Phylogenetic Cluster Analysis Identifies Virological and Behavioral Drivers of Human Immunodeficiency Virus Transmission in Men Who Have Sex With Men

Quantifying and Predicting Ongoing Human Immunodeficiency Virus Type 1 Transmission Dynamics in Switzerland Using a Distance-Based Clustering Approach

BACKGROUND

Despite effective prevention approaches, ongoing human immunodeficiency virus 1 (HIV-1) transmission remains a public health concern indicating a need for identifying its drivers.

METHODS

We combined a network-based clustering method using evolutionary distances between viral sequences with statistical learning approaches to investigate the dynamics of HIV transmission in the Swiss HIV Cohort Study and to predict the drivers of ongoing transmission.

RESULTS

We found that only a minority of clusters and patients acquired links to new infections between 2007 and 2020. While the growth of clusters and the probability of individual patients acquiring new links in the transmission network was associated with epidemiological, behavioral, and virological predictors, the strength of these associations decreased substantially when adjusting for network characteristics. Thus, these network characteristics can capture major heterogeneities beyond classical epidemiological parameters. When modeling the probability of a newly diagnosed patient being linked with future infections, we found that the best predictive performance (median area under the curve receiver operating characteristic AUCROC = 0.77) was achieved by models including characteristics of the network as predictors and that models excluding them performed substantially worse (median AUCROC = 0.54).

CONCLUSIONS

These results highlight the utility of molecular epidemiology-based network approaches for analyzing and predicting ongoing HIV transmission dynamics. This approach may serve for real-time prospective assessment of HIV transmission.

Community Insights in Phylogenetic HIV Research: The CIPHR Project Protocol

Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in 'real world' programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team's protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process - The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.

From components to communities: bringing network science to clustering for molecular epidemiology

Defining clusters of epidemiologically related infections is a common problem in the surveillance of infectious disease. A popular method for generating clusters is pairwise distance clustering, which assigns pairs of sequences to the same cluster if their genetic distance falls below some threshold. The result is often represented as a network or graph of nodes. A connected component is a set of interconnected nodes in a graph that are not connected to any other node. The prevailing approach to pairwise clustering is to map clusters to the connected components of the graph on a one-to-one basis. We propose that this definition of clusters is unnecessarily rigid. For instance, the connected components can collapse into one cluster by the addition of a single sequence that bridges nodes in the respective components. Moreover, the distance thresholds typically used for viruses like HIV-1 tend to exclude a large proportion of new sequences, making it difficult to train models for predicting cluster growth. These issues may be resolved by revisiting how we define clusters from genetic distances. Community detection is a promising class of clustering methods from the field of network science. A community is a set of nodes that are more densely inter-connected relative to the number of their connections to external nodes. Thus, a connected component may be partitioned into two or more communities. Here we describe community detection methods in the context of genetic clustering for epidemiology, demonstrate how a popular method (Markov clustering) enables us to resolve variation in transmission rates within a giant connected component of HIV-1 sequences, and identify current challenges and directions for further work.

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.

Undiagnosed HIV Infections May Drive HIV Transmission in the Era of "Treat All": A Deep-Sampling Molecular Network Study in Northeast China during 2016 to 2019

Universal antiretroviral therapy (ART, “treat all”) was recommended by the World Health Organization in 2015; however, HIV-1 transmission is still ongoing. This study characterizes the drivers of HIV transmission in the “treat all” era. Demographic and clinical information and HIV pol gene were collected from all newly diagnosed cases in Shenyang, the largest city in Northeast China, during 2016 to 2019. Molecular networks were constructed based on genetic distance and logistic regression analysis was used to assess potential transmission source characteristics. The cumulative ART coverage in Shenyang increased significantly from 77.0% (485/630) in 2016 to 93.0% (2598/2794) in 2019 (p < 0.001). Molecular networks showed that recent HIV infections linked to untreated individuals decreased from 61.6% in 2017 to 28.9% in 2019, while linking to individuals with viral suppression (VS) increased from 9.0% to 49.0% during the same time frame (p < 0.001). Undiagnosed people living with HIV (PLWH) hidden behind the links between index cases and individuals with VS were likely to be male, younger than 25 years of age, with Manchu nationality (p < 0.05). HIV transmission has declined significantly in the era of “treat all”. Undiagnosed PLWH may drive HIV transmission and should be the target for early detection and intervention.

An Approach to Quantifying the Interaction between Behavioral and Transmission Clusters

We hypothesize that patterns of sexual behavior play a role in the conformation of transmission networks, i.e., the way you behave might influence whom you have sex with. If that was the case, behavioral grouping might in turn correlate with, and potentially predict transmission networking, e.g., proximity in a viral phylogeny. We rigorously present an intuitive approach to address this hypothesis by quantifying mapped interactions between groups defined by similarities in sexual behavior along a virus phylogeny while discussing power and sample size considerations. Data from the Swiss HIV Cohort Study on condom use and hepatitis C virus (HCV) sequences served as proof-of-concept. In this case, a strict inclusion criteria contrasting with low HCV prevalence hindered our possibilities to identify significant relationships. This manuscript serves as guide for studies aimed at characterizing interactions between behavioral patterns and transmission networks. Large transmission networks such as those of HIV or COVID-19 are prime candidates for applying this methodological approach.

Increasing Frequency and Transmission of HIV-1 Non-B Subtypes among Men Who Have Sex with Men in the Swiss HIV Cohort Study

BACKGROUND

In Switzerland, HIV-1 transmission among men who have sex with men (MSM) has been dominated by subtype B, whilst non-B subtypes are commonly attributed to infections acquired abroad among heterosexuals. Here, we evaluated the temporal trends of non-B subtypes and the characteristics of molecular transmission clusters (MTCs) among MSM.

METHODS

Sociodemographic and clinical data and partial pol sequences were obtained from participants enrolled in the Swiss HIV Cohort Study (SHCS). For non-B subtypes, maximum likelihood trees were constructed, from which Swiss MTCs were identified and analysed by transmission group.

RESULTS

Non-B subtypes were identified in 8.1% (416/5,116) of MSM participants. CRF01_AE was the most prevalent strain (3.5%), followed by A (1.2%), F (1.1%), CRF02_AG (1.1%), C (0.9%), and G (0.3%). Between 1990 and 2019, an increase in the proportion of newly diagnosed individuals (0/123[0%] to 11/32 [34%]) with non-B subtypes in MSM was found. Across all non-B subtypes, the majority of MSM MTCs were European. Larger MTCs were observed for MSM than heterosexuals.

CONCLUSIONS

We found a substantial increase in HIV-1 non-B subtypes among MSM in Switzerland and the occurrence of large MTCs, highlighting the importance of molecular surveillance in guiding public health strategies targeting the HIV-1 epidemic.

Using longitudinally sampled viral nucleotide sequences to characterize the drivers of HIV-1 transmission

OBJECTIVES

Understanding the drivers of HIV-1 transmission is of importance for curbing the ongoing epidemic. Phylogenetic methods based on single viral sequences allow us to assess whether two individuals are part of the same viral outbreak, but cannot on their own assess who potentially transmitted the virus. We developed and assessed a molecular epidemiology method with the main aim to screen cohort studies for and to characterize individuals who are 'potential HIV-1 transmitters', in order to understand the drivers of HIV-1 transmission.

METHODS

We developed and validated a molecular epidemiology approach using longitudinally sampled viral Sanger sequences to characterize potential HIV-1 transmitters in the Swiss HIV Cohort Study.

RESULTS

Our method was able to identify 279 potential HIV-1 transmitters and allowed us to determine the main epidemiological and virological drivers of transmission. We found that the directionality of transmission was consistent with infection times for 72.9% of 85 potential HIV-1 transmissions with accurate infection date estimates. Being a potential HIV-1 transmitter was associated with risk factors including viral load [adjusted odds ratio_{multivariable} (95% confidence interval): 1.86 (1.49-2.32)], syphilis coinfection [1.52 (1.06-2.19)], and recreational drug use [1.45 (1.06-1.98)]. By contrast for the potential HIV-1 recipients, this association was weaker or even absent [1.18 (0.82-1.72), 0.89 (0.52-1.55) and 1.53 (0.98-2.39), respectively], indicating that inferred directionality of transmission is useful at the population level.

CONCLUSIONS

Our results indicate that longitudinally sampled Sanger sequences do not provide sufficient information to identify transmitters with high certainty at the individual level, but that they allow the drivers of transmission at the population level to be characterized.