Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Jair K

PROVIDER: S-EPMC6456221 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Jair Kamwing K McCann Chase D CD Reed Harrison H Castel Amanda D AD Pérez-Losada Marcos M Wilbourn Brittany B Greenberg Alan E AE Jordan Jeanne A JA

PloS one 20190409 4

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 exper ...[more]

PMID: 30964884

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Project description:Washington, DC consistently has one of the highest annual rates of new HIV-1 diagnoses in the United States over the last 10 years. To guide intervention and prevention strategies to combat DC HIV infection, it is helpful to understand HIV transmission dynamics in a historical context. Toward this aim, we conducted a retrospective study (years 1987-2015) of 3,349 HIV pol sequences (1,026 bp) from 1,996 individuals living in the DC area belonging to three different cohorts. We coupled HIV sequence data with clinical information (sex, risk factor, race/ethnicity, viral load, subtype, anti-retroviral regimen) to identify circulating drug resistant mutations (DRM) and transmission clusters and assess their persistence over time. Of the transmission clusters identified in the DC area, 78.0 and 31.7% involved MSM and heterosexuals, respectively. The longest spread of time for a single cluster was 5 years (2007-2012) using a distance-based network inference approach and 27 years (1987-2014) using a maximum likelihood phylogenetic approach. We found eight subtypes and nine recombinants. Genetic diversity increased steadily over time with a slight peak in 2009 and remained constant thereafter until 2015. Nucleotide diversity also increased over time while relative genetic diversity (BEAST) remained relatively steady over the last 28 years with slight increases since 2000 in subtypes B and C. Sequences from individuals on drug therapy contained the highest total number of DRMs (1,104-1,600) and unique DRMs (63-97) and the highest proportion (>20%) of resistant individuals. Heterosexuals (43.94%), MSM (40.13%), and unknown (44.26%) risk factors showed similar prevalence of DRMs, while injection drug users had a lower prevalence (33.33%). Finally, there was a 60% spike in the number of codons with DRMs between 2007 and 2010. Past patterns of HIV transmission and DRM accumulation over time described here will help to predict future efficacy of ART drugs based on DRMs persisting over time and identify risk groups of interest for prevention and intervention efforts within the DC population. Our results show how longitudinal data can help to understand the temporal dynamics of HIV-1 at the local level.

Project description:BACKGROUND:Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions. RESULTS:We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side. CONCLUSIONS:The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.

Project description:ST11-KL64 is an internationally distributed lineage of carbapenem-resistant Klebsiella pneumoniae and is the most common type in China. The international and interprovincial (in China) transmission of ST11-KL64 CRKP remains to be elucidated. We used both static clusters defined based on a fixed cutoff of ≤21 pairwise single-nucleotide polymorphisms and dynamic groups defined by modeling the likelihood to be linked by a transmission threshold to investigate the transmission of ST11-KL64 strains based on genome sequences mining. We analyzed all publicly available genomes (n = 730) of ST11-KL64 strains, almost all of which had known carbapenemase genes with KPC-2 being dominant. We identified 4 clusters of international transmission and 14 clusters of interprovincial transmission across China of ST11-KL64 strains. We found that dynamic grouping could provide further resolution for determining clonal relatedness in addition to the widely adopted static clustering and therefore increases the confidence for inferring transmission.IMPORTANCECarbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious challenge for clinical management and is prone to spread in and between healthcare settings. ST11-KL64 is the dominant CRKP type in China with a worldwide distribution. Here, we used two different methods, the widely used clustering based on a fixed single-nucleotide polymorphism (SNP) cutoff and the recently developed grouping by modeling transmission likelihood, to mine all 730 publicly available ST11-KL64 genomes. We identified international transmission of several strains and interprovincial transmission in China of a few, which warrants further investigations to uncover the mechanisms for their spread. We found that static clustering based on ≤21 fixed SNPs is sensitive to detect transmission and dynamic grouping has higher resolutions to provide complementary information. We suggest the use of the two methods in combination for analyzing transmission of bacterial strains. Our findings highlight the need of coordinated actions at both international and interprovincial levels for tackling multi-drug resistant organisms.

Dataset Information

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

Publications

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

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