Sustained, drug-free control of HIV-1 replication is naturally achieved in less than 0.5% of infected individuals (here termed ‘elite controllers’), despite the presence of a replication-competent viral reservoir1. Inducing such an ability to spontaneously maintain undetectable plasma viraemia is a major objective of HIV-1 cure research, but the characteristics of proviral reservoirs in elite controllers remain to be determined. Here, using next-generation sequencing of near-full-length single HIV-1 genomes and corresponding chromosomal integration sites, we show that the proviral reservoirs of elite controllers frequently consist of oligoclonal to near-monoclonal clusters of intact proviral sequences. In contrast to individuals treated with long-term antiretroviral therapy, intact proviral sequences from elite controllers were integrated at highly distinct sites in the human genome and were preferentially located in centromeric satellite DNA or in Krüppel-associated box domain-containing zinc finger genes on chromosome 19, both of which are associated with heterochromatin features. Moreover, the integration sites of intact proviral sequences from elite controllers showed an increased distance to transcriptional start sites and accessible chromatin of the host genome and were enriched in repressive chromatin marks. These data suggest that a distinct configuration of the proviral reservoir represents a structural correlate of natural viral control, and that the quality, rather than the quantity, of viral reservoirs can be an important distinguishing feature for a functional cure of HIV-1 infection. Moreover, in one elite controller, we were unable to detect intact proviral sequences despite analysing more than 1.5 billion peripheral blood mononuclear cells, which raises the possibility that a sterilizing cure of HIV-1 infection, which has previously been observed only following allogeneic haematopoietic stem cell transplantation2,3, may be feasible in rare instances.
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RNA-seq and ATAC-seq data have been deposited in the NCBI GEO (accession number GSE144334). Owing to study participant confidentiality concerns, full-length viral sequencing data cannot be publicly released, but will be made available to investigators upon reasonable request and after signing a coded tissue agreement. The Los Alamos HIV Sequence Database Hypermut 2.0 and the Los Alamos HIV Immunology Database 2.0 are available at https://www.hiv.lanl.gov/content/index. The iMethyl database is available at http://imethyl.iwate-megabank.org. ROADMAP epigenomic data are available at http://www.roadmapepigenomics.org.
Sáez-Cirión, A. & Pancino, G. HIV controllers: a genetically determined or inducible phenotype? Immunol. Rev. 254, 281–294 (2013).
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).
Gupta, R. K. et al. HIV-1 remission following CCR5Δ32/Δ32 haematopoietic stem-cell transplantation. Nature 568, 244–248 (2019).
McLaren, P. J. & Carrington, M. The impact of host genetic variation on infection with HIV-1. Nat. Immunol. 16, 577–583 (2015).
Migueles, S. A. et al. Lytic granule loading of CD8+ T cells is required for HIV-infected cell elimination associated with immune control. Immunity 29, 1009–1021 (2008).
Gaiha, G. D. et al. Structural topology defines protective CD8+ T cell epitopes in the HIV proteome. Science 364, 480–484 (2019).
Migueles, S. A. & Connors, M. Success and failure of the cellular immune response against HIV-1. Nat. Immunol. 16, 563–570 (2015).
Blankson, J. N. et al. Isolation and characterization of replication-competent human immunodeficiency virus type 1 from a subset of elite suppressors. J. Virol. 81, 2508–2518 (2007).
Veenhuis, R. T. et al. Long-term remission despite clonal expansion of replication-competent HIV-1 isolates. JCI Insight 3, e122795 (2018).
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).
Popper, K. Die Logik der Forschung. Zur Erkenntnistheorie der modernen Naturwissenschaft (Springer, 1935).
Ho, Y. C. et al. Replication-competent noninduced proviruses in the latent reservoir increase barrier to HIV-1 cure. Cell 155, 540–551 (2013).
Melamed, A. et al. Genome-wide determinants of proviral targeting, clonal abundance and expression in natural HTLV-1 infection. PLoS Pathog. 9, e1003271 (2013).
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).
Wagner, T. A. et al. Proliferation of cells with HIV integrated into cancer genes contributes to persistent infection. Science 345, 570–573 (2014).
Cohn, L. B. et al. HIV-1 integration landscape during latent and active infection. Cell 160, 420–432 (2015).
Maldarelli, F. et al. Specific HIV integration sites are linked to clonal expansion and persistence of infected cells. Science 345, 179–183 (2014).
McNulty, S. M. & Sullivan, B. A. Alpha satellite DNA biology: finding function in the recesses of the genome. Chromosome Res. 26, 115–138 (2018).
Carteau, S., Hoffmann, C. & Bushman, F. Chromosome structure and human immunodeficiency virus type 1 cDNA integration: centromeric alphoid repeats are a disfavored target. J. Virol. 72, 4005–4014 (1998).
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).
Lewinski, M. K. et al. Genome-wide analysis of chromosomal features repressing human immunodeficiency virus transcription. J. Virol. 79, 6610–6619 (2005).
Schröder, A. R. et al. HIV-1 integration in the human genome favors active genes and local hotspots. Cell 110, 521–529 (2002).
Ecco, G., Imbeault, M. & Trono, D. KRAB zinc finger proteins. Development 144, 2719–2729 (2017).
Lukic, S., Nicolas, J. C. & Levine, A. J. The diversity of zinc-finger genes on human chromosome 19 provides an evolutionary mechanism for defense against inherited endogenous retroviruses. Cell Death Differ. 21, 381–387 (2014).
Vogel, M. J. et al. Human heterochromatin proteins form large domains containing KRAB-ZNF genes. Genome Res. 16, 1493–1504 (2006).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Chomont, N. et al. HIV reservoir size and persistence are driven by T cell survival and homeostatic proliferation. Nat. Med. 15, 893–900 (2009).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).
Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Lucic, B. et al. Spatially clustered loci with multiple enhancers are frequent targets of HIV-1 integration. Nat. Commun. 10, 4059 (2019).
Komaki, S. et al. iMETHYL: an integrative database of human DNA methylation, gene expression, and genomic variation. Hum. Genome Var. 5, 18008 (2018).
Affinito, O. et al. Nucleotide distance influences co-methylation between nearby CpG sites. Genomics 112, 144–150 (2020).
Kauder, S. E., Bosque, A., Lindqvist, A., Planelles, V. & Verdin, E. Epigenetic regulation of HIV-1 latency by cytosine methylation. PLoS Pathog. 5, e1000495 (2009).
Marini, B. et al. Nuclear architecture dictates HIV-1 integration site selection. Nature 521, 227–231 (2015).
Ciuffi, A. et al. A role for LEDGF/p75 in targeting HIV DNA integration. Nat. Med. 11, 1287–1289 (2005).
Achuthan, V. et al. Capsid–CPSF6 interaction licenses nuclear HIV-1 trafficking to sites of viral DNA integration. Cell Host Microbe 24, 392–404 (2018).
Debyser, Z., Vansant, G., Bruggemans, A., Janssens, J. & Christ, F. Insight in HIV integration site selection provides a block-and-lock strategy for a functional cure of HIV infection. Viruses 11, 12 (2018).
Battivelli, E. et al. Distinct chromatin functional states correlate with HIV latency reactivation in infected primary CD4+ T cells. eLife 7, e34655 (2018).
Estes, J. D. et al. Defining total-body AIDS-virus burden with implications for curative strategies. Nat. Med. 23, 1271–1276 (2017).
Weinberger, L. S., Burnett, J. C., Toettcher, J. E., Arkin, A. P. & Schaffer, D. V. Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell 122, 169–182 (2005).
Burtt, N. P. Whole-genome amplification using Φ29 DNA polymerase. Cold Spring Harb. Protoc. 2011, pdb.prot5552 (2011).
Lee, G. Q. et al. HIV-1 DNA sequence diversity and evolution during acute subtype C infection. Nat. Commun. 10, 2737 (2019).
Hiener, B. et al. Identification of genetically intact HIV-1 proviruses in specific CD4+ T cells from effectively treated participants. Cell Rep. 21, 813–822 (2017).
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).
Rose, P. P. & Korber, B. T. Detecting hypermutations in viral sequences with an emphasis on G→A hypermutation. Bioinformatics 16, 400–401 (2000).
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Siepel, A. C., Halpern, A. L., Macken, C. & Korber, B. T. A computer program designed to screen rapidly for HIV type 1 intersubtype recombinant sequences. AIDS Res. Hum. Retroviruses 11, 1413–1416 (1995).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Serrao, E., Cherepanov, P. & Engelman, A. N. Amplification, next-generation sequencing, and genomic DNA mapping of retroviral integration sites. J. Vis. Exp. 109, e53840 (2016).
Trombetta, J. J. et al. Preparation of single-cell RNA-seq libraries for next generation sequencing. Curr. Protoc. Mol. Biol. 107, 4.22.1–4.22.17 (2014).
Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
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).
Bruner, K. M. et al. A quantitative approach for measuring the reservoir of latent HIV-1 proviruses. Nature 566, 120–125 (2019).
Chen, H. et al. CD4+ T cells from elite controllers resist HIV-1 infection by selective upregulation of p21. J. Clin. Invest. 121, 1549–1560 (2011).
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).
Kuo, H. H. et al. Anti-apoptotic protein BIRC5 maintains survival of HIV-1-infected CD4+ T cells. Immunity 48, 1183–1194 (2018).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Roederer, M., Nozzi, J. L. & Nason, M. C. SPICE: exploration and analysis of post-cytometric complex multivariate datasets. Cytometry 79A, 167–174 (2011).
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).
Robson, M. I. et al. Constrained release of lamina-associated enhancers and genes from the nuclear envelope during T-cell activation facilitates their association in chromosome compartments. Genome Res. 27, 1126–1138 (2017).
X.G.Y. is supported by NIH grants HL134539, AI116228, AI078799, DA047034 and the Bill and Melinda Gates Foundation (INV-002703). M.L. is supported by NIH grants AI098487, AI135940, AI114235, AI117841, AI120008 and DK120387. M.L. and X.G.Y. are Associated Members of the BEAT-HIV Martin Delaney Collaboratory (UM1 AI126620). A.N.E. is supported by NIH grant AI052014. Support was also provided by the Harvard University (HU) and University of California at San Francisco (UCSF)/Gladstone Institute for HIV Cure Research Centers for AIDS Research (P30 AI060354 and P30 AI027763, respectively), which are supported by the following institutes and centers that are co-funded by and associated with the US National Institutes of Health: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC and OAR, and by HU CFAR Developmental Awards (S.H.). We thank the MGH DNA core facility. R.F.S. and J.M.S. are supported by the NIH Martin Delaney I4C (UM1 AI126603), BEAT-HIV (UM1 AI126620) and the Delaney AIDS Research Enterprise (DARE; UM1 AI126611) Collaboratories and by the Howard Hughes Medical Institute and the Bill and Melinda Gates Foundation (OPP1115715). Additional support for the SCOPE cohort was provided by DARE (AI096109 and AI127966) and the amfAR Institute for HIV Cure Research (amfAR 109301). G.M.L. is supported by NSF grant 1738428 and NIH grant R44AI124996. The International HIV Controller Cohort is supported by the Bill and Melinda Gates Foundation (OPP1066973), the Ragon Institute of MGH, MIT and Harvard, the NIH (R37 AI067073 to B.D.W.) and the Mark and Lisa Schwartz Family Foundation. This project has been funded in whole or in part with federal funds from the Frederick National Laboratory for Cancer Research, under contract no. HHSN261200800001E. This research was supported in part by the Intramural Research Program of the NIH, Frederick National Lab, Center for Cancer Research and Intramural Programs of NIDCR, NIH. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.
A.N.E. has received fees from ViiV Healthcare within the past year for work unrelated to this project. All other authors declare no competing interests.
Peer review information Nature thanks Nicolas Chomont, Philippe Lemey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
a, Genetic distance (expressed as the average number of base pair substitutions) among all intact near-full-length proviral sequences obtained from each study participant. Clonal sequences were considered to be individual sequences; participants with at least two intact proviruses are included (n = 175 intact proviral sequences from 24 elite controllers and n = 147 intact proviral sequences from 26 ART-treated individuals). b, Frequencies of proviral species (copies per million resting CD4+ T cells) detected by IPDA from EC2. c, Proportion of optimal CTL epitopes (restricted by autologous HLA class I isotypes) with wild-type sequences within intact HIV-1 clade B sequences. Each dot represents one intact proviral sequence. n = 182 and n = 133 HIV-1 clade B intact sequences from 47 elite controllers and 34 ART-treated individuals are included, respectively. Optimal CTL epitopes matching the clade B consensus sequences were considered to be wild-type sequences. Clonal sequences were considered to be individual sequences. d, e, Average proportions of autologous HLA-class I restricted optimal CTL epitopes with wild-type sequences calculated from intact proviruses in each study participant. Clonal sequences were counted either once (d) or as individual sequences (e). Each dot represents one study participant. f, Proportions of optimal CTL epitopes containing escape variants (restricted by HLA-A01/A02 supertypes, HLA-A03 supertype or HLA-B*27/B*57) within intact proviruses from elite controllers and ART-treated individuals. Each dot represents one intact proviral sequence. Clonal sequences were counted individually. g, h, Proportion of clonal intact proviruses among all intact proviruses within each study participant (g) or within all intact proviruses from elite controllers and ART-treated individuals (h). Study participants for whom at least two intact proviruses were detected are included in g and h. Two-tailed Mann–Whitney U-tests were used for data shown in a, c–g; two-sided Fisher’s exact test was used for data shown in h.
Extended Data Fig. 2 Longitudinal evolution of CD4+ T cell counts and HIV-1 viral loads in EC1–EC13.
The recorded diagnosis date of HIV-1 infection for each study participant is shown as the first date on the x axis. PBMC sampling time points are indicated by red arrows.
Virograms reflect the genetic coverage of individual sequences of proviral genomes analysed in EC3–EC13. Numbers of total near-full-length proviral sequences obtained from each individual are shown on the y axis; numbers of independent sequences are indicated in brackets. Open boxes indicate clonal clusters.
Extended Data Fig. 4 The variations in HIV-1 DNA sequences in 5′ LTR regions from intact proviruses isolated from the indicated elite controllers, relative to HXB2.
Numbers of 5′ LTR sequences of intact proviruses obtained from each individual are shown on the vertical axis. Open boxes indicate clonal clusters.
Extended Data Fig. 5 Features of the chromosomal integration sites of intact proviruses from elite controllers after counting clonal sequences individually.
a, Heat map indicating the relative proportion of proviral integration sites of intact proviruses in each chromosome in elite controllers, relative to corresponding data from long-term ART-treated individuals14. Proviral integration site data from previous publications9,15,17 are shown for comparison; integration sites from intact and defective proviruses were not distinguished in these studies. Contributions of each chromosome to the total number of genes (first row) and to the total size of the human genome (second row) are included as references. b, c, Proportion of near-full-length intact proviruses located in the indicated genomic regions. Data from near-full-length intact proviral sequences in long-term ART-treated individuals are shown as a reference14; chromosomal integration sites from unselected (intact and defective) proviral sequences in elite controllers9 and in ART-treated individuals15,17 are also shown for comparison. d, SPICE diagrams59 showing the proportion of intact proviruses with the indicated chromosomal integration site features in elite controllers and ART-treated individuals. e, f, Chromosomal distance between integration sites of intact proviruses and the most proximal transcriptional start sites (determined by RNA-seq) (e) or to the most proximal ATAC-seq peak (f) in autologous total, central memory and effector memory CD4+ T cells and in the Genome Browser (GB). Horizontal lines show the geometric mean. g, Proportions of proviral sequences located in structural compartments A and B, as determined using previously published Hi-C-seq data29. Chromosomal integration regions not covered in the previous study29 were excluded from the analysis. f, g, Sequences in genomic regions included in the blacklist for functional genomics analysis identified by the ENCODE and modENCODE consortia28 were excluded owing to the absence of reliable ATAC-seq and Hi-C-seq reads in such repetitive regions. a–g, All members of clonal clusters were included as individual sequences. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05; FDR-adjusted two-sided Fisher’s exact tests were used for data shown in b and c; two-sided Fisher’s exact tests were used for data shown in d and g; FDR-adjusted two-tailed Mann–Whitney U-tests were used for data shown in e and f; all comparisons were made between elite controllers and reference groups.
Extended Data Fig. 6 Epigenetic features of the chromosomal integration sites of intact proviruses from elite controllers.
a–d, Numbers of DNA-sequencing reads associated with activating (H3K27ac) or repressive (H3K27me3) histone protein modifications in proximity to integration sites from elite controllers and long-term ART-treated individuals; median and confidence intervals (defined by one standard deviation) of ChIP–seq data from primary memory CD4+ T cells included in the ROADMAP repository26 are shown. Negative distances indicate genomic regions upstream of the HIV-1 5′ LTR host–viral junction; positive distances indicate regions downstream of the 3′ LTR viral–host junction. DNA-sequencing reads associated with H3K36me3, a chromatin mark that is atypically enriched in KRAB-ZNF genes on chromosome 19, are also shown29. e, f, Proportions of intact proviral sequences located in structural compartments A and B (and associated sub-compartments) by counting clonal sequences once (e) or by counting clonal sequences individually (f), as determined based on the alignment of chromosomal integration sites of intact proviruses to Hi-C-seq data from Jurkat cells30. Chromosomal integration regions not covered in the Jurkat cell study30 were excluded from the analysis. Compartment B4 was not assessed in the source data30 for this analysis. Two-sided Fisher’s exact tests were used for statistical comparisons; nominal P values are reported. a–f, Sequences in genomic regions included in the blacklist for functional genomics analysis identified by the ENCODE and modENCODE consortia28 were excluded owing to the absence of reliable ChIP–seq and Hi-C-seq reads in such repetitive regions.
Extended Data Fig. 7 Accessory features of chromosomal integration sites of intact proviral sequences from elite controllers.
a, Expression of host genes that contain intact proviral sequences in elite controllers and long-term ART-treated individuals, as determined by autologous RNA-seq data in total, central memory and effector memory CD4+ T cells. Gene expression percentiles are indicated. b, c, Orientation of intact proviruses relative to host genes in elite controllers and long-term ART-treated individuals. All data for genic integration sites are included, except for integration sites in genic regions associated with multiple genes in opposing orientations. Integration site data from previous studies of elite controllers9 and ART-treated individuals15,17 are shown for comparative purposes. d, e, Proportion of intact proviruses from elite controllers and long-term ART-treated individuals in lamina-associated domains, determined using Lamin B1–DNA adenine methyltransferase identification (DamID)61 for resting Jurkat cells. Integration site data from previous studies of elite controllers9 and ART-treated individuals15,17 are shown for comparative purposes. b, d, Clonal proviruses were counted once. c, e, Clonal proviruses were counted as individual sequences (FDR-adjusted two-sided Fisher’s exact tests). f, Expression of LEDGF (also known as PSIP1 or p75) and CPSF6 mRNA in autologous total CD4+ T cells from elite controllers and long-term ART-treated individuals, as determined by RNA-seq. Gene expression percentiles are indicated. a, f, Horizontal lines show the geometric mean. All comparisons were made between elite controllers and reference groups.
Extended Data Fig. 8 Features of chromosomal integration sites of in vitro-infected CD4+ T cells from elite controllers and HIV-1-negative study participants.
a, Heat map showing the relative proportion of proviral integration sites in sorted GFP+ or GFP− in vitro-infected CD4+ T cells (determined by ligation-mediated PCR49) from elite controllers and HIV-1-negative study participants (HIVNs), relative to proviral integration sites of intact proviruses in each chromosome in elite controllers; integration sites from intact and defective proviruses were not distinguished in in vitro-infection studies. Data from GFP+ (n = 74,055) and GFP− (n = 15,105) CD4+ T cell populations from elite controllers and from GFP+ (n = 31,682) and GFP− (n = 4,229) CD4+ T cell populations from HIV-1-negative study participants were included. Contributions of each chromosome to the total number of genes (first row) and to the total size of the human genome (second row) are included as references. b, c, Proportion of proviral integration sites located in indicated genomic regions (b) or defined genes (c). Data from near-full-length intact proviral sequences in elite controllers are indicated for reference. ****P < 0.0001, ***P < 0.001, *P < 0.05; FDR-adjusted two-sided Fisher’s exact tests or two-tailed χ2 tests were used as appropriate; P values indicating comparisons made between intact proviruses from elite controllers (determined ex vivo) and each in vitro-infection group are shown in corresponding colours.
Supplementary Table 1: Integration sites of all intact proviral sequences from elite controllers.
Supplementary Table 2: Integration sites detected in in-vitro infected CD4+ T cells. EC_GFP_N and EC_GFP_P denotes integration sites from GFP-negative (n=15,251 total IS; n=15,105 unique IS) and GFP-positive (n=74,551 total IS; n=74,055 unique IS) CD4+ T cells from 12 elite controllers; Neg_GFP_N and Neg_GFP_P denotes integration sites from GFP-negative (n=4,527 total IS; n=4,229 unique IS) and GFP-positive (n=31,801 total IS; n=31,682 unique IS) CD4+ T cells from 9 HIV-1-negative persons.
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Jiang, C., Lian, X., Gao, C. et al. Distinct viral reservoirs in individuals with spontaneous control of HIV-1. Nature 585, 261–267 (2020). https://doi.org/10.1038/s41586-020-2651-8
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