A public health model for the molecular surveillance of HIV transmission in San Diego, California

Abstract

Background Current public health efforts often use molecular technologies to identify and contain communicable disease networks, but not for HIV. Here, we investigate how molecular epidemiology can be used to identify highly-related HIV networks within a population and how voluntary contact tracing of sexual partners can be used to selectively target these networks. Methods We evaluated the use of HIV-1 pol sequences obtained from participants of a community-recruited cohort (n=268) and a primary infection research cohort (n=369) to define highly related transmission clusters and the use of contact tracing to link other individuals (n=36) within these clusters. The presence of transmitted drug resistance was interpreted from the pol sequences (Calibrated Population Resistance v3.0). Results Phylogenetic clustering was conservatively defined when the genetic distance between any two pol sequences was <1%, which identified 34 distinct transmission clusters within the combined community-recruited and primary infection research cohorts containing 160 individuals. Although sequences from the epidemiologically-linked partners represented approximately 5% of the total sequences, they clustered with 60% of the sequences that clustered from the combined cohorts (O.R. 21.7; p=<0.01). Major resistance to at least one class of antiretroviral medication was found in 19% of clustering sequences. Conclusions Phylogenetic methods can be used to identify individuals who are within highly related transmission groups, and contact tracing of epidemiologically-linked partners of recently infected individuals can be used to link into previously-defined transmission groups. These methods could be used to implement selectively targeted prevention interventions.

Targeting HIV Prevention Based on Molecular Epidemiology Among Deeply Sampled Subnetworks of Men Who Have Sex With Men

“…Even though only 179 participants were enrolled, phylogenetic analysis identified clustering among 64% of all participants, despite a diversity of HIV-1 subtypes and CRFs. Although somewhat higher than in other studies, this level of clustering is consistent with deep sampling of the local network such as other MSM cohorts [3,5,[33][34][35][36][37], and the epidemic is relatively new with rapid transmission. This high rate of clustering allowed us to determine that the local network structure followed a "preferential attachment" model, similar to other networks among MSM [3,23].…”

“…Participants were identified with primary infection by screening 2000 MSM for HIV infection every 2 months using enzyme-linked immunosorbent assay for antibody testing and nested polymerase chain reaction for HIV RNA testing [2,7]. All participants were diagnosed with HIV within 2 months of their infection and had their blood collected for CD4 T-cell count, viral load measurements, and HIV genotyping [2,5,8]. Sequences covered a 1.3-kb region of the HIV-1 pol, including protease (amino acids 1-99) and partial reverse transcriptase (amino acids 1-305; GenBank accession numbers KP250654-KP250829).…”

Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

“…3 However, genetic distance and branch support cutoffs markedly vary between studies and the rationale for a given cluster definition is rarely specified. [10][11][12][13][14][15] Many factors can influence the choice of genetic distance and branch support cutoff values used to define clusters, such as the spatial and temporal scale of analysis, HIV subtype, the underlying mode of transmission (e.g., heterosexual vs. injection drug use), and the viral genomic region(s) being analyzed. Branch support values may also be affected by the statistical model used to reconstruct the phylogenies (e.g., maximum likelihood, Bayesian) and the total amount of viral genetic diversity in the dataset.…”

Molecular analysis allows inference into HIV transmission among young men who have sex with men in the United States

“…Previous research examining the role of age and racial dynamics in the sexual networks of young MSM relied on small sample sizes [2,3,4,5,7,8] or survey data [4,8]. Whereas survey data can provide information about social and sexual networks, identifying genetically highly similar HIV sequences can allow us to infer HIV transmission networks and describe transmission between the persons within these networks [10][11][12][13][14][15][16][17][18][19]. In an effort to better understand the role that age and race/ethnicity of sexual partners may play in HIV acquisition for young MSM, we used HIV sequence data reported to U.S. National HIV Surveillance System (NHSS) to investigate HIV transmission dynamics for young MSM.…”

“…Estimated percentage of infections from older partners among men who have sex with men who are aged [13][14][15][16][17][18][19][20][21][22][23][24] The multivariable model included all variables listed and also controlled for year of diagnosis.…”