# Presentation: 470838

## Behavioral Risk and HIV-1 Molecular Diversity: Making the ConnectionsDrug Abuse and Risky Behaviors:  The Evolving Dynamics of HIV/AIDSNIDA, NIH, May 8, 2007

- Chris Beyrer, S. Tovanabutra2, G. Kijak2,  T. Sripaipan1   E. Sanders-Buell2,  K. Rungruengthanakit3, J. Jittiwutikarn4,  DD. Celentano1,  FE. McCutchan2
- 1. Johns Hopkins University; 2. WRAIR; 3. Chiang Mai University; 4. Northern Drug Treatment Center, Chiang Mai, Thailand

## Outline 

- Introduction
- Molecular epidemiology and segregation by risks
- Risk and complexity
- Associations in the Opiate Users Research Cohort
- Breakpoint analyses and networks
- Conclusions

## Introduction: Molecular Epidemiology

- From a public health perspective, the advent of molecular epidemiology, which allows tracking of pathogens based on unique genetic sequences or antigenic properties, has revolutionized how epidemiologists investigate and evaluate epidemics and assess endemic diseases.
- Robertson BH, Nicholson JK. New microbiology tools for public health and their implications. Annual Review of Public Health. 2005;26:281-302

## Introduction:  HIV Genetic Diversity

- HIV-1 is a genetically diverse virus with high rates of genetic change:  mutation, recombination, dual infection, super-infection
- The genetic diversity of HIV challenges the human immune system, vaccine development, measures of anti-viral drug resistance
- HIV-1 genetic diversity allows for epidemiologic investigations

## Slide 5

## Global Distribution of Subtypes and Recombinants

**C**

**A**

**D**

**CRF02_AG****, other recombinants**

**F,G,H,J,K,****CRF01 other recombinants**

**B**

**B, BF recombinant**

**A, B, AB recombinant**

**CRF01_AE, B**

**B, C, BC recombinant**

**Insufficient data**

## Uses of Molecular Epidemiology in HIV-1

- 	_**Established**_
- Powerful tool in understanding epidemic dynamics
- Has regional utility, particularly for **common border epidemics**
- Allows use of the virus to track movements of **people **(truckers, sex workers, migrants, soldiers) and **narcotics**
**	****	**_**Novel**_
- Linking diversity to risks could allow for targeting interventions, identifying “hot spots”
- Potential tool for mapping networks

## Molecular Epidemiology and segregation by risks

## Early Studies of Risks and Subtypes

- South Africa 1990s
- 	MSM with B, African Heterosexuals with C
- Thailand 1990s
- 	IDU with B, Heterosexuals with E (CRF01-A/E)
- Malaysia 1990s

## HIV-1 subtypes in Malaysia among different primary risk categories, 1994-1996.

_** **_
_**Risk **__**	**__**	**__**B**__**	**__**	**__**E***__**	**__**	**__**B/E**__**	**__**B/C**__**	**__**Non-typable**_
**All****	****(N=89)****	****34 (38%)****	****48 (54%)****	****2 (2%)****	****1 (1%)****	****4(4.5%)**
** **
**IDU****	****(N=53)****	****29 (55%)****	****19 (36%)****	****2 (4%)****	****1 (2%)****	****2 (4%)**
**Hetero****	**** (N=27)****	**** ****4 (15%)****	****	****23 (85%)****	****-****	****--****	****--**
**SW****	****(N=9)****	****  1 (11%)****	****6 (67%)****	****	****--****	****--****	****2 (22%)****	**
**_____________________________________________________________________**
-  * E now called CRF01_A/E
- IDU significantly more likely to have HIV-1 subtype B than those with sexual risks (heterosexuals and SW combined) **OR 5.9 (95% CI 1.9, 18.5) p < .001.  **
**All of the 3 dually reactive sera were from IDU.**

- Beyrer, et al. AIDS & Hum Retro, 1998

## Risk and Complexity

- Multi-region Hybridization Assays
- (MHA)

## Dual Infection

**Many different recombinants can emerge in a dual infected individual, who may transmit them to others; dual infection is an accelerator of HIV diversity in populations**
**Many recombinant strains are generated within high risk social networks, which also have high rates of transmission; this coincidence of factors can accelerate the initial spread of new variants**

## Multi-region Hybridization Assay (MHA) to study HIV-1 genetic diversity in Asia

**MHAbf**

**MHAcrf02**

**MHAacd**

**MHAbce**

_**The MHA family**_

**Courtesy Dr. F. McCutchan, USMHRP/HJF**

**A clear picture of the evolving HIV-1 epidemics in Asia can only be achieved through the study of large cohorts, using **_**high-throughput**_** and **_**high-resolution**_** subtyping**

**Multi-region Hybridization Assay (MHA) to study HIV-1 genetic diversity in Asia**

**F**

**Q**

**Y**

**F**

**Q**

**X**

**F**

**Q**

**Z**

_**Principle of  MHA**_

**Real-time PCR with Clade-specific probes**

**MHAbnb**

## Distinguishing HIV-1 molecular forms in Asia

**Distinguishing HIV-1 molecular forms in Asia**

## Comparative Epidemiology in Thailand

**MHA Genotypes (N)**
**336**
**177**
**293**
**806**

**Comparative Epidemiology in Thailand**

**Province**
**Chiang Mai**
**Lampang**
**Rayong-Chon Buri**

## The cohorts

**RV109**

**OUR**

**RV148**

**Location**

**Lampang**

**Chiang Mai**

**Rayong Chon Buri**

**Year**

**1996-1998**

**2004-2006**

**Cohort Characteristics**

**MTCT **

**Opiate **
**users **

**Community**

**Genotyped samples**

**177/180**
**(98.3%)**

**336/347**
**(96.8%)**

**376/391**
**(96.2%)**

**100% ****♀**

**7% ****♀**** **

**48% ****♀ **

**Gender**

**Risk factors**

**heterosexual**

**IVDU**

**heterosexual**

**Chiang Mai**

**Lampang**

**Rayong**

**Chon Buri**

***Bangkok***

_**The cohorts**_

**Total**
**889 / 918**
**(96.8%)**

**HIV sero-prevalence**

**ca. 3 %**

**15.6 %**

**1.6 %**

**Participants**

**180**

**2,231**

**26,675**

## Proportions of Subtypes, Recombinants, Dual Infections

**Proportions of Subtypes, Recombinants, Dual Infections**

**Antenatal**

**Trial Volunteers**

**IDU**

**CRF01_AE****	****94.9%****	****	****91.8%****	****	****81.8%**
**Subtype B****	****  2.3%****	****	**** 2.0%****	****	**** 3.9%**
**Recombinant****	****  2.8%****	****	**** 5.5%****	****	**** 9.2%**
**Dual****	****	****  0.0%****	****	**** 0.7%****	****	**** 5.1%**

## HIV diversity and risks in Thai IDU

- _336 isolates from Thai IDU in the OUR cohort_
- 81.8%		CRF01_AE
-   3.9% 		B
-   9.2% 		Recombinants: CRF01_AE  and  B
-   5.1%		Dual infections
- _Subtype B:_
- 30 years old or older 		OR: 6.9; 95%CI:   1.5-31.7
- _Dual infection:_
- lower education level 		AOR=5.0  95%CI: 1.4-17.5
- initiated injecting _<_ 3 years  	AOR=3.4  95%CI: 1.2-9.8
- _Recombinants and duals:_
- needle sharing last 3 months	AOR=4.1, 95% CI: 1.41.7

## Comparative Epidemiology in East Africa

**Population**
**High risk ♀ (Sex Workers)**
**Urban and rural communities**
**Rural communities**
**Agricultural Plantation**

**Country**
**Tanzania**
**Tanzania**
**Uganda**
**Kenya**

**Cohort**
**HISIS**
**CODE**
**MER**
**Kericho**

**Comparative Epidemiology in East Africa**

**Genotypes**
**(N)**
**238**
**487**
**329**
**366**
**1420**

**MHA**

## Proportions of Recombinant HIV and Dual Infections in A, D, C Subtype Zone 

**HISIS**

**CODE**

**MER**

**KERICHO**

**A**

**Agricultural/Rural**

**Rural/Urban**

**Urban**
**High Risk**

**Proportions of Recombinant HIV and Dual Infections in A, D, C Subtype Zone **

- URF

**26.4%**

**29.5%**

**35.9%**

**50.8%**

- Dual

**7.0%**

**7.1%**

**15.0%**

**16.4%**

## Breakpoint Analyses and Networks

- Fine mapping of recombinant breakpoints

## Describing a Recombinant Strain: Subtypes and Breakpoints

**Subtype A**

**Subtype C**

**AC Recombinant**

**1**

**9200**

**1**

**9200**

**380**

**4600**

**5700**

**1900**

## A

**380**

**4600**

**1900**

**5700**

**A**

**A**

**A**

**C**

**C**

**Through recombination, parts of the parental strains are lost, and cannot be regained until another dual infection provides opportunity to recombine again**

**Lost **
**Genetic**
**Material**

**Irreversibility lends stability**
**Could recombination breakpoints serve as stable markers through many cycles of transmission, permitting mapping of the social networks in which HIV spreads? **

**C**

**C**

**C**

**A**

**A**

## Hypotheses

**The structure and relationships of recombinant strains may provide information about the social networks in which they spread, providing new focus for interventions**

**Hypotheses**

## Recombinant Strains in “Low Risk” Groups

**Transmission**

**single**

**Recombinant Strains in “Low Risk” Groups**

**Sampling**

**single**

**single**

**single**

**single**

**Complete sharing of breakpoints**

## Recombinant Strains in High Risk Groups

**Recombinant Strains in High Risk Groups**

**Transmission**

**dual**

**dual**

**single**

**single**

**single**

**single**

**Partial sharing of breakpoints**

## Recombinant HIV Networks and Risk Groups in Asia

**Recombinant HIV Networks and Risk Groups in Asia**

**24 CRF01_AE/B recombinants from Thailand and Burma**
**11 from IDU**
**13 from heterosexual transmission**

## Slide 28

## Network Visualization Software*

**Each strain is a node**
**Each shared breakpoint is a connection, represented by a line**
**Highly interconnected strains form dense clusters, with less connected strains at the periphery**

***UCiNET and NetDraw by S. Borgatti Boston College/Analytic Technologies**

## Slide 30

## What can be learned about social networks from the relationships among recombinant strains circulating within them? 

## Slide 32

## By country

**By country**

**By date**

**By risk**

**Recombinant Networks in Asia**

**China**
**Burma**
**Thailand**

**1999 or before**
**2000-2005**

**IDU**
**Het**
**n.a.**

## In Asia

**Heterosexual and IDU Networks in Thailand are strongly interconnected and these connections were already established during the first decade of the Thailand epidemic**
**Fewer connections across national borders**
**Strains from Burma/Myanmar bridge China and Thailand epidemics**

## Slide 35

## Connections Across National Borders

**B/C**

**CRF01_AE/B**

**AD**

**A2D**

**CD**

**AC**

**Connections Across National Borders**

**Asia**

**E. Africa**

**Tanzania ****Uganda**** ****Kenya**

**China**
**Myanmar**
**Thailand**

## Contributors 

**Participants in cohort development and other studies in Tanzania, Uganda, Kenya, Thailand, China, Myanmar**
**Oliver Hoffmann, Steffan Geis, Leonard Maboko, Donan Mmbando, Eluter Samky, Michael Hoelscher, and other members of the Mbeya Medical Research Programme, Tanzania**
**David Serwadda, Nelson Sewankambo, Maria Wawer, Ron Gray Makerere University and Uganda Virus Research Institute, Uganda,Columbia University and Johns Hopkins University, USA and other members of the Rakai Project, Uganda**
**Carl Mason, USAMRU-K, Monique Wassuna, KEMRI, and other contributors to the Kenya Blood Bank Study**
**David Celentano, Chris Beyrer, Vinai Suriyanon, Jaroon Jittiwutikarn, Thira Sirisanthana, Myat Htoo Razak and other contributors to the Opiate Users Research Study, Thailand**
**Vilaiwan Gulgolgarn, Manu Wera-arpachai, Chirasak Khamboonrueng, Kenrad Nelson, Nakorn Dabbhasuta  and others from the Lampang perinatal transmission cohort study, Thailand**
**Supachai Rerks-Ngarm, Sonchai Wattana, Wiwat Wiriyakijja, Sorachai Nitayaphan, Chirapa Eamsila, Jerome Kim, Michael Benenson, Arthur Brown and others for samples from volunteers deferred from enrollment in the Phase III prime-boost vaccine trial in Rayong-Chonburi Provinces, Thailand**
**Special thanks to Jocelyn Chiu  and her mentors, Sodsai Tovanabutra, and Eric Sanders-Buell, for inspection and analysis of 1,125,000 nucleotides of sequence alignment**

## Implications for Prevention

**Targeting prevention to the most mobile sectors of a given population may also contribute to limiting the overall complexity of strains in an epidemic**
**Effective size of the social network in which HIV-1 is spreading  in E. Africa may be much larger than in Asia, with implications for dissemination of new strains**
**Heroin trafficking routes appear to predict HIV-1 subtype spread and should be priority zones for prevention**

**Implications for Prevention**

## Discussion and Conclusions

**Recombinant strains can represent highly informative tools to gain new understanding of the global epidemiology of HIV**
**Molecular data is more informative when closely linked to demographic data and becomes more useful when closely and systematically analyzed and when epidemiology and narcotics data are included**
**The structure of social networks, particularly the geographic and social  mobility of the highest risk groups,  can play key roles in the generation and spread of new HIV diversity generated by recombination**