Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Recommendations for measuring HIV reservoir size in cure-directed clinical trials

Abstract

Therapeutic strategies are being clinically tested either to eradicate latent HIV reservoirs or to achieve virologic control in the absence of antiretroviral therapy. Attaining this goal will require a consensus on how best to measure the numbers of persistently infected cells with the potential to cause viral rebound after antiretroviral-therapy cessation in assessing the results of cure-directed strategies in vivo. Current measurements assess various aspects of the HIV provirus and its functionality and produce divergent results. Here, we provide recommendations from the BEAT-HIV Martin Delaney Collaboratory on which viral measurements should be prioritized in HIV-cure-directed clinical trials.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The collective view of the BEAT-HIV Collaboratory on the priorities for measuring HIV reservoir size in blood and tissues during HIV-cure-related clinical trials.

References

  1. 1.

    Chargin, A. et al. Identification and characterization of HIV-1 latent viral reservoirs in peripheral blood. J. Clin. Microbiol. 53, 60–66 (2015).

    CAS  PubMed  Google Scholar 

  2. 2.

    Chun, T. W. et al. Early establishment of a pool of latently infected, resting CD4+ T cells during primary HIV-1 infection. Proc. Natl Acad. Sci. USA 95, 8869–8873 (1998).

    CAS  PubMed  Google Scholar 

  3. 3.

    Wong, J. K. et al. Recovery of replication-competent HIV despite prolonged suppression of plasma viremia. Science 278, 1291–1295 (1997).

    CAS  PubMed  Google Scholar 

  4. 4.

    Wandeler, G., Johnson, L. F. & Egger, M. Trends in life expectancy of HIV-positive adults on antiretroviral therapy across the globe: comparisons with general population. Curr. Opin. HIV AIDS 11, 492–500 (2016).

    CAS  PubMed  Google Scholar 

  5. 5.

    Biggar, R. J., Chaturvedi, A. K., Goedert, J. J. & Engels, E. A., HIV/AIDS Cancer Match Study. AIDS-related cancer and severity of immunosuppression in persons with AIDS. J. Natl. Cancer Inst. 99, 962–972 (2007).

  6. 6.

    Carbone, A., Volpi, C. C., Gualeni, A. V. & Gloghini, A. Epstein-Barr virus associated lymphomas in people with HIV. Curr. Opin. HIV AIDS 12, 39–46 (2017).

    CAS  PubMed  Google Scholar 

  7. 7.

    Zucchetto, A. et al. Non-AIDS-defining cancer mortality: emerging patterns in the late HAART era. J. Acquir. Immune Defic. Syndr. 73, 190–196 (2016).

    PubMed  Google Scholar 

  8. 8.

    Rodger, A. J. et al. Mortality in well controlled HIV in the continuous antiretroviral therapy arms of the SMART and ESPRIT trials compared with the general population. AIDS 27, 973–979 (2013).

    CAS  PubMed  Google Scholar 

  9. 9.

    Eisele, E. & Siliciano, R. F. Redefining the viral reservoirs that prevent HIV-1 eradication. Immunity 37, 377–388 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Crooks, A. M. et al. Precise quantitation of the latent HIV-1 reservoir: implications for eradication strategies. J. Infect. Dis. 212, 1361–1365 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Finzi, D. et al. Latent infection of CD4+ T cells provides a mechanism for lifelong persistence of HIV-1, even in patients on effective combination therapy. Nat. Med. 5, 512–517 (1999).

    CAS  PubMed  Google Scholar 

  12. 12.

    Chun, T. W. et al. Quantification of latent tissue reservoirs and total body viral load in HIV-1 infection. Nature 387, 183–188 (1997).

    CAS  PubMed  Google Scholar 

  13. 13.

    Richman, D. D. et al. The challenge of finding a cure for HIV infection. Science 323, 1304–1307 (2009).

    CAS  PubMed  Google Scholar 

  14. 14.

    Banga, R. et al. PD-1+ and follicular helper T cells are responsible for persistent HIV-1 transcription in treated aviremic individuals. Nat. Med. 22, 754–761 (2016).

    CAS  PubMed  Google Scholar 

  15. 15.

    Chun, T. W. et al. Persistence of HIV in gut-associated lymphoid tissue despite long-term antiretroviral therapy. J. Infect. Dis. 197, 714–720 (2008).

    CAS  PubMed  Google Scholar 

  16. 16.

    Yukl, S. A. et al. Differences in HIV burden and immune activation within the gut of HIV-positive patients receiving suppressive antiretroviral therapy. J. Infect. Dis. 202, 1553–1561 (2010).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Estes, J. D. et al. Defining total-body AIDS-virus burden with implications for curative strategies. Nat. Med. 23, 1271–1276 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Pantaleo, G. et al. HIV infection is active and progressive in lymphoid tissue during the clinically latent stage of disease. Nature 362, 355–358 (1993).

    CAS  PubMed  Google Scholar 

  19. 19.

    Bruner, K. M. et al. Defective proviruses rapidly accumulate during acute HIV-1 infection. Nat. Med. 22, 1043–1049 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Ho, Y. C. et al. Replication-competent noninduced proviruses in the latent reservoir increase barrier to HIV-1 cure. Cell 155, 540–551 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Finzi, D. et al. Identification of a reservoir for HIV-1 in patients on highly active antiretroviral therapy. Science 278, 1295–1300 (1997).

    CAS  PubMed  Google Scholar 

  22. 22.

    Siliciano, J. D. et al. Long-term follow-up studies confirm the stability of the latent reservoir for HIV-1 in resting CD4+ T cells. Nat. Med. 9, 727–728 (2003).

    CAS  PubMed  Google Scholar 

  23. 23.

    Hosmane, N. N. et al. Proliferation of latently infected CD4+ T cells carrying replication-competent HIV-1: potential role in latent reservoir dynamics. J. Exp. Med. 214, 959–972 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Lorenzi, J. C. et al. Paired quantitative and qualitative assessment of the replication-competent HIV-1 reservoir and comparison with integrated proviral DNA. Proc. Natl Acad. Sci. USA 113, E7908–E7916 (2016).

    CAS  PubMed  Google Scholar 

  25. 25.

    Lee, G. Q. et al. Clonal expansion of genome-intact HIV-1 in functionally polarized Th1 CD4+ T cells. J. Clin. Invest. 127, 2689–2696 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Pinzone, M. R. et al. Longitudinal HIV sequencing reveals reservoir expression leading to decay which is obscured by clonal expansion. Nat. Commun. 10, 728 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Bruner, K. M. et al. A quantitative approach for measuring the reservoir of latent HIV-1 proviruses. Nature 566, 120–125 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Gaebler, C. et al. Combination of quadruplex qPCR and next-generation sequencing for qualitative and quantitative analysis of the HIV-1 latent reservoir. J. Exp. Med. 216, 2253–2264 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Peluso, M. J. et al. Differential decay of intact and defective proviral DNA in HIV-1-infected individuals on suppressive antiretroviral therapy. JCI Insight 5, e132997 (2020).

    PubMed Central  Google Scholar 

  30. 30.

    Kwon, K. J. et al. Different human resting memory CD4+ T cell subsets show similar low inducibility of latent HIV-1 proviruses. Sci. Transl. Med. 12, eaax6795 (2020).

    CAS  PubMed  Google Scholar 

  31. 31.

    Morón-López, S. et al. Switching from a protease inhibitor-based regimen to a dolutegravir-based regimen: a randomized clinical trial to determine the effect on peripheral blood and ileum biopsies from antiretroviral therapy-suppressed human immunodeficiency virus-infected individuals. Clin. Infect. Dis. 69, 1320–1328 (2019).

    PubMed  Google Scholar 

  32. 32.

    Morón-López, S. et al. Sensitive quantification of the HIV-1 reservoir in gut-associated lymphoid tissue. PLoS ONE 12, e0175899 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Metcalf Pate, K. A. et al. A murine viral outgrowth assay to detect residual HIV type 1 in patients with undetectable viral loads. J. Infect. Dis. 212, 1387–1396 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Henrich, T. J. et al. HIV-1 persistence following extremely early initiation of antiretroviral therapy (ART) during acute HIV-1 infection: an observational study. PLoS Med. 14, e1002417 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Lewinski, M. K. et al. Genome-wide analysis of chromosomal features repressing human immunodeficiency virus transcription. J. Virol. 79, 6610–6619 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Sherrill-Mix, S. et al. HIV latency and integration site placement in five cell-based models. Retrovirology 10, 90 (2013).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Schröder, A. R. et al. HIV-1 integration in the human genome favors active genes and local hotspots. Cell 110, 521–529 (2002).

    Google Scholar 

  38. 38.

    Wang, G. P., Ciuffi, A., Leipzig, J., Berry, C. C. & Bushman, F. D. HIV integration site selection: analysis by massively parallel pyrosequencing reveals association with epigenetic modifications. Genome Res. 17, 1186–1194 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Einkauf, K. B. et al. Intact HIV-1 proviruses accumulate at distinct chromosomal positions during prolonged antiretroviral therapy. J. Clin. Invest. 129, 988–998 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Cohn, L. B. et al. HIV-1 integration landscape during latent and active infection. Cell 160, 420–432 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Patro, S. C. et al. Combined HIV-1 sequence and integration site analysis informs viral dynamics and allows reconstruction of replicating viral ancestors. Proc. Natl Acad. Sci. USA 116, 25891–25899 (2019).

    CAS  PubMed  Google Scholar 

  42. 42.

    Cesana, D. et al. HIV-1-mediated insertional activation of STAT5B and BACH2 trigger viral reservoir in T regulatory cells. Nat. Commun. 8, 498 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Wu, G. et al. HDAC inhibition induces HIV-1 protein and enables immune-based clearance following latency reversal. JCI Insight 2, e92901 (2017).

    PubMed Central  Google Scholar 

  44. 44.

    Pitman, M. C., Lau, J. S. Y., McMahon, J. H. & Lewin, S. R. Barriers and strategies to achieve a cure for HIV. Lancet HIV 5, e317–e328 (2018).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Pollack, R. A. et al. Defective HIV-1 proviruses are expressed and can be recognized by cytotoxic T lymphocytes, which shape the proviral landscape. Cell Host Microbe 21, 494–506.e494 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Imamichi, H. et al. Defective HIV-1 proviruses produce viral proteins. Proc. Natl Acad. Sci. USA 117, 3704–3710 (2020).

    CAS  PubMed  Google Scholar 

  47. 47.

    Passaes, C. P. B. et al. Ultrasensitive HIV-1 p24 assay detects single infected cells and differences in reservoir induction by latency reversal agents. J. Virol. 91, e02296–16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Cai, Y. et al. BCL6 inhibitor-mediated downregulation of phosphorylated SAMHD1 and T cell activation are associated with decreased HIV infection and reactivation. J. Virol. 93, e01073–18 (2019).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Fidler, S. et al. Antiretroviral therapy alone versus antiretroviral therapy with a kick and kill approach, on measures of the HIV reservoir in participants with recent HIV infection (the RIVER trial): a phase 2, randomised trial. Lancet 395, 888–898 (2020).

    CAS  PubMed  Google Scholar 

  50. 50.

    Ruiz, A. et al. Antigen production after latency reversal and expression of inhibitory receptors in CD8+ T cells limit the killing of HIV-1 reactivated cells. Front. Immunol. 9, 3162 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Bertagnolli, L. N. et al. The role of CD32 during HIV-1 infection. Nature 561, E17–E19 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Descours, B. et al. CD32a is a marker of a CD4 T-cell HIV reservoir harbouring replication-competent proviruses. Nature 543, 564–567 (2017).

    CAS  PubMed  Google Scholar 

  53. 53.

    Abdel-Mohsen, M. et al. CD32 is expressed on cells with transcriptionally active HIV but does not enrich for HIV DNA in resting T cells. Sci. Transl. Med. 10, eaar6759 (2018).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Baxter, A. E. et al. Multiparametric characterization of rare HIV-infected cells using an RNA-flow FISH technique. Nat. Protoc. 12, 2029–2049 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Grau-Expósito, J. et al. A novel single-cell FISH-flow assay identifies effector memory CD4+ T cells as a major niche for HIV-1 transcription in HIV-infected patients. MBio 8, e00876–17 (2017).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Pardons, M. et al. Single-cell characterization and quantification of translation-competent viral reservoirs in treated and untreated HIV infection. PLoS Pathog. 15, e1007619 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Cillo, A. R. et al. Quantification of HIV-1 latency reversal in resting CD4+ T cells from patients on suppressive antiretroviral therapy. Proc. Natl Acad. Sci. USA 111, 7078–7083 (2014).

    CAS  PubMed  Google Scholar 

  58. 58.

    Plantin, J., Massanella, M. & Chomont, N. Inducible HIV RNA transcription assays to measure HIV persistence: pros and cons of a compromise. Retrovirology 15, 9 (2018).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Massanella, M. et al. Improved assays to measure and characterize the inducible HIV reservoir. EBioMedicine 36, 113–121 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Chen, H. C., Martinez, J. P., Zorita, E., Meyerhans, A. & Filion, G. J. Position effects influence HIV latency reversal. Nat. Struct. Mol. Biol. 24, 47–54 (2017).

    CAS  PubMed  Google Scholar 

  61. 61.

    Pasternak, A. O. et al. Highly sensitive methods based on seminested real-time reverse transcription-PCR for quantitation of human immunodeficiency virus type 1 unspliced and multiply spliced RNA and proviral DNA. J. Clin. Microbiol. 46, 2206–2211 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Pasternak, A. O., Lukashov, V. V. & Berkhout, B. Cell-associated HIV RNA: a dynamic biomarker of viral persistence. Retrovirology 10, 41 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Yukl, S. A. et al. HIV latency in isolated patient CD4+ T cells may be due to blocks in HIV transcriptional elongation, completion, and splicing. Sci. Transl. Med. 10, eaap9927 (2018).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Li, J. Z. et al. The size of the expressed HIV reservoir predicts timing of viral rebound after treatment interruption. AIDS 30, 343–353 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Pasternak, A. O. et al. Cell-associated HIV-1 RNA predicts viral rebound and disease progression after discontinuation of temporary early ART. JCI Insight 5, e134196 (2020).

    PubMed Central  Google Scholar 

  66. 66.

    Procopio, F. A. et al. A novel assay to measure the magnitude of the inducible viral reservoir in HIV-infected individuals. EBioMedicine 2, 874–883 (2015).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Dinoso, J. B. et al. Treatment intensification does not reduce residual HIV-1 viremia in patients on highly active antiretroviral therapy. Proc. Natl Acad. Sci. USA 106, 9403–9408 (2009).

    CAS  PubMed  Google Scholar 

  68. 68.

    Hong, F. et al. Associations between HIV-1 DNA copy number, proviral transcriptional activity, and plasma viremia in individuals off or on suppressive antiretroviral therapy. Virology 521, 51–57 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Palmer, S. et al. New real-time reverse transcriptase-initiated PCR assay with single-copy sensitivity for human immunodeficiency virus type 1 RNA in plasma. J. Clin. Microbiol. 41, 4531–4536 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Deleage, C., Chan, C. N., Busman-Sahay, K. & Estes, J. D. Next-generation in situ hybridization approaches to define and quantify HIV and SIV reservoirs in tissue microenvironments. Retrovirology 15, 4 (2018).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Deleage, C. et al. Defining HIV and SIV reservoirs in lymphoid tissues. Pathog. Immun. 1, 68–106 (2016).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    O’Doherty, U., Swiggard, W. J., Jeyakumar, D., McGain, D. & Malim, M. H. A sensitive, quantitative assay for human immunodeficiency virus type 1 integration. J. Virol. 76, 10942–10950 (2002).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Strain, M. C. & Richman, D. D. New assays for monitoring residual HIV burden in effectively treated individuals. Curr. Opin. HIV AIDS 8, 106–110 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Yu, J. J. et al. A more precise HIV integration assay designed to detect small differences finds lower levels of integrated DNA in HAART treated patients. Virology 379, 78–86 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Eriksson, S. et al. Comparative analysis of measures of viral reservoirs in HIV-1 eradication studies. PLoS Pathog. 9, e1003174 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Williams, J. P. et al. HIV-1 DNA predicts disease progression and post-treatment virological control. eLife 3, e03821 (2014).

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Emmanouil Papasavvas, L.A. et al. Intact HIV reservoir associates with levels of total and integrated proviruses in the blood during suppressive antiretroviral therapy. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciaa809 (2020).

  78. 78.

    Lada, S. M. et al. Quantitation of integrated HIV provirus by pulsed-field gel electrophoresis and droplet digital PCR. J. Clin. Microbiol. 56, e01158–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Liszewski, M. K., Yu, J. J. & O’Doherty, U. Detecting HIV-1 integration by repetitive-sampling Alu-gag PCR. Methods 47, 254–260 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Vandergeeten, C. et al. Cross-clade ultrasensitive PCR-based assays to measure HIV persistence in large-cohort studies. J. Virol. 88, 12385–12396 (2014).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Butler, S. L., Hansen, M. S. & Bushman, F. D. A quantitative assay for HIV DNA integration in vivo. Nat. Med. 7, 631–634 (2001).

    CAS  PubMed  Google Scholar 

  82. 82.

    Lee, E. et al. Memory CD4+ T-cells expressing HLA-DR contribute to HIV persistence during prolonged antiretroviral therapy. Front. Microbiol. 10, 2214 (2019).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Chomont, N. et al. HIV reservoir size and persistence are driven by T cell survival and homeostatic proliferation. Nat. Med. 15, 893–900 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Zerbato, J. M., McMahon, D. K., Sobolewski, M. D., Mellors, J. W. & Sluis-Cremer, N. Naive CD4+ T cells harbor a large inducible reservoir of latent, replication-competent human immunodeficiency virus type 1. Clin. Infect. Dis. 69, 1919–1925 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Abreu, C. M. et al. Myeloid and CD4 T cells comprise the latent reservoir in antiretroviral therapy-suppressed SIVmac251-infected macaques. MBio 10, e01659–19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Ganor, Y. et al. HIV-1 reservoirs in urethral macrophages of patients under suppressive antiretroviral therapy. Nat. Microbiol. 4, 633–644 (2019).

    CAS  PubMed  Google Scholar 

  87. 87.

    Chaillon, A. et al. HIV persists throughout deep tissues with repopulation from multiple anatomical sources. J. Clin. Invest. 130, 1699–1712 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Gondim, M. et al. Marked variation in the susceptibility of HIV-1 to type 1 interferon inhibition during early, late and rebound infection. J. Virus Erad. 5, abstr. PP 3.14 (2019). (Suppl. 3).

    Google Scholar 

  89. 89.

    Andrade, V. M. et al. A minor population of macrophage-tropic HIV-1 variants is identified in recrudescing viremia following analytic treatment interruption. Proc. Natl Acad. Sci. USA 117, 9981–9990 (2020).

    CAS  PubMed  Google Scholar 

  90. 90.

    Hatano, H. et al. Cell-based measures of viral persistence are associated with immune activation and programmed cell death protein 1 (PD-1)-expressing CD4+ T cells. J. Infect. Dis. 208, 50–56 (2013).

    CAS  PubMed  Google Scholar 

  91. 91.

    Chew, G. M. et al. TIGIT marks exhausted T cells, correlates with disease progression, and serves as a target for immune restoration in HIV and SIV infection. PLoS Pathog. 12, e1005349 (2016).

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Fromentin, R. et al. CD4+ T cells expressing PD-1, TIGIT and LAG-3 contribute to HIV persistence during ART. PLoS Pathog. 12, e1005761 (2016).

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Abdel-Mohsen, M. et al. Select host restriction factors are associated with HIV persistence during antiretroviral therapy. AIDS 29, 411–420 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Keating, S. M. et al. HIV antibody level as a marker of HIV persistence and low-level viral replication. J. Infect. Dis. 216, 72–81 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Burbelo, P. D. et al. HIV antibody characterization as a method to quantify reservoir size during curative interventions. J. Infect. Dis. 209, 1613–1617 (2014).

    CAS  PubMed  Google Scholar 

  96. 96.

    Yukl, S. A. et al. Challenges in detecting HIV persistence during potentially curative interventions: a study of the Berlin patient. PLoS Pathog. 9, e1003347 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Luzuriaga, K. et al. Viremic relapse after HIV-1 remission in a perinatally infected child. N. Engl. J. Med. 372, 786–788 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Hill, A. L., Rosenbloom, D. I., Fu, F., Nowak, M. A. & Siliciano, R. F. Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1. Proc. Natl Acad. Sci. USA 111, 13475–13480 (2014).

    CAS  PubMed  Google Scholar 

  99. 99.

    Tebas, P. et al. Gene editing of CCR5 in autologous CD4 T cells of persons infected with HIV. N. Engl. J. Med. 370, 901–910 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Cohen, Y. Z. et al. Relationship between latent and rebound viruses in a clinical trial of anti-HIV-1 antibody 3BNC117. J. Exp. Med. 215, 2311–2324 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Salantes, D. B. et al. HIV-1 latent reservoir size and diversity are stable following brief treatment interruption. J. Clin. Invest. 128, 3102–3115 (2018).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Vibholm, L. K. et al. Characterization of intact proviruses in blood and lymph node from HIV-infected individuals undergoing analytical treatment interruption. J. Virol. 93, e01920–18 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    De Scheerder, M. A. et al. HIV rebound is predominantly fueled by genetically identical viral expansions from diverse reservoirs. Cell Host Microbe 26, 347–358.e347 (2019).

    PubMed  Google Scholar 

  104. 104.

    Pacanowski, J. et al. Early plasmacytoid dendritic cell changes predict plasma HIV load rebound during primary infection. J. Infect. Dis. 190, 1889–1892 (2004).

    PubMed  Google Scholar 

  105. 105.

    Papasavvas, E. et al. Plasmacytoid dendritic cell and functional HIV Gag p55-specific T cells before treatment interruption can inform set-point plasma HIV viral load after treatment interruption in chronically suppressed HIV-1+ patients. Immunology 145, 380–390 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Tomescu, C. et al. A correlate of HIV-1 control consisting of both innate and adaptive immune parameters best predicts viral load by multivariable analysis in HIV-1 infected viremic controllers and chronically-infected non-controllers. PLoS ONE 9, e103209 (2014).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Giron, L. B. et al. Plasma and antibody glycomic biomarkers of time to HIV rebound and viral setpoint. AIDS 34, 681–686 (2020).

    CAS  PubMed  Google Scholar 

  108. 108.

    Papasavvas, E. et al. Analytical antiretroviral therapy interruption does not irreversibly change preinterruption levels of cellular. Hiv. AIDS 32, 1763–1772 (2018).

    Google Scholar 

  109. 109.

    van Lunzen, J. & Hoffmann, C. Virological rebound and its consequences during treatment interruption. Curr. Opin. HIV AIDS 2, 1–5 (2007).

    PubMed  Google Scholar 

  110. 110.

    Julg, B. et al. Recommendations for analytical antiretroviral treatment interruptions in HIV research trials-report of a consensus meeting. Lancet HIV 6, e259–e268 (2019).

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Chun, T. W. et al. Presence of an inducible HIV-1 latent reservoir during highly active antiretroviral therapy. Proc. Natl Acad. Sci. USA 94, 13193–13197 (1997).

    CAS  PubMed  Google Scholar 

  112. 112.

    Laird, G. M. et al. Rapid quantification of the latent reservoir for HIV-1 using a viral outgrowth assay. PLoS Pathog. 9, e1003398 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Lee, S. K. et al. Quantification of the latent HIV-1 reservoir using ultra deep sequencing and primer ID in a viral outgrowth assay. J. Acquir. Immune Defic. Syndr. 74, 221–228 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Sherman, E. et al. INSPIIRED: a pipeline for quantitative analysis of sites of new DNA integration in cellular genomes. Mol. Ther. Methods Clin. Dev. 4, 39–49 (2016).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Berry, C. C. et al. INSPIIRED: quantification and visualization tools for analyzing integration site distributions. Mol. Ther. Methods Clin. Dev. 4, 17–26 (2016).

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Cabrera, C., Chang, L., Stone, M., Busch, M. & Wilson, D. H. Rapid, fully automated digital immunoassay for p24 protein with the sensitivity of nucleic acid amplification for detecting acute HIV infection. Clin. Chem. 61, 1372–1380 (2015).

    CAS  PubMed  Google Scholar 

  117. 117.

    Bullen, C. K., Laird, G. M., Durand, C. M., Siliciano, J. D. & Siliciano, R. F. New ex vivo approaches distinguish effective and ineffective single agents for reversing HIV-1 latency in vivo. Nat. Med. 20, 425–429 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Kumar, A. M., Borodowsky, I., Fernandez, B., Gonzalez, L. & Kumar, M. Human immunodeficiency virus type 1 RNA levels in different regions of human brain: quantification using real-time reverse transcriptase-polymerase chain reaction. J. Neurovirol. 13, 210–224 (2007).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the NIH-funded BEAT-HIV Martin Delaney Collaboratory to cure HIV-1 infection (1UM1Al126620). L.J.M. is also supported by NIH R01 AI065279, U01 AI065279, R01 DA048728, R01 DA049666, the Kean Family Professorship and the Philadelphia Foundation (Roberts I. Jacobs Fund). M.A.-M. is supported by NIH grants (R01 DK123733, R01 AG062383, R01NS117458, R21 AI143385, R21 AI129636 and R21 NS106970), The Foundation for AIDS Research (amfAR) impact grant 109840-65-RGRL, W.W. Smith Charitable Trust grant A1901, The Campbell Foundation, Mizutani Foundation for Glycoscience, Wistar Cancer Center Support grant P30 CA010815-49S2 and the Penn Center for AIDS Research (P30 AI 045008). M.J.B. is supported by The Miguel Servet program funded by the Spanish Health Institute Carlos III (CP17/00179). M.L. is supported by NIH grants AI117841, AI120008, AI124776, AI130005, AI122377 and AI135940. X.G.Y. is supported by NIH grants AI116228, AI078799, HL134539, AI125109 and DA047034. R.F.S. is supported by NIH grants UM1 AI126603, UM1 AI126620, UM1 AI12661 and P30 AI094189, the Howard Hughes Medical Institute and the Bill and Melinda Gates Foundation (OPP1115715). V.P. is supported by NIH grants R01 AI143567 and AI 124843. Y.-C.H. is supported by a Yale Top Scholar Award, a Rudolf J. Anderson Fellowship, NIH grants AI141009, DA047037, AI118402, P50 AI150464, P30 AI094189 and R37 AI14868, a W.W. Smith AIDS Research Grant, a Gilead AIDS Research Grant and a Gilead Research Scholar Grant. J.D.E. is supported by the NIH and Bill and Melinda Gates Foundation grants 75N93019C00070, AI133706, AI110164, AI141258, AI143411-01A1, AI149672, CA206466, DK119945, INV-002704, OD011092-60 and OPPO1108533. D.R. is supported by the NIH CARE Martin Delaney Collaboratory (1UM1AI126619 0), the UCSD NIH CFAR (AI306214), the Department of Veterans Affairs and the James B. Pendleton Charitable Trust. J.L.R. is supported by NIH grants U19AI117950, U19AI149680 and UM1AI126620.

Author information

Affiliations

Authors

Consortia

Contributions

All authors contributed to the writing and editing of the manuscript.

Corresponding author

Correspondence to Luis J. Montaner.

Ethics declarations

Competing interests

B.J.H. and D.H. are employees of Merck & Co.; R.F.S. is an inventor on a patent application on the IPDA filed by Johns Hopkins University and licensed to AccelevirDx (R.F.S. holds no equity in AccelevirDx).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Note

Supplementary Note

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abdel-Mohsen, M., Richman, D., Siliciano, R.F. et al. Recommendations for measuring HIV reservoir size in cure-directed clinical trials. Nat Med 26, 1339–1350 (2020). https://doi.org/10.1038/s41591-020-1022-1

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing