Glycolysis downregulation is a hallmark of HIV-1 latency and sensitizes infected cells to oxidative stress

HIV-1 infects lymphoid and myeloid cells, which can harbor a latent proviral reservoir responsible for maintaining lifelong infection. Glycolytic metabolism has been identified as a determinant of susceptibility to HIV-1 infection, but its role in the development and maintenance of HIV-1 latency has not been elucidated. By combining transcriptomic, proteomic and metabolomic analysis, we here show that transition to latent HIV-1 infection downregulates glycolysis, while viral reactivation by conventional stimuli reverts this effect. Decreased glycolytic output in latently infected cells is associated with downregulation of NAD+/NADH. Consequently, infected cells rely on the parallel pentose phosphate pathway and its main product, the antioxidant NADPH, fueling antioxidant pathways maintaining HIV-1 latency. Of note, blocking NADPH downstream effectors, thioredoxin and glutathione, favors HIV-1 reactivation from latency in lymphoid and myeloid cellular models. This provides a “shock and kill effect” decreasing proviral DNA in cells from people-living-with-HIV/AIDS. Overall, our data show that downmodulation of glycolysis is a metabolic signature of HIV-1 latency that can be exploited to target latently infected cells with eradication strategies.


Introduction
Decades after its outbreak, the HIV/AIDS pandemic remains one of the main causes of morbidity and mortality of humankind, leading to almost one million victims per year (source: UNAIDS). Moreover, due to the severe medical and economic crisis caused by Coronavirus Disease 2019 , adherence to and availability of antiretroviral therapies (ART) is decreasing in areas where HIV/AIDS prevalence is particularly high (Jiang et al, 2020;Jewell et al, 2020). This worsening of the death toll highlights the fragility of current therapeutic approaches, based on lifelong ART administration. Therefore, a cure for HIV/AIDS is an unmet medical need of growing importance for people-livingwith-HIV/AIDS (PLWH). The quest for a cure is hampered by persistence of transcriptionally silent proviruses within latently infected cells, which render these cells hard to discriminate from their uninfected counterparts (Finzi et al, 1999). Latent HIV-1 DNA can be mainly found in viral reservoirs such as CD4 + T-cells (Van Lint et al, 2013), however myeloid cells (in particular microglia) can also contribute to persistence of the infection during ART (Sattentau & Stevenson, 2016). Pinpointing molecular features to allow selective targeting of latently HIV-1 infected cells would represent a significant, and perhaps decisive, step in the quest for an HIV/AIDS cure.
A possible approach to reach this goal is the investigation of the metabolic pathways exploited by the retrovirus to actively replicate and enter a latent state. In this regard, it is interesting to point out that the cells more susceptible to HIV-1 infection are characterized by an increased glycolytic rate (Valle-Casuso et al, 2019). Cellular activation, leading to increased glycolysis, is necessary for active HIV-1 replication; of note, in CD4 + T-cells and monocytes of HIV-1 infected individuals, glucose consumption is increased and the expression of the glucose transporter GLUT-1 is upregulated (Palmer et al, 2014a(Palmer et al, , 2014b. Conversely, reports on the effect of HIV-1 infection on glycolysis have been sparse and, in some respects, conflicting. Starting from the 1990s, studies have shown that cells infected with HIV-1 display decreased levels of NAD + , an important substrate of glycolytic reactions (Murray et al, 1995), and that supplementation of the NAD + precursor nicotinamide influences viability of productively HIV-1 infected cells (Savarino et al, 1996). Moreover, after ART implementation, PLWH showed to be more susceptible to hyperglycemia than the general population (Dubé et al, 1997). On the other hand, upregulated glucose metabolism was reported to favor apoptosis of infected CD4 + T-cells (Hegedus et al, 2014). Although these combined sets of data strongly support a role of active glycolysis in determining susceptibility to HIV-1 infection as well as an altered glucose metabolism in PLWH, the role of glycolysis in viral latency, and therefore in possible curing strategies, has as yet remained unclear.
The importance of understanding the relationship between cell metabolism and the infection is emphasized by the possible first long-term remission of HIV-1 without bone marrow transplantation (Abstract Supplement Oral Abstracts from the 23rd International AIDS Conference 2020). This individual had been treated with a combination of intensified ART and the NAD + precursor nicotinamide, thus suggesting a possible contribution of glycolysis regulation in the therapeutic result obtained. The anecdotal character of this case, however, renders difficult the drawing of definitive conclusions.
Indirect evidence of an interplay between glycolysis and HIV-1 latency can also be drawn by the dysregulation of redox pathways, which are intertwined with glycolytic metabolism in several cell types and pathological conditions (Locasale & Cantley, 2011;Kondoh et al, 2007). This interconnection might be relevant, as we recently showed that HIV-1 infection leads to enhancement of antioxidant defenses in primary CD4 + T-cells (Shytaj et al, 2020). HIV-1 infection causes an initial oxidative stress (Daussy et al, 2020), which then leads to the nuclear translocation of the master antioxidant transcription factor Nrf2. This translocation in turn induces transcription of several proteins involved in antioxidant response, including glucose 6-phosphate dehydrogenase (G6PDH), which diverts glucose 6-phosphate from the glycolytic pathway to the pentose cycle, responsible for production of the antioxidant NADPH.
Therefore, elucidating the specific glycolysis/redox state interconnection during HIV-1 infection would be of pivotal importance for understanding the molecular events which lead infected cells to either die or establish a latent infection.
Herein, we combine transcriptomic, proteomic, and metabolomic datasets, including single cell analyses, to show that, during transition to HIV-1 latency, glycolysis is downregulated. In line with this, our results show that latently infected cells able to undergo HIV-1 reactivation are also characterized by prompter reactivation of glycolysis. Moreover, we show that downregulation of glycolysis in latently infected cells is accompanied by higher reliance on the antioxidant thioredoxin (Trx) and glutathione (GSH) systems for cell survival. Our results highlight the possibility to exploit glycolytic imbalances induced by HIV-1 infection for the elimination of retrovirally infected cells. This result may improve our knowledge of pathways that can be targeted by strategies aimed at eradicating HIV/AIDS.

HIV-1 infection downregulates expression of glycolytic enzymes in CD4 + T-cells
To study transcriptomic profiles upon infection, we used microarray and RNA-Seq data sets generated in primary CD4 + T-cells infected with HIV-1 pNL4-3 . The cellular model employed is based on longitudinal sample collection to cover different time points, from early HIV-1 infection [day 3 post-infection (p.i.)] to peak retroviral replication (days 7-9 p.i.) and latency/survival of infected cells (day 14 p.i.). The detailed features and validations of this cellular model have been described previously elsewhere (Shytaj et al, 2020).
Microarray results highlighted significant downregulation of the glycolytic pathway in infected cell cultures, which was independently evidenced by Gene Set Enrichment Analysis [GSEA; (Subramanian et al, 2005)] either using a customized gene set comprising enzymes involved in human glycolysis (henceforth, HUMAN-GLYCOLYSIS) or using the Reactome or Biocarta databases ( Figure 1A; Additional file 1). Entrance of the infected cells into a hypometabolic state was also shown by downregulation of glycolysis-independent transcriptional and translational pathways, although downregulation of these pathways did not reach the same level of convergence among the databases examined as compared to the glycolytic pathway (Additional file 1). Moreover, among the pathways more heavily perturbed by HIV-1 infection, there was the interferon pathway (Additional file 1), as expected, and pathways associated with both apoptosis and cell cycle, in line with the fact that, in an HIV-1 infected cell cultures, some cells succumb to infection and others survive developing a proviral latent state.
We then analyzed in detail the glycolytic enzymes of the pathway HUMAN-GLYCOLYSIS ( Figure 1B). The glycolytic enzymes characterized by significantly downregulated transcription upon infection were hexokinase 2 (HK2), glucose 6-phosphate isomerase (GPI), phosphofructokinase liver type (PFKL), aldolase fructose-bisphosphate C (ALDOC), triosephosphate isomerase 1 (TPI1) and enolase 2 (ENO2), thus suggesting a broad downregulation of the glycolytic pathway ( Figure 1B). In particular, the highly significant downregulation of GPI suggests a glycolysis-specific effect, as this enzyme commits metabolites to the glycolytic, rather than to the alternative pentose-phosphate pathway.
To expand these analyses, we explored an RNA-Seq dataset derived from the same primary CD4 + T-cell model, which was previously published elsewhere (Shytaj et al, 2020). In this dataset, the number of donors and time points was higher, allowing the study of the expression of glycolytic enzymes during each infection stage ( Figure 1C). In line with the microarray results, differential gene expression analysis of RNA-Seq data (DESeq2) (Love et al, 2014) highlighted significant downregulation of glycolysis in infected cells cultures, which was initiated at peak HIV-1 replication (7-9 days p.i.) and persisted after retroviral replication had ceased (14 days p.i.) ( Figure 1C). As seen in microarray analysis, enzyme transcriptional downregulation covered all main steps of glycolysis, and the RNA-Seq data set further suggested that various isoforms, in particular of PFK, might contribute differently to glycolysis downregulation during productive or latent infection [PFK-platelet (P); adjusted p value = 0.04 at 7 days p.i. PFK-muscle (M); adjusted p value = 0.04 at 14 days p.i. respectively]. Finally, analysis of a previously published proteomic dataset of the same CD4 + T-cell model (Shytaj et al, 2020) further corroborated the downregulating effect of HIV-1 on glycolysis, confirming significant downmodulation of GPI, PGK1 and TPI1 (Additional file 2).
Overall, these data show that HIV-1 infection during its transition to latency is associated with downregulated expression of glycolytic enzymes, in particular those catalyzing the early steps of glycolysis.

Expression of glycolytic enzymes is required for HIV-1 escape from latency in lymphoid and myeloid cells
The aforementioned results prove that glycolysis downregulation is initiated during productive infection and accompanies HIV-1 latency establishment. We then proceeded to specifically investigate the transcriptional regulation of the HUMAN-GLYCOLYSIS pathway upon the reverse process, i.e. reactivation from latency.
To this aim, we first analyzed two single cell RNA-Seq (scRNA-Seq) datasets independently published by the groups of Ciuffi and Nussenzweig, respectively (Golumbeanu et al, 2018;Cohn et al, 2018). In the first dataset, primary CD4 + T-cells infected with pseudotyped HIV-1-GFP/VSVG had been sorted according to GFP expression and allowed to revert to latency (Golumbeanu et al, 2018). Latently infected cells had then been either left untreated or subjected to HIV-1 reactivation by strong (α-CD3/CD28 antibodies) or weak stimuli [suberoyl anilide hydroxamic acid (SAHA)]. Eventually, the transcriptomic profile was analyzed by scRNA-Seq (Golumbeanu et al, 2018). Using principal component analysis, the authors identified two cell clusters, which were less (cluster 1) or more (cluster 2) susceptible to HIV-1 reactivation (Golumbeanu et al, 2018). We analyzed the expression of the enzymes of the glycolytic pathway in both clusters and found that glycolytic enzymes were downregulated in cells of cluster 1 ( Figure 1D). Interestingly, this relative downregulation, already visible in basal conditions, was maintained upon treatment with either anti-CD3/CD28 antibodies or SAHA ( Figure 1D). Moreover, GPI expression was lower in the cell subpopulation less responsive to HIV-1 reactivation as compared to the more susceptible cell subpopulation ( Figure 1E When infected cells were compared to their uninfected counterparts incubated under similar conditions, results showed a clear pattern of GPI downregulation being more significant in latent than in productive infection and accompanied by a more pronounced downregulation of the early glycolytic enzymes in the former (Additional File 3). Finally, sc-RNA-Seq profiling of HC69 cells highlighted a significantly positive correlation between baseline HIV-1 expression and key glycolytic enzymes, including GPI, in the subset of cells in which proviral transcription was detectable ( Figure 1G,H). Conversely, treatment with dexamethasone (DEXA), which is a known glycolysis inhibitor (Ma et al, 2013), decreased both the proportion of HC69 cells expressing the transcripts of the glycolytic pathway and the baseline percentage of cells expressing HIV-1 transcripts (Additional file 4).
Taken together, these results show that low expression of early glycolytic enzymes, in particular GPI, is associated with HIV-1 latency maintenance, and that at least partial restoration of glycolysis is required for latency disruption. Metabolite enrichment analysis showed significant changes in the glycolysis/gluconeogenesis pathway in both latently and productively infected cells as compared to their uninfected counterparts ( Figure 2C,D).

Decreased glycolytic metabolism during productive and latent HIV-1 infection
We then proceeded to analyze the glycolytic metabolites separately.
Results from latently infected cells showed significantly decreased levels of the metabolite produced by PFK, i.e. fructose 1,6 bisphosphate ( Figure 2E). Since the PFK enzyme acts immediately downstream of GPI, downregulation of fructose 1,6 bisphosphate is in line with the reduced expression of GPI in latently infected cells, as shown by both RNA-Seq and scRNA-Seq analysis ( Figure 1). There was also a trend towards reduction of glyceraldehyde 3-phosphate, a product of ALDO, the enzyme acting immediately downstream of PFK ( Figure 2E). These results, again, support the view that latently infected cells are characterized by a block in the early glycolytic steps.
Instead, the pattern observed in cells reactivated from latency, as compared to uninfected (C20) cells subjected to the same HIV-1 reactivating stimulus, showed a less clear impairment of glucose metabolism, with parameters indicative of glycolysis downregulation (a relative paucity of the initial glycolytic metabolites) and parameters indicative of glucose consumption (significantly decreased Dglucose levels and decreased ADP levels) ( Figure 2F).
We then explored the connection between decreased glycolytic metabolism and other, intertwined, metabolic pathways highlighted by the aforementioned enrichment analysis ( Figure  Network analysis supports the view that these cells rely on glutamate metabolism as an alternative source of pyruvate for fueling ADP utilization through the Krebs cycle ( Figure 2H).
Among the most significantly enriched pathways, we further considered metabolism of antioxidants (Additional file 5), due to its well described interconnection with glycolysis and NAD + , via the pentose pathway (Grant, 2008), and its relevance in the establishment and maintenance of HIV-1 latency (Shytaj et al, 2020;Benhar et al, 2016). Network analysis revealed a likely connection between NADH decrease and redox pathways in latently infected cells ( Figure 2G). Accordingly, increased levels of oxidized glutathione, consistent with higher oxidative stress upon infection, as previously described (Shytaj et al, 2020), were associated with a decrease not only in NADH but also in its precursor NAD + . The HIV-1related NAD + consumption was also associated with altered nicotinamide metabolism (Additional files 5,6) and with a trend towards increased expression of proteins consuming NAD + /NADH ( Figure 2G).
Among the most up-or downregulated transcripts (> 1 or < -1 Log 2 fold) involved in this pathway were G6PD and PARP2 in latent infection, the former being the enzyme initiating the pentose phosphate pathway and the latter being a NAD + -consuming enzyme involved in repair of oxidative-stress induced DNA damage (Chevanne et al, 2007) ( Figure 2G). Moreover, productively, but not latently, infected cells were characterized by higher levels of sirtuin (SIRT)1 and 3, which are known to favor HIV-1 replication (Pagans et al, 2005) and to counteract mitochondrial oxidative stress, respectively (Singh et al, 2018) ( Figure 2G). Productively infected cells also displayed decreased expression of CD38, an ectoenzyme responsible for hydrolysis of extracellular NAD + and intracellular recycling of its components, ADPribose and nicotinamide (Savarino et al, 2000) ( Figure 2G).
Overall, these data show that early glycolytic metabolites are downregulated in both latently and productively HIV-1 infected cells, as compared to uninfected cells. Moreover, the results indicate that HIV-1 infection diverts metabolism from glycolysis to the pentose cycle, as further described below.

Downstream block of the glycolysis-alternative pentose phosphate pathway can induce a "shock and kill" effect in latently infected cells
The data so far presented suggest that glycolysis is downregulated in latently HIV-1 infected cells to divert glucose 6-phosphate to the pentose cycle. In this regard, the standard balance between the two pathways would be altered by NAD + paucity ( Figures 2E-G), as NAD + is not only a main cofactor for glycolytic enzymes, but also a precursor of the main pentose cycle cofactor NADP + (Xiao et al, 2018). As diversion to the pentose phosphate pathway leads to regeneration of the antioxidant NADPH, we tested whether blocking the downstream effectors of NADPH could induce HIV-1 escape from latency and exploit NADH paucity to reduce the ability of infected cells to survive oxidative stress.
For this purpose, we chose the drugs auranofin (AF) and buthionine sulfoximine (BSO), which by inhibiting thioredoxin (Trx) and glutathione (GSH) regeneration, respectively (Benhar et al. 2016), can block the antioxidant/pro-latency effect of NADPH. These drugs were also preferred because of their translational potential, due to their clinical (separately) and pre-clinical (combined) testing as antireservoir compounds in PLWH and macaques infected with the simian immunodeficiency virus (SIV) (Diaz et al, 2019;Benhar et al, 2016). As expected, at the concentrations chosen for the reactivation experiments, the two drugs were able to synergistically increase oxidative stress in a previously described reporter model which allows measuring GSH potential in live cells (Bhaskar et al, 2015) (Additional File 7). When we analyzed HIV-1 production in a number of proviral latency models, we found that AF/BSO favored proviral reactivation, at different efficiencies, in both lymphoid and myeloid models ( Figure 3A, Additional Files 8,9).
Moreover, when cell viability was analyzed, results showed that combined inhibition of Trx and GSH led to the preferential killing of HIV-1 infected cells as compared to their uninfected counterparts ( Figure   3B), although specific leukemia/lymphoma cell lines were highly sensitive to AF and BSO treatment irrespective of HIV-1 infection, in line with the previously described sensitivity of such neoplasias to these drugs (Benhar et al, 2016;Fiskus et al, 2014).
We finally tested the combination of AF and BSO in primary cells derived from PLWH under ART.
Analysis of metabolic profiles of this primary cell model showed significant enrichment of the pentose cycle upon treatment with both AF and BSO as compared to cells treated with BSO only (Additional file 10). These effects were not detectable when cells treated with AF-only were compared to cells treated with BSO, thus supporting the specificity of the drug combination. Both results are in line with the well-known compensation of GSH inhibition by the Trx system (Benhar et al, 2016), thus confirming that only the combination of both drugs can lead to a sustained pro-oxidant effect. Finally, we tested the therapeutic potential of AF and BSO on CD4 + T-cells of PLWH receiving ART. As, in these ex-vivo samples, direct and precise analysis of cell viability (infected vs. control) was not possible due to the low frequency of HIV-1-infected cells, we indirectly tested the selective survival/elimination of HIV-1 infected cells by measuring the frequency of the integrated proviral DNA in cells sorted for viability after treatment.
Results showed that, despite only minor effects on HIV-1 reactivation as measured by Tat/rev Induced Limiting Dilution Assay [TILDA (Procopio et al, 2015)] (Additional file 11), proviral HIV-1 DNA was significantly lower in cell cultures that had received both AF and BSO ( Figure 3C). Of note, cells from two of the donors showed loss of integrated proviral DNA signal after AF/BSO treatment.
Overall, these data show that glycolysis downregulation and increased reliance on the pentose cycle in latently HIV-1 infected cells can be exploited by pro-oxidant drugs targeting Trx and GSH to induce HIV-1 reactivation and/or mortality of infected cells.

Discussion
The results of the present study identify downregulation of glycolytic activity as a determinant of the transition from productive to latent HIV-1 infection, an effect that is reversed upon proviral reactivation.
Our data show that it is HIV-1 infection per se that paves the way towards glycolysis downregulation, since productively HIV-1-infected cells display glycolytic profiles intermediate between those of uninfected and latently infected cells. Of note, HIV-1-associated downregulation of glycolysis was shown to occur mainly due to downmodulation of glycolysis-initiating enzymes (e.g. GPI) and paucity of precursors of the energetic products of glycolysis.
Our findings complement previous studies showing that the cell subsets most susceptible to HIV-1 infection are those displaying higher glycolytic activity and that this metabolic state supports HIV-1 replication (Valle-Casuso et al, 2019;Hegedus et al, 2014). The present study was instead aimed at a longer follow up, comprising transition to proviral latency from productive infection and proviral reactivation from latency. The overall evidence points toward a model where progressive downregulation of glycolysis is associated with gradual dampening of retroviral replication, perhaps selecting those cells that can survive the productive phase and undergo latent infection. The fact that latently infected cells undergo a metabolic transition, is further corroborated by our results showing that conventional HIV-1 reactivation stimuli can preferentially enhance glycolysis upon reversion to productive infection ( Figure   4).
One study that reached a partially different conclusion from ours is the work of Castellano et al. which analyzed a time course of macrophages infected in vitro with HIV-1 (Castellano et al, 2019). This study concluded that glycolysis might not be impaired in latently infected macrophages, although it pinpointed that these cells rely on glycolysis-independent energy sources such as glutamate. The authors based their conclusions on the analysis of the acidification capability (a byproduct of glycolysis). Our report, however, shows decreased production of lactate at the single cell level, which may have been missed using a cell culture containing HIV-1 infected cells surrounded by a majority of uninfected bystander cells. On the other hand, our report confirms that, when glycolysis is downregulated, glutamate becomes a readily alternative source for pyruvate in latently infected cells, so as to maintain sufficient levels of OXPHOS-derived ATP production compatible with cell survival. In this regard, another study analyzed a panel of 186 different metabolites in plasma of PLWH and highlighted glutamate was one out of the 6 metabolites that was unequivocally elevated among PLWH as compared to uninfected control individuals (Scarpelini et al, 2016).
Our analysis of the glycolytic pathway at a single cell resolution may reconcile our conclusions with previous results obtained studying the metabolism of PLWH. In this regard, Palmer et al. showed that glycolysis is upregulated in PLWH, independently of ART (Palmer et al, 2014a(Palmer et al, , 2014b. As the percentage of infected cells in vivo is relatively low, this effect could be attributed to chronic immune hyperactivation (Deeks et al, 2004), rather than to retroviral replication per se. In line with a possible contribution of bystander effects to the in-vivo observations, the data sets analyzed in the present work clearly showed transcriptional downregulation of the glycolysis pathway in single cells characterized by a deep latency state.
A hallmark of HIV-1 infection identified by our metabolomic analysis is the consumption of nicotinamide, which is a NAD + precursor. This molecule is also necessary for synthesis of NADP + , the precursor of the antioxidant NADPH, which is the main pentose cycle product. Therefore, nicotinamide consumption may contribute to divert metabolism from glycolysis to the pentose cycle, consistent with a model where glycolysis favors HIV-1 replication while the pentose cycle favors HIV-1 latency as well as antioxidant defenses. The latter phenomenon is in line with the previously described upregulation of antioxidant responses in latently HIV-1 infected cells (Shytaj et al, 2020). Moreover, oxidative stress immediately following HIV-1 infection was shown to induce increased transcription of G6PDH (Shytaj et al. 2020), therefore further validating the diversion of glucose 6-phosphate to the pentose cycle.
Moreover, these observations may shed new light on the previously reported contribution of nicotinamide to proviral escape from latency (Samer et al, 2020) and viability of HIV-1-infected cells (Savarino et al, 1997).
Our data also show that reinforcing glycolysis downmodulation through pro-oxidant drugs acting downstream of NADPH may lead to the selective elimination of infected cells. In particular, NADPH acts as a cofactor for the regeneration of the two most important antioxidant defenses, Trx and GSH (Benhar et al, 2016;Miller et al, 2018). In line with this, dual inhibition of the Trx and GSH pathways might induce a vicious cycle: on one side, subtracting metabolites to glycolysis and readdressing them to the antioxidant defense-generating pentose cycle, and on the other side blocking the downstream antioxidant effects of the pentose cycle (Figure 4). In this regard, an interesting parallel can be drawn with neoplastic cells, as previous studies showed increased susceptibility to the cytotoxic effects of combined Trx and GSH pathway inhibition in lung cancer cells with pharmacologically inhibited glycolysis (Fath et al, 2011;Li et al, 2015). Indeed, while our study privileged the use of a dual drug combination to decrease the likelihood of non-specific toxicity, it is tempting to speculate that including an inhibitor of glycolysis could enhance the effects observed on latently infected cells. Interestingly, the use of one such inhibitor A possible limitation of our study is that it focused mainly on glycolysis as the energy-producing pathway. In this regard, another major source of NADH is the Krebs cycle (Wu et al, 2015;Viña et al, 2016), which intervenes downstream of glycolysis. We believe that the complexity of these phenomena will require stepwise analysis. Future work might shed light on the regulation of the Krebs cycle during latent HIV-1 infection and thus provide a more complete and integrated picture of the metabolic alterations induced by HIV-1 infection.
The translational relevance of our findings resides in the fact that the drugs selected to inhibit the Trx and GSH pathways could become a useful tool for inducing a "shock-and-kill" effect, helping to purge the latently infected cells (Deeks, 2012). Indeed, the drugs used in the present work (AF and BSO) had already shown the potential to decrease the viral reservoir in macaques and PLWH (Shytaj et al, 2015;Diaz et al, 2019). A similar effect was observed in macaques treated with another prooxidant compound and Trx pathway inhibitor, i.e. arsenic trioxide (Yang et al, 2019). One characteristic of these strategies was not only the elimination of latently infected cells in vivo but also the enhancement of anti-HIV-1 cell mediated immunity (Shytaj et al, 2015). In light of the results of the present study, this immune enhancement could be interpreted as a consequence of the "shock" effect against viral latency provided by the combined inhibition of the Trx and GSH pathways. Future studies will be required to test this hypothesis and to understand the potential interplay between oxidative stress and cell-mediated immunity in the context of HIV-1 infection. Overall these results highlight glycolysis downregulation as a distinctive metabolic feature of latently HIV-1 infected cells which can be exploited to target latent reservoirs.

Cell cultures and HIV-1 infection
The following cell lines or primary cells were used as models of productive or latent HIV-1 infection: 1) Carbonell et al, 2017), c) primary CD4 + T-cells from healthy donors infected with HIV-1 in vitro (Shytaj et al, 2020) and d) primary CD4 + T-cells from PLWH with viral loads stably suppressed by ART; 2) myeloid cells: a) the THP-1 monocytic cell line, b) the U937 and U1
Latently HIV-1 infected Th17 cells were generated as previously described (Dobrowolski et al, 2019;Garcia-Mesa et al, 2017). Briefly, naive CD4 + T cells were isolated using a RoboSep CD4 + Naïve T cell negative selection kit (STEMCELL Technologies Inc., Vancouver, British Columbia, Canada), and 2 X 10 6 cells were resuspended in 10 mL RPMI medium and stimulated with 10 µg/mL concanavalin A (ConA) (EMD Millipore, Billerica, MA, USA) in the presence of subset-specific cytokines. Cells were cultured for 72 h at 37°C, followed by addition of 10 mL of fresh medium, additional 10 µg/mL ConA, polarization cocktail cytokines, and 120 IU/mL of IL-2. After 6 days, cells were washed and resuspended in RPMI medium supplemented with the growth cytokines IL-23 (50 ng/mL) and IL-2 (60 IU/mL). Cells were then infected in a 24-well plate using VSV glycoproteinpseudotyped virus expressing CD8 and GFP at a multiplicity of infection (MOI) of 2 at a cellular concentration of 5 X 10 6 cells per mL, in the presence of cell subset cytokines. Cells were spinoculated at 2,000 × g for 1.5 h at room temperature and then placed in an incubator overnight. Cells were adjusted to 1 × 10 6 per mL in the presence of cell subset-appropriate growth cytokines. After 48 h, infection efficiency was determined by GFP expression and infected cells were isolated using RoboSep mouse CD8a positive selection kit II (STEMCELL Technologies Inc., Vancouver, British Columbia, Canada). Cells (50 X 10 6 per mL) were pre-incubated with 50 µL/mL of antibody cocktail from the kit and 40 µL/mL of magnetic beads and diluted into 2.5 mL RoboSep buffer. Positive cells were recovered by magnetic bead separation, suspended in 1 mL of medium, and vortexed to release the cells and beads from the tube wall.

Primary CD4 + T-cells for in-vitro
HIV-1 infection were isolated from total blood of healthy individuals using the RosetteSep™ Human CD4 + T Cell Enrichment Cocktail (STEMCELL Technologies Inc., Vancouver, British Columbia, Canada) as previously described (Shytaj et al, 2020) and according to the manufacturer's instructions. The blood was obtained through the Heidelberg University Hospital Blood Bank following approval by the local ethics committee. To induce activation before HIV-1 infection Dynabeads® Human T Activator CD3/CD28 was added to cells for 72 h. Cells were then mock-infected or infected using 2ng p24 of HIV 1 pNL4 3 /10 6 cells. Mock-infected and HIV-1 infected cells were then cultured for two weeks in RPMI + 20% FBS with 10 ng/mL IL 2 at a density of 0.5-2 X 10 6 /mL. At 3,7,9 and 14 days post-infection 1 X 10 6 cells were pelleted and used for RNA extraction and transcriptomic analysis (microarray and RNA-Seq) as previously described (Shytaj et al, 2020). for at least 12 months. Culture conditions and HIV-1 reactivation experiments were conducted as described in (Procopio et al, 2015).

Myeloid cells
For producing latently HIV-1 infected THP-1 (ATCC number: TIB-202) cell cultures, uninfected cells were cultured on a 6-well plate at a density of 1 × 10 6 cells per well in RPMI growth medium containing 10% FBS, 1% penicillin/streptomycin, and 50 nM of 2-mercaptoethanol. Infection with a HIV-1-GFP virus was carried out by spinoculation, as described in . Positively selected cells were placed in RPMI medium with cell type-specific growth cytokines at 1 X 10 6 in upright flasks and allowed to expand for a week prior to treatments. During all assays suspension cells were cultured at a density of 1 X 10 6 cells per mL, in 96-well plates in a volume of 100 µL.
The procedure used to generate hµglia/HIV HC69 from C20 was previously described in ( HIV-1 pseudovirus bearing Gag-mCherry was produced as described previously (Coomer et al, 2020).

RNA extraction
Total cellular RNA was extracted using the InviTrap® Spin Universal RNA Mini Kit (Stratec Biomedical, Germany) according to the manufacturers' instructions and as previously described (Shytaj et al, 2020). RNA concentration was assessed using a P-class P 300 NanoPhotometer (Implen GmbH, Munich, Germany).

Microarray and RNA-Seq analyses
Primary CD4 ++ T-cells infected with HIV-1 or mock-infected were subjected to microarray and RNA-Seq using 500 ng of total RNA that was quality checked for integrity via Bioanalyzer. Microarray was performed using the HumanHT-12 beadchip (Illumina, Inc., 5200 Illumina Way San Diego, CA 92122 USA) and scanned using an iScan array scanner. Data extraction was done for all beads individually, and outliers were removed when the absolute difference to the median was greater than 2.5 times MAD (2.5 Hampelís method). Raw data are available at GSE163405.
Bead-level microarray raw data were converted to expression values using the lumi R package for quality control, variance stabilization, normalization, and gene annotation (Du et al, 2008). Briefly, raw data and control probes were loaded in R using the lumiR and addControlData2lumi functions. Raw signals were background corrected, estimating the background based on the control probe information with the bgAdjust method of the lumiB function. Background corrected data were then processed with the variance-stabilizing transformation (VST) of the lumiT function to stabilize the variance and were finally normalized using the quantile normalization implemented in lumiN. Gene expression data were annotated using the R package illuminaHumanv4.db that contains the mappings between Illumina identifiers and gene descriptions.
To identify the impact of HIV-1 infection on gene expression, we compared the expression levels of

CD4 + T-cells infected with HIV-1 with those of mock-infected cells using Significance Analysis of
Microarray (Tusher et al, 2001) algorithm coded in the same R package. In SAM, we estimated the percentage of false-positive predictions (i.e., false discovery rate, FDR) with 100 permutations and selected as differentially expressed those genes with an FDR q-value ≤ 0.05.
Over-representation analysis was performed using Gene Set Enrichment Analysis (Subramanian et al, 2005) and using the gene sets of the Biocarta and Reactome collections from the Broad Institute Molecular Signatures Database (http://software.broadinstitute.org/gsea/msigdb) as well as a customized gene set derived from AmiGO (see section "Glycolysis pathway" below). GSEA software (http://www.broadinstitute.org/gsea/index.jsp) was applied on Log 2 expression data of cells infected with HIV-1 or matched mock-infected controls. Gene sets were considered significantly enriched at FDR < 5% when using Signal2Noise as metric and 1,000 permutations of gene sets.

Proteomic analysis
Proteomic analysis of CD4 + T-cells infected in vitro with HIV-1 or mock-infected was retrieved from (Shytaj et al, 2020 by Despite Data-independent Acquisition (DIA) processing raw data with Spectronaut Pulsar X (version 11) using default and previously described parameters (Shytaj et al, 2020). Heatmaps were generated using the Morpheus tool (https://software.broadinstitute.org/morpheus).

Metabolomic analysis
Metabolomic analysis was performed as described previously (Li et al, 2020). Briefly, polar metabolites were extracted from cells using a cold extraction solution containing 80% methanol and 20% water. Two types of analyses were conducted. One set was run undiluted (1x), while the other set was concentrated 5fold before analysis (5x For pathway analysis, Human Metabolome Database (HMBD) (Wishart, 2020) IDs of the metabolites differentially expressed between the conditions compared were submitted to the MetaboAnalyst platform.
Comparisons were analyzed by Student's t-test using Holm's correction for multiple comparisons, so as to generate a q-value. The library 'Homo sapiens (human)' of the Human Metabolome Database was used for pathway analysis. For network generation, metabolite data were integrated with the RNA-Seq data of the same microglia model (described in its dedicated methods section). As input data, KEGG IDs, p-values and Log 2 fold changes were used for the selected compounds (metabolites and gene transcripts). In order to analyze a correlation network of the compounds in shared pathways, MetScape (Gao et al, 2010), an app implemented in Java and integrated with Cytoscape (version 3.5.1), was used.

Glycolysis pathway
Except for a priori analysis (

MTT assay of cell viability
Cell viability upon treatment with auranofin and/or BSO was measured using the CellTiter 96 ® Non Radioactive Cell Proliferation Assay (MTT) (Promega; Madison, WI, USA) according to the manufacturer's instructions, as described in (Shytaj et al, 2020). Absorbance values were measured using an Infinite 200 PRO (Tecan, Männedorf, Switzerland) plate reader. After blank subtraction, absorbance values were normalized using matched untreated controls.

Flow cytometry and cell sorting
To measure GFP expression in J-Lat 9.2 cells, 500 × 10 5 cells were fixed with 4% PFA in PBS, washed twice with PBS and resuspended in the FACS buffer. GFP fluorescence was measured using a BD

Redox Potential Measurement
Intracellular redox potential measurements in U1 cells were done as described earlier (Bhaskar et al, 2015). Briefly, the ratio-metric response of cells expressing the Grx1-roGFP2 sensor was obtained by measuring excitation at 405 and 488 nm at a fixed emission (510/10 nm) using a FACS Verse Flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA).

Real-time PCR and ALU-HIV PCR
HIV-1 expression (Gag-p24) in U1 cells was measured by qPCR as described previously (Bhaskar et al, 2015 To measure integrated HIV-1 DNA in live CD4 + T-cells of PLWH, Alu-HIV PCR was performed as described in (Chomont et al, 2009). HIV-1 reactivation in the same cell types was measured by Tat/rev Induced Limiting Dilution Assay as described in (Procopio et al, 2015).

Statistical analysis
Statistical analysis of in-silico data is described in the respective Methods subchapters. HIV-1 reactivation and cell viability data were analyzed by parametric (i.e. one or two way ANOVA tests) or nonparametric (i.e. Friedman test). Parametric testing was adopted when normality could be hypothesized (sample size ≤ 3) or restored through an appropriate transformation (e.g. tangent in the case of Figure 3B).
Data sets characterized by sample size > 3 which did not pass the normality tests (D´Agostino & Pearson or Shapiro-Wilk) and for which a transformation was not applicable were analyzed by non-parametric tests. For both parametric and non-parametric tests, post-test comparisons were used to compare specific groups as described in the Figure captions. Analyses were performed using GraphPad Prism (GraphPad Software, San Diego, CA, USA).  FOXK2  HK1  HK2  HK3  GCK  GPI  PFKL  PFKM  PFKP  ALDOA  ALDOB  ALDOC  ADPGK  TPI1  GAPDH  GAPDHS   PGK1  PGK2  BPGM  PGAM1  PGAM2  PGM2L1  ENO1  ENO2  ENO3  PKLR