Metabolic Reprogramming and Tunneling Nanotubes Cooperate to Regulate HIV-1 Latency Reactivation

Metabolic Reprogramming and Tunneling Nanotubes Cooperate to Regulate HIV-1 Latency Reactivation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic Reprogramming and Tunneling Nanotubes Cooperate to Regulate HIV-1 Latency Reactivation Marieke Böcker, Anoop Ambikan, Luis Villegas-Hernández, Sofja Poznakovs, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8529866/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract HIV-1 latency in myeloid cells remains a major obstacle to viral eradication. Here, we applied a systems biology approach combining transcriptomics, proteomics, metabolic modeling, targeted metabolite profiling, and advanced imaging to investigate metabolic alterations in a pre-monocytic latent cell model (U1). Upon latency reversal, we identified disrupted α-ketoglutarate (AKG) homeostasis driven by mitochondrial biogenesis and glutamine/glutamate metabolism, supporting energy production and M2-like macrophage polarization. Reporter metabolite analysis predicted cytoplasmic amino acid accumulation, and functional assays showed that tryptophan suppressed HIV reactivation by promoting mitochondrial and antioxidant metabolism. Additionally, we observed enhanced formation of tunneling nanotubes (TNTs), which facilitated intercellular transfer of mitochondria and viral components, potentially aiding viral persistence. Our study reveals cell-type–specific metabolic reprogramming and intercellular communication mechanisms underlying HIV-1 persistence. Our findings highlight the glutamine-AKG axis and TNTs as promising targets for strategies aimed at eliminating long-lived macrophage-associated HIV reservoirs. Biological sciences/Microbiology/Virology/Systems virology Biological sciences/Systems biology/Systems analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction HIV-1 latency remains a major obstacle to viral eradication, sustained by complex host-cell interactions and metabolic adaptations that allow the virus to persist in a transcriptionally silent yet replication-competent state 1-3 . While the role of CD4+ T cells as the primary reservoir for HIV is well-established, recent studies highlight the importance of myeloid lineage cells. Particularly, macrophages play an important role as long-lived reservoirs where the virus can persist in a latent state despite effective antiretroviral therapy (ART) 4 . Several studies have detected HIV DNA, including integrated proviral DNA, in circulating monocytes 5 and tissue-resident macrophages in various anatomical sites like the brain, gut, liver, and spleen, even in individuals on long-term ART [reviewed in 6 ]. A recent study also identified intact HIV genomes in monocytes from 40% of participants on cART, and higher levels of total HIV DNA were associated with reactivatable latent reservoirs in monocyte-derived macrophages (MDMs), which produced replication-competent virus capable of infecting bystander cells 7 . Understanding the metabolic and functional reprogramming of latently infected macrophages is therefore critical for developing effective latency-reversing or -sustaining strategies. 4 . This task is complicated by the intrinsic characteristics of macrophages, including their long lifespan, resistance to apoptosis, and plasticity in response to environmental cues 4,8,9 . In our recent clinical study, by applying systems biology and ex vivo assays, we identified the role of myeloid cells in system-level immunometabolic dysregulation 10 . We discovered impaired macrophage function due to metabolic training in well-treated people living with HIV (PWH) 11 . Despite growing evidence that myeloid cells serve as an important HIV reservoir, their role in immunometabolic reprogramming remains unclear. This highlights a critical gap in knowledge and the need for studies to elucidate mechanisms of their persistence and strategies for their elimination. In this study, we investigated metabolic remodeling associated with HIV-1 latency and reactivation in the pre-monocytic U1 cell model. We have used advanced multi-modality super-resolution optical microscopy and label-free quantitative optical microscopy (QPM). Our analyses revealed a pronounced disruption of α-ketoglutarate (AKG) homeostasis, which was linked to enhanced mitochondrial biogenesis and increased flux through glycolysis and the TCA cycle upon HIV-1 activation. Gene expression profiling further supported this metabolic shift, showing upregulation of pathways involved in cell-cell communication, inflammatory signaling, and energy metabolism. Quantitative metabolic assays confirmed increased glycolytic activity and altered levels of TCA intermediates, indicating a strategic metabolic reprogramming that accompanies latency reversal. Strikingly, the altered metabolic state was associated with a phenotypic transition of U1 cells toward an M2-like anti-inflammatory macrophage profile, characterized by disrupted AKG regulation and immunosuppressive features that may contribute to HIV-1 persistence. Additionally, we observed enhanced tunneling nanotube (TNT) formation in U1 cells during latency reversal, suggesting an intercellular communication mechanism that may facilitate the transfer of viral particles and mitochondria, further supporting viral survival and complicating efforts to eliminate latent reservoirs. Together, these findings underscore the importance of metabolic and morphological plasticity in HIV-1 latency and highlight AKG as a potential metabolic checkpoint in macrophage-associated viral persistence. Results Metabolic modeling predicted disrupted AKG homeostasis due to mitochondrial biogenesis in the latent cell model We activated the pre-monocytic latent cell model U1 and its parental cell line U937 with 10nM PMA for 48 hours. Following treatment, flow cytometry analysis revealed a median (IQR) viral activation of 78.25% (74.85–81.65%) in U1 cells, as measured by intracellular p24 expression. The PMA-stimulated U1 (hereafter referred to as MdU1) and U937 (MdU937 herein) cells exhibited macrophage-like phenotypes and were subsequently subjected to RNA sequencing using the NovaSeq X Plus platform.. Principal component analysis identified a clear separation of MdU937 from MdU1 treated with either PMA or the vehicle control DMSO (Fig. 1 A). Differential gene expression analysis identified 864 unique genes in PMA-treated MdU1 compared to MdU937 (Log fold change > 1.5, adjusted p < 0.05) after removing the genes that were significantly regulated in other comparisons (Fig. 1 B and Supplementary Table S1 ). The expression profile of the 864 genes is presented in Fig. 1 C. The key genes that were upregulated were part of cell-cell communications (e.g., CD151, CLDN9, SRC, TESK1, SIRPA, NECTIN2, and NECTIN1 ), genes involved in virus-related inflammatory processes, e.g., NF-kB signaling (e.g., TLR4, ICAM1 ), TNF-alpha signaling via NF-kB (e.g., EFNA1, BCL3, JUNB , and ICAM1 ), and specific to HIV-1 infection (e.g., TLR4, ICAM1 , and APOBEC3A ). Several genes in the PI3K/Akt/mTOR signaling pathway (e.g., EFNA1, NTRK1, RXRA, DDIT4, KDR, PPP2R2A, LAMC1, TLR4, ITGA9, AKT1S1, LAMTOR2 , and LAMTOR3 ) were also upregulated including genes of the mitochondrial biogenesis (e.g., RXRA, ATP5MC3 , and CALM1 ), suggesting an increase in energy production capabilities may be required to enhance the cell's ability to produce ATP efficiently during viral activation. Interestingly, several genes for metabolic processes were also upregulated, e.g., glutamine/glutamate metabolism (e.g., GLS ) and oxidative phosphorylation (e.g., ATP6V0E1, ATP5MC3 , and TCIRG1 ) which are also linked with energy metabolism. Therefore, upregulation of genes in these pathways and mitochondrial biogenesis can be a response to HIV-1 activation, requiring enhanced energy production. As the differential gene expression analysis does not capture the complexities of the metabolic process, we used context-specific genome-scale metabolic modeling (GEM) and flux balance analysis (FBA), with an aim to capture a comprehensive, systems-level representation of all metabolic functions within a cell in each context. We developed four different contextualized models, MdU1 and MdU937 PMA-treated and vehicle control (DMSO). Further, we performed FBA to computationally predict the rate of turnover for each reaction in the models. We then calculated unique metabolic reactions by identifying the reactions that are either absent or present or in opposite flux prediction in the PMA-treated MdU1 compared to the other three conditions. Among unique reactions, 60% of the reactions belong to transport reactions, indicating an increased flux of metabolites during latency reactivation (Supplementary Fig S1 ), including more predicted mitochondrial ATP production (Fig. 1 D). The prediction also identified disrupted endogenous AKG homeostasis in the activated MdU1 through aspartate, glutamate, and valine metabolism both in the mitochondria, e.g., AKG[m] + valine[m] = > 3 − methyl − 2−oxobutyrate[m] + glutamate[m] and cytoplasm e.g., AKG[c] + aspartate[c] = > glutamate[c] + OAA[c] which was either absent or having negative flux in other three conditions (Fig. 2 D). Several reactions of the TCA cycle were also altered. The proposed metabolic alteration during latency reversal in MdU1 is presented in Fig. 1 E. Combining all, these results suggest that AKG imbalance is a key metabolic feature associated with HIV-1 latency reversal, potentially reflecting increased demands for biosynthetic precursors and energy during viral reactivation. Tryptophan acts as a negative regulator of HIV latency reversal through coordinated host metabolic and immune modulation To identify metabolites associated with coordinated transcriptional changes indicating potential regulatory or functional hotspots within the metabolic network, we performed reporter metabolite analysis 12 . Reporter metabolites are those around which the most significant transcriptional changes occur. The significance of the reporter metabolites was evaluated using the distinct-directional class of gene set statistics, which incorporates the direction of gene expression changes. The analysis was performed for all pairwise comparisons between the study groups. It predicted 17 unique positive and 18 negative enrichments of reporter metabolites (adjusted p-value < 0.1) during the latency reversal in MdU1 (Fig. 2 A, Supplementary Table S2 ). It reveals that viral activation causes significant, unique regulation within the metabolic network. The volcano plot shows the results of the reporter metabolite analysis for U1_PMA compared to U1_DMSO, showing a higher number of changes in the metabolic network relative to the other groups. Predicted positive enrichment in the intracellular metabolites includes various amino acids, except glutamate. We therefore hypothesized that the accumulation of specific amino acids during latency reversal acts as a metabolic signal or provides substrates essential for HIV reactivation. We therefore treated the U1 cells with specific amino acids for 2 hours, followed by activation of the U1 cells with PMA, and identified that tryptophan (Trp) significantly suppresses the viral reactivation (p < 0.001, Fig. 2 B). Further, we primed the cells with Trp for 2 hours, followed by 48 hours of growth in the basal media and subsequently 48 hours of PMA treatment. The data showed a similar trend with significant suppression of the Gag expression in Trp-treated cells (Fig. 2 C). These results highlight a potential inhibitory role of Trp in the context of latency reversal and support further investigation into Trp metabolism as a modulatory axis in HIV persistence. To identify the mechanism, we further performed quantitative proteomics. Trp treatment significantly suppresses HIV gene expression (Gag-pol, env, and rev) as observed in the flow cytometry data (Fig. 2 D). It downregulates key host immune and transcriptional regulators such as interferon-stimulated genes ( IRF7, STAT1, STAT3 ) and nuclear transport factors ( KPN2A, NUP58 ), which may be critical for viral replication. The upregulation of TSG101, TRIM24 , AND NQO1 in Trp-treated cells suggests a coordinated host response involving altered viral budding machinery, epigenetic regulation, and enhanced antioxidant defense, potentially contributing to suppression of HIV reactivation. Further, the pathway enrichment analysis identified upregulation of the metabolic process towards mitochondrial and antioxidant metabolism, and antiviral mechanisms due to the intracellular viral particle, suggesting that Trp may prime antiviral immune pathways even as it suppresses viral reactivation. Moreover, the downregulation of proteins related to antigen processing and presentation, cell adhesion molecules, and Natural killer (NK) cell-mediated cytotoxicity indicates a suppression of immune surveillance, intercellular communication, and cytotoxic response mechanisms. AKG-mediated strategic metabolic reprogramming to meet biosynthetic and energy demands during latency reversal The upregulation of proteins in oxidative phosphorylation and glutathione metabolism in Trp-treated cells during latency reversal suggests that the cells are under oxidative pressure, likely due to enhanced mitochondrial activity (Fig. 2 E). Further, upregulation of OXPHOS increases the flux through AKG-consuming reactions in the TCA cycle (Fig. 1 D), potentially depleting intracellular AKG pools and disrupting its homeostasis. To understand how the disrupted endogenous AKG homeostasis impacts metabolite abundances during latency reversal to regulate cell fate, we measured the intra- and extracellular metabolites of the glycolysis (glucose, pyruvate, and lactate), TCA cycle (citrate, AKG, succinate, and fumarate) and glutamine/glutamate metabolism (glutamine and glutamate). There was a trend in increased glucose (p = 0.201) uptake in MdU1 cells compared to the MdU937 cells, with a significantly increased accumulation of pyruvate (p = 0.0002) and lactate (p = 0.002) (Fig. 3 A), indicating enhanced glycolytic activity in MdU1 cells during latency reversal. However, extracellular glucose and lactate were not significantly changed, indicating an accumulation of lactate in the cell (Fig. 3 B). Moreover, citrate (p = 0.0043) and AKG (p = 0.0004) were also significantly higher in MdU1, suggesting alterations in the TCA cycle, potentially due to increased influx of metabolic intermediates or reduced utilization of these metabolites for energy production. Interestingly, there was no increase in succinate, but fumarate is significantly high (p < 0.0001), indicating a metabolic reprogramming that supports the biosynthetic and energy needs during latency reversal. We also observed a significantly increased transcript level of glucose-6-phosphate dehydrogenase (G6PD) (p = 0.004), which could lead to increased production of NADPH generated through the oxidative pentose phosphate pathway (PPP), potentially due to the increased citrate and AKG levels in MdU1 cells for active lipid and amino acid biosynthesis. Further, the transcripts of isocitrate dehydrogenase 2 (IDH2) and glutamine synthetase (GS) were also significantly high (Fig. 3 C). As this resembles our early findings on plasma metabolomics 11 , 13 , we have performed a meta-analysis of the previously reported untargeted metabolomics from four different cohorts i.e. people living with HIV (PWH) without ART (n = 27) and with ART (n = 275), elite controllers (n = 14) and people without HIV (PWoH) (n = 100) from India, Cameroon, Sweden and Denmark. Following the batch correction and normalization we used 539 metabolites to identify the signature of the PWH on ART. While plasma AKG level was higher in all three PWH groups, EC has shown a significantly higher level of glutamate in EC compared to the other two PWH groups, indicating that it could potentially be linked with the viral persistence, as all the EC are seropositive but plasma viral load < 50 copies/mL (Fig. 3 D). Combining all the disrupted endogenous AKG homeostasis during HIV-1 latency reversal is associated with altered glycolytic and TCA cycle metabolite abundances and amino acid metabolism, indicating a metabolic reprogramming that supports biosynthetic demands and may be linked to viral persistence, as further supported by elevated AKG and glutamate levels in plasma metabolomics from PWH. Disrupted AKG homeostasis is characteristic of pre-monocytic latent cells To understand whether the disrupted AKG-homeostasis is a characteristic of all the HIV-1 latent cell models, we used J-Lat 10.6, which is a T-lymphocytic latent cell model, and compared it with MdU1. We measured the mitochondrial gls, glud1 , and idh3b , and AKG transporter SLC25A11 following 48 hours of PMA treatment, when more than 80% of the cells were activated in both the cell models (Fig. 4 A). There was a significant increase in gls in MdU1 cells compared to MdU937 but not in T-cell HIV-1 latent cell model J-Lat 10.6 compared to the parental Jurkat cells (Fig. 4 B). Interestingly, glud1 (Fig. 4 C), idh3B (Fig. 4 D), and AKG transporter SLC25A11 (Fig. 4 E) were significantly upregulated in MdU1 compared to MdU937 but significantly downregulated in J-Lat 10.6 compared to Jurkat. We also measured GLS in the MdU937 and MdU1. While western blot analysis confirmed the presence of p24 in MdU1 cells (Fig. 4 F), it also showed a significant increase in GLS (Fig. 4 F and G ). The significant upregulation of gls , glud1 , idh3b , and SLC25A11 in MdU1 cells supported by increased protein expression, contrasted with their downregulation in J-Lat 10.6, indicates that disrupted AKG homeostasis is a distinct feature of pre-monocytic latent cell models rather than T-lymphocytic models, highlighting cell type-specific metabolic reprogramming during latency reversal. Disrupted AKG-homeostasis reprograms MdU1 cells toward the M2-like phenotype upon activation Both AKG and lactate accumulation inhibit the pro-inflammatory M1 macrophage polarization while promoting the polarization of M2 macrophages towards an anti-inflammatory 14 . Following the PMA stimulation U1 and U937 cells can also acquire a macrophage-like phenotype (M0), we aimed to characterize the impact of HIV-1 latency in macrophage polarization. The majority of the M2-markers were significantly upregulated in PMA-stimulated MdU1 compared to MdU937, while some of the M1-markers were upregulated (Fig. 5 A). 3D super-resolution structured illumination microscopy (SIM) confirmed viral activation as detected by p24 in MdU1; these cells were also observed to be larger in size as compared to MdU937 (Fig. 5 B). Further, label-free low coherence quantitative phase microscopy (QPM) 15 was used to extract quantitative measurements of morphological changes between MdU1 and MdU937. QPM combines the intrinsic refractive index of individual cells and their thickness to generate high-contrast images 16 . QPM images of activated MdU937 and MdU1 showed a significantly higher optical thickness in MdU1 cells versus MdU937 (Fig. 5 C). The optical thickness is a product of cell's intrinsic refractive index and thickness. Thus, the multi-modal microscopy approach demonstrates MdU1 is both larger in footprint (via SIM) and optically denser (via QPM) than MdU937. Single-cell segmentation of more than 145 cells from the QPM dataset was used to extract quantitative parameters such as the volume, surface area, dry mass (non-water cell content, composed mostly of proteins and lipids) 17 , and the sphericity of individual cells. MdU1 cells showed overall higher volume, total area, and cell dry mass 18 , whereas MdU937 cells showed higher sphericity (Fig. 5 D). The results thus demonstrate that HIV-1 latency affects macrophage polarization, with MdU1 cells exhibiting distinct morphological and biochemical characteristics indicative of altered macrophage function. These findings suggest that HIV-1 latency alters macrophage morphology and intracellular composition, likely reflecting functional reprogramming consistent with a shift in polarization or activation state, which may influence how latently infected cells respond to immune signals or metabolic cues. The increase of cell dry mass in MdU1 suggests an alteration in the macrophage metabolism, possibly an increase in lipid storage or synthesis 19 ; and also upregulates cytokines, chemokines, and other proteins, increasing protein content, which contributes to dry mass 20 . Enhanced formation of tunneling nanotubes (TNTs) due to increased demands for energy during viral reactivation. We showed that HIV-1 latency in macrophage-like cells promotes polarization toward an anti-inflammatory M2 phenotype, accompanied by pronounced morphological changes (Fig. 5 ). Using SIM, we further observed the formation of TNTs, suggesting potential transfer of virus and mitochondria through these structures. Fixed samples of both PMA-stimulated latent (MdU1) and control (MdU1, 48 hours post-stimulation) macrophages were fluorescently labeled and imaged to detect mitochondria (GLS) and HIV-1 activation (Gag p24). After scanning 30 different 200 µm x 200 µm fields of view (FOV), TNTs were observed only in MdU1 cells. TNTs were found bridging between HIV-1 active cells and either inactive (Fig. 6 A,B) or other active cells (Fig. 6 C), with p24 directly present in the TNTs. 3D SIM, allow orthogonally (cross-sections) view (XZ/YZ) of the TNTs identified the presence of mitochondria both in short TNTs (Fig. 6 B) as well as in longer TNTs (Fig. 6 C). Similar patterns were also observed in other TNTs ( Figure S2 ). Further, we measured the thickness of the TNTs using QPM and found an average thickness of 500nm (Fig. 6 D), confirming that they are large enough to transfer both mitochondria (200-700nm) 21 and HIV-1 virions (~ 145nm) 22 . To understand the impact of the TNTs we used cytochalasin B (CytoB) that blocks TNT formation and observed the remarkable changes in the latency reversal. There was higher cell death in the CytoB treated cells (Fig. 6 E) and suppressed viral activation in the live cells (Fig. 6 F). Together, this data supports a hypothesis of TNT-mediated transfer of both HIV-1 protein and mitochondria through TNTs following reactivation and has impact on the latency reactivation. Discussion In this study, we demonstrate that HIV-1 latency reversal in a pre-monocytic cell model is characterized by disrupted AKG homeostasis that drives the U1 cells toward an M2-like anti-inflammatory macrophage phenotype upon activation. Transcriptomic and metabolic analyses revealed upregulation of pathways involved in glycolysis, the TCA cycle, and cell-cell communication, alongside cytoplasmic accumulation of amino acids. Functional assays confirmed increased glycolytic activity and altered TCA intermediates, indicating a shift toward a biosynthetically active state. Additionally, we observed enhanced formation of TNTs during latency reactivation, supporting the intercellular transfer of virus and mitochondria, and potentially contributing to viral persistence and immune evasion. Studies showed that the persistence of latently infected macrophages that survive with distinct metabolic alterations, characterized by mitochondrial fusion, lipid accumulation, and reliance on glutamine/glutamate metabolism, highlights new vulnerabilities that could be targeted to eliminate these long-lived viral reservoirs 23 . Our systems biology-driven analysis reveals that elevated AKG levels in PWH on long-term ART contribute to M2-like macrophage polarization and enhanced HIV-1 infection 24 . These findings demonstrate that the disrupted AKG homeostasis and enhanced glycolytic activity were found to drive the U1 cells toward an M2-like anti-inflammatory macrophage phenotype upon activation, which may aid in HIV-1 persistence. Amino acid metabolism plays a significant role in HIV persistence by influencing viral latency and reactivation mechanisms within the host cells. Glutamine plays a critical role in HIV-infected macrophages, where increased glutaminolysis supports energy production, redox balance, and HIV reactivation, highlighting how altered amino acid metabolism facilitates viral persistence under metabolic stress 23 . The study also highlighted the role of glutamate metabolism in HIV-1-infected macrophages, asserting that the production of AKG by glutaminolysis stabilizes ROS homeostasis, effectively mitigating oxidative stress that could otherwise hinder viral replication 25 . Therefore, the metabolic pathways involving glutamine, glutamate, AKG axis are crucial not only for energy production but also for sustaining the cellular environment that favors HIV latency and persistence. Interestingly, in our reporter metabolite analysis, we identified accumulation of most of the amino acids except glutamate, which is further supported by the significantly higher extracellular measurement of glutamate. Amino acid starvation has been implicated in reactivating latent HIV-1 provirus. Palmisano et al. (2012) demonstrated that deprivation of essential amino acids leads to the downregulation of histone deacetylase 4 (HDAC4), facilitating the reactivation of silenced transgenes, including latent HIV-1 26 . These findings suggest that manipulating amino acid availability could serve as a strategy to disrupt HIV latency. In our study tryptophan was found to suppress latency reactivation by promoting mitochondrial and antioxidant metabolism and activating antiviral pathways. This indicates that tryptophan may prime antiviral immune responses even as it inhibits HIV reactivation, highlighting the complex and context-dependent role of amino acid metabolism in regulating viral latency and immune defense. One of the key findings of our study was enhanced TNT formation in U1 cells during latency reactivation. M2-polarized macrophages have been shown to be more prone to forming intercellular communication structures such as TNTs 27 . The distinct morphological characteristics of MdU1 cells, including increased thickness and decreased sphericity, further indicate enhanced plasticity and membrane protrusion capacity, features known to facilitate TNT formation. Thus, HIV-1 latency may induce a cellular state conducive to TNT-mediated intercellular communication, potentially supporting viral persistence or reservoir maintenance through immune modulation. We therefore posit that TNTs support HIV-1 survival and persistence by allowing latent virus-infected cells to evade immune detection, maintain metabolic homeostasis through energy exchange with neighboring uninfected or non-reactivated cells, and facilitate the protected transfer of the virus itself. Despite the comprehensive nature of the study, our study has several limitations that merit comment. First, the findings are primarily based on a pre-monocytic latent cell model (U1) and may not fully capture the metabolic and immunological complexity of in vivo HIV-1 reservoirs, particularly in tissue-resident macrophages or other latent reservoirs in diverse anatomical compartments. Second, while our study links disrupted AKG homeostasis and glutamine/glutamate metabolism to HIV latency and reactivation, the specific enzymatic and regulatory checkpoints controlling this axis were not experimentally dissected. Despite these limitations, our study provides a comprehensive, systems-level understanding of HIV-1 latency reversal in myeloid cells by integrating transcriptomics, proteomics, metabolic modeling, targeted metabolite profiling, exposure studies, and advanced imaging. The identification of disrupted AKG homeostasis, glutamine/glutamate-driven metabolic reprogramming, and TNT-mediated intercellular communication highlights novel, cell-type–specific mechanisms underlying viral persistence. These insights offer valuable directions for developing targeted therapeutic strategies aimed at eliminating macrophage-associated HIV reservoirs. In conclusion, our integrative, systems-level analysis reveals that disrupted AKG homeostasis and glutamine/glutamate-driven metabolic reprogramming in HIV-1–infected pre-monocytic cells promote M2-like polarization and facilitate TNT-mediated intercellular communication, collectively supporting viral persistence. These findings highlight the metabolic vulnerabilities of macrophage-associated HIV reservoirs and underscore the therapeutic potential of targeting the glutamine–AKG axis and TNT formation. Future studies should focus on dissecting the regulatory checkpoints of amino acid metabolism that sustain HIV latency. Declarations Acknowledgments The Swedish Research Council funds the study grant 2021 − 01756 to UN. Karolinska Institute Consolidator Grants (2-117/2023) to UN. B.S.A. acknowledges the support from UiT Thematic Funding NASAR. Conflict of Interest : B.S.A. is the co-founder of the company Chip NanoImaging AS, which commercializes on-chip super-resolution microscopy systems. L.E.V.H is an employee of Chip NanoImaging AS. Other none to declare. Data Availability Statement: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org ) via the PRIDE partner repository 28 with the dataset identifier PXD065437 References Einkauf KB et al (2022) Parallel analysis of transcription, integration, and sequence of single HIV-1 proviruses. Cell 185:266–282e215. https://doi.org/10.1016/j.cell.2021.12.011 Bradley T, Ferrari G, Haynes BF, Margolis DM, Browne EP (2018) Single-Cell Analysis of Quiescent HIV Infection Reveals Host Transcriptional Profiles that Regulate Proviral Latency. Cell Rep 25:107–117e103. https://doi.org/10.1016/j.celrep.2018.09.020 Eisele E, Siliciano RF (2012) Redefining the viral reservoirs that prevent HIV-1 eradication. 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Sci Rep 9:14529. https://doi.org/10.1038/s41598-019-50971-x Perez-Riverol Y et al (2025) The PRIDE database at 20 years: 2025 update. Nucleic Acids Res 53:D543–d553. https://doi.org/10.1093/nar/gkae1011 Additional Declarations Yes there is potential Competing Interest. B.S.A. is the co-founder of the company Chip NanoImaging AS, which commercializes on-chip super-resolution microscopy systems. L.E.V.H is an employee of Chip NanoImaging AS. Other none to declare. Supplementary Files SupplementaryFigures.docx Supplementary Figures S1 - S3 SupplementaryTableS1.xlsx Supplementary Table S1 SupplementaryTableS2Rev.xlsx Supplementary Table S2 nrreportingsummary.pdf Reporting summary ThemassspectrometryproteomicsdatahavebeendepositedtotheProteomeXchangeConsortium.docx Access raw data SupplementaryFigures.pdf Supplementary Figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Samples are color-coded according to their respective groups, and ellipses represent the 90% confidence space. B) Venn diagram illustrating the number of differentially regulated genes (|log2 fold change| \u0026gt; 1.5, adjusted p \u0026lt; 0.05) identified in each comparison. The numbers indicate uniquely and commonly regulated genes across the comparisons. The number of genes uniquely regulated in MdU1 (PMA) compared to MdU937 (PMA) is highlighted in bold (n = 864). C) Heatmap showing the expression profiles of the uniquely regulated genes in MdU1 (PMA) compared to MdU937 (PMA) (n = 864) among all the samples. Columns represent samples grouped by treatment, with corresponding annotations; rows represent genes annotated by their log2 fold-change values. Selected key upregulated genes are labeled. D) Heatmap depicting reaction fluxes (mmol/gDW/h) estimated by flux balance analysis. Reactions with unique flux patterns in MdU1 (PMA) relative to other treatment groups are shown. Columns represent the averaged metabolic model for each treatment group, and rows are annotated and color-coded according to reaction type. E) Schematic representation of metabolic pathways illustrating the proposed metabolic alterations associated with latency reversal in MdU1 cells.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/a3c2c1f8b8d4212788310f44.png"},{"id":100780188,"identity":"06e814fa-a383-4350-8e67-4dc4e1383fde","added_by":"auto","created_at":"2026-01-21 11:38:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2363008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReporter metabolite and proteomic changes during tryptophan-modulated latency reversal.\u003c/strong\u003e A) Venn diagrams showing the number of reporter metabolites predicted to be downregulated (left) and upregulated (right) in each comparison (adjusted p \u0026lt; 0.1, Piano). The volcano plot displays the reporter metabolite analysis results for U1 PMA vs. U1 DMSO. Reporter metabolites represent metabolites proximal to major transcriptional changes. Significantly regulated reporter metabolites (adjusted p \u0026lt; 0.1, Piano) are color-coded in green (downregulated) and red (upregulated). The x-axis denotes gene set statistics computed by Piano, and the y-axis represents the negative log\u003csub\u003e10\u003c/sub\u003e-transformed adjusted p-values for the up and down directionality classes. Key regulated metabolites are labeled. D) Volcano plot illustrating the differential abundance analysis of proteomic data comparing tryptophan-treated U1 PMA cells to U1 PMA cells without tryptophan treatment. Significantly altered proteins (adjusted p\u0026lt;0.05, Limma) are highlighted in green (downregulated) and red (upregulated). Selected key proteins are labeled. E) Heatmap showing the abundance patterns of significantly regulated proteins in tryptophan-treated U1 PMA cells compared to U1 PMA controls (adjusted p \u0026lt; 0.05, Limma). Columns represent samples grouped by treatment with corresponding annotations; rows represent proteins annotated by their log10 fold-change values. Functional enrichment of the upregulated (n=803) and downregulated (n=767) proteins is shown as a bubble plot. Bubble size reflects the overlap of significantly enriched proteins within each pathway, while the color gradient represents the negative log\u003csub\u003e10\u003c/sub\u003e-transformed adjusted p-values.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/682869a28a408d309745aaf1.png"},{"id":100780633,"identity":"fd3f1879-a69c-4ae8-86e4-7261c6bae044","added_by":"auto","created_at":"2026-01-21 11:39:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1813036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic profiling of MdU1 and MdU937 cells and validation in clinical cohorts.\u003c/strong\u003e MdU1 and MdU937 metabolic characterization. A) Intracellular and B) extracellular quantification of the indicated metabolites and concentrations in MdU1 and MdU937. C) Bar plot of metabolic enzymes relative quantification in MdU1 and MdU937. Statistical analysis was performed using an unpaired t-test (significance level, p\u0026lt;0.05) and presented with a median with SD. D) Boxplots showing alpha-ketoglutarate and glutamate levels measured by untargeted metabolomics across four cohorts: people living with HIV (PWH) not on ART (n = 27) and on ART (n = 275), elite controllers (n = 14), and people without HIV (PWoH) (n = 100). Data represent batch-corrected and normalized metabolite intensities (n = 539).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/1b365a1691dd12435522a36f.png"},{"id":100780124,"identity":"cf3feb45-0f98-412e-b2e1-b1c8642469cf","added_by":"auto","created_at":"2026-01-21 11:38:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1547497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell type–specific disruption of AKG homeostasis in HIV-1 latency. A. \u003c/strong\u003eMdU1 (pre-monocytic) and J-Lat 10.6 (T-lymphocytic) latent models were analyzed after 48 h of PMA treatment (\u0026gt;80% activation) \u003cstrong\u003eB.\u003c/strong\u003e \u003cem\u003egls\u003c/em\u003e was upregulated in MdU1 but not in J-Lat 10.6 \u003cstrong\u003eC-E.\u003c/strong\u003e \u003cem\u003eGLUD1\u003c/em\u003e, \u003cem\u003eIDH3B\u003c/em\u003e, and \u003cem\u003eSLC25A11\u003c/em\u003e were increased in MdU1 and decreased in J-Lat 10.6. \u003cstrong\u003eF-G. \u003c/strong\u003eWestern blot confirmed p24 expression and elevated GLS protein in MdU1.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/793b90c42edada69963ee4e4.png"},{"id":100780626,"identity":"8aec3a61-ef79-4f3d-a49b-2a2e48015f83","added_by":"auto","created_at":"2026-01-21 11:39:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2015723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression of M1 and M2 phenotype marker genes in MdU1 (PMA) compared to MdU937.\u003c/strong\u003e A) Heatmap showing the expression profiles of the significantly regulated (adjusted p \u0026lt; 0.05, DESeq2) M1 and M1 phenotype marker genes in MdU1 (PMA) compared to MdU937 (PMA) among all the samples. Columns represent samples grouped by treatment, with corresponding annotations; rows represent the marker genes annotated by their log2 fold-change values and the phenotype. B) 3D super-resolution SIM confirming viral activation by p24 expression in MdU1 cells, which appeared larger in size compared to MdU937. C) Label-free quantitative phase microscopy (QPM) images showing significantly higher optical thickness in MdU1 cells, reflecting greater refractive index and density. D) Quantitative single-cell analysis (n\u0026gt;145) from QPM data illustrating increased volume, surface area, and dry mass in MdU1 cells, while MdU937 exhibited higher sphericity and variability.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/ec80b1592c9d2d92707704ca.png"},{"id":100780578,"identity":"726a2041-fe1c-43ad-bfef-af4bb95e61c3","added_by":"auto","created_at":"2026-01-21 11:39:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3381680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTNTs mediate intercellular transfer of mitochondria and HIV-1 proteins during latency reversal.\u003c/strong\u003e A-C) Representative 3D-SIM images showing TNTs observed exclusively in MdU1 cells after scanning 30 different 200 µm × 200 µm fields of view (FOV). TNTs were found bridging HIV-1 active cells with either inactive (A, B) or other active (C) cells, with p24 signal detected within the TNTs. Orthogonal (XZ/YZ) 3D-SIM views demonstrating the presence of mitochondria within both short (B) and long (C) TNTs. Similar observations were made across additional TNTs. D) QPM measurements showing an average TNT thickness of ~500 nm, sufficient to allow transfer of mitochondria (200–700 nm) and HIV-1 virions (~145 nm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE–F. \u003c/strong\u003eInhibition of TNT formation using cytochalasin B (CytoB) resulted in increased cell death (E) and reduced viral activation in surviving cells (F).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/4cc0b3571dddcf4f8c69c6fd.png"},{"id":102747350,"identity":"58a45078-ef27-4a89-b83b-05d3d67101f4","added_by":"auto","created_at":"2026-02-16 09:04:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15035935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/d59b9543-2954-4f72-ae64-b276f5919d70.pdf"},{"id":100779933,"identity":"82bcfc30-a4fa-4214-9dee-09463ce34020","added_by":"auto","created_at":"2026-01-21 11:37:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1871056,"visible":true,"origin":"","legend":"Supplementary Figures S1 - S3","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/23aa822731d51c858066aecb.docx"},{"id":100780419,"identity":"2a1bafde-cebb-4635-a00a-fb893e3d302d","added_by":"auto","created_at":"2026-01-21 11:38:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8526443,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S1\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/eb51d1d13c829b3145a1872a.xlsx"},{"id":100780567,"identity":"12f24cc7-d680-44e9-a68b-ca34d51fb7d5","added_by":"auto","created_at":"2026-01-21 11:39:16","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":247104,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S2\u003c/p\u003e","description":"","filename":"SupplementaryTableS2Rev.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/2c804bdb620ce3cd6c3fcee8.xlsx"},{"id":100780190,"identity":"a4e46bc4-1cf7-4d55-8a0f-03b2f4b219e2","added_by":"auto","created_at":"2026-01-21 11:38:07","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665993,"visible":true,"origin":"","legend":"Reporting summary","description":"","filename":"nrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/66a2a75a53362f75f4192d66.pdf"},{"id":100780420,"identity":"44caf9b2-6bd3-4738-a4f3-75a8e85d91f9","added_by":"auto","created_at":"2026-01-21 11:38:45","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":14125,"visible":true,"origin":"","legend":"Access raw data","description":"","filename":"ThemassspectrometryproteomicsdatahavebeendepositedtotheProteomeXchangeConsortium.docx","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/266e3774035d6d1afef20b28.docx"},{"id":100780581,"identity":"c915e549-322a-43a0-be6b-8e6715675ce8","added_by":"auto","created_at":"2026-01-21 11:39:19","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":854507,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8529866/v1/1024391b6ed62bf454973e2c.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nB.S.A. is the co-founder of the company Chip NanoImaging AS, which commercializes on-chip super-resolution microscopy systems. L.E.V.H is an employee of Chip NanoImaging AS. Other none to declare.","formattedTitle":"Metabolic Reprogramming and Tunneling Nanotubes Cooperate to Regulate HIV-1 Latency Reactivation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHIV-1 latency remains a major obstacle to viral eradication, sustained by complex host-cell interactions and metabolic adaptations that allow the virus to persist in a transcriptionally silent yet replication-competent state \u003csup\u003e1-3\u003c/sup\u003e.\u0026nbsp;While the role of CD4+ T cells as the primary reservoir for HIV is well-established, recent studies highlight the importance of myeloid lineage cells. Particularly, macrophages play an important role as long-lived reservoirs where the virus can persist in a latent state despite effective antiretroviral therapy (ART)\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e. Several studies have detected HIV DNA, including integrated proviral DNA, in circulating monocytes\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e and tissue-resident macrophages in various anatomical sites like the brain, gut, liver, and spleen, even in individuals on long-term ART [reviewed in\u0026nbsp;\u003csup\u003e6\u003c/sup\u003e]. A recent study also identified intact HIV genomes in monocytes from 40% of participants on cART, and higher levels of total HIV DNA were associated with reactivatable latent reservoirs in monocyte-derived macrophages (MDMs), which produced replication-competent virus capable of infecting bystander cells\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUnderstanding the metabolic and functional reprogramming of latently infected macrophages is therefore critical for developing effective latency-reversing or -sustaining strategies. \u003csup\u003e4\u003c/sup\u003e. This task is complicated by the intrinsic characteristics of macrophages, including their long lifespan, resistance to apoptosis, and plasticity in response to environmental cues \u003csup\u003e4,8,9\u003c/sup\u003e. In our recent clinical study, by applying systems biology and \u003cem\u003eex vivo\u003c/em\u003e assays, we identified the role of myeloid cells in system-level immunometabolic dysregulation\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e. We discovered impaired macrophage function due to metabolic training in well-treated people living with HIV (PWH)\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. Despite growing evidence that myeloid cells serve as an important HIV reservoir, their role in immunometabolic reprogramming remains unclear. This highlights a critical gap in knowledge and the need for studies to elucidate mechanisms of their persistence and strategies for their elimination.\u003c/p\u003e\n\u003cp\u003eIn this study, we investigated metabolic remodeling associated with HIV-1 latency and reactivation in the pre-monocytic U1 cell model. We have used advanced multi-modality super-resolution optical microscopy and label-free quantitative optical microscopy (QPM).\u0026nbsp;Our analyses revealed a pronounced disruption of α-ketoglutarate (AKG) homeostasis, which was linked to enhanced mitochondrial biogenesis and increased flux through glycolysis and the TCA cycle upon HIV-1 activation. Gene expression profiling further supported this metabolic shift, showing upregulation of pathways involved in cell-cell communication, inflammatory signaling, and energy metabolism. Quantitative metabolic assays confirmed increased glycolytic activity and altered levels of TCA intermediates, indicating a strategic metabolic reprogramming that accompanies latency reversal.\u003c/p\u003e\n\u003cp\u003eStrikingly, the altered metabolic state was associated with a phenotypic transition of U1 cells toward an M2-like anti-inflammatory macrophage profile, characterized by disrupted AKG regulation and immunosuppressive features that may contribute to HIV-1 persistence. Additionally, we observed enhanced tunneling nanotube (TNT) formation in U1 cells during latency reversal, suggesting an intercellular communication mechanism that may facilitate the transfer of viral particles and mitochondria, further supporting viral survival and complicating efforts to eliminate latent reservoirs. Together, these findings underscore the importance of metabolic and morphological plasticity in HIV-1 latency and highlight AKG as a potential metabolic checkpoint in macrophage-associated viral persistence.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic modeling predicted disrupted AKG homeostasis due to mitochondrial biogenesis in the latent cell model\u003c/h2\u003e \u003cp\u003eWe activated the pre-monocytic latent cell model U1 and its parental cell line U937 with 10nM PMA for 48 hours. Following treatment, flow cytometry analysis revealed a median (IQR) viral activation of 78.25% (74.85\u0026ndash;81.65%) in U1 cells, as measured by intracellular p24 expression. The PMA-stimulated U1 (hereafter referred to as MdU1) and U937 (MdU937 herein) cells exhibited macrophage-like phenotypes and were subsequently subjected to RNA sequencing using the NovaSeq X Plus platform.. Principal component analysis identified a clear separation of MdU937 from MdU1 treated with either PMA or the vehicle control DMSO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Differential gene expression analysis identified 864 unique genes in PMA-treated MdU1 compared to MdU937 (Log fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5, adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after removing the genes that were significantly regulated in other comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Supplementary \u003cb\u003eTable S1\u003c/b\u003e). The expression profile of the 864 genes is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. The key genes that were upregulated were part of cell-cell communications (e.g., \u003cem\u003eCD151, CLDN9, SRC, TESK1, SIRPA, NECTIN2, and NECTIN1\u003c/em\u003e), genes involved in virus-related inflammatory processes, e.g., NF-kB signaling (e.g., \u003cem\u003eTLR4, ICAM1\u003c/em\u003e), TNF-alpha signaling via NF-kB (e.g., \u003cem\u003eEFNA1, BCL3, JUNB\u003c/em\u003e, and \u003cem\u003eICAM1\u003c/em\u003e), and specific to HIV-1 infection (e.g., \u003cem\u003eTLR4, ICAM1\u003c/em\u003e, and \u003cem\u003eAPOBEC3A\u003c/em\u003e). Several genes in the PI3K/Akt/mTOR signaling pathway (e.g., \u003cem\u003eEFNA1, NTRK1, RXRA, DDIT4, KDR, PPP2R2A, LAMC1, TLR4, ITGA9, AKT1S1, LAMTOR2\u003c/em\u003e, and \u003cem\u003eLAMTOR3\u003c/em\u003e) were also upregulated including genes of the mitochondrial biogenesis (e.g., \u003cem\u003eRXRA, ATP5MC3\u003c/em\u003e, and \u003cem\u003eCALM1\u003c/em\u003e), suggesting an increase in energy production capabilities may be required to enhance the cell's ability to produce ATP efficiently during viral activation. Interestingly, several genes for metabolic processes were also upregulated, e.g., glutamine/glutamate metabolism (e.g., \u003cem\u003eGLS\u003c/em\u003e) and oxidative phosphorylation (e.g., \u003cem\u003eATP6V0E1, ATP5MC3\u003c/em\u003e, and \u003cem\u003eTCIRG1\u003c/em\u003e) which are also linked with energy metabolism. Therefore, upregulation of genes in these pathways and mitochondrial biogenesis can be a response to HIV-1 activation, requiring enhanced energy production. As the differential gene expression analysis does not capture the complexities of the metabolic process, we used context-specific genome-scale metabolic modeling (GEM) and flux balance analysis (FBA), with an aim to capture a comprehensive, systems-level representation of all metabolic functions within a cell in each context. We developed four different contextualized models, MdU1 and MdU937 PMA-treated and vehicle control (DMSO). Further, we performed FBA to computationally predict the rate of turnover for each reaction in the models. We then calculated unique metabolic reactions by identifying the reactions that are either absent or present or in opposite flux prediction in the PMA-treated MdU1 compared to the other three conditions. Among unique reactions, 60% of the reactions belong to transport reactions, indicating an increased flux of metabolites during latency reactivation (Supplementary \u003cb\u003eFig S1\u003c/b\u003e), including more predicted mitochondrial ATP production (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The prediction also identified disrupted endogenous AKG homeostasis in the activated MdU1 through aspartate, glutamate, and valine metabolism both in the mitochondria, e.g., AKG[m]\u0026thinsp;+\u0026thinsp;valine[m]\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026thinsp;\u0026minus;\u0026thinsp;methyl\u0026thinsp;\u0026minus;\u0026thinsp;2\u0026minus;oxobutyrate[m]\u0026thinsp;+\u0026thinsp;glutamate[m] and cytoplasm e.g., AKG[c]\u0026thinsp;+\u0026thinsp;aspartate[c]\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;glutamate[c]\u0026thinsp;+\u0026thinsp;OAA[c] which was either absent or having negative flux in other three conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Several reactions of the TCA cycle were also altered. The proposed metabolic alteration during latency reversal in MdU1 is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE. Combining all, these results suggest that AKG imbalance is a key metabolic feature associated with HIV-1 latency reversal, potentially reflecting increased demands for biosynthetic precursors and energy during viral reactivation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTryptophan acts as a negative regulator of HIV latency reversal through coordinated host metabolic and immune modulation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo identify metabolites associated with coordinated transcriptional changes indicating potential regulatory or functional hotspots within the metabolic network, we performed reporter metabolite analysis \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Reporter metabolites are those around which the most significant transcriptional changes occur. The significance of the reporter metabolites was evaluated using the distinct-directional class of gene set statistics, which incorporates the direction of gene expression changes. The analysis was performed for all pairwise comparisons between the study groups. It predicted 17 unique positive and 18 negative enrichments of reporter metabolites (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1) during the latency reversal in MdU1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplementary \u003cb\u003eTable S2\u003c/b\u003e). It reveals that viral activation causes significant, unique regulation within the metabolic network. The volcano plot shows the results of the reporter metabolite analysis for U1_PMA compared to U1_DMSO, showing a higher number of changes in the metabolic network relative to the other groups. Predicted positive enrichment in the intracellular metabolites includes various amino acids, except glutamate. We therefore hypothesized that the accumulation of specific amino acids during latency reversal acts as a metabolic signal or provides substrates essential for HIV reactivation. We therefore treated the U1 cells with specific amino acids for 2 hours, followed by activation of the U1 cells with PMA, and identified that tryptophan (Trp) significantly suppresses the viral reactivation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Further, we primed the cells with Trp for 2 hours, followed by 48 hours of growth in the basal media and subsequently 48 hours of PMA treatment. The data showed a similar trend with significant suppression of the Gag expression in Trp-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These results highlight a potential inhibitory role of Trp in the context of latency reversal and support further investigation into Trp metabolism as a modulatory axis in HIV persistence. To identify the mechanism, we further performed quantitative proteomics. Trp treatment significantly suppresses HIV gene expression (Gag-pol, env, and rev) as observed in the flow cytometry data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). It downregulates key host immune and transcriptional regulators such as interferon-stimulated genes (\u003cem\u003eIRF7, STAT1, STAT3\u003c/em\u003e) and nuclear transport factors (\u003cem\u003eKPN2A, NUP58\u003c/em\u003e), which may be critical for viral replication. The upregulation of \u003cem\u003eTSG101, TRIM24\u003c/em\u003e, AND \u003cem\u003eNQO1\u003c/em\u003e in Trp-treated cells suggests a coordinated host response involving altered viral budding machinery, epigenetic regulation, and enhanced antioxidant defense, potentially contributing to suppression of HIV reactivation. Further, the pathway enrichment analysis identified upregulation of the metabolic process towards mitochondrial and antioxidant metabolism, and antiviral mechanisms due to the intracellular viral particle, suggesting that Trp may prime antiviral immune pathways even as it suppresses viral reactivation. Moreover, the downregulation of proteins related to antigen processing and presentation, cell adhesion molecules, and Natural killer (NK) cell-mediated cytotoxicity indicates a suppression of immune surveillance, intercellular communication, and cytotoxic response mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAKG-mediated strategic metabolic reprogramming to meet biosynthetic and energy demands during latency reversal\u003c/h2\u003e \u003cp\u003eThe upregulation of proteins in oxidative phosphorylation and glutathione metabolism in Trp-treated cells during latency reversal suggests that the cells are under oxidative pressure, likely due to enhanced mitochondrial activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Further, upregulation of OXPHOS increases the flux through AKG-consuming reactions in the TCA cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), potentially depleting intracellular AKG pools and disrupting its homeostasis. To understand how the disrupted endogenous AKG homeostasis impacts metabolite abundances during latency reversal to regulate cell fate, we measured the intra- and extracellular metabolites of the glycolysis (glucose, pyruvate, and lactate), TCA cycle (citrate, AKG, succinate, and fumarate) and glutamine/glutamate metabolism (glutamine and glutamate). There was a trend in increased glucose (p\u0026thinsp;=\u0026thinsp;0.201) uptake in MdU1 cells compared to the MdU937 cells, with a significantly increased accumulation of pyruvate (p\u0026thinsp;=\u0026thinsp;0.0002) and lactate (p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), indicating enhanced glycolytic activity in MdU1 cells during latency reversal. However, extracellular glucose and lactate were not significantly changed, indicating an accumulation of lactate in the cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Moreover, citrate (p\u0026thinsp;=\u0026thinsp;0.0043) and AKG (p\u0026thinsp;=\u0026thinsp;0.0004) were also significantly higher in MdU1, suggesting alterations in the TCA cycle, potentially due to increased influx of metabolic intermediates or reduced utilization of these metabolites for energy production. Interestingly, there was no increase in succinate, but fumarate is significantly high (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a metabolic reprogramming that supports the biosynthetic and energy needs during latency reversal. We also observed a significantly increased transcript level of glucose-6-phosphate dehydrogenase (G6PD) (p\u0026thinsp;=\u0026thinsp;0.004), which could lead to increased production of NADPH generated through the oxidative pentose phosphate pathway (PPP), potentially due to the increased citrate and AKG levels in MdU1 cells for active lipid and amino acid biosynthesis. Further, the transcripts of isocitrate dehydrogenase 2 (IDH2) and glutamine synthetase (GS) were also significantly high (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). As this resembles our early findings on plasma metabolomics \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, we have performed a meta-analysis of the previously reported untargeted metabolomics from four different cohorts i.e. people living with HIV (PWH) without ART (n\u0026thinsp;=\u0026thinsp;27) and with ART (n\u0026thinsp;=\u0026thinsp;275), elite controllers (n\u0026thinsp;=\u0026thinsp;14) and people without HIV (PWoH) (n\u0026thinsp;=\u0026thinsp;100) from India, Cameroon, Sweden and Denmark. Following the batch correction and normalization we used 539 metabolites to identify the signature of the PWH on ART. While plasma AKG level was higher in all three PWH groups, EC has shown a significantly higher level of glutamate in EC compared to the other two PWH groups, indicating that it could potentially be linked with the viral persistence, as all the EC are seropositive but plasma viral load\u0026thinsp;\u0026lt;\u0026thinsp;50 copies/mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Combining all the disrupted endogenous AKG homeostasis during HIV-1 latency reversal is associated with altered glycolytic and TCA cycle metabolite abundances and amino acid metabolism, indicating a metabolic reprogramming that supports biosynthetic demands and may be linked to viral persistence, as further supported by elevated AKG and glutamate levels in plasma metabolomics from PWH.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDisrupted AKG homeostasis is characteristic of pre-monocytic latent cells\u003c/h3\u003e\n\u003cp\u003eTo understand whether the disrupted AKG-homeostasis is a characteristic of all the HIV-1 latent cell models, we used J-Lat 10.6, which is a T-lymphocytic latent cell model, and compared it with MdU1. We measured the mitochondrial \u003cem\u003egls, glud1\u003c/em\u003e, and \u003cem\u003eidh3b\u003c/em\u003e, and AKG transporter \u003cem\u003eSLC25A11\u003c/em\u003e following 48 hours of PMA treatment, when more than 80% of the cells were activated in both the cell models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). There was a significant increase in \u003cem\u003egls\u003c/em\u003e in MdU1 cells compared to MdU937 but not in T-cell HIV-1 latent cell model J-Lat 10.6 compared to the parental Jurkat cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Interestingly, \u003cem\u003eglud1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), \u003cem\u003eidh3B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), and AKG transporter SLC25A11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) were significantly upregulated in MdU1 compared to MdU937 but significantly downregulated in J-Lat 10.6 compared to Jurkat. We also measured GLS in the MdU937 and MdU1. While western blot analysis confirmed the presence of p24 in MdU1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), it also showed a significant increase in GLS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF and \u003cb\u003eG\u003c/b\u003e). The significant upregulation of \u003cem\u003egls\u003c/em\u003e, \u003cem\u003eglud1\u003c/em\u003e, \u003cem\u003eidh3b\u003c/em\u003e, and \u003cem\u003eSLC25A11\u003c/em\u003e in MdU1 cells supported by increased protein expression, contrasted with their downregulation in J-Lat 10.6, indicates that disrupted AKG homeostasis is a distinct feature of pre-monocytic latent cell models rather than T-lymphocytic models, highlighting cell type-specific metabolic reprogramming during latency reversal.\u003c/p\u003e\n\u003ch3\u003eDisrupted AKG-homeostasis reprograms MdU1 cells toward the M2-like phenotype upon activation\u003c/h3\u003e\n\u003cp\u003eBoth AKG and lactate accumulation inhibit the pro-inflammatory M1 macrophage polarization while promoting the polarization of M2 macrophages towards an anti-inflammatory \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Following the PMA stimulation U1 and U937 cells can also acquire a macrophage-like phenotype (M0), we aimed to characterize the impact of HIV-1 latency in macrophage polarization. The majority of the M2-markers were significantly upregulated in PMA-stimulated MdU1 compared to MdU937, while some of the M1-markers were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). 3D super-resolution structured illumination microscopy (SIM) confirmed viral activation as detected by p24 in MdU1; these cells were also observed to be larger in size as compared to MdU937 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Further, label-free low coherence quantitative phase microscopy (QPM)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was used to extract quantitative measurements of morphological changes between MdU1 and MdU937. QPM combines the intrinsic refractive index of individual cells and their thickness to generate high-contrast images \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. QPM images of activated MdU937 and MdU1 showed a significantly higher optical thickness in MdU1 cells versus MdU937 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The optical thickness is a product of cell's intrinsic refractive index and thickness. Thus, the multi-modal microscopy approach demonstrates MdU1 is both larger in footprint (via SIM) and optically denser (via QPM) than MdU937. Single-cell segmentation of more than 145 cells from the QPM dataset was used to extract quantitative parameters such as the volume, surface area, dry mass (non-water cell content, composed mostly of proteins and lipids) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and the sphericity of individual cells. MdU1 cells showed overall higher volume, total area, and cell dry mass \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, whereas MdU937 cells showed higher sphericity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The results thus demonstrate that HIV-1 latency affects macrophage polarization, with MdU1 cells exhibiting distinct morphological and biochemical characteristics indicative of altered macrophage function. These findings suggest that HIV-1 latency alters macrophage morphology and intracellular composition, likely reflecting functional reprogramming consistent with a shift in polarization or activation state, which may influence how latently infected cells respond to immune signals or metabolic cues. The increase of cell dry mass in MdU1 suggests an alteration in the macrophage metabolism, possibly an increase in lipid storage or synthesis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; and also upregulates cytokines, chemokines, and other proteins, increasing protein content, which contributes to dry mass\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnhanced formation of tunneling nanotubes (TNTs) due to increased demands for energy during viral reactivation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe showed that HIV-1 latency in macrophage-like cells promotes polarization toward an anti-inflammatory M2 phenotype, accompanied by pronounced morphological changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Using SIM, we further observed the formation of TNTs, suggesting potential transfer of virus and mitochondria through these structures. Fixed samples of both PMA-stimulated latent (MdU1) and control (MdU1, 48 hours post-stimulation) macrophages were fluorescently labeled and imaged to detect mitochondria (GLS) and HIV-1 activation (Gag p24). After scanning 30 different 200 \u0026micro;m x 200 \u0026micro;m fields of view (FOV), TNTs were observed only in MdU1 cells. TNTs were found bridging between HIV-1 active cells and either inactive (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B) or other active cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), with p24 directly present in the TNTs. 3D SIM, allow orthogonally (cross-sections) view (XZ/YZ) of the TNTs identified the presence of mitochondria both in short TNTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) as well as in longer TNTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Similar patterns were also observed in other TNTs (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Further, we measured the thickness of the TNTs using QPM and found an average thickness of 500nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), confirming that they are large enough to transfer both mitochondria (200-700nm) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and HIV-1 virions (~\u0026thinsp;145nm) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To understand the impact of the TNTs we used cytochalasin B (CytoB) that blocks TNT formation and observed the remarkable changes in the latency reversal. There was higher cell death in the CytoB treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) and suppressed viral activation in the live cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Together, this data supports a hypothesis of TNT-mediated transfer of both HIV-1 protein and mitochondria through TNTs following reactivation and has impact on the latency reactivation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrate that HIV-1 latency reversal in a pre-monocytic cell model is characterized by disrupted AKG homeostasis that drives the U1 cells toward an M2-like anti-inflammatory macrophage phenotype upon activation. Transcriptomic and metabolic analyses revealed upregulation of pathways involved in glycolysis, the TCA cycle, and cell-cell communication, alongside cytoplasmic accumulation of amino acids. Functional assays confirmed increased glycolytic activity and altered TCA intermediates, indicating a shift toward a biosynthetically active state. Additionally, we observed enhanced formation of TNTs during latency reactivation, supporting the intercellular transfer of virus and mitochondria, and potentially contributing to viral persistence and immune evasion.\u003c/p\u003e \u003cp\u003eStudies showed that the persistence of latently infected macrophages that survive with distinct metabolic alterations, characterized by mitochondrial fusion, lipid accumulation, and reliance on glutamine/glutamate metabolism, highlights new vulnerabilities that could be targeted to eliminate these long-lived viral reservoirs \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our systems biology-driven analysis reveals that elevated AKG levels in PWH on long-term ART contribute to M2-like macrophage polarization and enhanced HIV-1 infection \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These findings demonstrate that the disrupted AKG homeostasis and enhanced glycolytic activity were found to drive the U1 cells toward an M2-like anti-inflammatory macrophage phenotype upon activation, which may aid in HIV-1 persistence.\u003c/p\u003e \u003cp\u003eAmino acid metabolism plays a significant role in HIV persistence by influencing viral latency and reactivation mechanisms within the host cells. Glutamine plays a critical role in HIV-infected macrophages, where increased glutaminolysis supports energy production, redox balance, and HIV reactivation, highlighting how altered amino acid metabolism facilitates viral persistence under metabolic stress \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The study also highlighted the role of glutamate metabolism in HIV-1-infected macrophages, asserting that the production of AKG by glutaminolysis stabilizes ROS homeostasis, effectively mitigating oxidative stress that could otherwise hinder viral replication \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, the metabolic pathways involving glutamine, glutamate, AKG axis are crucial not only for energy production but also for sustaining the cellular environment that favors HIV latency and persistence. Interestingly, in our reporter metabolite analysis, we identified accumulation of most of the amino acids except glutamate, which is further supported by the significantly higher extracellular measurement of glutamate.\u003c/p\u003e \u003cp\u003eAmino acid starvation has been implicated in reactivating latent HIV-1 provirus. Palmisano et al. (2012) demonstrated that deprivation of essential amino acids leads to the downregulation of histone deacetylase 4 (HDAC4), facilitating the reactivation of silenced transgenes, including latent HIV-1 \u003csup\u003e26\u003c/sup\u003e. These findings suggest that manipulating amino acid availability could serve as a strategy to disrupt HIV latency. In our study tryptophan was found to suppress latency reactivation by promoting mitochondrial and antioxidant metabolism and activating antiviral pathways. This indicates that tryptophan may prime antiviral immune responses even as it inhibits HIV reactivation, highlighting the complex and context-dependent role of amino acid metabolism in regulating viral latency and immune defense.\u003c/p\u003e \u003cp\u003eOne of the key findings of our study was enhanced TNT formation in U1 cells during latency reactivation. M2-polarized macrophages have been shown to be more prone to forming intercellular communication structures such as TNTs \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The distinct morphological characteristics of MdU1 cells, including increased thickness and decreased sphericity, further indicate enhanced plasticity and membrane protrusion capacity, features known to facilitate TNT formation. Thus, HIV-1 latency may induce a cellular state conducive to TNT-mediated intercellular communication, potentially supporting viral persistence or reservoir maintenance through immune modulation. We therefore posit that TNTs support HIV-1 survival and persistence by allowing latent virus-infected cells to evade immune detection, maintain metabolic homeostasis through energy exchange with neighboring uninfected or non-reactivated cells, and facilitate the protected transfer of the virus itself.\u003c/p\u003e \u003cp\u003eDespite the comprehensive nature of the study, our study has several limitations that merit comment. First, the findings are primarily based on a pre-monocytic latent cell model (U1) and may not fully capture the metabolic and immunological complexity of \u003cem\u003ein vivo\u003c/em\u003e HIV-1 reservoirs, particularly in tissue-resident macrophages or other latent reservoirs in diverse anatomical compartments. Second, while our study links disrupted AKG homeostasis and glutamine/glutamate metabolism to HIV latency and reactivation, the specific enzymatic and regulatory checkpoints controlling this axis were not experimentally dissected. Despite these limitations, our study provides a comprehensive, systems-level understanding of HIV-1 latency reversal in myeloid cells by integrating transcriptomics, proteomics, metabolic modeling, targeted metabolite profiling, exposure studies, and advanced imaging. The identification of disrupted AKG homeostasis, glutamine/glutamate-driven metabolic reprogramming, and TNT-mediated intercellular communication highlights novel, cell-type\u0026ndash;specific mechanisms underlying viral persistence. These insights offer valuable directions for developing targeted therapeutic strategies aimed at eliminating macrophage-associated HIV reservoirs.\u003c/p\u003e \u003cp\u003eIn conclusion, our integrative, systems-level analysis reveals that disrupted AKG homeostasis and glutamine/glutamate-driven metabolic reprogramming in HIV-1\u0026ndash;infected pre-monocytic cells promote M2-like polarization and facilitate TNT-mediated intercellular communication, collectively supporting viral persistence. These findings highlight the metabolic vulnerabilities of macrophage-associated HIV reservoirs and underscore the therapeutic potential of targeting the glutamine\u0026ndash;AKG axis and TNT formation. Future studies should focus on dissecting the regulatory checkpoints of amino acid metabolism that sustain HIV latency.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe Swedish Research Council funds the study grant 2021\u0026thinsp;\u0026minus;\u0026thinsp;01756 to UN. Karolinska Institute Consolidator Grants (2-117/2023) to UN. B.S.A. acknowledges the support from UiT Thematic Funding NASAR.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflict of Interest\u003c/b\u003e: B.S.A. is the co-founder of the company Chip NanoImaging AS, which commercializes on-chip super-resolution microscopy systems. L.E.V.H is an employee of Chip NanoImaging AS. Other none to declare.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://proteomecentral.proteomexchange.org\u003c/span\u003e\u003cspan address=\"http://proteomecentral.proteomexchange.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the PRIDE partner repository \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e with the dataset identifier PXD065437\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEinkauf KB et al (2022) Parallel analysis of transcription, integration, and sequence of single HIV-1 proviruses. 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Nucleic Acids Res 53:D543\u0026ndash;d553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae1011\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae1011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8529866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8529866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHIV-1 latency in myeloid cells remains a major obstacle to viral eradication. Here, we applied a systems biology approach combining transcriptomics, proteomics, metabolic modeling, targeted metabolite profiling, and advanced imaging to investigate metabolic alterations in a pre-monocytic latent cell model (U1). Upon latency reversal, we identified disrupted α-ketoglutarate (AKG) homeostasis driven by mitochondrial biogenesis and glutamine/glutamate metabolism, supporting energy production and M2-like macrophage polarization. Reporter metabolite analysis predicted cytoplasmic amino acid accumulation, and functional assays showed that tryptophan suppressed HIV reactivation by promoting mitochondrial and antioxidant metabolism. Additionally, we observed enhanced formation of tunneling nanotubes (TNTs), which facilitated intercellular transfer of mitochondria and viral components, potentially aiding viral persistence. Our study reveals cell-type–specific metabolic reprogramming and intercellular communication mechanisms underlying HIV-1 persistence. Our findings highlight the glutamine-AKG axis and TNTs as promising targets for strategies aimed at eliminating long-lived macrophage-associated HIV reservoirs.\u003c/p\u003e","manuscriptTitle":"Metabolic Reprogramming and Tunneling Nanotubes Cooperate to Regulate HIV-1 Latency Reactivation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 10:11:08","doi":"10.21203/rs.3.rs-8529866/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ab5af9b-97a6-4814-8234-b25e60867b0b","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60745832,"name":"Biological sciences/Microbiology/Virology/Systems virology"},{"id":60745833,"name":"Biological sciences/Systems biology/Systems analysis"}],"tags":[],"updatedAt":"2026-02-13T12:05:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 10:11:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8529866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8529866","identity":"rs-8529866","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}