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

Abstract

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.

Fig 1. Study area.

Lake shore districts Masaka, Mukono and Wakiso.

Fig 2. Enrolment of study participants.

Fig 3. Maximum-likelihood phylogenetic tree showing the HIV transmission clusters in gag gene approximately 463 base pairs (n = 25) involving 86 of the 276 participant sequences.

Clusters with bootstrap support ≥ 95% and genetic distance of ≤ 3.5% are shown. Three clusters with > 2 members are shown while the rest of clusters had two members. Subtype A clusters are highlighted red and subtype D blue. The tree scale is shown.

Fig 4. Maximum-likelihood phylogenetic tree showing the HIV transmission clusters in env gene approximately 460 base pairs (n = 22) involving 79 of the 266 participant sequences shown while the rest of clusters had two members.

One cluster with bootstrap of 99% and genetic distance of ≤ 4.5% is included since it clustered in gag. Subtype A clusters are highlighted red, subtype C purple and subtype D blue. The tree scale is shown.

Fig 5. Discrete location of individuals with identified HIV clusters and gender.

In all figures, the gender is indicated by shape: square = female, circle = male. Non-sequenced is indicated by grey colour, and sequenced of the non-clustered individuals is indicated by yellow. Cluster memberships are indicated by colours other than grey and yellow: the individuals here share the same colour if and only if they belong to the same cluster. Filled circles/squares are incident cases, while non-filled ones are prevalent cases. In the figure, the location of each household is randomly allocated within the corresponding village (Masaka district (Lambu, Kamuwungu), Mukono district (Nsadzi), Wakiso district (Kasenyi, Nakiwogo). Household membership, gender and prevalent or incident HIV status of the HIV infected individuals are presented by village and by gene region.

Fig 6. Heat map depicting clustering proportions of individuals sequenced 34 clusters (n = 283) stratified by baseline characteristics.

The x-axis represents district, uc is un-clustered, c is clustered. The columns list on the y-axis are individual characteristics at study initiation and subsequent columns are the proportions clustered or un-clustered by district. Proportions are shown as a heat map to represent increasing proportions. Cells were colour coded with sliding colours as follows: blue corresponds to a low number of clustering, yellow corresponds to an intermediate amount of clustering and red corresponds to high level of clustering. Note that individuals in Masaka, Wakiso and Mukono; 2,2,1 had missing age, 25,3,1, had duration resident in area missing, 2,2,1 had missing marital status, 24,3,1 had missing occupation, 7,3,1 had alcohol use missing, 11,9,1 had partnerships missing, 7,3,1 had away 3 months missing and the remaining characteristics had none missing.