HIV-1 transmission patterns in antiretroviral therapy-naïve, HIV-infected North Americans based on phylogenetic analysis by population level and ultra-deep DNA sequencing

Leveraging Phylogenetics to Understand HIV Transmission and Partner Notification Networks

BACKGROUND

Partner notification is an important component of public health test and treat interventions. To enhance this essential function, we assessed the potential for molecular methods to supplement routine partner notification and corroborate HIV networks.

METHODS

All persons diagnosed with HIV infection in Wake County, NC, during 2012-2013 and their disclosed sexual partners were included in a sexual network. A data set containing HIV-1 pol sequences collected in NC during 1997-2014 from 15,246 persons was matched to HIV-positive persons in the network and used to identify putative transmission clusters. Both networks were compared.

RESULTS

The partner notification network comprised 280 index cases and 383 sexual partners and high-risk social contacts (n = 131 HIV-positive). Of the 411 HIV-positive persons in the partner notification network, 181 (44%) did not match to a HIV sequence, 61 (15%) had sequences but were not identified in a transmission cluster, and 169 (41%) were identified in a transmission cluster. More than half (59%) of transmission clusters bridged sexual network partnerships that were not recognized in the partner notification; most of these clusters were dominated by men who have sex with men.

CONCLUSIONS

Partner notification and HIV sequence analysis provide complementary representations of the existent partnerships underlying the HIV transmission network. The partner notification network components were bridged by transmission clusters, particularly among components dominated by men who have sex with men. Supplementing the partner notification network with phylogenetic data highlighted avenues for intervention.

HIV-1 molecular transmission clusters in nine European countries and Canada: association with demographic and clinical factors

BACKGROUND

Knowledge of HIV-1 molecular transmission clusters (MTCs) is important, especially in large-scale datasets, for designing prevention programmes and public health intervention strategies. We used a large-scale HIV-1 sequence dataset from nine European HIV cohorts and one Canadian, to identify MTCs and investigate factors associated with the probability of belonging to MTCs.

METHODS

To identify MTCs, we applied maximum likelihood inferences on partial pol sequences from 8955 HIV-positive individuals linked to demographic and clinical data. MTCs were defined using two different criteria: clusters with bootstrap support >75% (phylogenetic confidence criterion) and clusters consisting of sequences from a specific region at a proportion of >75% (geographic criterion) compared to the total number of sequences within the network. Multivariable logistic regression analysis was used to assess factors associated with MTC clustering.

RESULTS

Although 3700 (41%) sequences belonged to MTCs, proportions differed substantially by country and subtype, ranging from 7% among UK subtype C sequences to 63% among German subtype B sequences. The probability of belonging to an MTC was independently less likely for women than men (OR = 0.66; P < 0.001), older individuals (OR = 0.79 per 10-year increase in age; P < 0.001) and people of non-white ethnicity (OR = 0.44; P < 0.001 and OR = 0.70; P = 0.002 for black and 'other' versus white, respectively). It was also more likely among men who have sex with men (MSM) than other risk groups (OR = 0.62; P < 0.001 and OR = 0.69; P = 0.002 for people who inject drugs, and sex between men and women, respectively), subtype B (ORs 0.36-0.70 for A, C, CRF01 and CRF02 versus B; all P < 0.05), having a well-estimated date of seroconversion (OR = 1.44; P < 0.001), a later calendar year of sampling (ORs 2.01-2.61 for all post-2002 periods versus pre-2002; all P < 0.01), and being naïve to antiretroviral therapy at sampling (OR = 1.19; P = 0.010).

CONCLUSIONS

A high proportion (>40%) of individuals belonged to MTCs. Notably, the HIV epidemic dispersal appears to be driven by subtype B viruses spread within MSM networks. Expansion of regional epidemics seems mainly associated with recent MTCs, rather than the growth of older, established ones. This information is important for designing prevention and public health intervention strategies.

Understanding the complex evolution of rapidly mutating viruses with deep sequencing: Beyond the analysis of viral diversity

With the advent of affordable deep sequencing technologies, detection of low frequency variants within genetically diverse viral populations can now be achieved with unprecedented depth and efficiency. The high-resolution data provided by next generation sequencing technologies is currently recognised as the gold standard in estimation of viral diversity. In the analysis of rapidly mutating viruses, longitudinal deep sequencing datasets from viral genomes during individual infection episodes, as well as at the epidemiological level during outbreaks, now allow for more sophisticated analyses such as statistical estimates of the impact of complex mutation patterns on the evolution of the viral populations both within and between hosts. These analyses are revealing more accurate descriptions of the evolutionary dynamics that underpin the rapid adaptation of these viruses to the host response, and to drug therapies. This review assesses recent developments in methods and provide informative research examples using deep sequencing data generated from rapidly mutating viruses infecting humans, particularly hepatitis C virus (HCV), human immunodeficiency virus (HIV), Ebola virus and influenza virus, to understand the evolution of viral genomes and to explore the relationship between viral mutations and the host adaptive immune response. Finally, we discuss limitations in current technologies, and future directions that take advantage of publically available large deep sequencing datasets.