Abstract

A stable latent reservoir for HIV-1 in resting CD4+ T cells is the principal barrier to a cure1,2,3. Curative strategies that target the reservoir are being tested4,5 and require accurate, scalable reservoir assays. The reservoir was defined with quantitative viral outgrowth assays for cells that release infectious virus after one round of T cell activation1. However, these quantitative outgrowth assays and newer assays for cells that produce viral RNA after activation6 may underestimate the reservoir size because one round of activation does not induce all proviruses7. Many studies rely on simple assays based on polymerase chain reaction to detect proviral DNA regardless of transcriptional status, but the clinical relevance of these assays is unclear, as the vast majority of proviruses are defective7,8,9. Here we describe a more accurate method of measuring the HIV-1 reservoir that separately quantifies intact and defective proviruses. We show that the dynamics of cells that carry intact and defective proviruses are different in vitro and in vivo. These findings have implications for targeting the intact proviruses that are a barrier to curing HIV infection.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

The IPDA was developed through an analysis of published near full genome HIV-1 sequences (refs 8,9, GenBank accession numbers KX505390–KX505744 and KU677989–KU678196, respectively). All other data are available from the corresponding author upon reasonable request.

Additional information

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

References

  1. 1.

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

  2. 2.

    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).

  3. 3.

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

  4. 4.

    Archin, N. M. et al. Administration of vorinostat disrupts HIV-1 latency in patients on antiretroviral therapy. Nature 487, 482–485 (2012).

  5. 5.

    Borducchi, E. N. et al. Ad26/MVA therapeutic vaccination with TLR7 stimulation in SIV-infected rhesus monkeys. Nature 540, 284–287 (2016).

  6. 6.

    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).

  7. 7.

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

  8. 8.

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

  9. 9.

    Imamichi, H. et al. Defective HIV-1 proviruses produce novel protein-coding RNA species in HIV-infected patients on combination antiretroviral therapy. Proc. Natl Acad. Sci. USA 113, 8783–8788 (2016).

  10. 10.

    Sheehy, A. M., Gaddis, N. C., Choi, J. D. & Malim, M. H. Isolation of a human gene that inhibits HIV-1 infection and is suppressed by the viral Vif protein. Nature 418, 646–650 (2002).

  11. 11.

    Jordan, A., Bisgrove, D. & Verdin, E. HIV reproducibly establishes a latent infection after acute infection of T cells in vitro. EMBO J. 22, 1868–1877 (2003).

  12. 12.

    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).

  13. 13.

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

  14. 14.

    Maldarelli, F. et al. Specific HIV integration sites are linked to clonal expansion and persistence of infected cells. Science 345, 179–183 (2014).

  15. 15.

    Wagner, T. A. et al. Proliferation of cells with HIV integrated into cancer genes contributes to persistent infection. Science 345, 570–573 (2014).

  16. 16.

    Bui, J. K. et al. Proviruses with identical sequences comprise a large fraction of the replication-competent HIV reservoir. PLoS Pathog. 13, e1006283 (2017).

  17. 17.

    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).

  18. 18.

    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).

  19. 19.

    Wang, Z. et al. Expanded cellular clones carrying replication-competent HIV-1 persist, wax, and wane. Proc. Natl Acad. Sci. USA 115, E2575–E2584 (2018).

  20. 20.

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

  21. 21.

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

  22. 22.

    Ho, D. D. et al. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 373, 123–126 (1995).

  23. 23.

    Wei, X. et al. Viral dynamics in human immunodeficiency virus type 1 infection. Nature 373, 117–122 (1995).

  24. 24.

    Simonetti, F. R. et al. Clonally expanded CD4+ T cells can produce infectious HIV-1 in vivo. Proc. Natl Acad. Sci. USA 113, 1883–1888 (2016).

  25. 25.

    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 (2017).

  26. 26.

    Berry, C. C. et al. Estimating abundances of retroviral insertion sites from DNA fragment length data. Bioinformatics 28, 755–762 (2012).

  27. 27.

    Detels, R. et al. The multicenter AIDS Cohort Study, 1983 to … Public Health 126, 196–198 (2012).

  28. 28.

    Rose, P. P. & Korber, B. T. Detecting hypermutations in viral sequences with an emphasis on G→A hypermutation. Bioinformatics 16, 400–401 (2000).

  29. 29.

    Laird, G. M., Rosenbloom, D. I., Lai, J., Siliciano, R. F. & Siliciano, J. D. Measuring the frequency of latent HIV-1 in resting CD4+ T cells using a limiting dilution coculture assay. Methods Mol. Biol. 1354, 239–253 (2016).

  30. 30.

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

  31. 31.

    Rosenbloom, D. I. et al. Designing and interpreting limiting dilution assays: general principles and applications to the latent reservoir for human immunodeficiency virus-1. Open Forum Infect. Dis. 2, ofv123 (2015).

  32. 32.

    Sallusto, F., Lenig, D., Förster, R., Lipp, M. & Lanzavecchia, A. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708–712 (1999).

  33. 33.

    Durand, C. M. et al. HIV-1 DNA is detected in bone marrow populations containing CD4+ T cells but is not found in purified CD34+ hematopoietic progenitor cells in most patients on antiretroviral therapy. J. Infect. Dis. 205, 1014–1018 (2012).

  34. 34.

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

  35. 35.

    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 (2017).

Download references

Acknowledgements

We thank D. Finzi of NIAID for discussions leading to this work. This work was supported by the NIH Martin Delaney I4C (UM1 AI126603), Beat-HIV (UM1 AI126620) and DARE (UM1 AI12661) Collaboratories, by NIH grant 43222, by the Howard Hughes Medical Institute and the Bill and Melinda Gates Foundation (OPP1115715), and by NIH SBIR grants R43AI124996 and R44AI124996 and NSF grants 1621633 and 1738428 to Accelevir Diagnostics. Samples for some study participants were obtained from the Baltimore-Washington DC Center of the Multicenter AIDS Cohort Study (MACS) supported by NIH grants U01-AI-35042 and UL1-RR025005 (ICTR).

Author information

Author notes

    • Katherine M. Bruner

    Present address: Department of Molecular Biosciences, University of Texas, Austin, TX, USA

    • Ya-Chi Ho

    Present address: Department of Microbial Pathogenesis, Yale School of Medicine, New Haven, CT, USA

  1. These authors contributed equally: Katherine M. Bruner, Zheng Wang

Affiliations

  1. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Katherine M. Bruner
    • , Zheng Wang
    • , Francesco R. Simonetti
    • , Alexandra M. Bender
    • , Kyungyoon J. Kwon
    • , Srona Sengupta
    • , Emily J. Fray
    • , Subul A. Beg
    • , Annukka A. R. Antar
    • , Katharine M. Jenike
    • , Lynn N. Bertagnolli
    • , Adam A. Capoferri
    • , Joshua T. Kufera
    • , Andrew Timmons
    • , Ya-Chi Ho
    • , Joel N. Blankson
    • , Janet D. Siliciano
    •  & Robert F. Siliciano
  2. Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    • Christopher Nobles
    • , John Gregg
    •  & Frederic D. Bushman
  3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    • Nikolas Wada
  4. Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    • Hao Zhang
    •  & Joseph B. Margolick
  5. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    • Steven G. Deeks
  6. Accelevir Diagnostics, Baltimore, MD, USA

    • Gregory M. Laird
  7. Howard Hughes Medical Institute, Baltimore, MD, USA

    • Robert F. Siliciano

Authors

  1. Search for Katherine M. Bruner in:

  2. Search for Zheng Wang in:

  3. Search for Francesco R. Simonetti in:

  4. Search for Alexandra M. Bender in:

  5. Search for Kyungyoon J. Kwon in:

  6. Search for Srona Sengupta in:

  7. Search for Emily J. Fray in:

  8. Search for Subul A. Beg in:

  9. Search for Annukka A. R. Antar in:

  10. Search for Katharine M. Jenike in:

  11. Search for Lynn N. Bertagnolli in:

  12. Search for Adam A. Capoferri in:

  13. Search for Joshua T. Kufera in:

  14. Search for Andrew Timmons in:

  15. Search for Christopher Nobles in:

  16. Search for John Gregg in:

  17. Search for Nikolas Wada in:

  18. Search for Ya-Chi Ho in:

  19. Search for Hao Zhang in:

  20. Search for Joseph B. Margolick in:

  21. Search for Joel N. Blankson in:

  22. Search for Steven G. Deeks in:

  23. Search for Frederic D. Bushman in:

  24. Search for Janet D. Siliciano in:

  25. Search for Gregory M. Laird in:

  26. Search for Robert F. Siliciano in:

Contributions

K.M.B., Z.W., G.M.L. and R.F.S. designed the study. K.M.B., Z.W., G.M.L., F.R.S., A.M.B., K.J.K., S.S., E.J.F., A.A.R.A., K.M.J., L.N.B., J.T.K. and Y.-C.H. performed experiments. K.M.B., Z.W., F.R.S., A.T., N.W., J.D.S., G.M.L. and R.F.S. analysed data. C.N., J.G. and F.D.B. provided integration site analysis. H.Z. performed cell sorting. S.A.B., A.A.C., J.B.M., J.N.B. and S.G.D. provided patient samples. K.M.B. and R.F.S. wrote the manuscript.

Competing interests

Aspects of IPDA are subject of a patent application PCT/US16/28822 filed by Johns Hopkins University. K.M.B. and R.F.S. are inventors on this application. Accelevir Diagnostics holds an exclusive license for this patent application. G.M.L. is an employee of and shareholder in Accelevir Diagnostics. R.F.S. holds no equity interest in Accelevir Diagnostics. R.F.S. is a consultant on cure-related HIV research for Merck and Abbvie.

Corresponding author

Correspondence to Robert F. Siliciano.

Extended data figures and tables

  1. Extended Data Fig. 1 Analysis of hypermutation.

    a, Most hypermutated proviruses show both GG→AG and GA→AA patterns of hypermutation. Analysis based on hypermutated full-genome sequences in the database (n = 100). Sequences were analysed for GG→AG and GA→AA patterns using all available sequences for each clone and the Los Alamos Hypermut algorithm17. b, Hypermutation discrimination using two probes in the RRE of the env gene. The intact probe hybridizes with a region containing two adjacent APOBEC3G consensus sites (red underline) in intact proviruses. It is labelled with a fluorophore (VIC) and a quencher (Q). Also present in the reaction is a hypermutated probe that lacks the fluorophore and does not bind (arrow) to intact proviral sequences owing to G→A mutations at both APOBEC3G consensus sites. Dashed boxes indicate the nucleotide positions of sequence differences between the intact and hypermutated probes. The hypermutated probe preferentially binds to the same region in hypermutated proviruses. It lacks a fluorophore and prevents binding of the fluorophore-labelled intact probe (arrow). Therefore, no fluorescent signal is generated for 95% of hypermutated proviruses (Fig. 2f).

  2. Extended Data Fig. 2 Plasmid controls show the specificity of the IPDA.

    a, Maps of proviral plasmid control templates. Plasmids E44E11, 39G2 and 4F11 have deletions in the indicated regions (white). Plasmid 2G10 is a heavily hypermutated patient-derived sequence with G→A mutations in the probe-binding region of the env amplicon (enlarged region). Plasmid 19B3 has GA point mutations in this region. These G→A mutations (red) occur at two APOBEC3G consensus sites (TGGG, underlined) in this region. These plasmids have been previously described12,34. be, IPDA on plasmids representing the indicated defective proviruses showing positive droplets only in the expected quadrants.

  3. Extended Data Fig. 3 IPDA accuracy, reproducibility and limit of quantification.

    a, Correlation between expected and IPDA-measured frequencies of intact proviruses per 106 cells. Genomic DNA from uninfected donor CD4+ T cells was spiked with JLat6.3 DNA cell equivalents and subjected to a serial fourfold dilution. This material was then analysed by the IPDA, and the IPDA-measured frequencies of intact proviruses per million cells were compared to the expected frequencies (998, 249.5, 62.4, 15.6 and 3.9 intact proviruses per 106 CD4+ T cells). This experiment was performed independently three times. The agreement between the expected and IPDA-measured frequencies of intact proviruses was determined using Pearson correlation. b, Reproducibility of the IPDA across independent assay runs. Reproducibility was assessed by determining the coefficient of variation (CV) across three independent assay measurements of genomic DNA from uninfected donor CD4+ T cells spiked with JLat6.3 cell equivalents and subject to serial fourfold dilution, as described in a.

  4. Extended Data Fig. 4 IPDA reproducibility.

    ac, Frequencies of cells containing proviruses with 3′ deletions and/or hypermutation (a), 5′ deletions (b), or no defects (intact; c) in CD4+ T cells from 28 treated patients. Each data point represents a replicate IPDA determination from a single sample from the indicated patient. The mean and s.e.m. of the replicates are plotted. The variability between patients is much greater than the variation between replicates from a single patient. Technical replicates are shown to indicate low intrinsic variability of the IPDA.

  5. Extended Data Fig. 5 Plasmid controls confirm specificity of the IPDA.

    a, Map of the plasmid pNL4-3 carrying an intact HIV-1 provirus. Positions of the Ψ (blue) and env (green) IPDA amplicons and of a distinct set of plasmid shearing control (PSC, magenta boxes) amplicons are indicated. Spacing between PSC amplicons is equal to spacing between Ψ and env amplicons. Dotted lines indicate positions of deletions in plasmids carrying previously described12 defective proviruses E44E11 and 4F12 with 5′ and 3′ deletions, respectively. b, IPDA analysis of the indicated ZraI-cut plasmids representing intact, 5′-deleted and 3′-deleted proviruses. c, Summary of droplet counts for the experiment shown in b. E44E11 and 4F12 give positive droplets only in quadrants 4 and 1 (Q4 and Q1), respectively. For pNL4-3, more than 95% of droplets are in Q2, with the remainder attributable to shearing between the Ψ and env amplicons. d, Analysis of shearing. For IPDA analysis of patient samples, shearing was measured using amplicons in the RPP30 gene (Fig. 3a, d, e). For plasmid control experiments, shearing of ZraI-cut plasmids was analysed using two sets of amplicons, the Ψ and env IPDA amplicons and the equally spaced PSC amplicons shown in a. ddPCR analysis was done on fresh (D0) maxipreps of pNL4-3 linearized with ZraI at a concentration mimicking patient samples. To assess the effects of higher levels of DNA fragmentation, IPDA analysis was also done on pNL4-3 DNA that had been incubated at 4 °C for 5 days (D5), and on pNL4-3 DNA cut with both ZraI and EcoRI (two cuts). The mean and range of duplicate determinations of the DSI is shown for each set of amplicons. The DSI was the same for the IPDA and PSC amplicons at three different levels of shearing. The DSI was used to correct the IPDA droplet counts in e. Negative values were set to 0. e, Uncorrected and DSI-corrected IPDA analysis of the intact proviral construct pNL4-3 at different levels of fragmentation. After correction, positive droplets were almost exclusively in Q2 even at higher levels of fragmentation.

  6. Extended Data Fig. 6 Sequence analysis of Q2 proviruses.

    a, Sorting of productively infected CD4+ T cells. Cell preparations with a high fraction of intact proviruses were obtained by infecting CD4+ T lymphoblasts with a replication-competent HIV-1 carrying GFP in the nef ORF (R7-GFP37). After 48 h, GFP+ cells were collected by sorting. Genomic DNA was isolated, subjected to pulse field electrophoresis to remove unintegrated intermediates, and analysed by IPDA. b, IPDA analysis of high molecular mass DNA from sorted cells. Droplets in Q1 and Q4 largely reflect the shearing of intact proviruses (DSI = 0.46) during in DNA isolation and purification. c, Frequency of intact proviruses in GFP and GFP+ cells before and after correction for shearing. After correction for shearing, the frequency of intact proviruses in sorted GFP+ cells is close to the expected value of 1. d, Map of the HIV-1 genome in GFP-expressing HIV-1 vector R7-GFP used in a. GFP is inserted in the nef ORF. Positions of outer primers in the LTR and GFP used in single genome amplifications are indicated. e, Sequence analysis of nine independent single genomes. Arrows indicate positions of the Ψ and env IPDA amplicons. Orange lines indicate intact sequence without deletions or hypermutation and identical to R7-GFP except for single base mutations (black lines).

  7. Extended Data Fig. 7 DSI for patient samples.

    The DSI was determined by ddPCR using two amplicons in a cellular gene (RPP30) spaced at exactly the same distance as the Ψ and env amplicons. It is the fraction of templates in which DNA shearing has occurred between the amplicons. Horizontal bars indicate median and interquartile range; data from n = 62 patient samples.

  8. Extended Data Fig. 8 In vivo decay rates of cells with intact and defective proviruses.

    The frequency of cells carrying intact proviruses, proviruses with 3′ deletion and/or hypermutation (3′ del/hyper), and proviruses with 5′ deletions (5′ del) was measured in resting CD4+ T cells from patients on long-term suppressive ART. Data are plotted in terms of decay rate assuming exponential decay. Half-life values for the same decay curves are shown in Fig. 4c. Negative decay rate indicates proliferation.

  9. Extended Data Fig. 9 Variability in decay slopes.

    Shown are the mean and s.d. of the decay slopes for intact and defective proviruses in infected individuals on ART sampled longitudinally (n = 14). Analysis based on decay data in Fig. 4a.

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Tables S1-S6 and References.

  2. Reporting Summary

About this article

Publication history

Received

Accepted

Published

Issue Date

DOI

https://doi.org/10.1038/s41586-019-0898-8

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.