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Multi-omics analyses reveal that HIV-1 alters CD4+ T cell immunometabolism to fuel virus replication

Abstract

Individuals infected with human immunodeficiency virus type-1 (HIV-1) show metabolic alterations of CD4+ T cells through unclear mechanisms with undefined consequences. We analyzed the transcriptome of CD4+ T cells from patients with HIV-1 and revealed that the elevated oxidative phosphorylation (OXPHOS) pathway is associated with poor outcomes. Inhibition of OXPHOS by the US Food and Drug Administration–approved drug metformin, which targets mitochondrial respiratory chain complex-I, suppresses HIV-1 replication in human CD4+ T cells and humanized mice. In patients, HIV-1 peak viremia positively correlates with the expression of NLRX1, a mitochondrial innate immune receptor. Quantitative proteomics and metabolic analyses reveal that NLRX1 enhances OXPHOS and glycolysis during HIV-1-infection of CD4+ T cells to promote viral replication. At the mechanistic level, HIV infection induces the association of NLRX1 with the mitochondrial protein FASTKD5 to promote expression of mitochondrial respiratory complex components. This study uncovers the OXPHOS pathway in CD4+ T cells as a target for HIV-1 therapy.

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Fig. 1: Transcriptome analysis of CD4+ T cells from patients with HIV-1.
Fig. 2: Inhibition of OXPHOS reduces HIV-1 replication in human primary CD4+ T cells and NRG-hu CD4+ mice.
Fig. 3: NLRX1 expression positively correlates with HIV-1 viremia and the OXPHOS pathway.
Fig. 4: NLRX1-dependent upregulation of OXPHOS in HIV-1-infected T cells.
Fig. 5: NLRX1 is required for HIV-1 replication in human CD4+ T cells.
Fig. 6: NLRX1 associates with FASTKD5 and modulates the OXPHOS pathway and HIV-1 replication in T cells.
Fig. 7: FASTKD5 upregulates electron transport chain components upon HIV-1 infection.

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Data availability

The authors declare that the data supporting the findings of this study are available with the paper and its supplementary information files. Source data for all figures are provided with the paper. The RV217 CD4+ T cell transcriptome data have been uploaded to the Gene Expression Omnibus (accession number GSE165841). The mass spectrometry proteomics data have been deposited to the ProteomeXchange with identifier PXD023565. The public databases used include UniProt human protein sequence database https://www.uniprot.org/proteomes/UP000005640; KEGG pathway database https://www.genome.jp/kegg/pathway.html; Hallmark gene set https://www.gsea-msigdb.org/gsea/msigdb/genesets.jsp?collection=H; and Reactome Pathway database https://reactome.org/. Source data are provided with this paper.

References

  1. Gaardbo, J. C., Hartling, H. J., Gerstoft, J. & Nielsen, S. D. Incomplete immune recovery in HIV infection: mechanisms, relevance for clinical care, and possible solutions. Clin. Dev. Immunol. 2012, 670957 (2012).

    Article  Google Scholar 

  2. Palmer, C. S. et al. Increased glucose metabolic activity is associated with CD4+ T-cell activation and depletion during chronic HIV infection. AIDS 28, 297–309 (2014).

    Article  CAS  Google Scholar 

  3. Palmer, C. S., Palchaudhuri, R., Albargy, H., Abdel-Mohsen, M. & Crowe, S. M. Exploiting immune cell metabolic machinery for functional HIV cure and the prevention of inflammaging. F1000Res 7, 125 (2018).

    Article  Google Scholar 

  4. Dagenais-Lussier, X. et al. Current topics in HIV-1 pathogenesis: the emergence of deregulated immuno-metabolism in HIV-infected subjects. Cytokine Growth Factor Rev. 26, 603–613 (2015).

    Article  CAS  Google Scholar 

  5. Gerriets, V. A. & Rathmell, J. C. Metabolic pathways in T cell fate and function. Trends Immunol. 33, 168–173 (2012).

    Article  CAS  Google Scholar 

  6. Valle-Casuso, J. C. et al. Cellular metabolism is a major determinant of HIV-1 reservoir seeding in CD4+ T cells and offers an opportunity to tackle infection. Cell Metab. 29, 611–626 (2019).

  7. Palmer, C. S. et al. Glucose transporter 1–expressing proinflammatory monocytes are elevated in combination antiretroviral therapy–treated and untreated HIV+ subjects. J. Immunol. 193, 5595–5603 (2014).

  8. Korencak, M. et al. Effect of HIV infection and antiretroviral therapy on immune cellular functions. JCI Insight 4, e126675 (2019).

  9. Xia, X. et al. NLRX1 negatively regulates TLR-induced NF-κB signaling by targeting TRAF6 and IKK. Immunity 34, 843–853 (2011).

    Article  CAS  Google Scholar 

  10. Moore, C. B. et al. NLRX1 is a regulator of mitochondrial antiviral immunity. Nature 451, 573–577 (2008).

    Article  CAS  Google Scholar 

  11. Allen, I. C. et al. NLRX1 protein attenuates inflammatory responses to infection by interfering with the RIG-I-MAVS and TRAF6-NF-κB signaling pathways. Immunity 34, 854–865 (2011).

    Article  CAS  Google Scholar 

  12. Koblansky, A. A. et al. The innate immune receptor NLRX1 functions as a tumor suppressor by reducing colon tumorigenesis and key tumor-promoting signals. Cell Rep. 14, 2562–2575 (2016).

    Article  CAS  Google Scholar 

  13. Stokman, G. et al. NLRX1 dampens oxidative stress and apoptosis in tissue injury via control of mitochondrial activity. J. Exp. Med. 214, 2405–2420 (2017).

    Article  CAS  Google Scholar 

  14. Guo, H. et al. NLRX1 sequesters STING to negatively regulate the interferon response, thereby facilitating the replication of HIV-1 and DNA viruses. Cell Host Microbe 19, 515–528 (2016).

    Article  CAS  Google Scholar 

  15. Barouch, D. H. et al. Rapid inflammasome activation following mucosal SIV infection of rhesus monkeys. Cell 165, 656–667 (2016).

    Article  CAS  Google Scholar 

  16. Chan, E. Y. et al. Dynamic host energetics and cytoskeletal proteomes in human immunodeficiency virus type 1-infected human primary CD4 cells: analysis by multiplexed label-free mass spectrometry. J. Virol. 83, 9283–9295 (2009).

    Article  CAS  Google Scholar 

  17. Ringrose, J. H., Jeeninga, R. E., Berkhout, B. & Speijer, D. Proteomic studies reveal coordinated changes in T-cell expression patterns upon infection with human immunodeficiency virus type 1. J. Virol. 82, 4320–4330 (2008).

    Article  CAS  Google Scholar 

  18. Stanley, T. L. & Grinspoon, S. K. Body composition and metabolic changes in HIV-infected patients. J. Infect. Dis. 205, S383–S390 (2012).

    Article  CAS  Google Scholar 

  19. Valle-Casuso, J. C. et al. Cellular metabolism is a major determinant of HIV-1 reservoir seeding in CD4+ T cells and offers an opportunity to tackle infection. Cell Metab. https://doi.org/10.1016/j.cmet.2018.11.015 (2019).

  20. Robb, M. L. et al. Prospective study of acute HIV-1 infection in adults in East Africa and Thailand. N. Engl. J. Med. 374, 2120–2130 (2016).

  21. Mellors, J. W. et al. Prognosis in HIV-1 infection predicted by the quantity of virus in plasma. Science 272, 1167–1170 (1996).

    Article  CAS  Google Scholar 

  22. Kelley, C. F., Barbour, J. D. & Hecht, F. M. The relation between symptoms, viral load, and viral load set point in primary HIV infection. J. Acquir. Immune Defic. Syndr. 45, 445–448 (2007).

    Article  Google Scholar 

  23. Wheaton, W. W. et al. Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis. Elife 3, e02242 (2014).

  24. Ye, C. et al. Glycosylphosphatidylinositol-anchored anti-HIV scFv efficiently protects CD4 T cells from HIV-1 infection and deletion in hu-PBL mice. J. Virol. https://doi.org/10.1128/JVI.01389-16 (2017).

  25. Martin-Montalvo, A. et al. Metformin improves healthspan and lifespan in mice. Nat. Commun. 4, 2192 (2013).

    Article  Google Scholar 

  26. McCune, J. M. The dynamics of CD4+ T-cell depletion in HIV disease. Nature 410, 974–979 (2001).

    Article  CAS  Google Scholar 

  27. Pernicova, I. & Korbonits, M. Metformin—mode of action and clinical implications for diabetes and cancer. Nat. Rev. Endocrinol. 10, 143–156 (2014).

  28. Hegedus, A., Kavanagh Williamson, M. & Huthoff, H. HIV-1 pathogenicity and virion production are dependent on the metabolic phenotype of activated CD4+ T cells. Retrovirology 11, 98 (2014).

    Article  Google Scholar 

  29. Lagouge, M. et al. Resveratrol improves mitochondrial function and protects against metabolic disease by activating SIRT1 and PGC-1α. Cell 127, 1109–1122 (2006).

    Article  CAS  Google Scholar 

  30. Csiszar, A. et al. Resveratrol induces mitochondrial biogenesis in endothelial cells. Am. J. Physiol. Heart Circ. Physiol. 297, H13–H20 (2009).

    Article  CAS  Google Scholar 

  31. Lei, Y. et al. The mitochondrial proteins NLRX1 and TUFM form a complex that regulates type I interferon and autophagy. Immunity 36, 933–946 (2012).

    Article  CAS  Google Scholar 

  32. Lei, Y. et al. EGFR-targeted mAb therapy modulates autophagy in head and neck squamous cell carcinoma through NLRX1–TUFM protein complex. Oncogene 35, 4698–4707 (2016).

  33. Vermeire, J. et al. HIV triggers a cGAS-dependent, Vpu- and Vpr-regulated type I interferon response in CD4+ T cells. Cell Rep. 17, 413–424 (2016).

  34. Li, S., Wang, L., Berman, M., Kong, Y. Y. & Dorf, M. E. Mapping a dynamic innate immunity protein interaction network regulating type I interferon production. Immunity 35, 426–440 (2011).

    Article  CAS  Google Scholar 

  35. Jourdain, A. A. et al. A mitochondria-specific isoform of FASTK is present in mitochondrial RNA granules and regulates gene expression and function. Cell Rep. 10, 1110–1121 (2015).

    Article  CAS  Google Scholar 

  36. Antonicka, H. & Shoubridge, E. A. Mitochondrial RNA granules are centers for posttranscriptional RNA processing and ribosome biogenesis. Cell Rep. 10, 920–932 (2015).

    Article  CAS  Google Scholar 

  37. Tarancon-Diez, L. et al. Immunometabolism is a key factor for the persistent spontaneous elite control of HIV-1 infection. EBioMedicine 42, 86–96 (2019).

    Article  Google Scholar 

  38. Zuniga, J. A., Easley, K. A., Shenvi, N., Nguyen, M. L. & Holstad, M. The impact of diabetes on CD4 recovery in persons with HIV in an urban clinic in the United States. Int J. STD AIDS 29, 63–71 (2018).

    Article  Google Scholar 

  39. Moyo, D. et al. Cohort study of diabetes in HIV-infected adult patients: evaluating the effect of diabetes mellitus on immune reconstitution. Diabetes Res. Clin. Pr. 103, e34–e36 (2014).

    Article  CAS  Google Scholar 

  40. Carr, A. Toxicity of antiretroviral therapy and implications for drug development. Nat. Rev. Drug Discov. 2, 624–634 (2003).

  41. Routy, J. P. et al. Effect of metformin on the size of the HIV reservoir in non-diabetic ART-treated individuals: single-arm non-randomised Lilac pilot study protocol. BMJ Open 9, e028444 (2019).

    Article  Google Scholar 

  42. Committee for the Update of the Guide for the Care and Use of Laboratory Animals et al. Guide for the Care and Use of Laboratory Animals 8th edn (National Academies Press, 2011).

  43. Konig, R. et al. Global analysis of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell 135, 49–60 (2008).

    Article  CAS  Google Scholar 

  44. Guo, H., Gao, J., Taxman, D. J., Ting, J. P. & Su, L. HIV-1 infection induces interleukin-1β production via TLR8 protein-dependent and NLRP3 inflammasome mechanisms in human monocytes. J. Biol. Chem. 289, 21716–21726 (2014).

    Article  Google Scholar 

  45. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the \(2^{-\Delta\Delta{\rm{C}}_{{\rm{T}}}}\) method. Methods 25, 402–408 (2001).

  46. Uchimura, T. et al. The innate immune sensor NLRC3 acts as a rheostat that fine-tunes T cell responses in infection and autoimmunity. Immunity 49, 1049–1061 (2018).

    Article  CAS  Google Scholar 

  47. Chen, X., Smith, L. M. & Bradbury, E. M. Site-specific mass tagging with stable isotopes in proteins for accurate and efficient protein identification. Anal. Chem. 72, 1134–1143 (2000).

    Article  CAS  Google Scholar 

  48. Zecha, J. et al. TMT labeling for the masses: a robust and cost-efficient, in-solution labeling approach. Mol. Cell Proteom. 18, 1468–1478 (2019).

    Article  CAS  Google Scholar 

  49. Smyth, G. K. et al. in Bioinformatics and Computational Biology Solutions using R and Bioconductor 1st edn (eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R. & Dudoit, S.) 379–420 (NY Springer, 2005).

  50. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  Google Scholar 

  51. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, S4 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the UNC Flow Cytometry Core Facility, supported in part by P30 CA016086 Cancer Center Core Support Grant to the UNC Lineberger Comprehensive Cancer Center, for assistance with flow cytometry. We thank S. K. Chanda at Sanford Burnham Prebys Medical Discovery Institute for providing the pNL4-3.EGFP.E- construct. This work was supported by NIH grants R01-AI029564 (J.P.-Y.T.), U19AI109965 (J.P.-Y.T.), AI127346 (L.S.) and DK119937 (L.S.); and UNC CFAR Award P30 AI50410 (H.G.). The RV217 study was supported by a cooperative agreement (W81XWH-18-2-0040) between the Henry M. Jackson Foundation for the Advancement of Military Medicine and the US Department of Defense.

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Authors

Contributions

H.G., L.S. and J.P.-Y.T. designed the experiments; H.G., Q.W., L.W., K.G., E.R., E.H.G., L.C., M.L.R. and L.A.E. conducted the studies; K.G. performed the data and statistical analyses of the RV217 study. X.C. and R.-P.S. assisted with the experiments and provided intellectual input; D.M.M. and C.G. provided critical reagents; J.P.-Y.T. supervised the study; H.G., Q.W., L.S. and J.P.-Y.T. interpreted the data and wrote the manuscript.

Corresponding authors

Correspondence to Lishan Su or Jenny P.-Y. Ting.

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The authors declare no competing interests. The views expressed are those of the authors and should not be construed to represent the positions of the US Army, the Department of Defense or the Henry M. Jackson Foundation.

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Peer review information Nature Immunology thanks Xiao-Ning Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 The network of leading edge genes in the OXPHOS pathway associated with set-point viral load.

a, Cytoscape was used to plot a network highlighting the top genes contributing to enrichment. The ClueGO app was used to infer network connections. Nodes represent genes, and edges reflect the association between these genes. b, STRING was used to plot a network highlighting the top genes contributing to enrichment. Nodes represent genes, and edges reflect the association between these genes.

Source data

Extended Data Fig. 2 Metabolic inhibitors suppress HIV-1 replication in CD4 T cells and NRG-hu CD4 mice.

a, The effect of OXPHOS inhibitors on virus replication in VSV-G-NL4-3-Luc-infected Jurkat cells. Rotenone (0.2 µM or 1 µM), metformin (1 mM or 5 mM, mitochondrial complex I inhibitor) and antimycin A (0.2 µM or 1 µM, mitochondrial complex III inhibitor). Ethanol is the vehicle control for rotenone and antimycin A, and sterile water is the vehicle control for metformin. n = 3 cell cultures per experiment. b, The oxygen consumption rate (OCR) was measured in Jurkat cells infected with VSV-G-NL4-3-Luc or left uninfected (mock) in the presence of metformin or the vehicle control (H2O). n = 4 cell cultures per experiment. c, The basal and maximal OCR and reserved respiratory capacity of Jurkat cells infected with VSV-G-NL4-3-Luc or left uninfected (mock) in the presence of metformin or vehicle control (H2O). n = 12 cell cultures per experiment. d, The effect of 2-DG (5 mM or 10 mM), a glycolysis inhibitor, on virus replication in VSV-G-NL4-3-Luc infected Jurkat cells. H2O was used as vehicle control. n = 3 cell cultures per experiment. e, Representative FACS plots of human CD4 T cells from peripheral blood stained with the indicated markers. Cells were obtained from mock or HIV-1 R3A infected NRG-hu CD4 mice with or without metformin treatment. Data are representative of three independent experiments shown as the mean ± s.e.m. Statistical significance was tested by one-way ANOVA (a,d) or two-way ANOVA followed by Tukey’s multiple comparisons test (c).

Source data

Extended Data Fig. 3 NLRX1 is upregulated in HIV-1 infected CD4 T cells.

a, Jurkat cells were infected with VSV-G-pseudotyped HIV NL4-3-EGFP virus (MOI = 0.5). After 48 hours post-infection (hpi), cells were stained with an anti-NLRX1 antibody and subjected to ImageStreamX MkII imaging flow cytometry. GFP positive cells indicate HIV-1 infection. b, The mean fluorescence intensity (MFI) of NLRX1 staining in GFP negative (n = 532 cells) and positive cells (n = 222 cells). P value was calculated by two-tailed unpaired Student’s t-test. c, The linear relationship between the expression of NLRX1 and GFP. R, Pearson’s correlation coefficient; Correlation is significant at P < 0.0001. d, Jurkat cells were infected by VSV-G-NL4-3-EGFP pseudovirus (MOI = 1). At 48 hpi, NLRX1 transcripts were assessed by qPCR. n = 3 cell cultures per experiment. P value was calculated by two-tailed unpaired Student’s t-test. e, NLRX1 transcripts were assessed in human primary CD4 T cells infected by three different HIV-1 clinical isolates at 48 hpi by qPCR. n = 3 cell cultures per experiment. Significance was tested by one-way ANOVA followed by Dunnett’s multiple comparisons test. All data are representative of three independent experiments. Error bar is s.e.m.

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Extended Data Fig. 4 The effect of metformin on NLRX1 expression in CD4 T cells.

a–d, CD3/CD28 antibody-activated human primary CD4 T cells were left uninfected (a) or infected by three different HIV-1 clinical isolates, #409 (b), #413 (c), and #414 (d) in the presence of 5 mM metformin or the vehicle control, sterile water. NLRX1 transcripts were assessed at 48 hpi by qPCR. Representative data of three independent experiments are shown as the mean ± standard deviation (s.d). n = 4 cell cultures per experiment. Statistical significance was tested by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 5 Silencing NLRX1 protects HIV-1 caused human CD4 T cell depletion in the NRG-hu CD4 mouse model.

a, Representative fluorescence-activated cell sorting (FACS) plot of human CD4 T cells expressing the indicated markers in peripheral blood from mock or HIV-1 R3A infected NRG-hu CD4 mice at 1 and 2 wpi. b, Spleens from mock or HIV-1 R3A infected NRG-hu CD4 mice at 3 wpi. n = 6 mice per group. c, Splenocyte numbers of mock or HIV-1 R3A infected NRG-hu CD4 mice at 3 wpi. The data are a pool of two independent experiments. n = 8 mice for mock groups; n = 9 mice for HIV-1 infection groups. Data are presented as the mean ± s.e.m. Statistical significance was tested by two-way ANOVA followed by Sidak’s multiple comparisons test.

Source data

Extended Data Fig. 6 NLRX1 promotion of HIV-1 replication in CD4 T cells is independent of IFN-I, autophagy, or ER stress.

a, RLU in Jurkat sh-Ctr or sh-NLRX1 cells infected with VSV-G-NL4-3-Luc (MOI=1) or left uninfected in the presence or absence of JAK1/2 inhibitor Ruxolitinib at 24 hpi. n = 3 cell cultures per experiment. b, Ruxolitinib suppressed VSV-G-NL4-3-Luc infection-induced phosphorylation of STAT1 at 24 hpi. β-actin was used as the loading control. c, Similar to a except for using autophagy inhibitor 3-Methyladenine (3-MA) to treat cells. Numbers on top of the bar are the fold differences. n = 3 cell cultures per experiment. d, Similar to a except for using ER stress inhibitor sodium tauroursodeoxycholate (TUDCA) to treat cells. Luciferase activities were determined at 48 hpi. n = 3 cell cultures per experiment. Representative data of three independent experiments are presented as the mean ± s.e.m. Two-way ANOVA followed by Sidak’s multiple comparisons test.

Source data

Extended Data Fig. 7 Expression of FASTKD5 in Jurkat cells and primary human CD4 T cells.

a, Candidates of NLRX1 interacting proteins identified by immunoprecipitation (IP)-mass spectrometry (MS) using overexpressed NLRX1 as the bait in a previous publication. b, Jurkat-sh-Ctr and Jurkat-sh-NLRX1 cells were left uninfected (mock) or infected by VSV-G-NL4-3-Luc pseudovirus (MOI = 1). FASTKD5 transcripts were assessed at 24 hpi by qPCR. Data are shown as the mean ± s.e.m. n = 4 cell cultures per experiment. Statistical significance was tested by two-way ANOVA followed by Sidak’s multiple comparisons test. c, CD3/CD28 antibody-activated human primary CD4 T cells were infected by three different HIV-1 clinical isolates (10 ng p24), and FASTKD5 transcripts were assessed at 24 and 48 hpi by qPCR. Data are shown as the mean ± s.e.m. n = 4 cell cultures per experiment. Statistical significance was tested by one-way ANOVA followed by Dunnett’s multiple comparisons test. d, e, Doxycycline-induced silencing of FASTKD5 (d) and PRDX3 (e) in Jurkat cells transduced by 2 different shRNA containing lentiviruses. β-actin was used as the loading control. Data (b–e) are representative of three independent experiments.

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Guo, H., Wang, Q., Ghneim, K. et al. Multi-omics analyses reveal that HIV-1 alters CD4+ T cell immunometabolism to fuel virus replication. Nat Immunol 22, 423–433 (2021). https://doi.org/10.1038/s41590-021-00898-1

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