Impact of sampling density on the extent of HIV clustering

AUTO-TUNE: selecting the distance threshold for inferring HIV transmission clusters

Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained heterosexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.

AUTO-TUNE: SELECTING THE DISTANCE THRESHOLD FOR INFERRING HIV TRANSMISSION CLUSTERS

Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained hetero-sexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.

Molecular epidemiology to aid virtual elimination of HIV transmission in Australia

The Global UNAIDS 95/95/95 targets aim to increase the percentage of persons who know their HIV status, receive antiretroviral therapy, and have achieved viral suppression. Achieving these targets requires efforts to improve the public health response to increase access to care for those living with HIV, identify those yet undiagnosed with HIV early, and increase access to prevention for those most at risk of HIV acquisition. HIV infections in Australia are among the lowest globally having recorded significant declines in new diagnoses in the last decade. However, the HIV epidemic has changed with an increasing proportion of newly diagnosed infections among those born outside Australia observed in the last five years. Thus, the current prevention efforts are not enough to achieve the UNAIDS targets and virtual elimination across all population groups. We believe both are possible by including molecular epidemiology in the public health response. Molecular epidemiology methods have been crucial in the field of HIV prevention, particularly in demonstrating the efficacy of treatment as prevention. Cluster detection using molecular epidemiology can provide opportunities for the real-time detection of new outbreaks before they grow, and cluster detection programs are now part of the public health response in the USA and Canada. Here, we review what molecular epidemiology has taught us about HIV evolution and spread. We summarize how we can use this knowledge to improve public health measures by presenting case studies from the USA and Canada. We discuss the successes and challenges of current public health programs in Australia, and how we could use cluster detection as an add-on to identify gaps in current prevention measures easier and respond quicker to growing clusters. Lastly, we raise important ethical and legal challenges that need to be addressed when HIV genotypic data is used in combination with personal data.

Modeling homophily in dynamic networks with application to HIV molecular surveillance

BACKGROUND

Efforts to control the HIV epidemic can benefit from knowledge of the relationships between the characteristics of people who have transmitted HIV and those who became infected by them. Investigation of this relationship is facilitated by the use of HIV genetic linkage analyses, which allows inference about possible transmission events among people with HIV infection. Two persons with HIV (PWH) are considered linked if the genetic distance between their HIV sequences is less than a given threshold, which implies proximity in a transmission network. The tendency of pairs of nodes (in our case PWH) that share (or differ in) certain attributes to be linked is denoted homophily. Below, we describe a novel approach to modeling homophily with application to analyses of HIV viral genetic sequences from clinical series of participants followed in San Diego. Over the 22-year period of follow-up, increases in cluster size results from HIV transmissions to new people from those already in the cluster-either directly or through intermediaries.

METHODS

Our analytical approach makes use of a logistic model to describe homophily with regard to demographic, clinical, and behavioral characteristics-that is we investigate whether similarities (or differences) between PWH in these characteristics are associated with their sequences being linked. To investigate the performance of our methods, we conducted on a simulation study for which data sets were generated in a way that reproduced the structure of the observed database.

RESULTS

Our results demonstrated strong positive homophily associated with hispanic ethnicity, and strong negative homophily, with birth year difference. The second result implies that the larger the difference between the age of a newly-infected PWH and the average age for an available cluster, the lower the odds of a newly infected person joining that cluster. We did not observe homophily associated with prior diagnosis of sexually transmitted diseases. Our simulation studies demonstrated the validity of our approach for modeling homophily, by showing that the estimates it produced matched the specified values of the statistical network generating model.

CONCLUSIONS

Our novel methods provide a simple and flexible statistical network-based approach for modeling the growth of viral (or other microbial) genetic clusters from linkage to new infections based on genetic distance.

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

Identifying HIV-1 Transmission Clusters in Uzbekistan through Analysis of Molecular Surveillance Data

The CRF02_AG and sub-subtype A6 are currently the predominant HIV-1 variants in the Republic of Uzbekistan, but little is known about their time-spatial clustering patterns in high-risk populations. We have applied molecular evolution methods and network analyses to better understand the transmission patterns of these subtypes by analyzing 316 pol sequences obtained during the surveillance study of HIV drug resistance. Network analysis showed that about one third of the HIV infected persons were organized into clusters, including large clusters with more than 35 members. These clusters were composed mostly of injecting drug users and/or heterosexuals, with women having mainly high centrality within networks identified in both subtypes. Phylogenetic analyses of the 'Uzbek' sequences, including those publicly available, show that Russia and Ukraine played a role as the main sources of the current subtype A6 epidemic in the Republic. At the same time, Uzbekistan has been a local center of the CRF02_AG epidemic spread in the former USSR since the early 2000s. Both of these HIV-1 variants continue to spread in Uzbekistan, highlighting the importance of identifying transmission networks and transmission clusters to prevent further HIV spread, and the need for HIV prevention and education campaigns in high-risk groups.

Phylogeographic Assessment Reveals Geographic Sources of HIV-1 Dissemination Among Men Who Have Sex With Men in Kenya

HIV-1 transmission dynamics involving men who have sex with men (MSM) in Africa are not well understood. We investigated the rates of HIV-1 transmission between MSM across three regions in Kenya: Coast, Nairobi, and Nyanza. We analyzed 372 HIV-1 partial pol sequences sampled during 2006-2019 from MSM in Coast (N = 178, 47.9%), Nairobi (N = 137, 36.8%), and Nyanza (N = 57, 15.3%) provinces in Kenya. Maximum-likelihood (ML) phylogenetics and Bayesian inference were used to determine HIV-1 clusters, evolutionary dynamics, and virus migration rates between geographic regions. HIV-1 sub-subtype A1 (72.0%) was most common followed by subtype D (11.0%), unique recombinant forms (8.9%), subtype C (5.9%), CRF 21A2D (0.8%), subtype G (0.8%), CRF 16A2D (0.3%), and subtype B (0.3%). Forty-six clusters (size range 2-20 sequences) were found-half (50.0%) of which had evidence of extensive HIV-1 mixing among different provinces. Data revealed an exponential increase in infections among MSM during the early-to-mid 2000s and stable or decreasing transmission dynamics in recent years (2017-2019). Phylogeographic inference showed significant (Bayes factor, BF > 3) HIV-1 dissemination from Coast to Nairobi and Nyanza provinces, and from Nairobi to Nyanza province. Strengthening HIV-1 prevention programs to MSM in geographic locations with higher HIV-1 prevalence among MSM (such as Coast and Nairobi) may reduce HIV-1 incidence among MSM in Kenya.

Quantifying rates of HIV-1 flow between risk groups and geographic locations in Kenya: A country-wide phylogenetic study

In Kenya, HIV-1 key populations including men having sex with men (MSM), people who inject drugs (PWID) and female sex workers (FSW) are thought to significantly contribute to HIV-1 transmission in the wider, mostly heterosexual (HET) HIV-1 transmission network. However, clear data on HIV-1 transmission dynamics within and between these groups are limited. We aimed to empirically quantify rates of HIV-1 flow between key populations and the HET population, as well as between different geographic regions to determine HIV-1 'hotspots' and their contribution to HIV-1 transmission in Kenya. We used maximum-likelihood phylogenetic and Bayesian inference to analyse 4058 HIV-1 pol sequences (representing 0.3 per cent of the epidemic in Kenya) sampled 1986-2019 from individuals of different risk groups and regions in Kenya. We found 89 per cent within-risk group transmission and 11 per cent mixing between risk groups, cyclic HIV-1 exchange between adjoining geographic provinces and strong evidence of HIV-1 dissemination from (i) West-to-East (i.e. higher-to-lower HIV-1 prevalence regions), and (ii) heterosexual-to-key populations. Low HIV-1 prevalence regions and key populations are sinks rather than major sources of HIV-1 transmission in Kenya. Targeting key populations in Kenya needs to occur concurrently with strengthening interventions in the general epidemic.

Sorting by Race/Ethnicity Across HIV Genetic Transmission Networks in Three Major Metropolitan Areas in the United States

An important component underlying the disparity in HIV risk between race/ethnic groups is the preferential transmission between individuals in the same group. We sought to quantify transmission between different race/ethnicity groups and measure racial assortativity in HIV transmission networks in major metropolitan areas in the United States. We reconstructed HIV molecular transmission networks from viral sequences collected as part of HIV surveillance in New York City, Los Angeles County, and Cook County, Illinois. We calculated assortativity (the tendency for individuals to link to others with similar characteristics) across the network for three candidate characteristics: transmission risk, age at diagnosis, and race/ethnicity. We then compared assortativity between race/ethnicity groups. Finally, for each race/ethnicity pair, we performed network permutations to test whether the number of links observed differed from that expected if individuals were sorting at random. Transmission networks in all three jurisdictions were more assortative by race/ethnicity than by transmission risk or age at diagnosis. Despite the different race/ethnicity proportions in each metropolitan area and lower proportions of clustering among African Americans than other race/ethnicities, African Americans were the group most likely to have transmission partners of the same race/ethnicity. This high level of assortativity should be considered in the design of HIV intervention and prevention strategies.

Longitudinal typing of molecular HIV clusters in a statewide epidemic

BACKGROUND

HIV molecular epidemiology is increasingly integrated into public health prevention. We conducted cluster typing to enhance characterization of a densely sampled statewide epidemic towards informing public health.

METHODS

We identified HIV clusters, categorized them into types, and evaluated their dynamics between 2004 and 2019 in Rhode Island. We grouped sequences by diagnosis year, assessed cluster changes between paired phylogenies, t0 and t1, representing adjacent years and categorized clusters as stable (cluster in t0 phylogeny = cluster in t1 phylogeny) or unstable (cluster in t0 ≠ cluster in t1). Unstable clusters were further categorized as emerging (t1 phylogeny only) or growing (larger in t1 phylogeny). We determined proportions of each cluster type, of individuals in each cluster type, and of newly diagnosed individuals in each cluster type, and assessed trends over time.

RESULTS

A total of 1727 individuals with available HIV-1 subtype B pol sequences were diagnosed in Rhode Island by 2019. Over time, stable clusters and individuals in them dominated the epidemic, increasing over time, with reciprocally decreasing unstable clusters and individuals in them. Conversely, proportions of newly diagnosed individuals in unstable clusters significantly increased. Within unstable clusters, proportions of emerging clusters and of individuals in them declined; whereas proportions of newly diagnosed individuals in growing clusters significantly increased over time.

CONCLUSION

Distinct molecular cluster types were identified in the Rhode Island epidemic. Cluster dynamics demonstrated increasing stable and decreasing unstable clusters driven by growing, rather than emerging clusters, suggesting consistent in-state transmission networks. Cluster typing could inform public health beyond conventional approaches and direct interventions.

Human Immunodeficiency Virus (HIV) Drug Resistance, Phylogenetic Analysis, and Superinfection Among Men Who Have Sex with Men and Transgender Women in Sub-Saharan Africa: HIV Prevention Trials Network (HPTN) 075 Study

BACKGROUND

The HIV Prevention Trials Network (HPTN) 075 study evaluated the feasibility of enrolling and retaining men who have sex with men (MSM) and transgender women (TGW) from Kenya, Malawi, and South Africa. During the study follow-up, 21 participants acquired human immunodeficiency virus (HIV) (seroconverters). We analyzed HIV subtype diversity, drug resistance, transmission dynamics, and HIV superinfection data among MSM and TGW enrolled in HPTN 075.

METHODS

HIV genotyping and drug resistance testing were performed for participants living with HIV who had viral loads >400 copies/mL at screening (prevalent cases, n = 124) and seroconverters (n = 21). HIV pol clusters were identified using Cluster Picker. Superinfection was assessed by a longitudinal analysis of env and pol sequences generated by next-generation sequencing.

RESULTS

HIV genotyping was successful for 123/124 prevalent cases and all 21 seroconverters. The major HIV subtypes were A1 (Kenya) and C (Malawi and South Africa). Major drug resistance mutations were detected in samples from 21 (14.6%) of 144 participants; the most frequent mutations were K103N and M184V/I. Phylogenetic analyses identified 11 clusters (2-6 individuals). Clusters included seroconverters only (n = 1), prevalent cases and seroconverters (n = 4), and prevalent cases only (n = 6). Superinfections were identified in 1 prevalent case and 2 seroconverters. The annual incidence of superinfection was higher among seroconverters than among prevalent cases, and was higher than the rate of primary HIV infection in the cohort.

CONCLUSIONS

This report provides important insights into HIV genetic diversity, drug resistance, and superinfection among MSM and TGW in sub-Saharan Africa. These findings may help to inform future HIV prevention interventions in these high-risk groups.

The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review

To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995-2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.

Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control

Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs.

HIV transmission network analysis allows identifying unreported risk factors in HIV-positive blood donors in France

OBJECTIVES

As sex between men is a major route of human immunodeficiency virus (HIV) infection in most western countries, restrictive deferral rules for blood donation have largely been implemented regarding men having sex with men (MSM). Here, we sought here to assign unreported HIV risk factors in blood donors (BDs) and reevaluated the MSM-associated fraction of HIV transfusion residual risk (%RR_MSM ).

METHODS

We applied a genetic distance-based approach to infer an HIV transmission network for 384 HIV sequences from French BDs and 1337 HIV sequences from individuals with known risk factors (ANRS PRIMO primary HIV infection cohort). We validated the possibility of assigning a risk factor according to clustering using assortative mixing. Finally, we recalculated the %RR_MSM .

RESULTS

A total of 81 of 284 (28.5%) male and 5 of 100 (5%) female BDs belonged to a cluster; 72 (88.9%) of the 81 male BDs belonged to MSM clusters. After cluster correction, 8 of 67 (11.9%), 4 of 21 (19.0%), and 19 of 88 (21.6%) HIV-positive (HIV+) male BDs with heterosexual, other, or unknown risk factors could be reclassified as MSM, accounting for 10.9% of the total HIV+ male BDs. Overall, 139 of 284 HIV+ male donors (48.9%) could be considered MSM between 2000 and 2016 in France. Between 2005 and 2016, the %RR_MSM increase varied from 0 to 19%, without differing significantly from the %RR_MSM before reclassification.

CONCLUSION

Network inference can be used to complement declaration data on risk factors for HIV infection in BDs. This approach, complementary to behavioral studies, is a valuable tool to evaluate the effect of changes in deferral criteria on BD compliance.

Molecular Epidemiology of HIV-1 in Jiangsu Province, Southeast China: Genotypes and HIV-1 Transmission Networks Among Newly Diagnosed Men Having Sex with Men in 2017

Investigation of disease and intervention in populations of men having sex with men (MSM) has garnered attention globally, a primary reason being the rapid increase in the prevalence of human immunodeficiency virus (HIV)-1 among MSM. The purpose of this study was to understand the current HIV-1 molecular characteristics and characterize HIV-1 transmission networks in the MSM population. Nine hundred and fourteen newly diagnosed HIV-positive MSM were included in this study. The HIV-1 pol gene region was amplified and sequenced. A maximum likelihood phylogenetic tree was constructed, and transmission clusters were identified using 1.5% distance and 0.9 bootstrap values. In total, 767 sequences were successfully obtained, with CRF01_AE being the major genotype (43.3%, 332/767), followed by CRF07_BC (31.3%, 240/767), CRF67_01B (7.2%, 55/767), and URF (6.4%, 49/767). The transmitted HIV drug resistance rate was 4.0% (31/767), and the most common mutations were E138G (n = 4) and G190A (n = 4). A total of 182 (23.7%) sequences were included in the HIV-1 transmission networks, forming 79 clusters. Four clusters were identified as fast-growing, and the proportion of young MSM was higher than that of non-MSM (51.6% vs. 31.8%). The genetic diversity of HIV-1 in Jiangsu was complex, and cross-region transmission might exist for CRF67_01B. Transmission among young MSM within networks was greater than the other age groups; thus, they could be essential in the control of the HIV epidemic in Jiangsu. This study was approved by the ethical review board of the National Center for AIDS/STD Control and Prevention (Project No. X140617334).

High Prevalence of HIV-1 Drug Resistance and Dynamics of Transmission Among High-Risk Populations in Port-au-Prince, Haiti

BACKGROUND

In low HIV prevalence settings, understanding the transmission dynamics and the impact of drug resistance is critical to curb down the epidemic. This study aims to explore the prevalence and dynamics of transmission of HIV drug-resistance mutations (DRMs) among key populations in Haiti.

SETTINGS

Eligible participants (naive, treated) were selected from 7 key population friendly health care centers in Port-au-Prince, Haiti, from September 2018 to July 2019.

METHODS

A total of 119 HIV-1 pol sequences were analyzed from men having sex with men (MSM), female sex workers (FSWs), and their sexual partners. Screening for HIV DRMs was performed using the Stanford University Drug Resistance Database. Phylogenetic and network analyses using HIV-TRACE software were performed to infer putative relationships and shared DRMs.

RESULTS

Of the 119 participants, 62.2% were men (74/119), and 75.7% of them (56/74) reported MSM as a main risk factor. The overall DRM prevalence was 58.8% (70/119). A DRM was observed in 37.5% of MSM (21/56), 82.2% of FSWs (37/45), and 66.7% (12/18) among FSWs' clients. In a multivariate model, age and FSWs were significant predictors for DRMs (P = 0.001). Transmission network analysis found 24 of the 119 (20.2%) genetically linked individuals forming 8 clusters. Clustering participants were mostly MSM (15/24; 62.5%). Five clusters (62.5%) had shared DRMs, and K103N and M184V were the main shared mutations.

CONCLUSIONS

High prevalence of HIV DRMs was observed among MSM, FSWs, and their clients in Port-au-Prince, Haiti. Network analysis revealed frequent DRM transmission among genetically linked individuals, highlighting the need for appropriate interventions to limit HIV transmission in these high-risk populations.

Mapping of HIV-1C Transmission Networks Reveals Extensive Spread of Viral Lineages Across Villages in Botswana Treatment-as-Prevention Trial

BACKGROUND

Phylogenetic mapping of HIV-1 lineages circulating across defined geographical locations is promising for better understanding HIV transmission networks to design optimal prevention interventions.

METHODS

We obtained near full-length HIV-1 genome sequences from people living with HIV (PLWH), including participants on antiretroviral treatment in the Botswana Combination Prevention Project, conducted in 30 Botswana communities in 2013-2018. Phylogenetic relationships among viral sequences were estimated by maximum likelihood.

RESULTS

We obtained 6078 near full-length HIV-1C genome sequences from 6075 PLWH. We identified 984 phylogenetically distinct HIV-1 lineages (molecular HIV clusters) circulating in Botswana by mid-2018, with 2-27 members per cluster. Of these, dyads accounted for 62%, approximately 32% (n = 316) were found in single communities, and 68% (n = 668) were spread across multiple communities. Men in clusters were approximately 3 years older than women (median age 42 years, vs 39 years; P < .0001). In 65% of clusters, men were older than women, while in 35% of clusters women were older than men. The majority of identified viral lineages were spread across multiple communities.

CONCLUSIONS

A large number of circulating phylogenetically distinct HIV-1C lineages (molecular HIV clusters) suggests highly diversified HIV transmission networks across Botswana communities by 2018.

Factors associated with phylogenetic clustering of hepatitis C among people who inject drugs in Baltimore

Background

The availability of effective, oral direct acting antivirals (DAAs) for hepatitis C virus (HCV) treatment has put elimination of HCV as a public health challenge within reach. However, little is known about the characteristics of transmission networks of people who inject drugs (PWID).

Methods

Sequencing of a segment of the HCV genome was performed on samples collected from a community-based cohort of PWID between August 2005 and December 2016. Phylogenetic trees were inferred, and clusters were identified (70% bootstrap threshold; 0.04 maximum genetic distance threshold). We describe sex, race, age difference, and HIV infection status of potential transmission partners. Logistic regression was used to assess factors associated with being in an HCV cluster.

Results

Of 508 HCV genotype 1 viremic PWID, 8% (n = 41) were grouped into 20 clusters, consisting of 19 pairs and 1 triad. In adjusted analyses, female sex (odds ratio [OR] 2.3 [95% confidence interval (CI) 1.2–4.5]) and HIV infection (OR 5.7 [CI 2.7–11.9]) remained independently associated with being in an HCV infection cluster.

Conclusions

Molecular epidemiological analysis reveals that, in this cohort of PWID in Baltimore, HIV infection and female sex were associated with HCV clustering. Combination HCV prevention interventions targeting HIV infected PWID and addressing HCV infection prevention needs of women have potential to advance HCV elimination efforts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05546-x.

HIV-1 Transmission Patterns Within and Between Risk Groups in Coastal Kenya

HIV-1 transmission patterns within and between populations at different risk of HIV-1 acquisition in Kenya are not well understood. We investigated HIV-1 transmission networks in men who have sex with men (MSM), injecting drug users (IDU), female sex workers (FSW) and heterosexuals (HET) in coastal Kenya. We used maximum-likelihood and Bayesian phylogenetics to analyse new (N = 163) and previously published (N = 495) HIV-1 polymerase sequences collected during 2005-2019. Of the 658 sequences, 131 (20%) were from MSM, 58 (9%) IDU, 109 (17%) FSW, and 360 (55%) HET. Overall, 206 (31%) sequences formed 61 clusters. Most clusters (85%) consisted of sequences from the same risk group, suggesting frequent within-group transmission. The remaining clusters were mixed between HET/MSM (7%), HET/FSW (5%), and MSM/FSW (3%) sequences. One large IDU-exclusive cluster was found, indicating an independent sub-epidemic among this group. Phylodynamic analysis of this cluster revealed a steady increase in HIV-1 infections among IDU since the estimated origin of the cluster in 1987. Our results suggest mixing between high-risk groups and heterosexual populations and could be relevant for the development of targeted HIV-1 prevention programmes in coastal Kenya.

Factors influencing HIV-1 phylogenetic clustering

PURPOSE OF REVIEW

A major goal of public health in relation to HIV/AIDS is to prevent new transmissions in communities. Phylogenetic techniques have improved our understanding of the structure and dynamics of HIV transmissions. However, there is still no consensus about phylogenetic methodology, sampling coverage, gene target and/or minimum fragment size.

RECENT FINDINGS

Several studies use a combined methodology, which includes both a genetic or patristic distance cut-off and a branching support threshold to identify phylogenetic clusters. However, the choice about these thresholds remains an inherently subjective process, which affects the results of these studies. There is still a lack of consensus about the genomic region and the size of fragments that should be used, although there seems to be emerging a consensus that using longer segments, allied with the use of a realistic model of evolution and a codon alignment, increases the likelihood of inferring true transmission clusters. The pol gene is still the most used genomic region, but recent studies have suggested that whole genomes and/or sequences from nef and gp41 are also good targets for cluster reconstruction.

SUMMARY

The development and application of standard methodologies for phylogenetic clustering analysis will advance our understanding of factors associated with HIV transmission. This will lead to the design of more precise public health interventions.

Molecular network-based intervention brings us closer to ending the HIV pandemic

Precise identification of HIV transmission among populations is a key step in public health responses. However, the HIV transmission network is usually difficult to determine. HIV molecular networks can be determined by phylogenetic approach, genetic distance-based approach, and a combination of both approaches. These approaches are increasingly used to identify transmission networks among populations, reconstruct the history of HIV spread, monitor the dynamics of HIV transmission, guide targeted intervention on key subpopulations, and assess the effects of interventions. Simulation and retrospective studies have demonstrated that these molecular network-based interventions are more cost-effective than random or traditional interventions. However, we still need to address several challenges to improve the practice of molecular network-guided targeting interventions to finally end the HIV epidemic. The data remain limited or difficult to obtain, and more automatic real-time tools are required. In addition, molecular and social networks must be combined, and technical parameters and ethnic issues warrant further studies.

Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection

Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.

Analysis of HIV-1 diversity, primary drug resistance and transmission networks in Croatia

Molecular epidemiology of HIV-1 infection in treatment-naive HIV-1 infected persons from Croatia was investigated. We included 403 persons, representing 92.4% of all HIV-positive individuals entering clinical care in Croatia in 2014–2017. Overall prevalence of transmitted drug resistance (TDR) was estimated at 16.4%. Resistance to nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside RTI (NNRTIs) and protease inhibitors (PIs) was found in 11.4%, 6.7% and 2.5% of persons, respectively. Triple-class resistance was determined in 2.2% of individuals. In addition, a single case (1.0%) of resistance to integrase strand-transfer inhibitors (InSTIs) was found. Deep sequencing was performed on 48 randomly selected samples and detected additional TDR mutations in 6 cases. Phylogenetic inference showed that 347/403 sequences (86.1%) were part of transmission clusters and identified forward transmission of resistance in Croatia, even that of triple-class resistance. The largest TDR cluster of 53 persons with T215S was estimated to originate in the year 1992. Our data show a continuing need for pre-treatment HIV resistance testing in Croatia. Even though a low prevalence of resistance to InSTI was observed, surveillance of TDR to InSTI should be continued.

Reconstructing the Dissemination Dynamics of the Major HIV-1 Subtype B Non-Pandemic Lineage Circulating in Brazil

Non-pandemic variants of the Human Immunodeficiency Virus Type 1 (HIV-1) subtype B accounts for a significant fraction of HIV infections in several Caribbean islands, Northeastern South American countries and the Northern Brazilian states of Roraima and Amazonas. In this paper, we used a comprehensive dataset of HIV-1 subtype B pol sequences sampled in Amazonas and Roraima between 2007 and 2017 to reconstruct the phylogeographic and demographic dynamics of the major HIV-1 subtype B non-pandemic Brazilian lineage, designated as B_CAR-BR-I. Our analyses revealed that its origin could be traced to one of many viral introductions from French Guiana and Guyana into Northern Brazil, which probably occurred in the state of Amazonas around the late 1970s. The B_CAR-BR-I clade was rapidly disseminated from Amazonas to Roraima, and the epidemic grew exponentially in these Northern Brazilian states during the 1980s and 1990s, coinciding with a period of economic and fast population growth in the region. The spreading rate of the B_CAR-BR-I clade, however, seems to have slowed down since the early 2000s, despite the continued expansion of the HIV-1 epidemic in this region in the last decade.

A Short-Term Assessment of Nascent HIV-1 Transmission Clusters Among Newly Diagnosed Individuals Using Envelope Sequence-Based Phylogenetic Analyses

The identification of transmission clusters (TCs) of HIV-1 using phylogenetic analyses can provide insights into viral transmission network and help improve prevention strategies. We compared the use of partial HIV-1 envelope fragment of 1,070 bp with its loop 3 (108 bp) to determine its utility in inferring HIV-1 transmission clustering. Serum samples of recently (n = 106) and chronically (n = 156) HIV-1-infected patients with status confirmed were sequenced. HIV-1 envelope nucleotide-based phylogenetic analyses were used to infer HIV-1 TCs. Those were constructed using ClusterPickerGUI_1.2.3 considering a pairwise genetic distance of ≤10% threshold. Logistic regression analyses were used to examine the relationship between the demographic factors that were likely associated with HIV-1 clustering. Ninety-eight distinct consensus envelope sequences were subjected to phylogenetic analyses. Using a partial envelope fragment sequence, 42 sequences were grouped into 15 distinct small TCs while the V3 loop reproduces 10 clusters. The agreement between the partial envelope and the V3 loop fragments was significantly moderate with a Cohen's kappa (κ) coefficient of 0.59, p < .00001. The mean age (<38.8 years) and HIV-1 B subtype are two factors identified that were significantly associated with HIV-1 transmission clustering in the cohort, odds ratio (OR) = 0.25, 95% confidence interval (CI, 0.04-0.66), p = .002 and OR: 0.17, 95% CI (0.10-0.61), p = .011, respectively. The present study confirms that a partial fragment of the HIV-1 envelope sequence is a better predictor of transmission clustering. However, the loop 3 segment may be useful in screening purposes and may be more amenable to integration in surveillance programs.

Enhanced use of phylogenetic data to inform public health approaches to HIV among men who have sex with men

The multidimensional nature and continued evolution of HIV epidemics among men who have sex with men (MSM) requires innovative intervention approaches. Strategies are needed that recognise the individual, social and structural factors driving HIV transmission; that can pinpoint networks with heightened transmission risk; and that can help target intervention in real time. HIV phylogenetics is a rapidly evolving field with strong promise for informing innovative responses to the HIV epidemic among MSM. Currently, HIV phylogenetic insights are providing new understandings of characteristics of HIV epidemics involving MSM, social networks influencing transmission, characteristics of HIV transmission clusters involving MSM, targets for antiretroviral and other prevention strategies and dynamics of emergent epidemics. Maximising the potential of HIV phylogenetics for HIV responses among MSM will require attention to key methodological challenges and ethical considerations, as well as resolving key implementation and scientific questions. Enhanced and integrated use of HIV surveillance, sociobehavioural and phylogenetic data resources are becoming increasingly critical for informing public health approaches to HIV among MSM.

Phylogeographic Analyses Reveal the Early Expansion and Frequent Bidirectional Cross-Border Transmissions of Non-pandemic HIV-1 Subtype B Strains in Hispaniola

The human immunodeficiency virus-type 1 (HIV-1) subtype B has probably been circulating on the island of Hispaniola since the 1960s, but information about the early viral history on this Caribbean island is scarce. In this study, we reconstruct the dissemination dynamics of early divergent non-pandemic subtype B lineages (designated B_CAR) on Hispaniola by analyzing a country-balanced dataset of HIV-1 B_CARpol sequences from Haiti (n = 103) and the Dominican Republic (n = 123). Phylogenetic analyses supported that B_CAR strains from Haiti and the Dominican Republic were highly intermixed between each other, although the null hypothesis of completely random mixing was rejected. Bayesian phylogeographic analyses placed the ancestral B_CAR virus in Haiti and the Dominican Republic with the same posterior probability support. These analyses estimate frequent viral transmissions between Haiti and the Dominican Republic since the early 1970s onwards, and the presence of local B_CAR transmission networks in both countries before first AIDS cases was officially recognized. Demographic reconstructions point that the B_CAR epidemic in Hispaniola grew exponentially until the 1990s. These findings support that the HIV-1 epidemics in Haiti and the Dominican Republic have been connected by a recurrent bidirectional viral flux since the initial phase, which poses a great challenge in tracing the geographic origin of the B_CAR epidemic within Hispaniola using only genetic data. These data also reinforce the notion that prevention programs have successfully reduced the rate of new HIV-1 transmissions in Hispaniola since the end of the 1990s.

Trust and Expectations of Researchers and Public Health Departments for the Use of HIV Molecular Epidemiology

Background: Molecular epidemiology (ME) is a technique used to study the dynamics of pathogen transmission through a population. When used to study HIV infections, ME generates powerful information about how HIV is transmitted, including epidemiologic patterns of linkage and, potentially, transmission direction. Thus, ME raises challenging questions about the most responsible way to protect individual privacy while acquiring and using these data to advance public health and inform HIV intervention strategies. Here, we report on stakeholders' expectations for how researchers and public health agencies might use HIV ME. Methods: We conducted in-depth semistructured interviews with 40 key stakeholders to find out how these individuals respond to the proposed risks and benefits of HIV ME. Transcripts were coded and analyzed using Atlas.ti. Expectations were assessed through analysis of responses to hypothetical scenarios designed to help interviewees think through the implications of this emerging technique in the contexts of research and public health. Results: Our analysis reveals a wide range of imagined responsibilities, capabilities, and trustworthiness of researchers and public health agencies. Specifically, many respondents expect researchers and public health agencies to use HIV ME carefully and maintain transparency about how data will be used. Informed consent was discussed as an important opportunity for notification of privacy risks. Furthermore, some respondents wished that public health agencies were held to the same form of oversight and accountability represented by informed consent in research. Conclusions: To prevent HIV ME from becoming a barrier to testing or a source of public mistrust, the sense of vulnerability expressed by some respondents must be addressed. In research, informed consent is an obvious opportunity for this. Without giving specimen donors a similar opportunity to opt out, public health agencies may find it difficult to adopt HIV ME without deterring testing and treatment.

Hotspots of Transmission Driving the Local Human Immunodeficiency Virus Epidemic in the Cologne-Bonn Region, Germany

BACKGROUND

Geographical allocation of interventions focusing on hotspots of human immunodeficiency virus (HIV) transmission has the potential to improve efficiency. We used phylogeographic analyses to identify hotspots of the HIV transmission in Cologne-Bonn, Germany.

METHODS

We included 714 HIV-1 infected individuals, followed up at the University Hospitals Cologne and Bonn. Distance-based molecular network analyses were performed to infer putative relationships. Characteristics of genetically linked individuals and assortativity (shared characteristics) were analyzed. Geospatial diffusion (ie, viral gene flow) was evaluated using a Slatkin-Maddison approach. Geospatial dispersal was determined by calculating the average distance between the residences of linked individuals (centroids of 3-digit zip code).

RESULTS

In sum, 217/714 (30.4%) sequences had a putative genetic linkage, forming 77 clusters (size range: 2-8). Linked individuals were more likely to live in areas surrounding the city center (P = .043), <30 years of age (P = .009). and infected with HIV-1 subtype B (P = .002). Clustering individuals were nonassortative by area of residency (-.0026, P = .046). Geospatial analyses revealed a median distance between genetically linked individuals of 23.4 kilometers (km), lower than expected (P < .001). Slatkin-Maddison analyses revealed increased gene flow from central Cologne toward the surrounding areas (P < .001).

CONCLUSION

Phylogeographic analysis suggests that central Cologne may be a significant driver of the regional epidemic. Although clustering individuals lived closer than unlinked individuals, they were less likely to be linked to others from their same zip code. These results could help public health entities better understand transmission dynamics, facilitating allocation of resources to areas of greatest need.

Estimating Effects of HIV Sequencing Data Completeness on Transmission Network Patterns and Detection of Growing HIV Transmission Clusters

HIV nucleotide sequence data can identify clusters of persons with genetically similar strains suggesting transmission. We simulated the effect of lowered data completeness, defined by the percent of persons with diagnosed HIV with a reported sequence, on transmission patterns and detection of growing HIV transmission clusters. We analyzed HIV surveillance data for persons with HIV diagnosed during 2008-2014 who resided in Michigan or Washington. We calculated genetic distances, constructed the inferred transmission network for each jurisdiction, and compared transmission network characteristics and detection of growing transmission clusters in the full dataset with artificially reduced datasets. Simulating lower levels of completeness resulted in decreased percentages of persons linked to a cluster from high completeness (full dataset) to low completeness (5%) (Michigan: 54%-18%; Washington, 46%-16%). Patterns of transmission between certain populations remained robust as data completeness level was reduced. As data completeness was artificially decreased, sensitivity of cluster detection substantially diminished in both states. In Michigan, sensitivity decreased from 100% with the full dataset, to 62% at 50% completeness and 21% at 25% completeness. In Washington, sensitivity decreased from 100% with the full dataset, to 71% at 50% completeness and 29% at 25% completeness. Lower sequence data completeness limits the ability to detect clusters that may benefit from investigation; however, inferences can be made about transmission patterns even with low data completeness, given sufficient numbers. Data completeness should be prioritized, as lack of or delays in detection of transmission clusters could result in additional infections.

Exploring Evolutionary and Transmission Dynamics of HIV Epidemic in Serbia: Bridging Socio-Demographic With Phylogenetic Approach

Previous molecular studies of Serbian HIV epidemic identified the dominance of subtype B and presence of clusters related HIV-1 transmission, in particular among men who have sex with men (MSM). In order to get a deeper understanding of the complexities of HIV sub-epidemics in Serbia, epidemic trends, temporal origin and phylodynamic characteristics in general population and subpopulations were analyzed by means of mathematical modeling, phylogenetic analysis and latent class analysis (LCA). Fitting of the logistic curve of trends for a cumulative annual number of new HIV cases in 1984–2016, in general population and MSM transmission group, was performed. Both datasets fitted the logistic growth model, showing the early exponential phase of the growth curve. According to the suggested model, in the year 2030, the number of newly diagnosed HIV cases in Serbia will continue to grow, in particular in the MSM transmission group. Further, a detailed phylogenetic analysis was performed on 385 sequences from the period 1997–2015. Identification of transmission clusters, estimation of population growth (Ne), of the effective reproductive number (Re) and time of the most recent common ancestor (tMRCA) were estimated employing Bayesian and maximum likelihood methods. A substantial proportion of 53% of subtype B sequences was found within transmission clusters/network. Phylodynamic analysis revealed Re over one during the whole period investigated, with the steepest slopes and a recent tMRCA for MSM transmission group subtype B clades, in line with a growing trend in the number of transmissions in years approaching the end of the study period. Contrary, heterosexual clades in both studied subtypes – B and C – showed modest growth and stagnation. LCA analysis identified five latent classes, with transmission clusters dominantly present in 2/5 classes, linked to MSM transmission living in the capital city and with the high prevalence of co-infection with HBV and/or other STIs.Presented findings imply that HIV epidemic in Serbia is still in the exponential growth phase, in particular, related to the MSM transmission, with estimated steep growth curve until 2030. The obtained results imply that an average new HIV patient in Serbia is a young man with concomitant sexually transmitted infection.

Insights on transmission of HIV from phylogenetic analysis to locally optimize HIV prevention strategies

PURPOSE OF REVIEW

Phylogenetic analysis can identify transmission networks by clustering genetically related HIV genotypes that are routinely collected. In this study, we will review phylogenetic insights gained on transmission of HIV and phylogenetically optimized HIV prevention strategies.

RECENT FINDINGS

Phylogenetic analysis reports that HIV transmission varies by geographical region and by route of transmission. In high-income countries, HIV is predominantly transmitted between recently infected MSM who live in the same country. In rural Uganda, transmission of HIV is frequently between different communities. Age-discrepant transmission has been reported across the world. Four studies have used phylogenetic optimization of HIV prevention. Three studies predict that immediate treatment after diagnosis would have prevented 19-42% of infections, and that preexposure prophylaxis would have prevented 66% of infections. One phylogenetic study guided a public health response to an actively ongoing HIV outbreak. Phylogenetic clustering requires a dense sample of patients and small time-gaps between infection and diagnosis.

SUMMARY

Phylogenetic analysis can be an important tool to identify a local strategy that prevents most infections. Future studies that use phylogenetic analysis for optimizing HIV prevention strategies should also include cost-effectiveness so that the most cost-effective prevention method is identified.

In-depth Sampling of High-risk Populations to Characterize HIV Transmission Epidemics Among Young MSM Using PrEP in France and Quebec

Background

A better understanding of HIV transmission dynamics among populations at high risk is important for development of prevention strategies. We determined HIV transmission networks from infected individuals enrolled in the pre-exposure prophylaxis (PrEP) IPERGAY trial in combination with the ANRS PRIMO and Montreal PHI cohorts to identify and characterize active clusters of transmission in this high-risk population.

Methods

Genotypic resistance tests were performed on plasma samples from 31 IPERGAY participants. Reverse transcriptase sequences were analyzed in combination with unique HIV pol sequences from 1351 individuals enrolled in the PRIMO ANRS cohort (1999–2014) and 511 individuals enrolled in the Montreal PHI cohort (1996–2016). Network analyses were performed to infer putative relationships between all participants.

Results

Overall, 1893 participants were included. Transmission network analyses revealed that 14 individuals (45.2%) from the IPERGAY trial were involved in 13 clusters sampled over a median period (interquartile range) of 2 (0.3–7.8) years, including 7 dyads and 6 larger clusters ranging from 4 to 28 individuals. When comparing characteristics between clustering individuals enrolled in the PRIMO cohort (n = 377) and in IPERGAY (n = 14), we found that IPERGAY participants had a higher viral load (5.93 vs 5.20 log₁₀ copies/mL, P = .032) and reported a higher number of partners in the last 2 months (P < .01).

Conclusions

These results demonstrate high rates of HIV transmission clustering among young high-risk MSM enrolled in the IPERGAY trial. In-depth sampling of high-risk populations may help to uncover unobserved transmission intermediaries and improve prevention efforts that could be targeted to the most active clusters.

Pretreatment human immunodeficiency virus type 1 (HIV-1) drug resistance in transmission clusters of the Cologne-Bonn region, Germany

OBJECTIVES

In Germany, previous reports have demonstrated transmitted human immunodeficiency virus type 1 (HIV-1) drug-resistance mutations (DRM) in 11% of newly diagnosed individuals, highlighting the importance of drug-resistance screening before the initiation of antiretroviral therapy (ART). Here, we sought to understand the molecular epidemiology of HIV DRM transmission in the Cologne-Bonn region of Germany, given one of the highest rates of new HIV diagnoses in western Europe (13.7 per 100 000 habitants).

METHODS

We analysed 714 HIV-1 ART-naive infected individuals diagnosed at the University Hospitals Cologne and Bonn between 2001 and 2016. Screening for DRM was performed according to the Stanford University Genotypic Resistance Interpretation. Shared DRM were defined as any DRM present in genetically linked individuals (<1.5% genetic distance). Phylogenetic and network analyses were performed to infer putative relationships and shared DRM.

RESULTS

The prevalence of any DRM at time of diagnosis was 17.2% (123/714 participants). Genetic transmission network analyses showed comparable frequencies of DRM in clustering versus non-clustering individuals (17.1% (85/497) versus 17.5% (38/217)). The observed rate of DRM in the region was higher than previous reports 10.8% (87/809) (p < 0.001), revealing the need to reduce onward transmission in this area. Genetically linked individuals harbouring shared DRM were more likely to live in suburban areas (24/38) than in central Cologne (1/38) (p < 0.001).

CONCLUSION

The rate of DRM was exceptionally high. Network analysis elucidated frequent cases of shared DRM among genetically linked individuals, revealing the potential spread of DRM and the need to prevent onward transmission of DRM in the Cologne-Bonn area.

Inferring the age difference in HIV transmission pairs by applying phylogenetic methods on the HIV transmission network of the Swiss HIV Cohort Study

Age-mixing patterns are of key importance for understanding the dynamics of human immunodeficiency virus (HIV)-epidemics and target public health interventions. We use the densely sampled Swiss HIV Cohort Study (SHCS) resistance database to study the age difference at infection in HIV transmission pairs using phylogenetic methods. In addition, we investigate whether the mean age difference of pairs in the phylogenetic tree is influenced by sampling as well as by additional distance thresholds for including pairs. HIV-1 pol-sequences of 11,922 SHCS patients and approximately 240,000 Los Alamos background sequences were used to build a phylogenetic tree. Using this tree, 100 per cent down to 1 per cent of the tips were sampled repeatedly to generate pruned trees (N = 500 for each sample proportion), of which pairs of SHCS patients were extracted. The mean of the absolute age differences of the pairs, measured as the absolute difference of the birth years, was analyzed with respect to this sample proportion and a distance criterion for inclusion of the pairs. In addition, the transmission groups men having sex with men (MSM), intravenous drug users (IDU), and heterosexuals (HET) were analyzed separately. Considering the tree with all 11,922 SHCS patients, 2,991 pairs could be extracted, with 954 (31.9 per cent) MSM-pairs, 635 (21.2 per cent) HET-pairs, 414 (13.8 per cent) IDU-pairs, and 352 (11.8 per cent) HET/IDU-pairs. For all transmission groups, the age difference at infection was significantly (P < 0.001) smaller for pairs in the tree compared with randomly assigned pairs, meaning that patients of similar age are more likely to be pairs. The mean age difference in the phylogenetic analysis, using a fixed distance of 0.05, was 9.2, 9.0, 7.3 and 5.6 years for MSM-, HET-, HET/IDU-, and IDU-pairs, respectively. Decreasing the cophenetic distance threshold from 0.05 to 0.01 significantly decreased the mean age difference. Similarly, repeated sampling of 100 per cent down to 1 per cent of the tips revealed an increased age difference at lower sample proportions. HIV-transmission is age-assortative, but the age difference of transmission pairs detected by phylogenetic analyses depends on both sampling proportion and distance criterion. The mean age difference decreases when using more conservative distance thresholds, implying an underestimation of age-assortativity when using liberal distance criteria. Similarly, overestimation of the mean age difference occurs for pairs from sparsely sampled trees, as it is often the case in sub-Saharan Africa.

Molecular Epidemiology of the HIV Epidemic in Three German Metropolitan Regions - Cologne/Bonn, Munich and Hannover, 1999-2016

Using HIV sequence data to characterize clusters of HIV transmission may provide insight into the epidemic. Phylogenetic and network analyses were performed to infer putative relationships between HIV-1 partial pol sequences from 2,774 individuals receiving care in three German regions between 1999-2016. The regions have in common that they host some of the largest annual festivals in Europe (Carnival and Oktoberfest). Putative links with sequences (n = 150,396) from the Los Alamos HIV Sequence database were evaluated. A total of 595/2,774 (21.4%) sequences linked with at least one other sequence, forming 184 transmission clusters. Clustering individuals were significantly more likely to be younger, male, and report sex with men as their main risk factor (p < 0.001 each). Most clusters (77.2%) consisted exclusively of men; 41 (28.9%) of these included men reporting sex with women. Thirty-two clusters (17.4%) contained sequences from more than one region; clustering men were significantly more likely to be in a position bridging regional HIV epidemics than clustering women (p = 0.027). We found 236 clusters linking 547 sequences from our sample with sequences from the Los Alamos database (n = 1407; 31% from other German centres). These results highlight the pitfalls of focusing HIV prevention efforts on specific risk groups or specific locales.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis—where individuals are sampled sooner post-infection—rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Many pathogens evolve so rapidly that they accumulate genetic differences within a host before becoming transmitted to the next host. Consequently, clusters of sampled infections with nearly identical genomes may reveal outbreaks of recent or ongoing transmissions. There is rapidly growing interest in using model-free genetic clustering methods to guide public health responses to epidemics in near real-time, including HIV, Ebola virus and tuberculosis. However, we show that current methods are relatively ineffective at detecting transmission outbreaks; instead, they are predominantly influenced by how infections are sampled from the population. We describe a fundamentally new approach to genetic clustering that is based on modelling changes in transmission rates during the spread of the epidemic. We use simulated and real pathogen sequence data sets to demonstrate that this model-based approach is substantially more effective for detecting transmission outbreaks, and remains fast enough for real-time applications to large sequence databases.

HIV-1 transmission networks in high risk fishing communities on the shores of Lake Victoria in Uganda: A phylogenetic and epidemiological approach

BACKGROUND

Fishing communities around Lake Victoria in sub-Saharan Africa have been characterised as a population at high risk of HIV-infection.

METHODS

Using data from a cohort of HIV-positive individuals aged 13-49 years, enrolled from 5 fishing communities on Lake Victoria between 2009-2011, we sought to identify factors contributing to the epidemic and to understand the underlying structure of HIV transmission networks. Clinical and socio-demographic data were combined with HIV-1 phylogenetic analyses. HIV-1 gag-p24 and env-gp-41 sub-genomic fragments were amplified and sequenced from 283 HIV-1-infected participants. Phylogenetic clusters with ≥2 highly related sequences were defined as transmission clusters. Logistic regression models were used to determine factors associated with clustering.

RESULTS

Altogether, 24% (n = 67/283) of HIV positive individuals with sequences fell within 34 phylogenetically distinct clusters in at least one gene region (either gag or env). Of these, 83% occurred either within households or within community; 8/34 (24%) occurred within household partnerships, and 20/34 (59%) within community. 7/12 couples (58%) within households clustered together. Individuals in clusters with potential recent transmission (11/34) were more likely to be younger 71% (15/21) versus 46% (21/46) in un-clustered individuals and had recently become resident in the community 67% (14/21) vs 48% (22/46). Four of 11 (36%) potential transmission clusters included incident-incident transmissions. Independently, clustering was less likely in HIV subtype D (adjusted Odds Ratio, aOR = 0.51 [95% CI 0.26-1.00]) than A and more likely in those living with an HIV-infected individual in the household (aOR = 6.30 [95% CI 3.40-11.68]).

CONCLUSIONS

A large proportion of HIV sexual transmissions occur within house-holds and within communities even in this key mobile population. The findings suggest localized HIV transmissions and hence a potential benefit for the test and treat approach even at a community level, coupled with intensified HIV counselling to identify early infections.

Genetic characterization of human immunodeficiency virus type 1 transmission in the Middle East and North Africa

BACKGROUND

The HIV-1 spread in the Middle East and North Africa (MENA) has not been previously characterised using the phylogenetic approach. The aim of the current study was to investigate the genetic diversity and domestic transmission of HIV-1 in the MENA.

METHODS

A total of 2036 HIV-1 sequences available in Genbank and collected in the MENA during 1988-2016 were used together with 715 HIV-1 reference sequences that were retrieved from Genbank based on genetic similarity with the MENA sequences. The REGA and COMET tools were used to determine HIV-1 subtypes and circulating recombinant forms. Maximum Likelihood and Bayesian phylogenetic analyses were used to identify and date HIV-1 transmission clusters.

RESULTS

At least 21 HIV-1 subtypes and recombinant forms were prevalent in the MENA. Subtype B was the most common variant (39%), followed by CRF35_AD (19%) and CRF02_AG (14%). The most common genetic region was pol, and 675 partial pol sequences (average of 1005 bp) were eligible for detailed phylogenetic analysis. Fifty-four percent of the MENA sequences formed HIV-1 transmission clusters. Whereas numerous clusters were country-specific, some clusters indicated transmission links between countries for subtypes B, C and CRF02_AG. This was more common in North Africa compared with the Middle East (p < 0.001). Recombinant forms had a larger proportion of clustering compared to pure subtypes (p < 0.001). The largest MENA clusters dated back to 1991 (an Algerian CRF06_cpx cluster of 43 sequences) and 2002 (a Tunisian CRF02_AG cluster of 48 sequences).

CONCLUSIONS

We found an extensive HIV-1 diversity in the MENA and a high proportion of sequences in transmission clusters. This study highlights the need for preventive measures in the MENA to limit HIV-1 spread in this region.

Defining HIV-1 transmission clusters based on sequence data

Understanding HIV-1 transmission dynamics is relevant to both screening and intervention strategies of HIV-1 infection. Commonly, HIV-1 transmission chains are determined based on sequence similarity assessed either directly from a sequence alignment or by inferring a phylogenetic tree. This review is aimed at both nonexperts interested in understanding and interpreting studies of HIV-1 transmission, and experts interested in finding the most appropriate cluster definition for a specific dataset and research question. We start by introducing the concepts and methodologies of how HIV-1 transmission clusters usually have been defined. We then present the results of a systematic review of 105 HIV-1 molecular epidemiology studies summarizing the most common methods and definitions in the literature. Finally, we offer our perspectives on how HIV-1 transmission clusters can be defined and provide some guidance based on examples from real life datasets.

Recent advances in understanding HIV evolution

The human immunodeficiency virus (HIV) evolves rapidly owing to the combined activity of error-prone reverse transcriptase, recombination, and short generation times, leading to extensive viral diversity both within and between hosts. This diversity is a major contributing factor in the failure of the immune system to eradicate the virus and has important implications for the development of suitable drugs and vaccines to combat infection. This review will discuss the recent technological advances that have shed light on HIV evolution and will summarise emerging concepts in this field.

Spatiotemporal dynamics of HIV-1 transmission in France (1999-2014) and impact of targeted prevention strategies

Background

Characterizing HIV-1 transmission networks can be important in understanding the evolutionary patterns and geospatial spread of the epidemic. We reconstructed the broad molecular epidemiology of HIV from individuals with primary HIV-1 infection (PHI) enrolled in France in the ANRS PRIMO C06 cohort over 15 years.

Results

Sociodemographic, geographic, clinical, biological and pol sequence data from 1356 patients were collected between 1999 and 2014. Network analysis was performed to infer genetic relationships, i.e. clusters of transmission, between HIV-1 sequences. Bayesian coalescent-based methods were used to examine the temporal and spatial dynamics of identified clusters from different regions in France. We also evaluated the use of network information to target prevention efforts. Participants were mostly Caucasian (85.9%) and men (86.7%) who reported sex with men (MSM, 71.4%). Overall, 387 individuals (28.5%) were involved in clusters: 156 patients (11.5%) in 78 dyads and 231 participants (17%) in 42 larger clusters (median size: 4, range 3–41). Compared to individuals with single PHI (n = 969), those in clusters were more frequently men (95.9 vs 83%, p < 0.01), MSM (85.8 vs 65.6%, p < 0.01) and infected with CRF02_AG (20.4 vs 13.4%, p < 0.01). Reconstruction of viral migrations across time suggests that Paris area was the major hub of dissemination of both subtype B and CRF02_AG epidemics. By targeting clustering individuals belonging to the identified active transmission network before 2010, 60 of the 143 onward transmissions could have been prevented.

Conclusion

These analyses support the hypothesis of a recent and rapid rise of CRF02_AG within the French HIV-1 epidemic among MSM. Combined with a short turnaround time for sample processing, targeting prevention efforts based on phylogenetic monitoring may be an efficient way to deliver prevention interventions but would require near real time targeted interventions on the identified index cases and their partners.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-017-0339-4) contains supplementary material, which is available to authorized users.

Reconstructing contact network parameters from viral phylogenies

Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on approximate Bayesian computation (ABC), a likelihood-free inference strategy, for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barabási-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from ten published HIV sequence datasets. This model incorporates a feature called preferential attachment (PA), whereby individuals with more existing contacts accumulate new contacts at a higher rate. On simulated data, we found that the strength of PA and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, was not estimable with ABC. We observed sub-linear PA power in all datasets, as well as higher PA power in networks of injection drug users. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics.

Evolutionary Dynamics and Complicated Genetic Transmission Network Patterns of HIV-1 CRF01_AE among MSM in Shanghai, China

To explore the evolutionary dynamics and molecular transmission patterns of HIV-1 CRF01_AE in depth among men who have sex with men (MSM) in Shanghai, we constructed phylogenetic tree and genetic transmission networks based on 1, 152 pol sequences from MSM, 282 from other risk groups and 795 references. Phylogenetic analyses identified two distinct major CRF01_AE lineages and a Shanghai-based sub-lineage. The estimated tMRCAs for lineage 1 and 2 were 1996.0 (1992.9-1999.2) and 1997.8 (1994.3-2001.4), respectively. Of the 1, 152 MSM, 681 (59.1%) were identified as belonging to 241 separate networks. Of these 681 individuals in networks, 74.2% were linked to cases diagnosed in different years, 4.3% were linked to heterosexual women, and 0.7% were linked to persons who inject drugs. A total of 71 networks including 180 individuals diagnosed in Shanghai with the same domicile were found. Recent infection (P = 0.022) and sampling year after 2011 (P < 0.001) were significantly associated with potential transmission links among the networks. Besides, a significant transmission of viruses with drug resistant mutations at V179D/E were found in the networks. Given these findings, we propose that genetic transmission analysis is a useful tool in HIV intervention strategies to curb the spread of virus and promoting public health.

Connecting the dots: network data and models in HIV epidemiology

Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks most commonly comprise chains of sexual relationships, but in some populations, sharing of contaminated needles is also an important, or even the main mechanism that connects people in the network. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years, because of advances in molecular biology and the use of partner notification services in HIV prevention and treatment programmes. We review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network. We discuss the relative strengths and weaknesses of existing methods and models, and we propose a research agenda for advancing network analysis in HIV epidemiology. We make the case for a combination approach that integrates multiple data sources into a coherent statistical framework.

Near real-time monitoring of HIV transmission hotspots from routine HIV genotyping: an implementation case study

BACKGROUND

HIV evolves rapidly and therefore infections with similar genetic sequences are likely linked by recent transmission events. Clusters of related infections can represent subpopulations with high rates of transmission. We describe the implementation of an automated near real-time system to monitor and characterise HIV transmission hotspots in British Columbia, Canada.

METHODS

In this implementation case study, we applied a monitoring system to the British Columbia drug treatment database, which holds more than 32 000 anonymised HIV genotypes for nearly 9000 residents of British Columbia living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated reanalysis of the entire database. We extracted clusters of five or more individuals with short phylogenetic distances between their respective HIV sequences. The system generated monthly reports of the growth and characteristics of clusters that were distributed to public health officers.

FINDINGS

In June, 2014, the monitoring system detected the expansion of a cluster by 11 new cases during 3 months, including eight cases with transmitted drug resistance. This cluster generally comprised young men who have sex with men. The subsequent report precipitated an enhanced public health follow-up to ensure linkage to care and treatment initiation in the affected subpopulation. Of the nine cases associated with this follow-up, all had already been linked to care and five cases had started treatment. Subsequent to the follow-up, three additional cases started treatment and most cases achieved suppressed viral loads. During the next 12 months, we detected 12 new cases in this cluster with reduction in the onward transmission of drug resistance.

INTERPRETATION

Our findings show the first application of an automated phylogenetic system monitoring a clinical database to detect a recent HIV outbreak and support the ensuing public health response. By making secondary use of routinely collected HIV genotypes, this approach is cost-effective, attains near real-time monitoring of new cases, and can be implemented in all settings in which HIV genotyping is the standard of care.

FUNDING

BC Centre for Excellence in HIV/AIDS, the Canadian Institutes for Health Research, the Genome Canada-CIHR Partnership in Genomics and Personalized Health, and the US National Institute on Drug Abuse.

Phylogenetic Investigation of a Statewide HIV-1 Epidemic Reveals Ongoing and Active Transmission Networks Among Men Who Have Sex With Men

BACKGROUND

Molecular epidemiological evaluation of HIV-1 transmission networks can elucidate behavioral components of transmission that can be targets for intervention.

METHODS

We combined phylogenetic and statistical approaches using pol sequences from patients diagnosed between 2004 and 2011 at a large HIV center in Rhode Island, following 75% of the state's HIV population. Phylogenetic trees were constructed using maximum likelihood, and putative transmission clusters were evaluated using latent class analyses to determine association of cluster size with underlying demographic/behavioral characteristics. A logistic growth model was used to assess intracluster dynamics over time and predict "active" clusters that were more likely to harbor undiagnosed infections.

RESULTS

Of the 1166 HIV-1 subtype B sequences, 31% were distributed among 114 statistically supported, monophyletic clusters (range: 2-15 sequences/cluster). Sequences from men who have sex with men (MSM) formed 52% of clusters. Latent class analyses demonstrated that sequences from recently diagnosed (2008-2011) MSM with primary HIV infection (PHI) and other sexually transmitted infections (STIs) were more likely to form larger clusters (odds ratio: 1.62-11.25, P < 0.01). MSM in clusters were more likely to have anonymous partners and meet partners at sex clubs and pornographic stores. Four large clusters with 38 sequences (100% male, 89% MSM) had a high probability of harboring undiagnosed infections and included younger MSM with PHI and STIs.

CONCLUSIONS

In this first large-scale molecular epidemiological investigation of HIV-1 transmission in New England, sexual networks among recently diagnosed MSM with PHI and concomitant STIs contributed to the ongoing transmission. Characterization of transmission dynamics revealed actively growing clusters, which may be targets for intervention.