HIV-1 Dual Infection and Correlates of Neutralization

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Gabriel A. Wagner, MD, of UC San Diego Owen Clinic, presents "HIV-1 Dual Infection and Correlates of Neutralization"

Gabriel A. Wagner, MD, of UC San Diego Owen Clinic, presents "HIV-1 Dual Infection and Correlates of Neutralization"

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  • 1. The UC San Diego AntiViral Research Center sponsors weekly presentations by infectious disease clinicians, physicians and researchers. The goal of these presentations is to provide the most current research, clinical practices and trends in HIV, HBV, HCV, TB and other infectious diseases of global significance. The slides from the AIDS Clinical Rounds presentation that you are about to view are intended for the educational purposes of our audience. They may not be used for other purposes without the presenter’s express permission. AIDS CLINICAL ROUNDS
  • 2. HIV-1 Dual Infection and Correlates of Neutralization Gabriel A. Wagner Postdoctoral Research Fellow University of California San Diego Friday, July 12, 2013
  • 3. Outline • What is HIV-1 dual infection? • What does it mean to the global epidemic? • How common is it? • What are the individual consequences? • What does it portend for rational vaccine design?
  • 4. Types of HIV-1 Dual Infection (DI) Strain 1 + Strain 2 Coinfection (CI) Time Strain 1 Strain 2 Superinfection (SI) Time Intrasubtype (same subtype) or Intersubtype (different subtypes) M.Pacold
  • 5. Global Distribution of HIV-1 M Subtypes Hemelaar J. Trends Mol Med 2012
  • 6. Artenstein et al. JID 1995;171:805-10
  • 7. Previous Investigative Methods • Single genome sequencing (SGS): obtain the sequences of 20-30 individual HIV genomes • Lacks sensitivity required to discern low minority populations (<5%) • Cannot be used in large cohorts
  • 8. Ultradeep Sequencing (UDS) • Shorter reads, but many more of them • High coverage depth suitable for detecting minority variants (<1%) [Archer et al., PLOS One 2012; 7(11)] [Modified from Bushman et al., AIDS 2008] 400 100 50 960 000 250 000 000 519 000 000 http://www.genomeweb.com/sequencing/survey-illumina-solid-and-454-gain-ground-research-labs-most-users-mull-addition
  • 9. Clinical cohorts • San Diego Primary Infection Cohort – Longitudinal (including acute and early infection) – Predominantly MSM, White – Subtype B – ART-naive • IAVI Protocol C Cohort – Longitudinal – Heterosexual discordant, MSM, SW (varies by clinical site) – Subtypes C, A, D (varies by clinical site) – ART-naive
  • 10. Methods: UDS • Blood → HIV-1 RNA → cDNA → PCR 3 regions • Pooled 3 PCR products per sample • Sequenced 16 samples concurrently on a 454 GS FLX Titanium plate • Processed reads and generated phylogenies • DI: nucleotide divergence> 2.5% (RT, gag) and > 5% (env), confirmed by phylogenetic bootstrap env: C2-V3-C3 (416 bp) pol: RT (534 bp) gag: p24 (253 bp) Pacold et al., AIDS 2012, 26:157–165
  • 11. Phylogenies: Dual vs. Monoinfection P265 env, 3rd year of infection Divergence: 16% D381 env, 6th year of infection Divergence: 14% High bootstrap support for dual infection
  • 12. Time Point Sampling • Last time point per participant sampled with UDS – (N=118) • If DI, baseline sample was deep sequenced • If DI at baseline: coinfection (CI) • If MI at baseline: superinfection (SI), and timing of SI was determined
  • 13. Outline • What is HIV-1 dual infection? • What does it mean to the global epidemic? • How common is it? • What are the individual consequences? • What does it portend for rational vaccine design?
  • 14. Redd et al. JID 2012;206:267–74 • Heterosexual open, rural cohort • 7 out of 149 identified with inter- or intra-subtype superinfection • Rate of HIV superinfection: 1.44 per 100 PYs • Unadjusted primary HIV incidence rate: 1.15 per 100 PYs • Adjusted primary incidence 3.28 per 100 PYs (borderline statistical significance)
  • 15. Piantadosi et al. PLoS Pathog 2007;3(11) • High-risk Kenyan women cohort • 7 out of 36 individuals intrasubtype A • Frequency of HIV-1 superinfection: 3.7% per year • Incidence of primary infection in this cohort, 8% per year
  • 16. Study Cohort Baseline Characteristics Wagner et al., Poster 510, CROI 2013, Atlanta, GA
  • 17. Rates of HIV-1 Dual Infection in SD • Of 118 cohort participants: – 7 baseline co-infected (5.9% prevalence, 95% CI 2.4%−11.8%) at a median time from EDI of 2.8 months (IQR 2.3−3.2 months) – 10 superinfections identified over 201.6 person-years, resulting in an overall incidence of superinfection of 4.96 per 100 person-years (95% CI 2.67–9.22) • 7 superinfections occurred in the first year (6.3% first-year incidence, 95% CI 2.6%−12.6%), and 3 in the second year (2.9% second-year incidence, 95% CI 0.6%−8.2%)
  • 18. Wagner et al., Poster 510, CROI 2013, Atlanta, GA
  • 19. Incidence of Primary HIV in MSM in San Diego • Primary HIV incidence in the cohort was calculated for repeat testers who were initially negative and subsequently tested positive: – 4.37 per 100 person-years (95% CI 3.56–5.36) • Incidence of HIV-1 superinfection comparable to incidence of primary infection
  • 20. Wagner et al., Poster 510, CROI 2013, Atlanta, GA Cumulative Prevalence of HIV-1 Dual Infection Throughout 215 person-years of follow-up, the cumulative prevalence of HIV- 1 dual infection (co-infections and superinfections) was 14.4% (95% CI 8.6%−22.1%).
  • 21. Outline • What is HIV-1 dual infection? • What does it mean to the global epidemic? • How common is it? • What are the individual consequences? • What does it portend for rational vaccine design?
  • 22. Pacold et al., AIDS 2012, 26:157–165
  • 23. Methods: Case Control • Cohort Subset: – 4 coinfected – 7 superinfected – 19 monoinfected • Applied linear mixed-effects models to longitudinal viral load and CD4 data
  • 24. Comparison of VL Progressions • Compared to MI, SI had a significantly faster viral load increase (p<0.05). • The difference between MI and CI was not significant (p=0.06). N CI 4 3 2 1 1 1 per SI 7 6 3 2 2 1 time MI 19 18 12 8 9 6 point LogViralLoad Months since Initial Infection Pacold et al., AIDS 2012, 26:157–165
  • 25. Comparison of CD4 Progressions • MI, CI, SI CD4 progressions are not significantly different from each other (p>0.05) N CI 4 3 2 1 1 1 per SI 7 6 3 2 2 1 time MI 18 18 11 8 9 6 point SquareRootCD4 Months since Initial Infection Pacold et al., AIDS 2012, 26:157–165
  • 26. Subject HLA-A HLA-B C2-V3 RT pol 1 (K6) 23, 29 44, 44 In=Out In>Out In>Out 2 (K9) 03, 29 44, 57 Out>In In=Out In>Out 3 (D2) 03, 32 35, 47 Out>In Out>In NA 4 (P2) 01, 68 35, 57 In=Out NA NA 5 (P8) 24, 31 35, 41 In=Out In=Out NA 6 (S1) 24, 66 35, 41 In=Out In=Out NA 7 (U7) 01, 03 08, 35 In=Out In=Out NA Evidence of CTL Escape by SI Virus • Amino acid differences between Initial versus Superinfecting viruses are compared inside vs. outside epitopes. Bold: p<0.05. • Unique characteristics of K6 and K9 among SI participants: • Complete replacement of initial by SI virus • Evidence of CTL escape Pacold et al., AIDS 2012, 26:157–165
  • 27. DI and coreceptor usage • Both DI and infection with CXCR4 (X4)-using virus have been associated with accelerated disease progression • Coreceptor usage can be determined phenotypically or predicted from genotype • UDS increases sensitivity of coreceptor usage prediction
  • 28. Wagner et al. JID 2013;208:271–4
  • 29. Methods: Case Control • Cohort Subset: – N=102 • Co‐receptor usage predicted – geno2pheno 454 [Thielen et al. Intervirology 2012; 55:113‐7] – Samples classified as X4‐capable when >1% of the viral population predicted as X4-using variants [Daumer et al. BMC Med Inform Decis Mak 2011; 11:30] • Nonparametric and correlation analyses performed to examine associations between X4-usage and dual infection
  • 30. Prevalence of X4 coreceptor usage • At baseline, X4 usage was high (23 of 102 subjects harbored X4 variants) • X4 usage was not associated with infection duration or DI Wagner et al. JID 2013;208:271–4
  • 31. HIV-1 superinfection and coreceptor usage • Longitudinal analysis of 47 participants: – 41 MI – 5 SI – 1 CI • Coreceptor usage changed in 12 of 47 participants – X4 usage emerged in 4 of 41 monoinfections vs 2 of 5 superinfections (P = 0.12) Wagner et al. JID 2013;208:271–4
  • 32. HIV-1 superinfection and coreceptor usage • In case G59, an increase in the proportion of X4 usage (black solid diamonds) coincided with the detection of a superinfecting strain (pie charts), and X4 variants disappeared when this strain was no longer detected Wagner et al. JID 2013;208:271–4
  • 33. Outline • What is HIV-1 dual infection? • What does it mean to the global epidemic? • How common is it? • What are the individual consequences? • What does it portend for rational vaccine design?
  • 34. Vaccines and HIV-1 Superinfection • Neutralizing antibodies (NAbs): best correlate of protection from re-infection with most viruses and of protection mediated by viral vaccines1 • Recent studies underscore need for better understanding of natural development of NAbs for vaccine design2,3 • HIV-1 Superinfection – an effective vaccine must contain immunogens broad and potent enough to protect from very diverse viral challenges. – unique opportunity to study correlates of protection 1Plotkin SA. C R Acad Sci III 1999,322:943-951 2Klein et al., Nature 2012; 492:118-122 3Walker et al., Nature 2011; 477:466-470
  • 35. Assessing neutralization breadth and potency • Monogram Biosciences: high-throughput neutralization assay against autologous and heterologous pseudoviruses – cross-clade heterologous panel used for the selection of best Protocol G donors and highly predictive of neutralization breadth on a larger panel1,2 NL43 (highly neutralization-susceptible clade B) = positive control aMLV (irrelevant mouse retrovirus) = negative control 94UG103 (clade A) 92BR020 (clade B) JRCSF (clade B) SF162 (clade B) IAVIC22 (clade C) 93IN905 (clade C) 92TH021 (CRF AE) Neutralization data analysis: Infectivity inhibition  IC50 titer  neutralization score (breadth and potency) Landais E., NAC Retreat 2012 1Richman et al., PNAS 2003; 100,7:4144-4149 2Simek et al., J.Virol. 2009; 83(14):7337
  • 36. What is the effect of viral genetic diversity on NAb potency and breadth?
  • 37. Methods: Diversity vs. neutralization • Cohort subset (N=34) • UDS maximal sequence divergence – estimate of diversity • Neutralization assays against heterologous pseudoviruses (Monogram Biosciences) • Correlation analysis between genetic maximal divergence (MDI) in each coding region vs. NAb
  • 38. NAb Score vs. env MDI Carter, Wagner et al., Poster 351, CROI 2013, Atlanta, GA
  • 39. Future NAb score vs. Baseline env MDI Carter, Wagner et al., Poster 351, CROI 2013, Atlanta, GA
  • 40. What are protective correlates of NAb response during intrasubtype HIV-1 superinfection?
  • 41. Comparing NAb development in superinfection vs. monoinfection • NAb activity against autologous and heterologous viruses before and after superinfection (SI) –Compared to monoinfected (MI) controls matched to duration of infection within 3 months • 10 SI • 19 MI • Nonparametric test performed at 3, 12, 24, and 36 months after EDI
  • 42. 3 Months after EDI: Heterologous NAb breadth and potency Wagner et al., Oral Abstract C-129, CROI 2013, Atlanta, GA
  • 43. 3 Months after EDI: Heterologous NAb to tier 1 clade B virus Wagner et al., Oral Abstract C-129, CROI 2013, Atlanta, GA At 3 months, those who would acquire SI (8) had significantly weaker NAb response against susceptible clade B viruses than MI (17), p = 0.011. p < 0.05
  • 44. 6 Months after EDI: Heterologous NAb breadth and potency Wagner et al., Oral Abstract C-129, CROI 2013, Atlanta, GA
  • 45. 6 Months after EDI: Heterologous NAb to tier 1 clade B virus Wagner et al., Oral Abstract C-129, CROI 2013, Atlanta, GA At 6 months, those who became superinfected (3) had significantly weaker NAb response against susceptible clade B heterologous viruses than MI (19). p < 0.05
  • 46. 12 months after EDI: Autologous NAb to 3M virus At 12 months, those who became SI (7) had significantly weaker NAb response against 3-month autologous virus than MI group (15). p < 0.05 Wagner et al., Oral Abstract C-129, CROI 2013, Atlanta, GA
  • 47. 12 months after EDI: Autologous NAb to 6M virus At 12 months, those who became SI had weaker NAb response against 6-month autologous virus, with a trend towards statistical significance. *p = 0.051 Wagner et al., Oral Abstract C-129, CROI 2013, Atlanta, GA
  • 48. 24 and 36 Months after EDI: Heterologous and Autologous NAb • No significant difference in NAb response against heterologous viruses • No significant difference in NAb response against 3-month or 12-month autologous viruses
  • 49. What are the viral dynamics of HIV-1 superinfection?
  • 50. Viral dynamics in SI and NAb response • Deep-sequencing and single-genome data for 7 SI – phylogenetic trees generated using BEAST – Screened for recombination – Viral dynamics evaluated using nucleotide entropy as a marker of diversity • NAb responses to autologous viruses (ANAb) were evaluated before and after superinfection and compared against viral diversity
  • 51. Chaillon, Wagner et al., Poster 177LB, CROI 2013. Manuscript submitted.
  • 52. Chaillon, Wagner et al., Poster 177LB, CROI 2013. Manuscript submitted. • A strong NAb response was observed to autologous virus at time of superinfection for G5 • G5 also displayed high viral diversity within env at the two latest time points available (respective mean entropy of 0.117 and 0.08) where recombination events were also observed Mean ANAb titers to contemporaneous viruses at the time of and shortly following superinfection.
  • 53. Limitations • Molecular evidence of HIV-1 dual infection – Limited to coding regions sequenced – Limited to compartment sampled – Recombination can homogenize viral populations and may make DI detection more difficult • Methodology bias – UDS platform – Bioinformatic analysis • Follow up – ART initiation in the cohort
  • 54. Overall Conclusions • High rates of intrasubtype B HIV-1 dual infection in high-risk cohort – Most cases occur in first year • Superinfection associated with higher VL, potential CTL escape and X4 coreceptor usage • env diversity likely driven by NAb selective pressure and not vice versa • ‘Window of susceptibility’ in the first year of primary infection where individuals with weaker heterologous and slower autologous NAb development are at risk of SI • Viral dynamics after superinfection fall into discrete patterns
  • 55. Acknowledgments Univ of California San Diego Davey Smith Doug Richman Caroline Ignacio Melissa Laird Antoine Chaillon Sara Gianella Demetrius Dela Cruz Funding U.S. Department of Veterans Affairs National Institutes of Health International AIDS Vaccine Initiative National Science Foundation James B. Pendleton Charitable Trust Monogram Biosciences Terri Wrin Pham Phung All Participants in the San Diego Primary Infection Cohort and IAVI Protocol C Cohort The Scripps Research Institute Pascal Poignard Elise Landais