In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoe-leather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, that is, on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from www.github.com/veg/hivtrace, along with the accompanying result visualization module from www.github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens.

/pdf/hiv-trace-transmission-cluster-engine-a-tool-for-large-scale-2g1v6909x9.pdf

HIV-TRACE (TRAnsmission Cluster Engine): a Tool for Large Scale Molecular Epidemiology of HIV-1 and Other Rapidly Evolving Pathogens.

CITATION: Wertheim, J. O. et al. 2017. Social and genetic networks of HIV-1 transmission in New York City. PLoS Pathogens, 13(1): e1006000, doi:10.1371/journal.ppat.1006000.

/pdf/social-and-genetic-networks-of-hiv-1-transmission-in-new-26p1jwzuxo.pdf

Social and genetic networks of HIV-1 transmission in New York City

Understanding HIV-1 transmission dynamics is relevant to both screening and intervention strategies of HIV-1 infection. Commonly, HIV-1 transmission chains are determined based on sequence similarity assessed either directly from a sequence alignment or by inferring a phylogenetic tree. This review is aimed at both nonexperts interested in understanding and interpreting studies of HIV-1 transmission, and experts interested in finding the most appropriate cluster definition for a specific dataset and research question. We start by introducing the concepts and methodologies of how HIV-1 transmission clusters usually have been defined. We then present the results of a systematic review of 105 HIV-1 molecular epidemiology studies summarising the most popular methods and definitions in the literature. Finally, we offer our perspectives on how HIV-1 transmission clusters can be defined and provide some guidance based on examples from real life datasets.

/pdf/defining-hiv-1-transmission-clusters-based-on-sequence-data-1b4fnxafeq.pdf

Defining HIV-1 transmission clusters based on sequence data.

BACKGROUND Detecting recent and rapid spread of HIV can help prioritize prevention and early treatment for those at highest risk of transmission. HIV genetic sequence data can identify transmission clusters, but previous approaches have not distinguished clusters of recent, rapid transmission. We assessed an analytic approach to identify such clusters in the United States. METHODS We analyzed 156,553 partial HIV-1 polymerase sequences reported to the National HIV Surveillance System and inferred transmission clusters using 2 genetic distance thresholds (0.5% and 1.5%) and 2 periods for diagnoses (all years and 2013-2015, ie, recent diagnoses). For rapidly growing clusters (with ≥5 diagnoses during 2015), molecular clock phylogenetic analysis estimated the time to most recent common ancestor for all divergence events within the cluster. Cluster transmission rates were estimated using these phylogenies. RESULTS A distance threshold of 1.5% identified 103 rapidly growing clusters using all diagnoses and 73 using recent diagnoses; at 0.5%, 15 clusters were identified using all diagnoses and 13 using recent diagnoses. Molecular clock analysis estimated that the 13 clusters identified at 0.5% using recent diagnoses had been diversifying for a median of 4.7 years, compared with 6.5-13.2 years using other approaches. The 13 clusters at 0.5% had a transmission rate of 33/100 person-years, compared with previous national estimates of 4/100 person-years. CONCLUSIONS Our approach identified clusters with transmission rates 8 times those of previous national estimates. This method can identify groups involved in rapid transmission and help programs effectively direct and prioritize limited public health resources.

Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data.

In January 2015, an outbreak of undiagnosed human immunodeficiency virus (HIV) infections among persons who inject drugs (PWID) was recognized in rural Indiana. By September 2016, 205 persons in this community of approximately 4400 had received a diagnosis of HIV infection. We report results of new approaches to analyzing epidemiologic and laboratory data to understand transmission during this outbreak. HIV genetic distances were calculated using the polymerase region. Networks were generated using data about reported high-risk contacts, viral genetic similarity, and their most parsimonious combinations. Sample collection dates and recency assay results were used to infer dates of infection. Epidemiologic and laboratory data each generated large and dense networks. Integration of these data revealed subgroups with epidemiologic and genetic commonalities, one of which appeared to contain the earliest infections. Predicted infection dates suggest that transmission began in 2011, underwent explosive growth in mid-2014, and slowed after the declaration of a public health emergency. Results from this phylodynamic analysis suggest that the majority of infections had likely already occurred when the investigation began and that early transmission may have been associated with sexual activity and injection drug use. Early and sustained efforts are needed to detect infections and prevent or interrupt rapid transmission within networks of uninfected PWID.

Detailed Transmission Network Analysis of a Large Opiate-Driven Outbreak of HIV Infection in the United States.

BACKGROUND Studying HIV transmission networks provides insight into the spread of HIV and opportunities for intervention. We identified transmission dynamics among risk groups and racial/ethnic groups in the United States. METHODS For HIV-1 pol sequences reported to the US National HIV Surveillance System during 2001-2012, we calculated pairwise genetic distance, identified linked pairs of sequences (those with distance ≤1.5%), and examined transmission category and race/ethnicity of these potential transmission partners. RESULTS Of 40,950 sequences, 12,910 (32%) were linked to ≥1 other sequence. Of men who have sex with men (MSM) who were linked to ≥1 sequence, 88% were linked to other MSM and only 4% were linked to heterosexual women. Of heterosexual women for whom we identified potential transmission partners, 29% were linked to MSM, 21% to heterosexual men, and 12% to persons who inject drugs. Older and black MSM were more likely to be linked to heterosexual women. Assortative mixing was present for all racial/ethnic groups; 81% of blacks/African Americans linked to other blacks. CONCLUSIONS This analysis is the first use of US surveillance data to infer an HIV transmission network. Our data suggest that HIV infections among heterosexual women predominantly originate from MSM, followed by heterosexual men. Although few MSM were linked to women, suggesting that a minority of MSM are involved in transmission with heterosexual women, these transmissions represent a substantial proportion of HIV acquisitions by heterosexual women. Interventions that reduce transmissions involving MSM are likely to also reduce HIV acquisition among other risk groups.

/pdf/using-molecular-hiv-surveillance-data-to-understand-204ekxav3m.pdf

Hall Hi

Papers

Using Molecular HIV Surveillance Data to Understand Transmission Between Subpopulations in the United States.