Phylogenetic and Demographic Characterization of Directed HIV-1 Transmission Using Deep Sequences from High-Risk and General Population Cohorts/Groups in Uganda

Abstract

Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) naïve participants with plasma viral load >1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source–recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68–3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care.

The Molecular Epidemiology and Transmission Dynamics of HIV Type 1 in a General Population Cohort in Uganda

“…Potential viral transmission in this study was associated with being ART naïve, being female and being above 25 years of age. The observation that the risk of HIV transmission was associated with older individuals >25 years is consistent with findings from our recent study [ 24 ]. An estimated 20.5% (84/409) of all pairs identified consisted of female-female pairs suggesting a larger proportion of unreached men in inferred HIV transmission networks that are still problematic in HIV intervention efforts and present challenges in molecular epidemiological analyses.…”

“…This also implies that partner exchange rates within the rural communities may be lower relative to those in the neighbouring urban areas. The heterogeneity of the HIV-1 epidemic in the GPC (situated about 16 km from the trans-African highway) and neighbouring high-risk populations like the fishing communities (approximately 40 km apart) has previously been described [ 7 ] and the viral migration patterns inferred in this study fit well with a diffusion model that supports the flow of virus from this low risk cohort to other higher-risk populations [ 23 , 24 , 68 ]. Nonetheless, the term ‘lower-risk population’ is a subjective over-simplification of the dynamics of HIV-1 transmission in the GPC as shown by the presence of large HIV-1 clusters and the inferred viral migration patterns.…”

“…The suboptimal sampling in the non-GPC was as a result of missing those individuals who were living with HIV-1 but were not linked to care. Persons living with HIV but not engaging in care are a major challenge that needs to be addressed in HIV testing/treatment programs [ 71 , 72 ] as well as in molecular epidemiological analyses [ 24 ]. Nonetheless, including non-GPC samples in our study provided additional insights into the HIV-1 transmission dynamics beyond the GPC.…”

“…In the study presented here, we analysed a total of 3796 HIV-1 pol sequences from samples collected between 2003–2015 from the GPC ( n = 1418), non-GPC ( n = 1223) and from other sites in Central ( n = 1010) and Eastern ( n = 145) Uganda. As described previously [ 23 , 24 ], we used a biometric fingerprint-scanning device with all study participants to avoid duplicate enrolments and also increase our certainty of the clusters identified in the HIV-1 transmission network analyses.…”

MicrobeTrace: Retooling Molecular Epidemiology for Rapid Public Health Response

“…For example, an HIV cluster in rural southeastern Indiana grew rapidly in 2015 but underwent slow growth for nearly a decade prior (Campbell, et al, 2017). The nearest connect neighbor method yields results similar to a non-exhaustive search for all minimum spanning trees, as has been previously described (Bbosa, et al, 2020;Campbell, et al, 2017). The threshold and nearest connected neighbor filters are not mutually exclusive and can therefore be applied simultaneously to ensure that genetically distant nodes remain disconnected.…”