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HIV-1 Serodiscordant Couples: Priority for Public Health and Pathogenesis Jairam Lingappa, MD, PhD Departments of Global Health and Medicine.

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Presentation on theme: "HIV-1 Serodiscordant Couples: Priority for Public Health and Pathogenesis Jairam Lingappa, MD, PhD Departments of Global Health and Medicine."— Presentation transcript:

1 HIV-1 Serodiscordant Couples: Priority for Public Health and Pathogenesis Jairam Lingappa, MD, PhD Departments of Global Health and Medicine

2 Key messages Infectious diseases are transmitted in an environmental context The study of host HIV-1 susceptibility factors should capture that context Discordant couples are an important model for public health and pathogenesis Ongoing studies of HIV-1 discordant couples in Africa will evaluate factors in heterosexual transmission

3 Caveats Focus on sexual transmission of HIV-1 Much more data is published on risk factors for other forms of HIV-1 transmission and HIV-1 disease

4 Sexual Transmission of HIV-1 All HIV-1 sexual transmission involves one HIV-1 infected/transmitting and one uninfected/exposed partner (discordant couple) This partnership is more traceable in some cases What are the factors that determine when transmission occurs

5 HIV-1 Transmission Factors The exposed partner ? The infected partner ? Behavior ?

6 HIV-1 Transmission Risk: Is it the number of sex acts or number of individuals? Model heterosexual and MSM transmission data : - Known HIV-1 infected and uninfected partners - Does infectivity depend on # sex acts or # partners (Peterman 1988; Grant 1987; Kaplan 1990) 55 heterosexual couples with 10 transmissions –Infectivity per sex act [ ] however: –26 couples with fewest sex acts yielded 70% of transmissions –27 couples with most sex acts accounted for 30% –1980s data Not consistent with model that transmission is proportional to exposure based solely on number of sex acts

7 HIV-1 Transmission: By individual or by sex act 138 MSM yielding 11 seroconversions –Infectivity per partner of [0.022 – 0.08] Transmission ProbabilityNumber of Partners Probability of transmission varied by number of partnerships, Highest probability is for two partners. Need additional biological data to characterize infectiousness and susceptibility

8 Heterosexual Couples Studies: Rakai, Uganda Community randomized trial of STD control for HIV prevention –415 HIV-1 discordant couples –174 monogamous Followed every 10 months for 3 visits; study conducted over 4 years –Sexual/clinical history –HIV-1 RNA, HIV-1 subtype, GUD/STD Compare SC to non-SC –Matched by age, gender and time of HIV RNA

9 Rakai Transmission data CharacteristicAdj RR Age y2.38 ( ) >30 y1 Genital Ulcer Disease Yes2.05 (1-4.1) No1 HIV RNA quantile (log 10 ) > ( ) ( ) (1-10.8) Stage of infection Incident4.98 (2-12.4) Prevalent1 Late stage3.5 ( ) Wawer et al JID 2005

10 Rakai HIV-1 transmission per coital act Incident index partner in early infection: – ( ) Prevalent index partner after months of follow-up – ( ) Compare to estimate from heterosexuals in US 5 years earlier (Kaplan et al 1990) – [ ]

11 Meta-Analysis of Heterosexual Transmission Risk Probability and Co-factors Review of 29 observational studies with 15 heterosexual infectivity estimates 9/15 were discordant couples studies –4 longitudinal –5 cross-sectional Summarize infectivity estimates Quantify cofactor effects on infectivity Powers et al Lancet 2008

12 Category# of estimatesRate/1000 (95% CI) Region USA/Europe80.59 ( ) Africa60·91 (0·59–1·22) Asia131·00 (25·00–40·00) Sex act Penile-vaginal50·84 (0·51–1·17) Penile-anal133·80 (18·51–49·09) Transmission MTF100·66 (0·54–0·79) FTM62·76 (1·19–4·33) GUD No43·72 (0·70–6·75) Yes530·55 (11·27–49·84) STI (in exp) No112·00 (6·00–25·00) Yes255·86 (4·43–107·29) Male Circumcision Circ25·13 (3·37–6·89) Not Circ297·33 (0·00–295·16) Age (exp) >30 y61·06 (0·56–1·56) <30 y215·71 (0·00–45·20) Disease Stage (inf) Mid40·71 (0·57–0·85) Early24·67 (0·00–10·46) Late43·18 (0·94–5·42) Powers et al Lancet 2008

13 Infectivity estimates and epidemiologic context Common estimate for HIV-1 infectivity in heterosexual contact: –Should be considered a lower bound Meta-analysis: –Penile-vaginal contacts – 0.1 –Penile-anal contact – 0.3 Epidemiologic context can greatly impact transmission risk

14 HIV-1 Transmission: Exposure Factors The exposed partner: –GUD/STD –Gender –Male Circumcision –Age The infected partner: –GUD/STD –Gender –Stage of infection –HIV-1 plasma/genital RNA Behavior: - Sex practices (anal vs. vaginal) - Condom use

15 HIV-1 Resistance: Cellular Immune Factors HIV-1 specific CD8-CTL –Kenyan CSW –US/Canadian discordant couples –US MSM +/- HIV-specific CD4+ IL-2/proliferative response –Kenyan CSW –Italian discordant couples Increased Immune activation –2 discordant couples studies –1 contradictory MSM study

16 HIV-1 Resistance: Humoral Immune Factors HIV-1 specific mucosal IgA responses –Italian discordant couples –Thai and Kenyan CSW –Contradictory findings in US discordant couples Autoantibodies (  -CD4,  CCR5,  -HLA) –Italian discordant couples contradictory

17 HIV-1 Resistance: Innate factors CCR5-  32 –MSM and discordant couples at low frequency –Not present in African cohorts (CCR2-64I) –CCR5D32/CCR5-2459A/G haplotype in MSM CCL3L1 (MIP-1  natural ligand of CCR5) –Higher copy number in cross-sectional cohorts

18 HIV-1 Resistance: Innate factors Class I HLA –A2 supertype protects Kenyan EU not Zambian –A*36 in transmission in HIV-infected partners –Lower Class I allele sharing between partners Class II HLA –DRB1/DQB1 associated in Zambian EU –DR5, DQ4 Non-classical HLA –HLA-E, HLA-G with increased susceptibility in African women (cross-sectional)

19 HIV-1 Resistance: Innate factors KIR/Class I HLA –CSW in Cote d’Ivoire EU with NK-cell inhibitory receptor (3DL1/2DL3) lacking HLA cognate (Bw4/C1) DC-SIGN/DC-SIGNR –DC-SIGN het repeat polymorphism in MSM –Cross-sectional cohort in North Indians (DC-SIGNR 7/7 homozygote with increased infection) –Cross-sectional MSM cohort (DC-SIGNR 7/5 het with resistance) Trim5 , APOBEC3G small MSM and cross- sectional heterosexual cohorts

20 Factors in HIV-1 Sexual Transmission How to make sense of it all: –Diversity in host factors could represent Many pathways to resistance Lots of false positive associations –Difficult to compare across many cohorts with candidate gene testing Given importance of characterizing exposure, use of convenience-based sampling of controls (blood donors or uncharacterized HIV-positives) may be comparing “apples” and “oranges”

21 What is needed Large cohorts Cases and comparison groups with well characterized HIV-1 exposure Wide evaluation of host genotypic, immunologic and viral characteristics in the same individuals

22 African HIV-1 Discordant Couples Public Health imperative: –>50% of all stable couples in which one partner is HIV-1 infected has an HIV-1 negative partner (i.e., are HIV-1 discordant) –50-60% of new HIV-1 infections in married couples may come from married partner (based on data from Kenya/Uganda) –Important to develop couples counseling capacity and community awareness of HIV-1 discordance in African communities

23 African HIV-1 Discordant Couples Research potential: –High rates of retention (couples counseling) –Prospective follow-up for both couples Sexual history, demographics, clinical data –Specimen collection for Host genetic Host immunologic Viral sequencing Drawbacks: Increased cost and infrastructure

24 UW Studies of HIV-1 Discordant Couples in Africa StudyPurposeCohort# SCLocationsFollow-upStatus Partners Study HSV-2 suppression to reduce HIV-1 transmission 3408 couples- HIV/HSV-2+ CD4>250 ~1507 East Africa 7 Southern Africa moFollow-up Complete COS Observational475 couples- few restrictions ~251 Uganda 1 RSA 12 moSept 2009 Partners PrEP Pre-Exposure prophylaxis in HIV- 1 negative to reduce HIV-1 transmission 3900 couples~1904 Uganda 4 Kenya moEnrl – 2010 F/u Total ---~7800 couples~ mo--

25 UW Discordant Couples Cohorts: Specimen Collection Specimen typeFrequency PlasmaQuarterly SerumQuarterly Cervical swabQuarterly Semen1 time point PBMC6-monthly* Whole blood RNA6-monthly* * Collected at 2 sites in Partners Study and COS

26 Partners Study: Baseline Demographic Characteristics Characteristic Couples w/HIV-infected WomenCouples w/HIV-infected Men HIV-infected Female (#, %) HIV-uninfected Male (#, %) HIV-infected Male (#, %) HIV-infected Female (#, %) Median age (IQR) 30 (25-35)35 (30-42)37 (32-45)31 (25-38) Yrs living w/ partner (median, IQR) † 5 (2-9)--6 (3-13)-- Number of children (median, IQR) † 2 (1-3)--3 (2-5)-- Total sex acts (median, IQR) † 4 (2-8)--4 (2-8)-- Unprotected sex acts (median, IQR) † 0 (0-1)--0 (0-1)-- Couples reporting any unprotected sex acts 661 (29%)--311 (28%)-- Use condoms for contraception 1062 (46%) (44%) Use no contraception 727 (32%) (33%)

27 Partners Study: Baseline Clinical and Lab Characteristics Characteristic Couples w/HIV-infected WomenCouples w/HIV-infected Men HIV-infected Female (#, %) HIV-uninfected Male (#, %) HIV-infected Male (#, %) HIV-infected Female (#, %) Symptoms of GUD (previous 3 mos) 174 (8%)46 (2%)63 (6%)54 (5%) HSV-2 seropositive2272 (99%)1361 (59%)1080 (97%)954 (85%) N. gonorrhoeae +(TMA)40 (2%)13 (1%)10 (1%) C. trachomatis + (TMA)46 (2%)66 (0.3%)16 (1%)19 (2%) T. vaginalis + (TMA)314 (14%)157 (7%)52 (5%)102 (9%) Positive RPR140 (6%)107 (5%)61 (6%)43 (4%) CD4 count (cells/mcL) (median, IQR) 483 ( )--424 ( )-- HIV-1 plasma RNA Log 10 (median,IQR) 4.0 ( )--4.4 ( )--

28 Pathogenesis Studies Planned Envelope sequencing of transmitted variants (Mullins) Genome-Wide Association Study (CHAVI) Candidate Gene Genotyping (D. Nickerson/M. Bamshad) HLA typing Pre-seroconversion Gene Expression Arrays studies Pre-seroconversion HIV-1 specific CD4 and CD8 studies and risk of HIV-1 acquisition (McElrath) Immune activation studies: Risk of acquisition, effect on set-point viral load in seroconverters (McElrath) HIV-1 clade studies: HIV transmission risk and set point in seroconverters Neutralizing antibody studies

29 Acknowledgments: Clinical Trial Coordinating Center Coordinating Center: Principal Investigator: Connie Celum Co-Investigators: Anna Wald, Julie McElrath, Jared Baeten, Jai Lingappa, Larry Corey Program Management: Linda Barnes Regional Directors: Nelly Mugo, & Andrew Mujugira, Patrick Ndase Clinical Monitors: Marothodi Semenya, Apollo Odika, Hilda O’Hara Coordinating Center Operations: Margaret Warner-Lubin, Dana Panteleeff, Meighan Krows, Heena Shaw, Ellen Wilcox Biostatisticians/Data Management: Jim Hughes, Deborah Donnell, Amalia Meier, Richard Wang, Erin Kahle, Lara Kidoguchi, Renee Hefforn, Jennifer Broad Fiscal/Admin: Linda Barnes, Darcie Somera, Carlos Flores, Becky Karschney, Matt Leidholm, Toni Maddox, Alice Rose, Troy Sexton, Calvin Tran, Christy Wilson Central Repository: Harald Haugen, Justin Brantley, Shauna Durbin, Vikram Nayani Coordinating Center Contractors: Site Laboratory Oversight: Wendy Stevens, Clinical Lab Services, Univ of Wits HIV-1 Retrovirology Labs: Bob Coombs, Joan Dragavon; Jane Kuypers, Reggie Sampoleo HSV-2 Virology Lab: Rhoda Ashley, Anne Cent HIV Virology (Endpoint Analysis) Lab: Jim Mullins, Mary Campbell Data Management Contractor: Darryl Pahl & Lisa Ondrajeck DSMB: Rich Whitley, Chair Funding: Bill & Melinda Gates Foundation

30 Partners Study Site Investigators –Nairobi: J Kiarie, C. Farquhar, G. John-Stewart –Kisumu: E. Bukusi, C. Cohen –Eldoret: E. Were, K. Fife –Thika: N. Mugo –Tanzania: R. Manongi, S. Kapiga –Kampala: E. Nakku-Joloba, L. Kavuma, A. Ronald, E. Katabira –Kigali: B Bekan, K. Kayatenkore, S. Allen –Soweto/PHRU: G. Gray, G. DeBryn, J. McIntyre –Orange Farm/RHRU: S. Delaney & H. Rees –Cape Town: A. DeCock, D. Coetzee –Gaborone: P. Dusara, J. Makhema, M. Essex –Lusaka, Ndola & Kitwe: M. Inambao, W. Kanweka, S. Allen  And above all: thanks to all study participants


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