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Human Immunodeficiency Virus Type 1 Phylodynamics to Detect And applyparastyle “fig//caption/p[1]” parastyle “FigCapt” SubSubBList3=SubBList=SubSubBList=SubBList SubSubBList2=SubBList=SubSubBList=SubBList SubBList2=BList=SubBList=BList Human Immunodeficiency Virus Type 1 Phylodynamics to HeadB/HeadA=HeadC=HeadB/HeadA=HeadC/HeadB HeadC/HeadB=HeadD=HeadC/HeadB=HeadC/HeadB Detect and Characterize Active Transmission Clusters in HeadC=NList_dot_numeric1=HeadC=NList_dot_numeric North Carolina HeadC/HeadB=NList_dot_numeric1=HeadC/HeadB=NList_dot_numeric Ann M. Dennis,1, Stéphane Hué,2, Rachael Billock,3 Sara Levintow,3 Joseph Sebastian,4 William C. Miller,5 and Joseph J. Eron1,3 HeadD=NList_dot_numeric1=HeadD=NList_dot_numeric 1Division of Infectious Diseases, University of North Carolina at Chapel Hill; 2London School of Hygiene and Tropical Medicine, United Kingdom; 3Department of Epidemiology, University of North HeadD/HeadC=NList_dot_numeric1=HeadD/HeadC=NList_dot_numeric Carolina at Chapel Hill; 4Campbell University School of Osteopathic Medicine, South Lillington, North Carolina; and 5Department of Epidemiology, Ohio State University, Columbus SubBList2=NList_dot_numeric2=SubBList=NList_dot_numeric2 (See the Editorial Commentary by France and Oster, on pages 1223–5.) SubBList2=NList_dot_numeric=SubBList=NList_dot_numeric NList_dot_numeric2=HeadB=NList_dot_numeric=HeadB Background. Human immunodeficiency virus type 1 (HIV-1) phylodynamics can be used to monitor epidemic trends and help NList_dot_numeric3=HeadB=NList_dot_numeric=HeadB target prevention through identification and characterization of transmission clusters. Methods. We analyzed HIV-1 pol sequences sampled in North Carolina from 1997 to 2014. Putative clusters were identified NList_dot_numeric2=SubBList1=NList_dot_numeric=SubBList1 using maximum-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference. Active clusters were defined as NList_dot_numeric3=SubBList1=NList_dot_numeric=SubBList1 clusters including internal nodes from 2009 to 2014. Effective reproductive numbers (Re) were estimated using birth-death models SubBList3=HeadD=SubBList_Before_Head=HeadD for large clusters that expanded ≥2-fold from 2009 to 2014. SubBList2=HeadD=SubBList_Before_Head=HeadD Results. Of 14 921 persons, 7508 (50%) sequences were identified in 2264 clusters. Only 288 (13%) clusters were active from SubBList2=HeadB=SubBList=HeadB 2009 to 2014; 37 were large (10–36 members). Compared to smaller clusters, large clusters were increasingly populated by men and ≥ ≥ SubBList3=HeadB=SubBList=HeadB younger persons; however, nearly 60% included 1 women. Clusters with 3 members demonstrated assortative mixing by sex, age, and sample region. Of 15 large clusters with ≥2-fold expansion, nearly all had Re approximately 1 by 2014. HeadC=NList_dot_numeric1(2Digit)=HeadC=NList_dot_numeric(2Digit) Conclusions. Phylodynamics revealed transmission cluster expansion in this densely sampled region and allowed estimates HeadC/HeadB=NList_dot_numeric1(2Digit)=HeadC/HeadB=NList_dot_numeric(2Digit) of Re to monitor active clusters, showing the propensity for steady, onward propagation. Associations with clustering and cluster HeadD=NList_dot_numeric1(2Digit)=HeadD=NList_dot_numeric(2Digit) characteristics vary by cluster size. Harnessing sequence-derived epidemiologic parameters within routine surveillance could allow HeadD/HeadC=NList_dot_numeric1(2Digit)=HeadD/HeadC=NList_dot_numeric(2Digit) refined monitoring of local subepidemics. SubBList2(2Digit)=NList_dot_numeric2(2Digit)=SubBList(2Digit)=NList_dot_numeric2(2Digit) Keywords. HIV-1; molecular epidemiology; transmission; phylogeny; southeastern United States. SubBList2(2Digit)=NList_dot_numeric(2Digit)=SubBList(2Digit)=NList_dot_numeric(2Digit) NList_dot_numeric2(2Digit)=HeadB=NList_dot_numeric(2Digit)=HeadB Successful human immunodeficiency virus type 1 (HIV-1) trans- public health planning and assessment of interventions [10]. NList_dot_numeric3(2Digit)=HeadB=NList_dot_numeric(2Digit)=HeadB mission prevention requires targeted approaches tailored to indi- While HIV-1 transmission clusters have been described in mul- NList_dot_numeric2(2Digit)=SubBList1(2Digit)=NList_dot_numeric(2Digit)=SubBList1(2Digit) vidual epidemics. Molecular epidemiology is increasingly used to tiple cohorts in the United States (US) [11–13], few studies have NList_dot_numeric3(2Digit)=SubBList1(2Digit)=NList_dot_numeric(2Digit)=SubBList1(2Digit) reconstruct HIV-1 transmission clusters from viral sequences [1], incorporated phylodynamics [14–16]. SubBList3(2Digit)=HeadD=SubBList(2Digit)=HeadD providing insights into subepidemics overlapping in geography, Phylodynamic analyses may be particularly informative in SubBList2(2Digit)=HeadD=SubBList(2Digit)=HeadD time, and contact networks. HIV-1 phylodynamic analyses com- regions such as the southern US, where HIV-1 incidence has bine viral sequence similarity, molecular clock modeling, and SubBList2(2Digit)=HeadB=SubBList(2Digit)=HeadB failed to substantially decline with traditional control measures. population dynamic inference to reconstruct viral transmission Southern states are now an epicenter of the US epidemic [17], SubBList3(2Digit)=HeadB=SubBList(2Digit)=HeadB over time [2]. These analyses allow the estimation of transmis- with higher HIV-1 diagnosis rates and prevalence compared to sion parameters among subgroups [3, 4], timing of regional viral other regions. Despite widespread antiretroviral therapy (ART) introduction [5], and spread [6]. HIV-1 phylodynamics have and advances in prevention, ongoing transmission continues also been used to identify links between subepidemics [7] and from the prevalent pool of diagnosed and undiagnosed persons estimate epidemic growth parameters [8, 9], which can inform living with HIV-1 [18]. As in other southern states, HIV-1 in- cidence in North Carolina (NC) has not appreciably declined Received 25 October 2018; editorial decision 22 February 2019; accepted 11 April 2019; since 2008 [19]; approximately 1400 new HIV-1 diagnoses are published online April 27, 2019. Presented in part: 23rd Conference on Retroviruses and Opportunistic Infections, Boston, reported annually [20]. While our prior studies revealed mul- Massachusetts, February 2016. tiple large clusters composed primarily of men who have sex Correspondence: A. M. Dennis, MD, 130 Mason Farm Road, Suite 2115, Chapel Hill, NC 27599-6134 ([email protected]). with men (MSM) [11, 21], sizeable local heterosexual transmis- sion was also identified [21]. Further investigation into HIV-1 DOI: 10.1093/infdis/jiz176 clustering dynamics, focusing on growing clusters, is needed to determine optimal approaches for public health response. We investigated the HIV-1 subtype B transmission dynamics Time-Scaled Clusters in NC to characterize ongoing transmission networks. We Subtype B sequences identified in putative clusters in the ML tree aimed to differentiate between historical and “active” clusters were further analyzed using Bayesian Markov Chain Monte Carlo with recent expansion in cluster size, and to estimate temporal (MCMC) inference in Bayesian Evolutionary Analysis Sampling Trees (BEAST) version 1.8.2 software [29]. Non-B subtypes were parameters and effective reproductive numbers (Re) of active clusters. Delineating demographic and geographic subgroups evaluated in a separate analysis [14]. Sequences belonging to pu- with high rates of clustering and likely future transmission may tative clusters were pooled in batches of <200 sequences to de- inform the design, allocation, and monitoring of combination crease computational time. Care was taken not to divide clusters prevention approaches, such as increased testing and imme- between files. Analyses were conducted using the SRD06 nucle- diate ART [22], allowing for efficient deployment of finite pre- otide substitution model, a log-normal relaxed molecular clock vention resources. model, and the Bayesian skyline model as coalescent tree prior. The MCMC chains were run for 50–100 million generations METHODS with 1–5 runs performed for each file. The BEAST log and tree files from multiple runs were combined in LogCombiner ver- Study Population sion 1.8.2 [29]; 10% of the generations were discarded as burn-in. We analyzed full-length protease (PR, 297 bp) and partial re- Convergence of the estimates was considered satisfactory when verse transcriptase (RT, 1200 bp) HIV-1 sequences generated the effective sample size was >200, as calculated in Tracer ver- by the Laboratory Corporation of America (LabCorp), the sion 1.6 [30]. Maximum clade credibility trees (MCCTs) were largest commercial laboratory in NC. Sequences were col- summarized using TreeAnnotator version 1.8.2 [29], keeping the lected from 1997 to mid-2014 from persons accessing clinical median height over the posterior distribution of trees. Clades with care in NC; the majority of the assays were HIV-1 GenoSure. posterior probability ≥0.90 were considered highly supported. We included samples from patients with a valid date of birth The time of the most recent common ancestor (tMRCA) of and aged ≥18 years at the time of sample acquisition. For per- the transmission clusters was estimated as the difference in the sons with multiple sequences, only the first available sequence sampling date of the most recent sequence in the tree from the was included. Demographic variables collected included sex median height of the basal node of the cluster identified in the and geographic location of sampling sites; diagnosis dates MCCTs. The MCCTs were further assessed for recent
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