleveland Immune Failure cohort. The Cleveland Immune Failure study examined immunologic indices in healthy controls and 2 groups of patients who had been receiving ART for at least 2 years with plasma HIV RNA levels below detectable levels using routine clinical assays; typically, less than 50 copies per milliliter. Transient blips in HIV RNA levels did not exclude participation if flanked by levels below limits of detection. Immune failure patients (INRs) had CD4 T cells <350/µL and immune success patients (IRs) had CD4 T cells >500/µL. Detailed clinical indices, anti-retroviral regimens and basic demographics are listed in Table S1. Cohort 2 (SCOPE): A total of 21 INRs and 20 IRs were identified from the University of California San Francisco (UCSF) SCOPE cohort. Viral suppression was defined by at least two longitudinal tests that showed plasma HIV RNA levels below the limit of detection using standard assays (<40 Abbot RealTime HIV-1 assay, <40 Roche COBAS^® Ampliprep/COBAS^® Taqman^® HIV-1 Test, <50 branched DNA); these tests were done 3 months prior to and on the date of specimen collection. Subjects with viral blips below 200 copies/ml in the time period since beginning ART (or since the end of last treatment gap) 2 years prior to the specimen date were not excluded from the study. Basic demographics and clinical readouts at the time of collection are listed in Table S2.

Microarray Pre-processing and differential gene expression analyses:

Whole blood (from all CLIF and SCOPE participants) was collected and lysed in RLT for RNA extraction (Qiagen, Valencia, CA, USA). CD4+ T (CM, EM, and Innate) cells were FACSorted from cyropreserved PBMCs directly ex vivo. 7 INRA-A, INR-B and IR subjects (n=42) were collected. These subsets were sorted in 2x buffer for RNA extraction. For the CLIF cohort, cDNA obtained after reverse transcription reaction was hybridized to the Illumina Human HT-12 version 4 Expression BeadChip according to the manufacturer’s instruction and quantified using the Illumina iScan System. The data were extracted using the GenomeStudio software. Similarly, the SCOPE cohort samples were run using an Affymetrix microarray system. Detailed analysis of the genome array output data was conducted using the R statistical language¹²⁸ and the Bioconductor suite¹²⁹. Arrays displaying unusually low median expression intensity and variability across all probes relative to all arrays were discarded from the analysis. Probes that do not map to annotated RefSeq genes and control probes were removed. Quantile normalization followed by a log2 transformation using the Bioconductor package LIMMA was applied to raw microarrays intensities. To determine differences between groups or against a continuous variable, the LIMMA package¹³⁰ was used to fit a linear model to each probe. A (moderated) Student’s t-test to assess significance in difference between groups, whereas a Pearson correlation test was used to assess significance against a continuous variable. The proportions of false positives were defined using the Benjamini and Hochberg method¹³¹. Unless indicated otherwise, genes that satisfied FDR <0.05 were selected for data mining and functional analyses.

Class identification, classifier training and validation: Class identification - In the CLIF cohort, 62 samples were hybridized by the Vaccine Genome Research Institute Genomics Core and 61 samples made it to the downstream analysis (44 INRs and 17 IRs) after outlier identification and removal. Initial exploratory analysis (MDS plot – Fig. 1a) followed by unsupervised analysis (heatmap of top 200 most variable probes – Supplementary Fig. 1c) revealed the discovery of two INR classes in the dataset. These presence of these subject cluster was confirmed when the Gap statistic technique⁸² was used on whole transcriptomic data to determine the optimal number of INR classes. The two classes of INRs were labeled: INR-A (an extreme INR phenotype, n = 15) and INR-B (INR phenotype proximal to IRs, n = 29). Differential expression analyses (described above) were used to identify the differences between IR and INR-A, IR and INR-B and INR-A and INR-B. Classifier training - Construction of the INR-A – INR-B Classifier (Fig. 1f, Supplementary Fig. 3) was done using the Prediction Analysis of Microarrays for R (PAM) package from R. PAM uses the nearest shrunken centroid methodology¹³². A standardized centroid for each class (INR-A and INR-B) was computed. Ten-fold cross-validation was used to optimize the shrunken criteria. The-shrunken criteria that resulted in the lowest cross-validated misclassification error rate was used to generate the optimized classifier on the CLIF cohort (training cohort). Supplementary Fig. 3a represents training of the 352 features used to build the classifier on the CLIF dataset (see features in Table S11). Classifier validation - Testing the classifier on the validation set (SCOPE cohort) is illustrated in Fig. 1f and Fig. 2a. There are no “true” labels that define INR-A or INR-B in the SCOPE testing cohort as they both fall under the label immune non-responders. In order to assess the performance of the classifier on the validation set, the two INR groups had to be defined. An unbiased unsupervised approach similar to the one used to discover the classes in the CLIF cohort was applied on the normalized Affymetrix microarray data of the SCOPE cohort. Supplementary Fig. 3a shows a heatmap representation of the expression of the top varying probesets (top 200 variant transcripts) on the full SCOPE dataset. Two clusters of INRs were identified using unsupervised clustering (color of branches in Supplementary Fig. 3a denote the discovered classes). This discovery allowed us to label the two subgroups of INRs in the SCOPE cohort. The 352-gene classifier was then applied on the SCOPE cohort. ROC analysis was then used to assess the accuracy (80%) of the classifier on the SCOPE cohort (Fig. 2a). Throughout the study the INR classes in the SCOPE cohort were defined based on the 352-gene classifier (PCA representation of classifier genes in Fig. 2b). Whole transcriptome profiling of classifier defined classes in the SCOPE cohort is shown in Supplementary Fig. 3b.

Pathway analyses. Gene Set Enrichment Analysis (GSEA)³⁰ throughout the study was performed to assess enrichment in pathways that discriminated between classes (Fig. 1b-d), were associated with inducible HIV (Supplementary Fig. 5c and Fig. 3e), were associated with Treg cluster 7 (Fig. 3e), were associated with low CD4 counts (Fig. 4e) and were associated with PD1^hiROS^hi CD4 TCMs (Fig. 4e, f). Briefly, the whole transcriptome probe-set was collapsed to genes by assessing the most variable probes, pre-ranking of this collapsed transcriptome was done (t-statistic) and enrichment of various genesets was tested after running 1000 permutations of enrichment. Hallmark MSigDB database²⁸ (v 7.0) was used to identify pathways that differentiated IR, INR-A and INR-B subjects in the CLIF cohort (Fig. 1b). This database was also used to assess pathways driving lower CD4 counts in the Senescent INRs (Fig. 4e), frequencies of PD^hiROS^hi CD4 TCMs in the Senescent INRs (Fig. 4e) and inducible HIV (Table S12) in the SCOPE cohort. Cell subset deconvolution: Major peripheral immune subset-specific gene expression signatures were obtained from Nakaya et. al³⁴, and their enrichment was assessed (Fig. 1c, Table S7). In addition, data from a comprehensive study with sorted hematopoietic stem cell-subsets³⁵ was used to generate gene-signatures specific to 38 unique subsets (each subset-specific signature was defined as genes found to be >2-fold higher with p<0.05 than in the pool of all other subsets) (Table S8). These signatures were used to assess variations in differentiated CD4 and CD8 T cell subsets in whole blood in Fig. 1d. Custom genesets: Senescence based gene signatures were extracted from Reactome database in MsigDB v7.0’s c2 module (Fig. 4f). Genesets specific to host antiviral restriction factors (Supplementary Fig. 6d, Table S12) and IFN signaling (Fig. 1b, Supplementary Fig. 6d, Table S12) were extracted from Abdel-Mohsen et. al¹³³ and Interferome database¹³⁴, respectively. SMAD2/3 and HDAC1/2 genesets were also extracted from MsigDB’s c2 module by searching this module for ‘SMAD2’, ‘SMAD3’, ‘HDAC1’ and ‘HDAC2’. The results of these analyses are shown in Fig. 3e (Table S17). The enrichment of the 352-geneset classifier gene-set was done where specified (Fig. 1i, Fig. 3e, Table S17). Leading Edge overlap and Sample Level Enrichment Analyses (SLEA): Leading edge genes from the analyses above were overlapped (significance of overlap was determined using Fischer’s Exact test; p < 0.05) to define new gene-lists that were associated with both “inducible HIV and Treg cluster 17” (Fig. 3e, Table S17) OR “CD4 counts and PD1^hiROS^hi CD4 TCMs” (Fig. 4e, Table S20). SLEA was used to generate a z-score normalized value for genelists in Fig. 3e, 4e and 4f. These scores were correlated (spearman correlation test) with each other, minor (Treg frequencies and PD1^hiROS^hi CD4 TCM frequencies) and major (inducible HIV and CD4 counts) outcomes to generate the integrated correlation network model in Fig. 6e and Table S20.

Over-representation, gene-mania/correlation networks and transcription Factor (TF) analyses: Over-representation of Reactome pathways in genes that are upregulated in the INR-As (vs both IRs and INR-Bs; FDR < 0.05) was assessed using the ClueGO plug-in from Cytoscape (http://apps.cytoscape.org/apps/cluego³⁶), and the results of these analyses were corrected for multiple comparisons using Bonferroni step-down correction (Fig. 1e, see Table S10 for full results). GeneMania and correlations network: GeneMania Networks¹³⁵ (http://genemania.org) were plotted to represent co-expression of genes (Supplementary Fig. 2b and Supplementary Fig. 2d). Overlap between the genes included in the networks and Gene Ontology (GO) biological process was assessed using a Fisher exact test. Correlation networks in Fig. 3d, Fig. 4d, Fig. 6e and Supplementary Fig. 5c are based on significant Spearman correlations (p-value < 0.05) between the features indicated in each figure. All networks were plotted using the Cytoscape Plug-in – specific value/category for all nodes and edges are available upon request. Transcription factor list was generated by combining genes with known ChIP-seq validated targets from CHEA¹³⁶ (https://amp.pharm.mssm.edu/Harmonizome/resource/ChIP-X+Enrichment+Analysis) and ENCODE (https://amp.pharm.mssm.edu/Harmonizome/dataset/ENCODE+Transcription+Factor+Targets) databases. The expression of these TFs was tested in INR-As vs INR-Bs or IRs (Supplementary Fig. 2c). IRF3 targets and FOXO3 targets were extracted from the ENCODE and CHEA databases cited above, respectively. Co-expressed targets of FOXO3 and IRF3 that correlated with inducible HIV are represented in Fig. 6d (Table S12).

Cell Preparation and flow cytometry: PBMCs were prepared from whole blood by ficoll-hypaque density sedimentation and cryopreserved in 10% dimethyl sulfoxide and 90% FBS. Two panels to evaluate Treg function and mitochondrial dysfunction in memory CD4s were run using previously titrated antibodies summarized in Table S13, S14 and S18. The cells were surface stained for 20 minutes in the dark at room temperature, washed, fixed and permeabilized using the eBioscience™ Foxp3 / Transcription Factor Staining Buffer Set (Cat# 00-5523-00), as per manufacture instructions. Intracellular staining was performed in Perm-Wash provided by the kit for 45 minutes at 4^oC. Samples were washed and re-suspended in staining buffer for acquisition. ~500,000 live-gated events were collected per sample on the LSRII flow cytometer (Becton Dickinson, San Jose, CA). Initial data cleaning and pre-gating was done using the Flow-Jo X software (TreeStar, Ashland, OR) (Supplementary Fig. 7). Briefly, lymphocyte gate based on FSC-A and SSC-A was defined. Single cells were then selected using a FSC-A x FCS-H gate. Live CD3+CD4+CD8- cells were gated and exported for unbiased clustering analyses (Supplementary Fig. 7). For both panels, projection of the density of cells expressing markers of interest (Table S14, Table S18) were visualized/plotted on a 2-dimensional UMAP (https://arxiv.org/abs/1802.03426, https://github.com/lmcinnes/umap). Clusters of cells using the RPhenograph package after (https://github.com/jacoblevine/PhenoGraph) after concatenating all samples per panel and bi-exponentially transforming each marker (Fig. 3a and c, Fig. 4a). Differences in cluster frequencies per group and MFI for each marker per cluster, for each are shown in Table S14 and Table S18.

Measurement of inducible reservoir: The HIV reservoir was measured using the tat/rev induced limiting dilution assay (TILDA). This method measures the inducible reservoir by RT-qPCR using the limiting dilution assay previously described⁷³. Briefly, 2 x10⁶ CD4 T cells from 20x10⁶ PBMCs were enriched using a CD4 T cell negative selection kit (Stem Cell). These cells were stimulated with PMA/Iono (100ng/ml and 1µg/ml, respectively) for 12 hours. A total of 744,000 stimulated cells were then distributed in a limiting dilution in a 96-well plate and tat/rev msRNA expression was directly quantified (without prior RNA extraction) by semi-nested real-time PCR⁷³. The frequencies of cells positive for inducible tat/rev msRNA per 10⁶ CD4+ T cells were determined by using the maximum likelihood method (http://bioinf.wehi.edu.au/software/elda) (Fig. 2 c,d).

Measurement of plasma biomarkers: Plasma IL-6 in the CLIF cohort was measured using a high sensitivity ELISA kit for human IL-6 (Quantikine HS) from R&D Systems (Minneapolis, MN). Plasma levels of IL-7 in the CLIF cohort were measured by high sensitivity IL-7 ELISA (Quantikine HS, R&D Systems, Minneapolis, MN). Plasma levels of IP10 in the CLIF cohort were measured by IP-10 ELISA (Quantikine, R&D Systems, Minneapolis, MN). Plasma levels of human Intestinal Fatty Acid Binding Protein (I-FABP) in the CLIF cohort were measured using a DuoSet ELISA Development kit from R&D Systems (Minneapolis, MN) following the manufacturer’s protocol. Levels of D-dimers in the CLIF cohort were measured using the Asserachrom D-DI immunoassay (Diagnostica Stago, Asnieres France) (Supplementary Fig 1. d-m). Measurement of LPS in the CLIF cohort: Plasma samples were diluted to 10% or 20% with endotoxin free water and then heated to 85^oC for 15 minutes to denature plasma proteins. We then quantified plasma levels of LPS with a commercially available Limulus Amebocyte Lysate (LAL) assay (QCL-1000, Lonza, Walkersville, MD) according to the manufacturer’s protocol. Multiplex ELISA (Mesoscale): U-PLEX assay (Meso Scale MULTI-ARRAY Technology) commercially available by Meso Scale Discovery (MSD) was used for plasma cytokine detection. The assay was performed according to the manufacturer’s instructions (https://www.mesoscale.com/en/technical_resources/technical_literature/techncal_notes_search). 25μL of plasma from each donor was combined with the biotinylated antibody plus the assigned linker and the SULFO-TAG™ conjugated detection antibody; in parallel a multi-analyte calibrator standard was prepared by doing 4-fold serial dilutions. Both samples and calibrators were mixed with the Read buffer and loaded in a 10-spot U-PLEX plate, which was read by the MESO QuickPlex SQ 120. The plasma cytokines values (pg/mL) were extrapolated from the standard curve of each specific analyte. Cytokine clustering (Supplementary Fig. 4a,b,c) was performed using independent methods: (gap statistic method to identify and characterize optimal number of k-means clusters, and hierarchical clustering (ward clustering; Euclidean distance⁸²).

Metabolome. In collaboration with Metabolon, plasma metabolite levels for up to 1300 metabolites were measured. Metabolon uses four ultra-high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) methods in a highly controlled environment to reduce noise and produce accurate results. The data generated using UHPLC/MS/MS were referenced against a well-established library of known and novel metabolites.

Treg differentiation in the presence of alpha-ketobutyrate: Naïve CD4 T cells extracted from 8 healthy donors were isolated using the EasySep™ Human Naïve CD4+ T Cell Isolation Kit (StemCell Technologies Catalog# 19555) and differentiated in vitro using the protocols described by Rudensky¹⁰⁴. Briefly, 100-200,000 naïve CD4 T cells were stimulated with Dynabeads Human T-Activator CD3/CD28 beads (ThermoFisher Scientific Cat# 111.31D, 1 bead/3 cells), 100 U/mL of IL-2 (R&D Systems Cat# 202-IL-500), 0 to 1ng/mL of TGF-b (R&D Systems Cat# 240-B-010) and alpha-ketobutyrate (Cat#, from 0mM to 5mM) for 3 days in 96-well round bottom plates. Viability post-stimulation, assessed post-stimulation by staining with a fixable viability dye, was found to be >80% in all stimulation conditions. Secreted cytokines in the supernatants were quantified using the Mesoscale Discovery platform/kits described above. CD4 T cells were intracellularly stained with the Treg phenotyping panel described (Table. S8).

Latency establishment and reactivation assay: The latency and reactivation assays developed by our group¹⁰⁶ was used to assess the latency establishment in memory CD4 T cells post-stimulation with increasing doses of TGF-b. Briefly, memory CD4 T cells spinoculated with HIV (HIV strain information listed¹⁰⁶) were incubated in the presence of antiretrovirals (effavirenz , Saquinavir and Ralegravir) and latency establishment media (supplemented with 0-40ng/mL of TGF-b) for 13 days (or when the frequencies of HIV-p24+, measured by flow cytometry, CD4 T cells was negligible). Integrated HIV DNA was measured at this stage using the protocol described¹⁰⁶. Reactivation of HIV post-latency was done after stimulation with 1ug/mL anti-CD3 and 1ug/mL of anti-CD28 for ~48 hours; reactivated cells were quantified by monitoring the frequencies of p24+ cells using flow cytometry.

Other statistical analyses: All univariate group-differences were analyzed using a non-parametric Wilcoxon-ranked test. All univariate correlation analyses were done using a non-parametric Spearman’s test. P<0.05 is reported as significant.

Full Data Availability Statement: The raw and normalized data matrices for gene expression analysis of the CLIF and SCOPE cohorts were deposited into the Gene Expression Omnibus (GEO) database; GSE143742.

Financial support: This work was supported by the National Institutes of Health (grants UO1 AI 105937 and RO1 AI 110334 and RO1 AI 11444201), the CWRU Center for AIDS Research (grant AI 36219), DARE (U19 AI 096109), and the Fasenmyer Foundation. Rafick-Pierre Sekaly is the Richard J. Fasenmyer Professor of Immunopathogenesis.

Potential conflicts of interest: All authors: No reported conflicts

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TGF-β associated senescence and impaired metabolism in central memory CD4 T cells promotes HIV persistence

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