IfHMaE, I. Global Burden of Disease Study 2021 (GBD 2021) Results. https://vizhub.healthdata.org/gbd-results/ (2022).
HIV.gov. The Global HIV and AIDS Epidemic. https://www.hiv.gov/federal-response/pepfar-global-aids/global-hiv-aids-overview (2025).
Rodger, A. J. et al. Risk of HIV transmission through condomless sex in serodifferent gay couples with the HIV-positive partner taking suppressive antiretroviral therapy (PARTNER): Final results of a multicentre, prospective, observational study. Lancet 393, 2428–2438. https://doi.org/10.1016/S0140-6736(19)30418-0 (2019).
Powell, T. R. et al. The behavioral, cellular and immune mediators of HIV-1 acquisition: New insights from population genetics. Sci. Rep. 10, 3304. https://doi.org/10.1038/s41598-020-59256-0 (2020).
Donnell, D. et al. HIV protective efficacy and correlates of tenofovir blood concentrations in a clinical trial of PrEP for HIV prevention. J. Acquir. Immune Defic. Syndr. 66, 340–348. https://doi.org/10.1097/QAI.0000000000000172 (2014).
Ngcobo, S. et al. Pre-infection plasma cytokines and chemokines as predictors of HIV disease progression. Sci. Rep. 12, 2437. https://doi.org/10.1038/s41598-022-06532-w (2022).
Liebenberg, L. J. et al. Genital-systemic chemokine gradients and the Risk of HIV acquisition in women. J. Acquir. Immune Defic. Syndr. 74, 318–325. https://doi.org/10.1097/QAI.0000000000001218 (2017).
Leeansyah, E., Malone, D. F., Anthony, D. D. & Sandberg, J. K. Soluble biomarkers of HIV transmission, disease progression and comorbidities. Curr. Opin. HIV AIDS 8, 117–124. https://doi.org/10.1097/COH.0b013e32835c7134 (2013).
de Lara, L. M., Parthasarathy, R. S. & Rodriguez-Garcia, M. Mucosal immunity and HIV acquisition in women. Curr. Opin. Physiol. 19, 32–38. https://doi.org/10.1016/j.cophys.2020.07.021 (2021).
Luo, M. Natural immunity against HIV-1: Progression of understanding after association studies. Viruses https://doi.org/10.3390/v14061243 (2022).
Morrison, C. S. et al. Concomitant imbalances of systemic and mucosal immunity increase HIV acquisition risk. J. Acquir. Immune Defic. Syndr. 84, 85–91. https://doi.org/10.1097/QAI.0000000000002299 (2020).
Huang, Y. et al. The role of a mutant CCR5 allele in HIV-1 transmission and disease progression. Nat. Med. 2, 1240–1243. https://doi.org/10.1038/nm1196-1240 (1996).
Deng, H. et al. Identification of a major co-receptor for primary isolates of HIV-1. Nature 381, 661–666. https://doi.org/10.1038/381661a0 (1996).
Wachira, D., Lihana, R., Okoth, V., Maiyo, A. & Khamadi, S. A. Chemokine coreceptor-2 gene polymorphisms among HIV-1 infected individuals in Kenya. Dis. Markers 2015, 952067. https://doi.org/10.1155/2015/952067 (2015).
Teran, L. M., Ramirez-Jimenez, F., Soid-Raggi, G. & Velazquez, J. R. Interleukin 16 and CCL17/thymus and activation-regulated chemokine in patients with aspirin-exacerbated respiratory disease. Ann. Allergy Asthma Immunol. 118, 191–196. https://doi.org/10.1016/j.anai.2016.11.004 (2017).
Lee, K. & Lim, C. Y. Mendelian randomization analysis in observational epidemiology. J. Lipid Atheroscler. 8, 67–77. https://doi.org/10.12997/jla.2019.8.2.67 (2019).
Emdin, C. A., Khera, A. V. & Kathiresan, S. Mendelian randomization. JAMA 318, 1925–1926. https://doi.org/10.1001/jama.2017.17219 (2017).
Wu, Y. et al. Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution. Hum. Mol. Genet. 28, 4161–4172. https://doi.org/10.1093/hmg/ddz263 (2019).
Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362, k601. https://doi.org/10.1136/bmj.k601 (2018).
Orru, V. et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet 52, 1036–1045. https://doi.org/10.1038/s41588-020-0684-4 (2020).
Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281. https://doi.org/10.1038/ng.3368 (2015).
Duarte, R. R. R., Pain, O., Furler, R. L., Nixon, D. F. & Powell, T. R. Transcriptome-wide association study of HIV-1 acquisition identifies HERC1 as a susceptibility gene. iScience 25, 104854. https://doi.org/10.1016/j.isci.2022.104854 (2022).
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518. https://doi.org/10.1038/s41586-022-05473-8 (2023).
Johnson, E. O. et al. Novel genetic locus implicated for HIV-1 acquisition with putative regulatory links to HIV replication and infectivity: A genome-wide association study. PLoS ONE 10, e0118149. https://doi.org/10.1371/journal.pone.0118149 (2015).
McLaren, P. J. et al. Association study of common genetic variants and HIV-1 acquisition in 6,300 infected cases and 7,200 controls. PLoS Pathog. 9, e1003515. https://doi.org/10.1371/journal.ppat.1003515 (2013).
Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665. https://doi.org/10.1002/gepi.21758 (2013).
Genomes Project, C. et al. A global reference for human genetic variation. Nature 526, 68–74, https://doi.org/10.1038/nature15393 (2015).
Burgess, S., Thompson, S. G. & Collaboration, C. C. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764. https://doi.org/10.1093/ije/dyr036 (2011).
Palmer, T. M. et al. Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am. J. Epidemiol. 173, 1392–1403. https://doi.org/10.1093/aje/kwr026 (2011).
Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 26, 2333–2355. https://doi.org/10.1177/0962280215597579 (2017).
Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42. https://doi.org/10.1097/EDE.0000000000000559 (2017).
Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525. https://doi.org/10.1093/ije/dyv080 (2015).
Chen, X., Li, A., Kuang, Y. & Ma, Q. Gastroesophageal reflux disease and atrial fibrillation: A bidirectional Mendelian randomization study. Int. J. Med. Sci. 21, 1321–1328. https://doi.org/10.7150/ijms.95518 (2024).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383. https://doi.org/10.1371/journal.pgen.1004383 (2014).
Watts, E. L. et al. Circulating insulin-like growth factors and risks of overall, aggressive and early-onset prostate cancer: A collaborative analysis of 20 prospective studies and Mendelian randomization analysis. Int. J. Epidemiol. 52, 71–86. https://doi.org/10.1093/ije/dyac124 (2023).
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918. https://doi.org/10.1038/s41467-018-03371-0 (2018).
Consortium, G. T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330, https://doi.org/10.1126/science.aaz1776 (2020).
McRae, A. F. et al. Identification of 55,000 replicated DNA methylation QTL. Sci. Rep. 8, 17605. https://doi.org/10.1038/s41598-018-35871-w (2018).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523. https://doi.org/10.1038/s41467-019-09234-6 (2019).
Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: Biological systems database as a model of the real world. Nucl. Acids Res. 53, D672–D677. https://doi.org/10.1093/nar/gkae909 (2025).
Kuleshov, M. V. et al. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucl. Acids Res. 44, W90-97. https://doi.org/10.1093/nar/gkw377 (2016).
Xie, Z. et al. Gene set knowledge discovery with enrichr. Curr. Protoc. 1, e90. https://doi.org/10.1002/cpz1.90 (2021).
Chen, E. Y. et al. Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf. 14, 128. https://doi.org/10.1186/1471-2105-14-128 (2013).
Fowke, K. R. et al. Resistance to HIV-1 infection among persistently seronegative prostitutes in Nairobi, Kenya. Lancet 348, 1347–1351. https://doi.org/10.1016/S0140-6736(95)12269-2 (1996).
Dabis, F. et al. Rates of mother-to-child transmission of HIV-1 in Africa, America, and Europe: Results from 13 perinatal studies. The Working Group on Mother-To-Child Transmission of HIV. J. Acquir. Immune. Defic. Syndr. Hum. Retrovirol. 8, 506–510. https://doi.org/10.1097/00042560-199504120-00011 (1995).
Pedraza, M. A. et al. Heterosexual transmission of HIV-1 is associated with high plasma viral load levels and a positive viral isolation in the infected partner. J Acquir Immune Defic Syndr 21, 120–125 (1999).
Patel, P. et al. Estimating per-act HIV transmission risk: a systematic review. AIDS 28, 1509–1519. https://doi.org/10.1097/QAD.0000000000000298 (2014).
McLaren, P. J. et al. Polymorphisms of large effect explain the majority of the host genetic contribution to variation of HIV-1 virus load. Proc. Natl. Acad. Sci. USA 112, 14658–14663. https://doi.org/10.1073/pnas.1514867112 (2015).
Ding, D. L., Liu, S. J. & Zhu, H. Z. Association between the CCR2-Val64Ile polymorphism and susceptibility to HIV-1 infection: A meta-analysis. Mol. Med. Rep. 4, 181–186. https://doi.org/10.3892/mmr.2010.400 (2011).
Ngoufack, M. N. et al. CCR2 polymorphism and HIV: Mutation in both mother and child is associated with higher transmission. Int. J. Biochem. Mol. Biol. 10, 42–48 (2019).
Qijian, S. et al. Distribution of CCR5-delta32, CCR2-64I, and SDF1-3’A in Guangxi Zhuang population. J. Int. Assoc. Physicians AIDS Care (Chic) 9, 145–149. https://doi.org/10.1177/1545109710367517 (2010).
Sundaravaradan, V., Mir, K. D. & Sodora, D. L. Double-negative T cells during HIV/SIV infections: Potential pinch hitters in the T-cell lineup. Curr. Opin. HIV AIDS 7, 164–171. https://doi.org/10.1097/COH.0b013e3283504a66 (2012).
Rodriguez-Rodriguez, N. et al. TCR-alpha/beta CD4(-) CD8(-) double negative T cells arise from CD8(+) T cells. J. Leukoc Biol. 108, 851–857. https://doi.org/10.1002/JLB.1AB0120-548R (2020).
Brandt, D. & Hedrich, C. M. TCRalphabeta(+)CD3(+)CD4(-)CD8(-) (double negative) T cells in autoimmunity. Autoimmun. Rev. 17, 422–430. https://doi.org/10.1016/j.autrev.2018.02.001 (2018).
Singleterry, W. L., Henderson, H. & Cruse, J. M. Depletion of pro-inflammatory CD161(+) double negative (CD3(+)CD4(-)CD8(-)) T cells in AIDS patients is ameliorated by expansion of the gammadelta T cell population. Exp. Mol. Pathol. 92, 155–159. https://doi.org/10.1016/j.yexmp.2011.11.002 (2012).
Matsumoto, M., Yasukawa, M., Inatsuki, A. & Kobayashi, Y. Human double-negative (CD4-CD8-) T cells bearing alpha beta T cell receptor possess both helper and cytotoxic activities. Clin. Exp. Immunol. 85, 525–530. https://doi.org/10.1111/j.1365-2249.1991.tb05761.x (1991).
Wu, Z. et al. CD3(+)CD4(-)CD8(-) (Double-Negative) T cells in inflammation. Immune Disord. Cancer. Front. Immunol. 13, 816005. https://doi.org/10.3389/fimmu.2022.816005 (2022).
Meziane, O. et al. HIV infection and persistence in pulmonary mucosal double negative T cells in vivo. J. Virol. https://doi.org/10.1128/JVI.01788-20 (2020).
Chachage, M. et al. CD25+ FoxP3+ Memory CD4 T cells are frequent targets of HIV infection in vivo. J. Virol. 90, 8954–8967. https://doi.org/10.1128/JVI.00612-16 (2016).
Pallikkuth, S., de Armas, L., Rinaldi, S. & Pahwa, S. T follicular helper cells and B cell dysfunction in aging and HIV-1 infection. Front. Immunol. 8, 1380. https://doi.org/10.3389/fimmu.2017.01380 (2017).
Carrillo, J. et al. Memory B cell dysregulation in HIV-1-infected individuals. AIDS 32, 149–160. https://doi.org/10.1097/QAD.0000000000001686 (2018).
Buckner, C. M. et al. Maintenance of HIV-specific memory B-Cell responses in elite controllers despite low viral burdens. J. Infect. Dis. 214, 390–398. https://doi.org/10.1093/infdis/jiw163 (2016).
Moir, S. & Fauci, A. S. B-cell responses to HIV infection. Immunol Rev 275, 33–48. https://doi.org/10.1111/imr.12502 (2017).
Ostrand-Rosenberg, S. & Sinha, P. Myeloid-derived suppressor cells: linking inflammation and cancer. J Immunol 182, 4499–4506. https://doi.org/10.4049/jimmunol.0802740 (2009).
Gabrilovich, D. I. & Nagaraj, S. Myeloid-derived suppressor cells as regulators of the immune system. Nat. Rev. Immunol. 9, 162–174. https://doi.org/10.1038/nri2506 (2009).
Dai, J., El Gazzar, M., Li, G. Y., Moorman, J. P. & Yao, Z. Q. Myeloid-derived suppressor cells: paradoxical roles in infection and immunity. J. Innate Immun. 7, 116–126. https://doi.org/10.1159/000368233 (2015).
Youn, J. I., Collazo, M., Shalova, I. N., Biswas, S. K. & Gabrilovich, D. I. Characterization of the nature of granulocytic myeloid-derived suppressor cells in tumor-bearing mice. J Leukoc. Biol. 91, 167–181. https://doi.org/10.1189/jlb.0311177 (2012).
Munder, M. et al. Arginase I is constitutively expressed in human granulocytes and participates in fungicidal activity. Blood 105, 2549–2556. https://doi.org/10.1182/blood-2004-07-2521 (2005).
Liu, C. Y. et al. Population alterations of L-arginase- and inducible nitric oxide synthase-expressed CD11b+/CD14(-)/CD15+/CD33+ myeloid-derived suppressor cells and CD8+ T lymphocytes in patients with advanced-stage non-small cell lung cancer. J. Cancer Res. Clin. Oncol. 136, 35–45. https://doi.org/10.1007/s00432-009-0634-0 (2010).
Ohl, K. & Tenbrock, K. Reactive oxygen species as regulators of MDSC-mediated immune suppression. Front Immunol. 9, 2499. https://doi.org/10.3389/fimmu.2018.02499 (2018).
Wang, L. et al. Expansion of myeloid-derived suppressor cells promotes differentiation of regulatory T cells in HIV-1+ individuals. AIDS 30, 1521–1531. https://doi.org/10.1097/QAD.0000000000001083 (2016).
Ren, J. P. et al. Hepatitis C virus-induced myeloid-derived suppressor cells regulate T-cell differentiation and function via the signal transducer and activator of transcription 3 pathway. Immunology 148, 377–386. https://doi.org/10.1111/imm.12616 (2016).
Ren, J. P. et al. Decline of miR-124 in myeloid cells promotes regulatory T-cell development in hepatitis C virus infection. Immunology 150, 213–220. https://doi.org/10.1111/imm.12680 (2017).
Wang, L. et al. HCV-associated exosomes promote myeloid-derived suppressor cell expansion via inhibiting miR-124 to regulate T follicular cell differentiation and function. Cell Discov 4, 51. https://doi.org/10.1038/s41421-018-0052-z (2018).
Agrati, C. et al. Myeloid Derived Suppressor Cells Expansion Persists After Early ART and May Affect CD4 T Cell Recovery. Front Immunol 10, 1886. https://doi.org/10.3389/fimmu.2019.01886 (2019).
Vollbrecht, T. et al. Chronic progressive HIV-1 infection is associated with elevated levels of myeloid-derived suppressor cells. AIDS 26, F31-37. https://doi.org/10.1097/QAD.0b013e328354b43f (2012).
Thakuri, B. K. C. et al. LncRNA HOTAIRM1 promotes MDSC expansion and suppressive functions through the HOXA1-miR124 axis during HCV infection. Sci Rep 10, 22033. https://doi.org/10.1038/s41598-020-78786-1 (2020).
Zhang, J. et al. Long noncoding RNA HOTAIRM1 promotes myeloid-derived suppressor cell expansion and suppressive functions through up-regulating HOXA1 expression during latent HIV infection. AIDS 34, 2211–2221. https://doi.org/10.1097/QAD.0000000000002700 (2020).
Zhang, J. et al. Long noncoding RNA RUNXOR promotes myeloid-derived suppressor cell expansion and functions via enhancing immunosuppressive molecule expressions during latent HIV infection. J. Immunol. 206, 2052–2060. https://doi.org/10.4049/jimmunol.2001008 (2021).
Board, N. L., Moskovljevic, M., Wu, F., Siliciano, R. F. & Siliciano, J. D. Engaging innate immunity in HIV-1 cure strategies. Nat. Rev. Immunol. 22, 499–512. https://doi.org/10.1038/s41577-021-00649-1 (2022).
Shi, Y. et al. The role of innate immunity in natural elite controllers of HIV-1 infection. Front Immunol. 13, 780922. https://doi.org/10.3389/fimmu.2022.780922 (2022).
