Identifying Transmission Clusters with Cluster Picker and HIV-TRACE

Threshold sensitivity analysis for HIV-1 transmission cluster detection using different genomic regions and subtypes

HIV-1 cluster analysis has been widely used in characterizing HIV-1 transmission and some countries have implemented such molecular epidemiology as part of their prevention strategy. However, HIV-1 sequences derive from varying genome regions, which affects phylogenetic clustering outputs. Here, we apply different tools to run a sensitivity analysis for assessing which threshold give the most cohesive clustering outputs for different data sources. We used a dataset of 174 full-length sequences of subtype B from the Swiss HIV Cohort Study and publicly available subtype C from South Africa. Each dataset was divided into sub-genomic sub-datasets covering gag, pol, and env. pol was further subdivided into regions commonly used in HIV-1 genotyping laboratories (pr-rt, rt-int, and pr-rt-int). Cluster analyses for each sub-genomic region was performed specifying varying distance thresholds of 0.5 %-4.5 % and tree branch support of 70 %, 90 % and 99 % in ClusterPicker. Tree topologies and clustering outputs were compared against each other to assess cluster similarity. Pylogenies using pol, pr-rt-int, or rt-int had more robust tree topologies compared to gag and env. Cluster composition changed with increasing genetic distance threshold but was not affected by branch support. Cluster identity was most similar around genetic distances of 2.5 (±0.5)% for all sub-genomic regions and for both subtype B and C. Our study demonstrated the value of performing a sensitivity analysis before setting a genetic distance threshold for clustering output and that the pol region is appropriate for clustering outputs and can be used for near real-time HIV-1 cluster detection.

Characterization of the HIV-1 molecular network in a middle-aged population aged 50 years and older in a City in Southern Sichuan, China

This study aimed to investigate the characteristics of the HIV-1 molecular network among newly diagnosed HIV-infected patients in southern Sichuan City. Plasma samples will be collected from eligible study subjects (n = 1249) during a cross-sectional survey conducted between 2016 and 2020. The HIV-1 polymerase (pol) gene sequences obtained from the collected samples will be used to perform phylogenetic analysis and characterize the genetic subtypes' molecular transmission networks. HIV-1 pol region sequences were successfully amplified in 898 cases, and seven genotypes were obtained, with CRF01_AE (331, 36.86%) and CRF07_BC (368, 40.98%) subtypes as the predominant prevalent strains in the region. 601 sequences entered the molecular transmission network. There were 302 highly connected individuals. Further multivariate analysis showed that the older the age (60-69 years, OR = 1.595, 95% CI: 1.026-2.479; ≥70 years, OR = 2.189, 95% CI: 1.295-3.699), RX and GJ counties (OR = 4.654, 95% CI: 2.776-7.803; OR = 6.847, 95% CI: 3.464-13.533) and CRF08_BC subtype (OR = 2.031, 95% CI: 1.225-3.367) were both more likely to be highly connected individuals. To effectively combat this local HIV-1 epidemic, HIV prevention and intervention programs should target older adults at least 60 years of age and registered residents in districts and counties within RX and GJ.

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing and adapted phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Despite the complementarity of the tools, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.

Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study

BACKGROUND

The HIV-1 molecular network is an innovative tool, using gene sequences to understand transmission attributes and complementing social and sexual network studies. While previous research focused on static network characteristics, recent studies' emphasis on dynamic features enhances our understanding of real-time changes, offering insights for targeted interventions and efficient allocation of public health resources.

OBJECTIVE

This study aims to identify the dynamic changes occurring in HIV-1 molecular transmission networks and analyze the primary influencing factors driving the dynamics of HIV-1 molecular networks.

METHODS

We analyzed and compared the dynamic changes in the molecular network over a specific time period between the baseline and observed end point. The primary factors influencing the dynamic changes in the HIV-1 molecular network were identified through univariate analysis and multivariate analysis.

RESULTS

A total of 955 HIV-1 polymerase fragments were successfully amplified from 1013 specimens; CRF01_AE and CRF07_BC were the predominant subtypes, accounting for 40.8% (n=390) and 33.6% (n=321) of the specimens, respectively. Through the analysis and comparison of the basic and terminal molecular networks, it was discovered that 144 sequences constituted static molecular networks, and 487 sequences contributed to the formation of dynamic molecular networks. The findings of the multivariate analysis indicated that the factors occupation as a student, floating population, Han ethnicity, engagement in occasional or multiple sexual partnerships, participation in anal sex, and being single were independent risk factors for the dynamic changes observed in the HIV-1 molecular network, and the odds ratio (OR; 95% CIs) values were 2.63 (1.54-4.47), 1.83 (1.17-2.84), 2.91 (1.09-7.79), 1.75 (1.06-2.90), 4.12 (2.48-6.87), 5.58 (2.43-12.80), and 2.10 (1.25-3.54), respectively. Heterosexuality and homosexuality seem to exhibit protective effects when compared to bisexuality, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. Additionally, the National Eight-Item score and sex education experience were also identified as protective factors against dynamic changes in the HIV-1 molecular network, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively.

CONCLUSIONS

The HIV-1 molecular network analysis showed 144 sequences in static networks and 487 in dynamic networks. Multivariate analysis revealed that occupation as a student, floating population, Han ethnicity, and risky sexual behavior were independent risk factors for dynamic changes, while heterosexuality and homosexuality were protective compared to bisexuality. A higher National Eight-Item score and sex education experience were also protective factors. The identification of HIV dynamic molecular networks has provided valuable insights into the characteristics of individuals undergoing dynamic alterations. These findings contribute to a better understanding of HIV-1 transmission dynamics and could inform targeted prevention strategies.

Epidemiological characteristics of HIV transmission in southeastern China from 2015 to 2020 based on HIV molecular network

OBJECTIVE

HIV/AIDS remains a global public health problem, and understanding the structure of social networks of people living with HIV/AIDS is of great importance to unravel HIV transmission, propose precision control and reduce new infections. This study aimed to investigate the epidemiological characteristics of HIV transmission in Fujian province, southeastern China from 2015 to 2020 based on HIV molecular network.

METHODS

Newly diagnosed, treatment-naive HIV/AIDS patients were randomly sampled from Fujian province in 2015 and 2020. Plasma was sampled for in-house genotyping resistance test, and HIV molecular network was created using the HIV-TRACE tool. Factors affecting the inclusion of variables in the HIV molecular network were identified using univariate and multivariate logistic regression analyses.

RESULTS

A total of 1,714 eligible cases were finally recruited, including 806 cases in 2015 and 908 cases in 2020. The dominant HIV subtypes were CRF01_AE (41.7%) and CRF07_BC (38.3%) in 2015 and CRF07_BC (53. 3%) and CRF01_AE (29.1%) in 2020, and the prevalence of HIV drug resistance was 4.2% in 2015 and 5.3% in 2020. Sequences of CRF07_BC formed the largest HIV-1 transmission cluster at a genetic distance threshold of both 1.5 and 0.5%. Univariate and multivariate logistic regression analyses showed that ages of under 20 years and over 60 years, CRF07_BC subtype, Han ethnicity, sampling in 2015, absence of HIV drug resistance, married with spouse, sampling from three cities of Jinjiang, Nanping and Quanzhou resulted in higher proportions of sequences included in the HIV transmission molecular network at a genetic distance threshold of 1.5% (p < 0.05).

CONCLUSION

Our findings unravel the HIV molecular transmission network of newly diagnosed HIV/AIDS patients in Fujian province, southeastern China, which facilitates the understanding of HIV transmission patterns in the province.

Whole-genome sequencing to investigate transmission of SARS-CoV-2 in the acute healthcare setting: a systematic review

BACKGROUND

Whole-genome sequencing (WGS) has been used widely to elucidate transmission of SARS-CoV-2 in acute healthcare settings, and to guide infection, prevention, and control (IPC) responses.

AIM

To systematically appraise available literature, published between January 1st, 2020 and June 30th, 2022, describing the implementation of WGS in acute healthcare settings to characterize nosocomial SARS-CoV-2 transmission.

METHODS

Searches of the PubMed, Embase, Ovid MEDLINE, EBSCO MEDLINE, and Cochrane Library databases identified studies in English reporting the use of WGS to investigate SARS-CoV-2 transmission in acute healthcare environments. Publications involved data collected up to December 31st, 2021, and findings were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

FINDINGS

In all, 3088 non-duplicate records were retrieved; 97 met inclusion criteria, involving 62 outbreak analyses and 35 genomic surveillance studies. No publications from low-income countries were identified. In 87/97 (90%), WGS supported hypotheses for nosocomial transmission, while in 46 out of 97 (47%) suspected transmission events were excluded. An IPC intervention was attributed to the use of WGS in 18 out of 97 (18%); however, only three (3%) studies reported turnaround times ≤7 days facilitating near real-time IPC action, and none reported an impact on the incidence of nosocomial COVID-19 attributable to WGS.

CONCLUSION

WGS can elucidate transmission of SARS-CoV-2 in acute healthcare settings to enhance epidemiological investigations. However, evidence was not identified to support sequencing as an intervention to reduce the incidence of SARS-CoV-2 in hospital or to alter the trajectory of active outbreaks.

HIV-1 molecular transmission network and drug resistance in Chongqing, China, among men who have sex with men (2018-2021)

BACKGROUND

Over the past few years, HIV transmission among men who have sex with men (MSM) in China has increased significantly. Chongqing, located in the southwest of China, has the highest prevalence of HIV among MSM in the country.

METHODS

Blood samples were taken from 894 MSM in Chongqing who had recently been diagnosed with HIV-1 infection and had not yet started getting treatment. In order to determine the distribution of HIV-1 subtypes, transmitted drug resistance, and assessments of molecularly transmitted clusters, we sequenced the Pol genes and employed them in phylogenetic analysis. The genetic distance between molecular clusters was 1.5%. To find potential contributing factors, logistic regression analyses were performed.

RESULTS

Of the 894 HIV-1 pol sequences acquired from study participants, we discovered that CRF07_BC (73.6%) and CRF01_AE (19.6%) were the two most prevalent HIV-1 genotypes in Chongqing among MSM, accounting for 93.2% of all infections. In addition, CRF08_BC (1.1%), B subtype (1.0%), CRF55_01B (3.4%), and URF/Other subtypes (1.3%) were less frequently observed. Among MSM in Chongqing, transmitted drug resistance (TDR) was reported to be present at a rate of 5.6%. 48 clusters with 600 (67.1%, 600/894) sequences were found by analysis of the molecular transmission network. The distributions of people by age, sexual orientation, syphilis, and genotype were significantly differentially related to being in clusters, according to the multivariable logistic regression model.

CONCLUSION

Despite the low overall prevalence of TDR, the significance of genotypic drug resistance monitoring needs to be emphasized. CRF07_BC and CRF01_AE were the two main genotypes that created intricate molecular transmission networks. In order to prevent the expansion of molecular networks and stop the virus's spread among MSM in Chongqing, more effective HIV intervention plans should be introduced.

Molecular epidemiology of HIV-1 among adult female sex workers at the Guatemala-Mexico border

ABSTRACTSex workers have been demonstrated to have increased vulnerabilities to HIV and a high population prevalence of the disease. Despite their increased risk, sex workers have been underrepresented in molecular epidemiology studies assessing HIV in Mesoamerica. This study aims to describe the sociodemographic characteristics and phylogenetic profile of HIV-1 within a cohort of HIV-positive female sex workers (FSW) situated at the Guatemala-Mexico border. HIV viral sequences were collected from a cohort of FSW ≥18 years of age from San Marcos, Guatemala (n = 6) and compared to viral sequences collected as part of the Mesoamerican Drug Resistance Monitoring Programme to assess HIV viral diversity in Mexico and Guatemala (n = 3956). All of the FSW sampled were determined to have genetically unrelated HIV infections, suggesting multiple introductions of the virus and/or the potential existence of populations not captured by current surveillance efforts. Many reported numerous vulnerabilities that may have heightened their risk of acquiring and transmitting HIV through sex work activities. Our phylogenetic analysis indicated that national surveillance programmes may not fully capture the viral diversity among FSW and their clients within this region. Additional research is needed to fully capture HIV diversity and transmission in Mesoamerica, especially in the Guatemala-Mexico border region.

Characterization of HIV-1 Transmission Clusters Inferred from the Brazilian Nationwide Genotyping Service Database

The study of HIV-1 transmission networks inferred from viral genetic data can be used to clarify important factors about the dynamics of HIV-1 transmission, such as network growth rate and demographic composition. In Brazil, HIV transmission has been stable since the early 2000s and the study of transmission clusters can provide valuable data to understand the drivers of virus spread. In this work, we analyzed a nation-wide database of approximately 53,000 HIV-1 nucleotide pol sequences sampled from genotyped patients from 2008-2017. Phylogenetic trees were reconstructed for the HIV-1 subtypes B, C and F1 in Brazil and transmission clusters were inferred by applying genetic distances thresholds of 1.5%, 3.0% and 4.5%, as well as high (>0.9) cluster statistical support. An odds ratio test revealed that young men (15-24 years) and individuals with more years of education presented higher odds to cluster. The assortativity coefficient revealed that individuals with similar demographic features tended to cluster together, with emphasis on features, such as place of residence and age. We also observed that assortativity weakens as the genetic distance threshold increases. Our results indicate that the phylogenetic clusters identified here are likely representative of the contact networks that shape HIV transmission, and this is a valuable tool even in sites with low sampling density, such as Brazil.

Optimized phylogenetic clustering of HIV-1 sequence data for public health applications

Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

Antiretroviral Drug Resistance in HIV Sequences From People Who Inject Drugs and Men Who Have Sex With Men Across 21 Cities in India

Background

Drug resistance testing is limited in public-sector human immunodeficiency virus (HIV) care in India, and there are few systematic samplings for prevalent drug resistance mutations (DRMs), particularly among men who have sex with men (MSM) and people who inject drugs (PWID).

Methods

We conducted genotypic resistance testing on 915 HIV sequences sampled from viremic self-reported antiretroviral therapy (ART) experienced and naive PWID and MSM recruited from 21 cities across India in 2016-2017. We analyzed factors associated with resistance using logistic regression and evaluated evidence for transmitted resistance using phylogenetic analyses.

Results

Of the 915 participants sequenced, median age was 31, 436 were MSM, and 191 were ART experienced. Overall, 62.8% of ART-experienced participants and 14.4% of ART-naive participants were found to have low-level resistance or higher to 1 or more classes of drugs. Prevalence of tenofovir disoproxil fumarate resistance was 25.7% in ART-experienced participants and 1.11% in ART-naive participants. The highest proportion of drug resistance was seen across nucleoside reverse transcriptase inhibitors and nonnucleoside reverse transcriptase inhibitors, and resistance was significantly more common among MSM participants than PWID. Phylogenetic analyses revealed that 54.6% of ART-naive participants with resistance who clustered had shared DRMs, suggesting transmitted resistance may have occurred.

Conclusions

Patients experiencing virologic failure on first-line therapy switched blindly to tenofovir/lamivudine/dolutegravir may effectively be receiving dolutegravir monotherapy due to resistance to tenofovir and lamivudine. While dolutegravir is expected to have full activity in the majority of patients in India, follow-up is needed to understand how resistance may affect long-term outcomes.

Detection of HIV-1 Transmission Clusters from Dried Blood Spots within a Universal Test-and-Treat Trial in East Africa

The Sustainable East Africa Research in Community Health (SEARCH) trial was a universal test-and-treat (UTT) trial in rural Uganda and Kenya, aiming to lower regional HIV-1 incidence. Here, we quantify breakthrough HIV-1 transmissions occurring during the trial from population-based, dried blood spot samples. Between 2013 and 2017, we obtained 549 gag and 488 pol HIV-1 consensus sequences from 745 participants: 469 participants infected prior to trial commencement and 276 SEARCH-incident infections. Putative transmission clusters, with a 1.5% pairwise genetic distance threshold, were inferred from maximum likelihood phylogenies; clusters arising after the start of SEARCH were identified with Bayesian time-calibrated phylogenies. Our phylodynamic approach identified nine clusters arising after the SEARCH start date: eight pairs and one triplet, representing mostly opposite-gender linked (6/9), within-community transmissions (7/9). Two clusters contained individuals with non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance, both linked to intervention communities. The identification of SEARCH-incident, within-community transmissions reveals the role of unsuppressed individuals in sustaining the epidemic in both arms of a UTT trial setting. The presence of transmitted NNRTI resistance, implying treatment failure to the efavirenz-based antiretroviral therapy (ART) used during SEARCH, highlights the need to improve delivery and adherence to up-to-date ART recommendations, to halt HIV-1 transmission.

Transmission networks of hepatitis C virus among HIV/HCV-coinfected patients in Guangdong, China

Background

Coinfection with hepatitis C virus (HCV) is common in human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients due to shared routes of transmission. We aimed to investigate the characteristics of HCV subgenotypes among HIV/HCV-coinfected patients in Guangdong and explore the molecular transmission networks and related risk factors for HCV strains.

Methods

Plasma samples were obtained from 356 HIV/HCV-coinfected patients for HCV NS5B region sequencing. A neighbor-joining phylogenetic tree was constructed to affirm HCV subgenotypes. The transmission networks based on maximum likelihood phylogenetic tree were determined by Cluster Picker, and visualized using Cytoscape 3.2.1.

Results

A total of 302 HCV NS5B sequences were successfully amplified and sequenced from the 356 plasma samples. A neighbor-joining phylogenetic tree based on the 302 NS5B sequences revealed the profile of HCV subgenotypes circulating among HIV/HCV coinfection patients in Guangdong. Two predominant strains were found to be 6a (58.28%, 176/302) and 1b (18.54%, 56/302), followed by 3a (10.93%, 33/302), 3b (6.95%, 21/302), 1a (3.64%, 11/302), 2a (0.99%, 3/302) and 6n (0.66%, 2/302). A molecular transmission network of five major HCV genotypes was constructed, with a clustering rate of 44.04%. The clustering rates of subgenotypes 1a, 3a, 3b, 1b, and 6a were 18.18% (2/11), 42.42%, 52.38%, 48.21%, and 44.89%, respectively. Multivariate logistic regression analysis showed no significant effects from sex, age, transmission route, geographical region, baseline CD4 + T cell count or subgenotype (P > 0.05), except marital status. Married or cohabiting people (compared with unmarried people) had more difficulty forming transmission networks.

Conclusions

In summary, this study, based on HCV NS5B subgenotypes, revealed the HCV subtype diversity and distribution among HIV/HCV-coinfected patients in Guangdong. Marital status inclined to be the factor influencing HCV transmission networks formation.

Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial - BCPP/ Ya Tsie trial

Background: Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence.

Methods: To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana. We sequenced HIV viral whole genomes from 5,114 trial participants among the 30 trial communities.

Results: Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 - 56.7] versus 3% [0.1 - 27.3]) than at baseline (7% [1.5 - 25.3] versus 5% [0.9 - 22.9]) compatible with a benefit from treatment-as-prevention.

Conclusion: Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies.

Funding: This study was supported by the National Institute of General Medical Sciences (U54GM088558); the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610); and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).

Sorting by Race/Ethnicity Across HIV Genetic Transmission Networks in Three Major Metropolitan Areas in the United States

An important component underlying the disparity in HIV risk between race/ethnic groups is the preferential transmission between individuals in the same group. We sought to quantify transmission between different race/ethnicity groups and measure racial assortativity in HIV transmission networks in major metropolitan areas in the United States. We reconstructed HIV molecular transmission networks from viral sequences collected as part of HIV surveillance in New York City, Los Angeles County, and Cook County, Illinois. We calculated assortativity (the tendency for individuals to link to others with similar characteristics) across the network for three candidate characteristics: transmission risk, age at diagnosis, and race/ethnicity. We then compared assortativity between race/ethnicity groups. Finally, for each race/ethnicity pair, we performed network permutations to test whether the number of links observed differed from that expected if individuals were sorting at random. Transmission networks in all three jurisdictions were more assortative by race/ethnicity than by transmission risk or age at diagnosis. Despite the different race/ethnicity proportions in each metropolitan area and lower proportions of clustering among African Americans than other race/ethnicities, African Americans were the group most likely to have transmission partners of the same race/ethnicity. This high level of assortativity should be considered in the design of HIV intervention and prevention strategies.

Spatiotemporal Phylodynamics of Hepatitis C Among People Who Inject Drugs in India

BACKGROUND AND AIMS

Implementing effective interventions for HCV requires a detailed understanding of local transmission dynamics and geospatial spread. Little is known about HCV phylodynamics, particularly among high-burden populations, such as people who inject drugs (PWID).

APPROACH AND RESULTS

We used 483 HCV sequences and detailed individual-level data from PWID across four Indian cities. Bayesian phylogeographic analyses were used to evaluate transmission hotspots and geospatial diffusion of the virus. Phylogenetic cluster analysis was performed to infer epidemiologic links and factors associated with clustering. A total of 492 HIV sequences were used to draw comparisons within the same population and, in the case of coinfections, evaluate molecular evidence for shared transmission pathways. Overall, 139/483 (28.8%) of HCV sequences clustered with a median cluster size of 3 individuals. Genetically linked participants with HCV were significantly younger and more likely to be infected with HCV subtype 3b as well as to live and inject close to one another. Phylogenetic evidence suggests likely ongoing HCV infection/reinfection with limited support for shared HIV/HCV transmission pathways. Phylogeographic analyses trace historic HCV spread back to Northeastern India and show diffusion patterns consistent with drug trafficking routes.

CONCLUSIONS

This study characterizes HCV phylodynamics among PWID in a low and middle-income country setting. Heterogeneity and recent genetic linkage of HCV across geographically disparate Indian states suggest that targeted interventions could help prevent reimportation of virus through drug trafficking routes.

Phylogenetic Analysis of Sequences in the HIV Database Revealed Multiple Potential Circulating Recombinant Forms in China

HIV recombination contributes greatly to its diversity and produces many circulating recombinant forms (CRFs) and unique recombinant forms (URFs). In China, 24 CRFs have been reported to date, and CRFs cause more than 80% of HIV infections. However, the prevalence of CRFs might still be underestimated, as a high level of onward transmission of URFs has been reported. In this study, we analyzed all Chinese pol region (2,253-3,252) sequences in the HIV Database to evaluate potential new CRFs in China. HIV-1 genotypes were verified by the Context-based Modeling for Expeditious Typing (COMET) tool. Maximum-likelihood (ML) trees were constructed based on sequences with unassigned genotypes. Cluster Picker 1.2.1 was used to identify transmission clusters. Meanwhile, a jumping-profile hidden Markov model (jpHMM) was used to perform recombination breakpoint analysis. Beast 1.7.5 was used to estimate the time of the most recent common ancestor of new CRFs. In the HIV databases, CRF01_AE was the most prevalent genetic form in China, accounting for 39.69% of all national infections, followed by CRF07_BC (20.47%), subtype B (17.50%), CRF08_BC (6.60%), subtype C (6.28%), CRF55_01B (2.06%), and other CRFs (1.77%). The URFs were responsible for 5.31% of all infections nationwide. Among URFs, genomes comprising BC, 01BC, 01B, and 01C were dominant. Finally, 17 potential CRFs and 1 novel CRF were identified. BEAST analysis indicates that novel CRF originated around in 2009. The data highlight that more CRFs have been spreading in China. HIV-1 pol sequences that are commonly used to explore drug resistance are helpful for the surveillance of epidemics of different HIV-1 genotypes.

Distribution characteristics of drug resistance mutations of HIV CRF01_AE, CRF07_BC and CRF08_BC from patients under ART in Ganzhou, China

BACKGROUND

Drug resistance mutation (DRM)-associated virological failure has become a critical issue for ART and the elimination of HIV.

OBJECTIVES

To investigate the distribution characteristics of DRMs of HIV CRF01_AE, CRF07_BC and CRF08_BC, the predominant subtypes in China.

METHODS

Patients receiving ART up to 31 August 2020 in Ganzhou in China were recruited. Full-length sequences of the HIV pol gene were amplified from patients with virological failure. DRMs and antiretroviral susceptibility were explored using the Stanford University HIV Drug Resistance Database HIVdb Program.

RESULTS

Overall, 279 of 2204 patients under ART were found to have virological failure. Nine HIV subtypes were identified among 211 sequences that were amplified successfully and CRF08_BC (37.0%), CRF01_AE (26.1%) and CRF07_BC (25.6%) were the most prevalent, with mutation frequencies of 44.9% (35/78), 52.7% (29/55) and 35.2% (19/54), respectively. The most common DRMs of these three subtypes were K103N and M184V, while the mutation frequencies of M41L, D67N, K70R, K101E, V106M, Y181C, K219E, H221Y and N348I were obviously different among subtypes. The resistance levels and frequencies for antiretroviral drugs for these three subtypes were similar and resistances to nevirapine, efavirenz, lamivudine and emtricitabine were the most frequently observed. Compared with CRF01_AE and CRF07_BC, CRF08_BC had higher proportions of DRMs for NRTIs and lower frequencies of resistance to NRTIs and NNRTIs.

CONCLUSIONS

The distribution characteristics of DRMs of HIV CRF01_AE, CRF07_BC and CRF08_BC were inconsistent and should be considered when selecting antiretroviral strategies, developing new drugs and controlling HIV strains containing DRMs.

Human Immunodeficiency Virus (HIV) Drug Resistance, Phylogenetic Analysis, and Superinfection Among Men Who Have Sex with Men and Transgender Women in Sub-Saharan Africa: HIV Prevention Trials Network (HPTN) 075 Study

BACKGROUND

The HIV Prevention Trials Network (HPTN) 075 study evaluated the feasibility of enrolling and retaining men who have sex with men (MSM) and transgender women (TGW) from Kenya, Malawi, and South Africa. During the study follow-up, 21 participants acquired human immunodeficiency virus (HIV) (seroconverters). We analyzed HIV subtype diversity, drug resistance, transmission dynamics, and HIV superinfection data among MSM and TGW enrolled in HPTN 075.

METHODS

HIV genotyping and drug resistance testing were performed for participants living with HIV who had viral loads >400 copies/mL at screening (prevalent cases, n = 124) and seroconverters (n = 21). HIV pol clusters were identified using Cluster Picker. Superinfection was assessed by a longitudinal analysis of env and pol sequences generated by next-generation sequencing.

RESULTS

HIV genotyping was successful for 123/124 prevalent cases and all 21 seroconverters. The major HIV subtypes were A1 (Kenya) and C (Malawi and South Africa). Major drug resistance mutations were detected in samples from 21 (14.6%) of 144 participants; the most frequent mutations were K103N and M184V/I. Phylogenetic analyses identified 11 clusters (2-6 individuals). Clusters included seroconverters only (n = 1), prevalent cases and seroconverters (n = 4), and prevalent cases only (n = 6). Superinfections were identified in 1 prevalent case and 2 seroconverters. The annual incidence of superinfection was higher among seroconverters than among prevalent cases, and was higher than the rate of primary HIV infection in the cohort.

CONCLUSIONS

This report provides important insights into HIV genetic diversity, drug resistance, and superinfection among MSM and TGW in sub-Saharan Africa. These findings may help to inform future HIV prevention interventions in these high-risk groups.

Syphilis Testing as a Proxy Marker for a Subgroup of Men Who Have Sex With Men With a Central Role in HIV-1 Transmission in Guangzhou, China

Objectives: The objectives of this study were to distinguish the role of men who have sex with men (MSM) with or without syphilis testing in HIV-1 transmission and to provide molecular evidence of syphilis testing as a proxy marker for identifying the subgroup of MSM.

Methods: HIV-1 transmission clusters were constructed by HIV-TRACE and Cluster Picker using HIV-1 pol sequences from 729 newly diagnosed HIV-infected MSM from 2008 to 2012 in Guangzhou, China. The role of MSM in HIV-1 transmission networks was determined by a node influence measurement and centrality analysis. The association between syphilis testing and factors related to HIV-1 transmission and antiretroviral treatment (ART) were analyzed by the Cox regression model.

Results: Among HIV-infected MSM, 56.7% did not test for syphilis at the time of HIV-1 diagnosis. MSM without syphilis testing was a specific subgroup of MSM with a larger closeness centrality and clustering coefficient than the recipients of syphilis testing (P < 0.001), indicating their central position in the HIV-1 transmission networks. The median degree and radiality within HIV-1 transmission networks as well as the median K-shell scores were also greater for MSM without syphilis testing (P < 0.001), suggesting their relatively greater contribution in transmitting HIV-1 than the receipts of syphilis testing. MSM with syphilis testing usually did not disclose their occupation or were more likely to be unemployed or to take non-skilled jobs, to have a history of sexually transmitted infections (STIs), and to be AIDS patients when diagnosed with HIV-1 infection (P < 0.05). Multivariable Cox regression analysis indicated that syphilis testing per se did not promote the engagement of ART (P = 0.233) or affect the speed of CD4⁺ T cell count recovery after treatment (P = 0.256).

Conclusions: Our study identifies syphilis testing as a proxy marker of a specific subgroup of HIV-infected MSM who refuse syphilis testing during HIV-1 diagnosis with an important role in HIV-1 transmission. Specific prevention and intervention targeting MSM without syphilis testing during HIV-1 care are urgently needed.

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees

Understanding age-mixing patterns in Human Immunodeficiency Virus (HIV) transmission networks can enhance the design and implementation of HIV prevention strategies in sub-Saharan Africa. Due to ethical consideration, it is less likely possible to conduct a benchmark study to assess which sampling strategy, and sub-optimal sampling coverage which can yield best estimates for these patterns. We conducted a simulation study, using phylogenetic trees to infer estimates of age-mixing patterns in HIV transmission, through the computation of proportions of pairings between men and women, who were phylogenetically linked across different age groups (15-24 years, 25-39 years, and 40-49 years); and the means, and standard deviations of their age difference. We investigated also the uncertainty around these estimates as a function of the sampling coverage in four sampling strategies: when missing sequence data were missing completely at random (MCAR), and missing at random (MAR) with at most 30%-50%-70% of women in different age groups being in the sample. The results suggested that age-mixing patterns in HIV transmission can be unveiled from proportions of phylogenetic pairings between men and women across age groups; and the mean, and standard deviation of their age difference. A 55% sampling coverage was sufficient to provide the best values of estimates of age-mixing patterns in HIV transmission with MCAR scenario. But we should be cautious in interpreting proportions of men phylogenetically linked to women because they may be overestimated or underestimated, even at higher sampling coverage. The findings showed that, MCAR was the best sampling strategy. This means, it is advisable not to use sequence data collected in settings where we can find a systematic imbalance of age and gender to investigate age-mixing in HIV transmission. If not possible, ensure to take into consideration the imbalance in interpreting the results.

Persistence of HIV transmission clusters among people who inject drugs

OBJECTIVE

We investigated the duration of HIV transmission clusters.

DESIGN

Fifty-four individuals newly infected at enrollment in the ALIVE cohort were included, all of whom had sequences at an intake visit (T1) and from a second (T2) and/or a third (T3) follow-up visit, median 2.9 and 5.4 years later, respectively.

METHODS

Sequences were generated using the 454 DNA sequencing platform for portions of HIV pol and env (HXB2 positions 2717-3230; 7941-8264). Genetic distances were calculated using tn93 and sequences were clustered over a range of thresholds (1--5%) using HIV-TRACE. Analyses were performed separately for individuals with pol sequences for T1 + T2 (n = 40, 'Set 1') and T1 + T3 (n = 25; 'Set 2'), and env sequences for T1 + T2 (n = 47, 'Set 1'), and T1 + T3 (n = 30; 'Set 2').

RESULTS

For pol, with one exception, a single cluster contained more than 75% of samples at all thresholds, and cluster composition was at least 90% concordant between time points/thresholds. For env, two major clusters (A and B) were observed at T1 and T2/T3, although cluster composition concordance between time points/thresholds was low (<60%) at lower thresholds for both sets 1 and 2. In addition, several individuals were included in clusters at T2/T3, although not at T1.

CONCLUSION

Caution should be used in applying a single threshold in population studies where seroconversion dates are unknown. However, the retention of some clusters even after 5 + years is evidence for the robustness of the clustering approach in general.

Dynamics of HIV-1 Molecular Networks Reveal Effective Control of Large Transmission Clusters in an Area Affected by an Epidemic of Multiple HIV Subtypes

This study reconstructed molecular networks of human immunodeficiency virus (HIV) transmission history in an area affected by an epidemic of multiple HIV-1 subtypes and assessed the efficacy of strengthened early antiretroviral therapy (ART) and regular interventions in preventing HIV spread. We collected demographic and clinical data of 2221 treatment-naïve HIV-1–infected patients in a long-term cohort in Shenyang, Northeast China, between 2008 and 2016. HIV pol gene sequencing was performed and molecular networks of CRF01_AE, CRF07_BC, and subtype B were inferred using HIV-TRACE with separate optimized genetic distance threshold. We identified 168 clusters containing ≥ 2 cases among CRF01_AE-, CRF07_BC-, and subtype B-infected cases, including 13 large clusters (≥ 10 cases). Individuals in large clusters were characterized by younger age, homosexual behavior, more recent infection, higher CD4 counts, and delayed/no ART (P < 0.001). The dynamics of large clusters were estimated by proportional detection rate (PDR), cluster growth predictor, and effective reproductive number (R_e). Most large clusters showed decreased or stable during the study period, indicating that expansion was slowing. The proportion of newly diagnosed cases in large clusters declined from 30 to 8% between 2008 and 2016, coinciding with an increase in early ART within 6 months after diagnosis from 24 to 79%, supporting the effectiveness of strengthened early ART and continuous regular interventions. In conclusion, molecular network analyses can thus be useful for evaluating the efficacy of interventions in epidemics with a complex HIV profile.

Short Communication: Choosing the Right Program for the Identification of HIV-1 Transmission Networks from Nucleotide Sequences Sampled from Different Populations

HIV-TRAnsmission Cluster Engine (HIV-TRACE) and Cluster Picker are some of the most widely used programs for identifying HIV-1 transmission networks from nucleotide sequences. However, choosing between these tools is subjective and often a matter of personal preference. Because these software use different algorithms to detect HIV-1 transmission networks, their optimal use is better suited with different sequence data sets and under different scenarios. The performance of these tools has previously been evaluated across a range of genetic distance thresholds without an assessment of the differences in the structure of networks identified. In this study, we tested both programs on the same HIV-1 pol sequence data set (n = 2,017) from three Ugandan populations to examine their performance across different risk groups and evaluate the structure of networks identified. HIV-TRACE that uses a single-linkage algorithm identified more nodes in the same networks that were connected by sparse links than Cluster Picker. This suggests that the choice of the program used for identifying networks should depend on the study aims, the characteristics of the population being investigated, dynamics of the epidemic, sampling design, and the nature of research questions being addressed for optimum results. HIV-TRACE could be more applicable with larger data sets where the aim is to identify larger clusters that represent distinct transmission chains and in more diverse populations where infection has occurred over a period of time. In contrast, Cluster Picker is applicable in situations where more closely connected clusters are expected in the studied populations.

Antiretroviral drug use and HIV drug resistance in female sex workers in Tanzania and the Dominican Republic

Objective

Female sex workers (FSW) have increased risk of HIV infection. Antiretroviral treatment (ART) can improve HIV outcomes and prevent HIV transmission. We analyzed antiretroviral (ARV) drug use and HIV drug resistance among HIV-positive FSW in the Dominican Republic and Tanzania.

Methods

Plasma samples collected at study entry with viral loads >1,000 copies/mL were tested for ARV drugs and HIV drug resistance. ARV drug testing was performed using a qualitative assay that detects 22 ARV drugs in five classes. HIV genotyping was performed using the ViroSeq HIV-1 Genotyping System. Phylogenetic analyses were performed to determine HIV subtype and assess transmission clusters.

Results

Among 410 FSW, 144 (35.1%) had viral loads >1,000 copies/mL (DR: n = 50; Tanzania: n = 94). ARV drugs were detected in 36 (25.0%) of 144 samples. HIV genotyping results were obtained for 138 (95.8%) cases. No transmission clusters were observed in either country. HIV drug resistance was detected in 54 (39.1%) of 138 samples (31/35 [88.6%] with drugs detected; 23/103 [22.3%] without drugs detected); 29/138 (21.0%) had multi-class resistance (MCR). None with MCR had integrase strand transfer inhibitor resistance. In eight cases, one or more ARV drug was detected without corresponding resistance mutations; those women were at risk of acquiring additional drug resistance. Using multivariate logistic regression, resistance was associated with ARV drug detection (p<0.001), self-reported ART (full adherence [p = 0.034]; partial adherence [p<0.001]), and duration of HIV infection (p = 0.013).

Conclusions

In this cohort, many women were on ART, but were not virally suppressed. High levels of HIV drug resistance, including MCR, were observed. Resistance was associated with detection of ARV drugs, self-report of ART with full or partial adherence, and duration of HIV infection. These findings highlight the need for better HIV care among FSW to improve their health, reduce HIV drug resistance, and decrease risk of transmission to others.

Empirical comparison of analytical approaches for identifying molecular HIV-1 clusters

Public health interventions guided by clustering of HIV-1 molecular sequences may be impacted by choices of analytical approaches. We identified commonly-used clustering analytical approaches, applied them to 1886 HIV-1 Rhode Island sequences from 2004-2018, and compared concordance in identifying molecular HIV-1 clusters within and between approaches. We used strict (topological support ≥ 0.95; distance 0.015 substitutions/site) and relaxed (topological support 0.80-0.95; distance 0.030-0.045 substitutions/site) thresholds to reflect different epidemiological scenarios. We found that clustering differed by method and threshold and depended more on distance than topological support thresholds. Clustering concordance analyses demonstrated some differences across analytical approaches, with RAxML having the highest (91%) mean summary percent concordance when strict thresholds were applied, and three (RAxML-, FastTree regular bootstrap- and IQ-Tree regular bootstrap-based) analytical approaches having the highest (86%) mean summary percent concordance when relaxed thresholds were applied. We conclude that different analytical approaches can yield diverse HIV-1 clustering outcomes and may need to be differentially used in diverse public health scenarios. Recognizing the variability and limitations of commonly-used methods in cluster identification is important for guiding clustering-triggered interventions to disrupt new transmissions and end the HIV epidemic.

FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences

MOTIVATION

The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain 'truth' sets on which to test them and properly measure their performance.

RESULTS

We introduce FrAmework for VIral Transmission and Evolution Simulation (FAVITES), a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters.

AVAILABILITY AND IMPLEMENTATION

FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prevalence and Phylogenetic Characterization of Hepatitis C Virus Among Indian Men Who Have Sex With Men: Limited Evidence for Sexual Transmission

BACKGROUND

Data from high-income countries suggest increasing hepatitis C virus (HCV) prevalence/incidence among human immunodeficiency virus (HIV)-infected men who have sex with men (MSM), but limited data derive from low-and-middle-income countries.

METHODS

We recruited 4994 MSM from 5 states across India using respondent-driven sampling. Logistic regression incorporating respondent-driven sampling weights and machine learning feature selection were used to identify correlates of prevalent HCV, and Bayesian phylogenetic analysis was used to examine genetic clustering.

RESULTS

The median age was 25 years, the HIV prevalence was 7.2%, and 49.3% of participants reported recent unprotected anal intercourse. The HCV prevalence was 1.3% (95% confidence interval, 1.0%-1.6%; site range, 0.2%-3.4%) and was 3.1% in HIV-positive versus 1.1% among HIV-negative men. HCV infection was significantly associated with injection drug use (odds ratio, 177.1; 95% confidence interval, 72.7-431.5) and HIV infection (4.34; 1.88-10.05). Machine learning did not uncover any additional epidemiologic signal. Phylogenetic analysis revealed 3 clusters suggestive of linked transmission; each contained ≥1 individual reporting injection drug use.

CONCLUSIONS

We observed a low HCV prevalence in this large sample of MSM despite a high prevalence of known risk factors, reflecting either the need for a threshold of HCV for sexual transmission and/or variability in sexual practices across settings.

Molecular network-based intervention brings us closer to ending the HIV pandemic

Precise identification of HIV transmission among populations is a key step in public health responses. However, the HIV transmission network is usually difficult to determine. HIV molecular networks can be determined by phylogenetic approach, genetic distance-based approach, and a combination of both approaches. These approaches are increasingly used to identify transmission networks among populations, reconstruct the history of HIV spread, monitor the dynamics of HIV transmission, guide targeted intervention on key subpopulations, and assess the effects of interventions. Simulation and retrospective studies have demonstrated that these molecular network-based interventions are more cost-effective than random or traditional interventions. However, we still need to address several challenges to improve the practice of molecular network-guided targeting interventions to finally end the HIV epidemic. The data remain limited or difficult to obtain, and more automatic real-time tools are required. In addition, molecular and social networks must be combined, and technical parameters and ethnic issues warrant further studies.

A cross-sectional study to characterize local HIV-1 dynamics in Washington, DC using next-generation sequencing

Washington, DC continues to experience a generalized HIV-1 epidemic. We characterized the local phylodynamics of HIV-1 in DC using next-generation sequencing (NGS) data. Viral samples from 68 participants from 2016 through 2017 were sequenced and paired with epidemiological data. Phylogenetic and network inferences, drug resistant mutations (DRMs), subtypes and HIV-1 diversity estimations were completed. Haplotypes were reconstructed to infer transmission clusters. Phylodynamic inferences based on the HIV-1 polymerase (pol) and envelope genes (env) were compared. Higher HIV-1 diversity (n.s.) was seen in men who have sex with men, heterosexual, and male participants in DC. 54.0% of the participants contained at least one DRM. The 40-49 year-olds showed the highest prevalence of DRMs (22.9%). Phylogenetic analysis of pol and env sequences grouped 31.9-33.8% of the participants into clusters. HIV-TRACE grouped 2.9-12.8% of participants when using consensus sequences and 9.0-64.2% when using haplotypes. NGS allowed us to characterize the local phylodynamics of HIV-1 in DC more broadly and accurately, given a better representation of its diversity and dynamics. Reconstructed haplotypes provided novel and deeper phylodynamic insights, which led to networks linking a higher number of participants. Our understanding of the HIV-1 epidemic was expanded with the powerful coupling of HIV-1 NGS data with epidemiological data.

Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort

Keywords: HIV; Canada; molecular phylogenetics; viral evolution; person-to-person transmission inference; transmission network; summary statistics.

Phylogenetic Clustering by Linear Integer Programming (PhyCLIP)

Subspecies nomenclature systems of pathogens are increasingly based on sequence data. The use of phylogenetics to identify and differentiate between clusters of genetically similar pathogens is particularly prevalent in virology from the nomenclature of human papillomaviruses to highly pathogenic avian influenza (HPAI) H5Nx viruses. These nomenclature systems rely on absolute genetic distance thresholds to define the maximum genetic divergence tolerated between viruses designated as closely related. However, the phylogenetic clustering methods used in these nomenclature systems are limited by the arbitrariness of setting intra and intercluster diversity thresholds. The lack of a consensus ground truth to define well-delineated, meaningful phylogenetic subpopulations amplifies the difficulties in identifying an informative distance threshold. Consequently, phylogenetic clustering often becomes an exploratory, ad hoc exercise. Phylogenetic Clustering by Linear Integer Programming (PhyCLIP) was developed to provide a statistically principled phylogenetic clustering framework that negates the need for an arbitrarily defined distance threshold. Using the pairwise patristic distance distributions of an input phylogeny, PhyCLIP parameterizes the intra and intercluster divergence limits as statistical bounds in an integer linear programming model which is subsequently optimized to cluster as many sequences as possible. When applied to the hemagglutinin phylogeny of HPAI H5Nx viruses, PhyCLIP was not only able to recapitulate the current WHO/OIE/FAO H5 nomenclature system but also further delineated informative higher resolution clusters that capture geographically distinct subpopulations of viruses. PhyCLIP is pathogen-agnostic and can be generalized to a wide variety of research questions concerning the identification of biologically informative clusters in pathogen phylogenies. PhyCLIP is freely available at http://github.com/alvinxhan/PhyCLIP, last accessed March 15, 2019.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection

Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.

TreeSwift: A massively scalable Python tree package

Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees.

Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data

BACKGROUND

Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data.

METHODS

We identified putative transmission clusters with ≥2 members through pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017.

RESULTS

Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples.

CONCLUSIONS

The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.

Correlates of hepatitis C viral clustering among people who inject drugs in Baltimore

This study examines correlates of hepatitis C virus (HCV) genetic clustering among community-recruited people who inject drugs enrolled in the AIDS Linked to the IntraVenous Experience cohort in Baltimore between 1988 and 1989. HCV RNA was extracted and the core/envelope-1 region was sequenced. Clusters were identified from maximum likelihood trees with 1000 bootstrap replicates using a 70% aLRT and a 4% genetic-distance threshold in Cluster Picker. Overall, 46% of participants were in a cluster, including 122 genotype-1a and 36 genotype-1b clusters with an average of 2-3 genetically linked HCV infections. The largest cluster consists of 9 participants. In univariable analysis, black race (PR = 1.66 [95% CI: 1.12-2.45]), age <35 years (PR = 1.18 [95% CI: 1.02-1.37]), and injection drug use of cocaine alone (PR = 1.30 [95% CI: 1.02-1.65]) were significantly associated with being in a cluster. Conversely, a history of medication-associated treatment (MAT) was negatively associated with being in a cluster (PR = 0.82 [95% CI: 0.71-0.95]). In multivariable analysis, black race (APR = 1.62 [95% CI: 1.11-2.38]) remained independently associated being in a cluster while MAT (APR = 0.85 [95% CI: 0.74-0.99]) remained negatively associated with clustering. Our findings suggest strong locally-propagated transmission networks during the early epidemic that was driven by younger PWID. In light of the current opioid epidemic in the US, these findings suggest an urgent need for preventive interventions to mitigate the growth of large HCV transmission networks.

A Short-Term Assessment of Nascent HIV-1 Transmission Clusters Among Newly Diagnosed Individuals Using Envelope Sequence-Based Phylogenetic Analyses

The identification of transmission clusters (TCs) of HIV-1 using phylogenetic analyses can provide insights into viral transmission network and help improve prevention strategies. We compared the use of partial HIV-1 envelope fragment of 1,070 bp with its loop 3 (108 bp) to determine its utility in inferring HIV-1 transmission clustering. Serum samples of recently (n = 106) and chronically (n = 156) HIV-1-infected patients with status confirmed were sequenced. HIV-1 envelope nucleotide-based phylogenetic analyses were used to infer HIV-1 TCs. Those were constructed using ClusterPickerGUI_1.2.3 considering a pairwise genetic distance of ≤10% threshold. Logistic regression analyses were used to examine the relationship between the demographic factors that were likely associated with HIV-1 clustering. Ninety-eight distinct consensus envelope sequences were subjected to phylogenetic analyses. Using a partial envelope fragment sequence, 42 sequences were grouped into 15 distinct small TCs while the V3 loop reproduces 10 clusters. The agreement between the partial envelope and the V3 loop fragments was significantly moderate with a Cohen's kappa (κ) coefficient of 0.59, p < .00001. The mean age (<38.8 years) and HIV-1 B subtype are two factors identified that were significantly associated with HIV-1 transmission clustering in the cohort, odds ratio (OR) = 0.25, 95% confidence interval (CI, 0.04-0.66), p = .002 and OR: 0.17, 95% CI (0.10-0.61), p = .011, respectively. The present study confirms that a partial fragment of the HIV-1 envelope sequence is a better predictor of transmission clustering. However, the loop 3 segment may be useful in screening purposes and may be more amenable to integration in surveillance programs.

Phylogenetic Methods Inconsistently Predict the Direction of HIV Transmission Among Heterosexual Pairs in the HPTN 052 Cohort

BACKGROUND

We evaluated use of phylogenetic methods to predict the direction of human immunodeficiency virus (HIV) transmission.

METHODS

For 33 pairs of HIV-infected patients (hereafter, "index patients") and their partners who acquired genetically linked HIV infection during the study, samples were collected from partners and index patients close to the time when the partner seroconverted (hereafter, "SC samples"); for 31 pairs, samples collected from the index patient at an earlier time point (hereafter, "early index samples") were also available. Phylogenies were inferred using env next-generation sequences (1 tree per pair/subtype). The direction of transmission (DoT) predicted from each tree was classified as correct or incorrect on the basis of which sequences (those from the index patient or the partner) were closest to the root. DoT was also assessed using maximum parsimony to infer ancestral node states for 100 bootstrap trees.

RESULTS

DoT was predicted correctly for both single-pair and subtype-specific trees in 22 pairs (67%) by using SC samples and in 23 pairs (74%) by using early index samples. DoT was predicted incorrectly for 4 pairs (15%) by using SC or early index samples. In the bootstrap analysis, DoT was predicted correctly for 18 pairs (55%) by using SC samples and for 24 pairs (73%) by using early index samples. DoT was predicted incorrectly for 7 pairs (21%) by using SC samples and for 4 pairs (13%) by using early index samples.

CONCLUSIONS

Phylogenetic methods based solely on the tree topology of HIV env sequences, particularly without consideration of phylogenetic uncertainty, may be insufficient for determining DoT.

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

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.

Validation of publicly-available software used in analyzing NGS data for HIV-1 drug resistance mutations and transmission networks in a Washington, DC, Cohort

The DC Cohort is an ongoing longitudinal observational study of persons living with HIV. To better understand HIV-1 drug resistance and potential transmission clusters among these participants, we performed targeted, paired-end next-generation sequencing (NGS) of protease, reverse transcriptase and integrase amplicons. We elected to use free, publicly-available software (HyDRA Web, Stanford HIVdb and HIV-TRACE) for data analyses so that laboratory personnel without extensive bioinformatics expertise could use it; making the approach accessible and affordable for labs worldwide. With more laboratories transitioning away from Sanger-based chemistries to NGS platforms, lower frequency drug resistance mutations (DRMs) can be detected, yet their clinical relevance is uncertain. We looked at the impact choice in cutoff percentage had on number of DRMs detected and found an inverse correlation between the two. Longitudinal studies will be needed to determine whether low frequency DRMs are an early indicator of emerging resistance. We successfully validated this pipeline against a commercial pipeline, and another free, publicly-available pipeline. RT DRM results from HyDRA Web were compared to both SmartGene and PASeq Web; using the Mantel test, R2 values were 0.9332 (p<0.0001) and 0.9097 (p<0.0001), respectively. PR and IN DRM results from HyDRA Web were then compared with PASeq Web only; using the Mantel test, R2 values were 0.9993 (p<0.0001) and 0.9765 (p<0.0001), respectively. Drug resistance was highest for the NRTI drug class and lowest for the PI drug class in this cohort. RT DRM interpretation reports from this pipeline were also highly correlative compared to SmartGene pipeline; using the Spearman's Correlation, rs value was 0.97757 (p<0.0001). HIV-TRACE was used to identify potential transmission clusters to better understand potential linkages among an urban cohort of persons living with HIV; more individuals were male, of black race, with an HIV risk factor of either MSM or High-risk Heterosexual. Common DRMs existed among individuals within a cluster. In summary, we validated a comprehensive, easy-to-use and affordable NGS approach for tracking HIV-1 drug resistance and identifying potential transmission clusters within the community.

Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these 'source' populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8-28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.

Inconsistent temporal patterns of genetic variation of HCV among high-risk subjects may impact inference of transmission networks

Hepatitis-C Virus (HCV) sequences are often used to establish networks of people who inject drugs (PWID). However, the degree to which within-host evolutionary dynamics affect those inferences has not been carefully studied. Here, we analyzed 702 longitudinally-sampled HCV E1 sequences from 88 HCV+ people who inject drugs (PWID) in the Baltimore Before and After Acute Study of Hepatitis (BBAASH) cohort. Individuals were tested for HCV RNA over multiple visits to the clinic, and the HCV E1 gene was sequenced for HCV+ samples. Genetic clustering was performed on the full set of sequences using a 3% genetic distance threshold to define epidemiological linkage. Maximum-likelihood (ML) phylogenies were inferred to assess evolutionary relationships. We found 22 clusters containing sequences sampled over five or more years (long-term clusters, LTC), of which 17 had >1 subject. In six of the multi-subject LTC, one subject had a sequence sampled >3 years earlier or later than the next-closest subject in the cluster (time-gap LTC). ML trees showed that, in three of the time-gap LTC, two subjects had identical sequences despite 7-10 years separating the sampling times. In four of the time-gap LTC for whom additional data were available, the subject with the later detected shared variant had both different variants and visits with no detectable HCV RNA (RNA-) prior to the appearance of the shared variant. In the subject with the earlier detection of the shared variant, different variants and RNA- visits were also detected in multiple cases subsequent to appearance of the shared variant. Complex patterns of shared viral variation among PWID reflect on-going re-infection, multiple transmission partners, and/or inconsistent detection of viral variants. Our results suggest that transmission events are currently underestimated by analysis of sequences at a single point in time.

Phylogeography of HIV-1 suggests that Ugandan fishing communities are a sink for, not a source of, virus from general populations

Although fishing communities (FCs) in Uganda are disproportionately affected by HIV-1 relative to the general population (GP), the transmission dynamics are not completely understood. We earlier found most HIV-1 transmissions to occur within FCs of Lake Victoria. Here, we test the hypothesis that HIV-1 transmission in FCs is isolated from networks in the GP. We used phylogeography to reconstruct the geospatial viral migration patterns in 8 FCs and 2 GP cohorts and a Bayesian phylogenetic inference in BEAST v1.8.4 to analyse the temporal dynamics of HIV-1 transmission. Subtype A1 (pol region) was most prevalent in the FCs (115, 45.1%) and GP (177, 50.4%). More recent HIV transmission pairs from FCs were found at a genetic distance (GD) <1.5% than in the GP (Fisher’s exact test, p = 0.001). The mean time depth for pairs was shorter in FCs (5 months) than in the GP (4 years). Phylogeographic analysis showed strong support for viral migration from the GP to FCs without evidence of substantial viral dissemination to the GP. This suggests that FCs are a sink for, not a source of, virus strains from the GP. Targeted interventions in FCs should be extended to include the neighbouring GP for effective epidemic control.

Population Structure, Antibiotic Resistance, and Uropathogenicity of Klebsiella variicola

Infections caused by antibiotic-resistant bacterial pathogens are a growing public health threat. Understanding of pathogen relatedness and biology is imperative for tracking outbreaks and developing therapeutics. Here, we detail the phylogenetic structure of 145 K. variicola genomes from different continents. Our results have important clinical ramifications as high-risk antibiotic resistance genes are present in K. variicola genomes from a variety of geographic locations and as we demonstrate that K. variicola clinical isolates can establish higher bladder titers than K. pneumoniae. Differential presence of these pilus genes inK. variicola isolates may indicate adaption for specific environmental niches. Therefore, due to the potential of multidrug resistance and pathogenic efficacy, identification of K. variicola and K. pneumoniae to a species level should be performed to optimally improve patient outcomes during infection. This work provides a foundation for our improved understanding of K. variicola biology and pathogenesis.

Klebsiella variicola is a member of the Klebsiella genus and often misidentified as Klebsiella pneumoniae or Klebsiella quasipneumoniae. The importance of K. pneumoniae human infections has been known; however, a dearth of relative knowledge exists for K. variicola. Despite its growing clinical importance, comprehensive analyses of K. variicola population structure and mechanistic investigations of virulence factors and antibiotic resistance genes have not yet been performed. To address this, we utilized in silico, in vitro, and in vivo methods to study a cohort of K. variicola isolates and genomes. We found that the K. variicola population structure has two distant lineages composed of two and 143 genomes, respectively. Ten of 145 K. variicola genomes harbored carbapenem resistance genes, and 6/145 contained complete virulence operons. While the β-lactam bla_LEN and quinolone oqxAB antibiotic resistance genes were generally conserved within our institutional cohort, unexpectedly 11 isolates were nonresistant to the β-lactam ampicillin and only one isolate was nonsusceptible to the quinolone ciprofloxacin. K. variicola isolates have variation in ability to cause urinary tract infections in a newly developed murine model, but importantly a strain had statistically significant higher bladder CFU than the model uropathogenic K. pneumoniae strain TOP52. Type 1 pilus and genomic identification of altered fim operon structure were associated with differences in bladder CFU for the tested strains. Nine newly reported types of pilus genes were discovered in the K. variicola pan-genome, including the first identified P-pilus in Klebsiella spp.

Combining Phylogenetic and Network Approaches to Identify HIV-1 Transmission Links in San Mateo County, California

The HIV epidemic in San Mateo County is sustained by multiple overlapping risk groups and is an important hub for HIV transmission in northern California. Limited access to care has led historically to delayed clinical presentation, higher rates of opportunistic infections, and an increased prevalence of antiretroviral drug resistance. The virologic and clinical consequences of treatment within these multiple ethnic and behavioral groups are poorly understood, highlighting the need for efficient surveillance strategies that are able to elucidate transmission networks and drug resistance patterns. We obtained sequence data from a group of 316 HIV-positive individuals in the San Mateo AIDS Program over a 14-year period and integrated epidemiologic, phylogenetic, and network approaches to characterize transmission clusters, risk factors and drug resistance. Drug resistance mutations were identified using the Stanford HIV Drug Resistance Database. A maximum likelihood tree was inferred in RAxML and subjected to clustering analysis in Cluster Picker. Network analysis using pairwise genetic distances was performed in HIV-TRACE. Participants were primarily male (60%), white Hispanics and non-Hispanics (32%) and African American (20.6%). The most frequent behavior risk factor was male-male sex (33.5%), followed by heterosexual (23.4%) and injection drug use (9.5%). Nearly all sequences were subtype B (96%) with subtypes A, C, and CRF01_AE also observed. Sequences from 65% of participants had at least one drug resistance mutation. Clustered transmissions included a higher number of women when compared to non-clustered individuals and were more likely to include heterosexual or people who inject drugs (PWID). Detailed analysis of the largest network (N = 47) suggested that PWID played a central role in overall transmission of HIV-1 as well as bridging men who have sex with men (MSM) transmission with heterosexual/PWID among primarily African American men. Combined phylogenetic and network analysis of HIV sequence data identified several overlapping risk factors in the epidemic, including MSM, heterosexual and PWID transmission with a disproportionate impact on African Americans and a high prevalence of drug resistance.

Burmese injecting drug users in Yunnan play a pivotal role in the cross-border transmission of HIV-1 in the China-Myanmar border region

Injecting drug users (IDUs) are the major risk group for HIV-1 infection in the China-Myanmar border area. There are a large number of Burmese IDUs living in Yunnan (Yunnan-mIDUs) who might be associated with the cross-border transmission of HIV-1. From 2010 to 2013, 617 Yunnan-mIDUs were recruited from three counties of Yunnan, 19.0% of whom were detected to be HIV-1 positive by serological testing. Partial HIV-1 p17, pol, vif-env, and env genes were amplified from the positive samples and were sequenced. Phylogenetic and HIV-1 subtyping analyses revealed that HIV-1 recombinant forms (RFs), including RF_BC (36.4%), RF_01BC (26.1%), RF_01C (9.1%) and RF_01B (1.1%), were predominant among this cohort. Of the identified HIV-1 strains, 14.8%, 9.1% and 3.4% belonged to subtype C, CRF01_AE and subtype B, respectively. Transmission cluster analysis showed that sequences from the Yunnan-mIDUs formed transmission clusters not only with those from Burmese IDUs but also with those from Chinese IDUs, indicating that Yunnan-mIDUs might acquire HIV-1 infection from or spread HIV-1 to both Burmese and Chinese IDUs. Phylogeographic analyses revealed three cross-border transmission patterns associated with Yunnan-mIDUs, in which Yunnan-mIDUs served as the crucial nodes linking the Burmese and Chinese IDUs. These results suggest that Yunnan-mIDUs are a potential viral reservoir for the diffusion of HIV-1 in Yunnan and play a pivotal role in the bidirectional cross-border transmission of HIV-1 in the China-Myanmar border region. More intervention efforts that focus on Yunnan-mIDUs are recommended in Yunnan’s campaign against HIV/AIDS.

Epidemic history of hepatitis C virus genotypes and subtypes in Portugal

Any successful strategy to prevent and control HCV infection requires an understanding of the epidemic behaviour among the different genotypes. Here, we performed the first characterization of the epidemic history and transmission dynamics of HCV subtypes in Portugal. Direct sequencing of NS5B was performed on 230 direct-acting antiviral drugs (DAA)-treatment naïve patients in Lisbon. Phylogenetic analysis was used for subtyping and transmission cluster identification. Bayesian methods were used to reconstruct the epidemic history of HCV subtypes. Sequences were analysed for resistance-associated substitutions (RAS). The majority of strains were HCV-GT1 (62.6%), GT3 (18.3%, all subtype 3a) and GT4 (16.1%). Among GT1, the most frequent were subtypes 1a (75.5%) and 1b (24.5%). Polyphyletic patterns were found in all but 12 lineages suggesting multiple introductions of the different subtypes in this population. Five distinct epidemics were identified. The first significant HCV epidemic in Portugal occurred between 1930s and 1960s, was caused almost exclusively by GT1b and was likely associated with blood transfusions. Rapid expansion of GT3a occurred in the 1960s and GT1a in the 1980s, associated with intravenous drug use. The most recent epidemics were caused by GT4a and GT4d and seem to be associated with the resurgence of opioid use. The C316N substitution was found in 31.4% of GT1b-patients. Close surveillance of patients bearing this mutation and undergoing dasabuvir-based regimens will be important to determine its impact on treatment outcome.

Ultradeep single-molecule real-time sequencing of HIV envelope reveals complete compartmentalization of highly macrophage-tropic R5 proviral variants in brain and CXCR4-using variants in immune and peripheral tissues

Despite combined antiretroviral therapy (cART), HIV+ patients still develop neurological disorders, which may be due to persistent HIV infection and selective evolution in brain tissues. Single-molecule real-time (SMRT) sequencing technology offers an improved opportunity to study the relationship among HIV isolates in the brain and lymphoid tissues because it is capable of generating thousands of long sequence reads in a single run. Here, we used SMRT sequencing to generate ~ 50,000 high-quality full-length HIV envelope sequences (> 2200 bp) from seven autopsy tissues from an HIV+/cART+ subject, including three brain and four non-brain sites. Sanger sequencing was used for comparison with SMRT data and to clone functional pseudoviruses for in vitro tropism assays. Phylogenetic analysis demonstrated that brain-derived HIV was compartmentalized from HIV outside the brain and that the variants from each of the three brain tissues grouped independently. Variants from all peripheral tissues were intermixed on the tree but independent of the brain clades. Due to the large number of sequences, a clustering analysis at three similarity thresholds (99, 99.5, and 99.9%) was also performed. All brain sequences clustered exclusive of any non-brain sequences at all thresholds; however, frontal lobe sequences clustered independently of occipital and parietal lobes. Translated sequences revealed potentially functional differences between brain and non-brain sequences in the location of putative N-linked glycosylation sites (N-sites), V1 length, V3 charge, and the number of V4 N-sites. All brain sequences were predicted to use the CCR5 co-receptor, while most non-brain sequences were predicted to use CXCR4 co-receptor. Tropism results were confirmed by in vitro infection assays. The study is the first to use a SMRT sequencing approach to study HIV compartmentalization in tissues and supports other reports of limited trafficking between brain and non-brain sequences during cART. Due to the long sequence length, we could observe changes along the entire envelope gene, likely caused by differential selective pressure in the brain that may contribute to neurological disease.