Phylogenetics: an added ingredient in HIV prevention

The HIV/AIDS pandemic is composed of multiple epidemics, fueled by an array of biological, behavioral and societal factors. In North America, epidemics are concentrated in specific populations, including MSM, people who inject drug, Afro-Americans, Latinos, Indigenous people, youth, women, and displaced persons. The spread of HIV is localized to major urban centers and rural geographic hotspots, influenced by patterns of population mobility and human migration. Antiretroviral therapy (ART) has revolutionized the long-term management of HIV-infected individuals, reducing community viral load and preventing HIV transmission at a population-level. The goal of treatment has shifted from addressing the health benefits of the individual to global control of the HIV pandemic. In December 2013, UNAIDS introduced the 90–90–90 initiative to reduce annual numbers of new infections to 500 000 by 2020. All nations have been called upon to diagnose 90% of new infections; treat 90% of those diagnosed and attain viral suppression in 90% of those treated (www.unaids.org). Revised guidelines have incentivized HIV testing, immediate initiation of antiviral therapy and expanded access to pre-exposure prophylaxis (PreP) and post-exposure prophylaxis for HIV-negative populations at high-risk for infection, for example, MSM [1]. Despite this concerted global health response, efforts to reach fewer than 500 000 infections by 2020 are off track with an estimated 1.7 million new infections in 2018. Data from 146 countries show modest to no reductions in HIV incidence with rising numbers of new infections in many regional settings (www.unaids.org). In the USA, new infections have plateaued at 39 000 yearly since 2014. In February 2019, the Center for Disease Control (USA) launched the Ending the HIV epidemic plan for America to reduce new infections by 90% by 2030. HIV phylogenetic surveillance has been added as a fourth pillar in test--treat--suppress prevention paradigms [2,3]. Viral genetic data, derived from centralized drug resistance testing programs, can be analyzed using novel bioinformatic and statistical tools to track local and regional epidemics in key risk groups. Application of these methods provides novel insights on epidemic drivers and gives new opportunities to uncover risk populations and transmission patterns that cannot be identified by traditional epidemiological approaches. Surveillance data can be harnessed to target interventions to populations that are of high risk of acquiring and spreading HIV. In this issue of AIDS, Werthiem et al. [4] performed a retrospective analysis of National HIV Surveillance System data from six states having comprehensive (>50%) genotypic coverage. The HIV-TRACE platform was used to identify 116 clusters having three or more incident infections in 2010–2012 (n = 759, median cluster size 5) [2,3]. The growth of each of these prioritized clusters was followed over the 5-year period from 2013 to 2018. Overall, 82 of these clusters experienced growth with 641 added new cases. Bayesian analysis on date-stamped sequences was used to infer the time to most recent common ancestor (TMRCA) of added members within each cluster. Phylodynamic inferences attributed the growth of 63% of prioritized clusters to new incident infections arising after 2012 while 59% of clusters added infections from undiagnosed persons acquiring infections in the 2010–2012 period. These findings are obtained consistently in large population-based studies in Quebec and the Netherlands, which estimate that 60–70% of the onward spread of HIV occurs among newly infected persons who are often unaware of their HIV status, with fewer than 5% of infections being from persons receiving ART [5–7]. Collectively, these findings demonstrate that gaps in testing and the initiation of ART are the primary drivers of incident infections. Wertheim et al.[4] applied univariate and multivariate logistic regression analyses to assess for predictors of the growth of priority clusters, using the binary outcome of at least one inferred undiagnosed case. The growth of prioritized clusters was not significantly associated with the size of cluster unless adjusted to ‘cluster age’ (TMRCA). Unexpectedly, the growth of priority clusters did not correlate with race/ethnicity or MSM transmission risk. These counterintuitive findings illustrate the challenges in linking phylogenetic and epidemiologic variables [8]. Genetic ‘cluster size’ is not a static measure but rather one that evolves with new transmissions. The dynamics that drive large cluster outbreaks are complex and may vary from cluster to cluster. For instance, growth trajectories of individual clusters may be influenced by routes of transmission, the size and duration of infectious acute outbreaks, and episodic risk among affected persons. An effective intervention, such as PreP among MSM, may prevent the potential growth of individual clusters. The inclusion of molecular phylogenetics as a fourth pillar in HIV prevention is an exciting new direction in HIV research. Phylogenetics and epidemiological data may be leveraged to gain new insights on the origin, drivers and control of sporadic HIV outbreaks. Ending the HIV epidemic is unattainable if significant proportions of people living with HIV remain undiagnosed [9]. Expanded genotypic coverage for all newly diagnosed persons prior to treatment initiation can provide high-quality data sources for investigation. It is important that ethical guidelines in phylogenetic research safeguard the individual and assure community engagement [10]. Acknowledgements Conflicts of interest There are no conflicts of interest.

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