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Mohamed S, Boulmé R, Sayada C. From Capillary Electrophoresis to Deep Sequencing: An Improved HIV-1 Drug Resistance Assessment Solution Using In Vitro Diagnostic (IVD) Assays and Software. Viruses 2023; 15:v15020571. [PMID: 36851783 PMCID: PMC9965321 DOI: 10.3390/v15020571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Drug-resistance mutations were mostly detected using capillary electrophoresis sequencing, which does not detect minor variants with a frequency below 20%. Next-Generation Sequencing (NGS) can now detect additional mutations which can be useful for HIV-1 drug resistance interpretation. The objective of this study was to evaluate the performances of CE-IVD assays for HIV-1 drug-resistance assessment both for target-specific and whole-genome sequencing, using standardized end-to-end solution platforms. METHODS A total of 301 clinical samples were prepared, extracted, and amplified for the three HIV-1 genomic targets, Protease (PR), Reverse Transcriptase (RT), and Integrase (INT), using the CE-IVD DeepChek® Assays; and then 19 clinical samples, using the CE-IVD DeepChek® HIV Whole Genome Assay, were sequenced on the NGS iSeq100 and MiSeq (Illumina, San Diego, CA, USA). Sequences were compared to those obtained by capillary electrophoresis. Quality control for Molecular Diagnostics (QCMD) samples was added to validate the clinical accuracy of these in vitro diagnostics (IVDs). Nineteen clinical samples were then tested with the same sample collection, handling, and measurement procedure for evaluating the use of NGS for whole-genome HIV-1. Sequencing analyzer outputs were submitted to a downstream CE-IVD standalone software tailored for HIV-1 analysis and interpretation. RESULTS The limits of range detection were 1000 to 106 cp/mL for the HIV-1 target-specific sequencing. The median coverage per sample for the three amplicons (PR/RT and INT) was 13,237 reads. High analytical reproducibility and repeatability were evidenced by a positive percent agreement of 100%. Duplicated samples in two distinct NGS runs were 100% homologous. NGS detected all the mutations found by capillary electrophoresis and identified additional resistance variants. A perfect accuracy score with the QCMD panel detection of drug-resistance mutations was obtained. CONCLUSIONS This study is the first evaluation of the DeepChek® Assays for targets specific (PR/RT and INT) and whole genome. A cutoff of 3% allowed for a better characterization of the viral population by identifying additional resistance mutations and improving the HIV-1 drug-resistance interpretation. The use of whole-genome sequencing is an additional and complementary tool to detect mutations in newly infected untreated patients and heavily experienced patients, both with higher HIV-1 viral-load profiles, to offer new insight and treatment strategies, especially using the new HIV-1 capsid/maturation inhibitors and to assess the potential clinical impact of mutations in the HIV-1 genome outside of the usual HIV-1 targets (RT/PR and INT).
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Affiliation(s)
| | | | - Chalom Sayada
- Advanced Biological Laboratories (ABL), 2550 Luxembourg, Luxembourg
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2
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Wilbourn B, Saafir-Callaway B, Jair K, Wertheim JO, Laeyendeker O, Jordan JA, Kharfen M, Castel A. Characterization of HIV Risk Behaviors and Clusters Using HIV-Transmission Cluster Engine Among a Cohort of Persons Living with HIV in Washington, DC. AIDS Res Hum Retroviruses 2021; 37:706-715. [PMID: 34157853 PMCID: PMC8501467 DOI: 10.1089/aid.2021.0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Molecular epidemiology (ME) is one tool used to end the HIV epidemic in the United States. We combined clinical and behavioral data with HIV sequence data to identify any overlap in clusters generated from different sequence datasets; to characterize HIV transmission clusters; and to identify correlates of clustering among people living with HIV (PLWH) in Washington, District of Columbia (DC). First, Sanger sequences from DC Cohort participants, a longitudinal HIV study, were combined with next-generation sequences (NGS) from participants in a ME substudy to identify clusters. Next, demographic and self-reported behavioral data from ME substudy participants were used to identify risks of secondary transmission. Finally, we combined NGS from ME substudy participants with Sanger sequences in the DC Molecular HIV Surveillance database to identify clusters. Cluster analyses used HIV-Transmission Cluster Engine to identify linked pairs of sequences (defined as distance ≤1.5%). Twenty-eight clusters of ≥3 sequences (size range: 3-12) representing 108 (3%) participants were identified. None of the five largest clusters (size range: 5-12) included newly diagnosed PLWH. Thirty-four percent of ME substudy participants (n = 213) reported condomless sex during their last sexual encounter and 14% reported a Syphilis diagnosis in the past year. Seven transmission clusters (size range: 2-19) were identified in the final analysis, each containing at least one ME substudy participant. Substudy participants in clusters from the third analysis were present in clusters from the first analysis. Combining HIV sequence, clinical and behavioral data provided insights into HIV transmission that may not be identified using traditional epidemiological methods alone. Specifically, the sexual risk behaviors and STI diagnoses reported in the substudy survey may not have been disclosed during Partner Services activities and the survey data complemented clinical data to fully characterize transmission clusters. These findings can be used to enhance local efforts to interrupt transmission and avert new infections.
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Affiliation(s)
- Brittany Wilbourn
- Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA
| | - Brittani Saafir-Callaway
- HIV/AIDS, Hepatitis, STD and TB Administration, DC Health, Washington, District of Columbia, USA
| | - Kamwing Jair
- Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA
| | - Joel O. Wertheim
- Department of Medicine, University of California San Diego, LA Jolla, California, USA
| | - Oliver Laeyendeker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, Maryland, USA
| | - Jeanne A. Jordan
- Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA
| | - Michael Kharfen
- HIV/AIDS, Hepatitis, STD and TB Administration, DC Health, Washington, District of Columbia, USA
| | - Amanda Castel
- Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA
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Bendall ML, Gibson KM, Steiner MC, Rentia U, Pérez-Losada M, Crandall KA. HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intrahost Viral Populations. Mol Biol Evol 2021; 38:1677-1690. [PMID: 33367849 PMCID: PMC8042772 DOI: 10.1093/molbev/msaa315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Deep sequencing of viral populations using next-generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intrahost viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here, we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.
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Affiliation(s)
- Matthew L Bendall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Keylie M Gibson
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Margaret C Steiner
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Uzma Rentia
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
| | - Keith A Crandall
- Computational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA.,Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
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Knyazev S, Hughes L, Skums P, Zelikovsky A. Epidemiological data analysis of viral quasispecies in the next-generation sequencing era. Brief Bioinform 2021; 22:96-108. [PMID: 32568371 PMCID: PMC8485218 DOI: 10.1093/bib/bbaa101] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 05/04/2020] [Indexed: 01/04/2023] Open
Abstract
The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.
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Noguera-Julian M, Lee ER, Shafer RW, Kantor R, Ji H. Dry Panels Supporting External Quality Assessment Programs for Next Generation Sequencing-Based HIV Drug Resistance Testing. Viruses 2020; 12:v12060666. [PMID: 32575676 PMCID: PMC7354622 DOI: 10.3390/v12060666] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/18/2020] [Accepted: 06/18/2020] [Indexed: 12/18/2022] Open
Abstract
External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.
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Affiliation(s)
- Marc Noguera-Julian
- IrsiCaixa AIDS Research Institute, Hospital Germans Trias i Pujol, s/n, Catalonia, 08196 Badalona, Spain
- Chair in AIDS and Related Illnesses, Centre for Health and Social Care Research (CESS), Faculty of Medicine, University of Vic, Central University of Catalonia, Can Baumann. Ctra. de Roda, 70, 08500 Vic, Spain
- Correspondence:
| | - Emma R. Lee
- National HIV and Retrovirology Laboratories, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada; (E.R.L.); (H.J.)
| | | | - Rami Kantor
- Division of Infectious Diseases, Brown University Alpert Medical School, Providence, RI 02903, USA;
| | - Hezhao Ji
- National HIV and Retrovirology Laboratories, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada; (E.R.L.); (H.J.)
- Department of Medical Microbiology and Infectious Diseases, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
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Ye M, Chen X, Wang Y, Zhou YH, Pang W, Zhang C, Zheng YT. HIV-1 Drug Resistance in ART-Naïve Individuals in Myanmar. Infect Drug Resist 2020; 13:1123-1132. [PMID: 32368103 PMCID: PMC7182463 DOI: 10.2147/idr.s246462] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 03/31/2020] [Indexed: 01/29/2023] Open
Abstract
Background Estimating the prevalence and characterizing the transmission of HIV-1 drug resistance in treatment-naïve individuals are very important in the prevention and control of HIV/AIDS. As one of the areas most affected by HIV/AIDS, few data are currently available for HIV-1 drug resistance in antiretroviral therapy (ART)-naïve individuals in Myanmar, which borders Yunnan, China. Methods HIV-1 pol sequences from ART-naïve HIV-1-infected individuals during 2008 and 2014 in Myanmar were retrieved from our previous studies. HIV-1 transmitted drug resistance (TDR) and susceptibility to antiretroviral drugs were predicted using the Stanford HIVdb program. HIV-1 transmission cluster (TC) was determined by Cluster Picker. Results A total of 169 partial pol sequences from ART-naïve HIV-1 positive Burmese were analyzed. The prevalence of TDR was 20.1%. CRF01_AE and BC recombinants appeared to have a higher prevalence of TDR than other subtypes. The V179D/T was found to be very common in the China–Myanmar border region and was involved in half of the transmission clusters formed by HIV-1 drug-resistance strains in this region. Comparison showed that drug-resistance mutation profile in Myanmar was very similar to that in Dehong prefecture of Yunnan. By further phylogenetic analysis with all available sequences from the China–Myanmar border region, four HIV-1 drug-resistance-related TCs were identified. Three of them were formed by Burmese long-distance truck drivers and the Burmese staying in Yunnan, and another was formed by Burmese injection drug users staying in Myanmar and Yunnan. These results suggest a potential transmission link of HIV-1 drug resistance between Myanmar and Yunnan. Conclusion Given the high prevalence of TDR in Myanmar, and the potential risk of cross-border transmission of HIV-1 drug-resistant strains between Myanmar and Yunnan, China, ongoing monitoring of HIV-1 drug resistance in ART-naïve individuals will provide a guideline for clinical antiretroviral treatment and benefit the prevention and control of HIV/AIDS in this border region.
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Affiliation(s)
- Mei Ye
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People's Republic of China.,Savaid Medical School, University of Chinese Academy of Sciences, Beijing 101408, People's Republic of China
| | - Xin Chen
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People's Republic of China.,Department of Pathogenic Biology, School of Basic Medical Sciences, Gannan Medical University, Ganzhou 341000, People's Republic of China
| | - Yu Wang
- KIZ-SU Joint Laboratory of Animal Model and Drug Development, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215000, People's Republic of China
| | - Yan-Heng Zhou
- Shaanxi Engineering and Technological Research Center for Conversation and Utilization of Regional Biological Resources, College of Life Sciences, Yan'an University, Yan'an, Shaanxi 716000, People's Republic of China
| | - Wei Pang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People's Republic of China
| | - Chiyu Zhang
- Pathogen Discovery and Evolution Unit, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200025, People's Republic of China
| | - Yong-Tang Zheng
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, People's Republic of China.,KIZ-SU Joint Laboratory of Animal Model and Drug Development, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215000, People's Republic of China
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Analysis of unusual and signature APOBEC-mutations in HIV-1 pol next-generation sequences. PLoS One 2020; 15:e0225352. [PMID: 32102090 PMCID: PMC7043932 DOI: 10.1371/journal.pone.0225352] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/30/2020] [Indexed: 12/31/2022] Open
Abstract
Introduction At low mutation-detection thresholds, next generation sequencing (NGS) for HIV-1 genotypic resistance testing is susceptible to artifactual detection of mutations arising from PCR error and APOBEC-mediated G-to-A hypermutation. Methods We analyzed published HIV-1 pol Illumina NGS data to characterize the distribution of mutations at eight NGS mutation detection thresholds: 20%, 10%, 5%, 2%, 1%, 0.5%, 0.2%, and 0.1%. At each threshold, we determined proportions of amino acid mutations that were unusual (defined as having a prevalence <0.01% in HIV-1 group M sequences) or signature APOBEC mutations. Results Eight studies, containing 855 samples, in the NCBI Sequence Read Archive were analyzed. As detection thresholds were lowered, there was a progressive increase in the proportion of positions with usual and unusual mutations and in the proportion of all mutations that were unusual. The median proportion of positions with an unusual mutation increased gradually from 0% at the 20% threshold to 0.3% at the 1% threshold and then exponentially to 1.3% (0.5% threshold), 6.9% (0.2% threshold), and 23.2% (0.1% threshold). In two of three studies with available plasma HIV-1 RNA levels, the proportion of positions with unusual mutations was negatively associated with virus levels. Although the complete set of signature APOBEC mutations was much smaller than that of unusual mutations, the former outnumbered the latter in one-sixth of samples at the 0.5%, 1%, and 2% thresholds. Conclusions The marked increase in the proportion of positions with unusual mutations at thresholds below 1% and in samples with lower virus loads suggests that, at low thresholds, many unusual mutations are artifactual, reflecting PCR error or G-to-A hypermutation. Profiling the numbers of unusual and signature APOBEC pol mutations at different NGS mutation detection thresholds may be useful to avoid selecting a threshold that is too low and poses an unacceptable risk of identifying artifactual mutations.
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Gibson KM, Jair K, Castel AD, Bendall ML, Wilbourn B, Jordan JA, Crandall KA, Pérez-Losada M. A cross-sectional study to characterize local HIV-1 dynamics in Washington, DC using next-generation sequencing. Sci Rep 2020; 10:1989. [PMID: 32029767 PMCID: PMC7004982 DOI: 10.1038/s41598-020-58410-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 12/31/2019] [Indexed: 11/08/2022] Open
Abstract
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.
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Grants
- P30 AI117970 NIAID NIH HHS
- U01 AI069503 NIAID NIH HHS
- UM1 AI069503 NIAID NIH HHS
- This study was supported by the DC Cohort Study (U01 AI69503-03S2), a supplement from the Women’s Interagency Study for HIV-1 (410722_GR410708), a DC D-CFAR pilot award, and a 2015 HIV-1 Phylodynamics Supplement award from the District of Columbia for AIDS Research, an NIH funded program (AI117970), which is supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC, NIGMS, NIDDK and OAR. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Affiliation(s)
- Keylie M Gibson
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
| | - Kamwing Jair
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Amanda D Castel
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Matthew L Bendall
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Brittany Wilbourn
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Jeanne A Jordan
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Keith A Crandall
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal
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Pérez-Losada M, Arenas M, Galán JC, Bracho MA, Hillung J, García-González N, González-Candelas F. High-throughput sequencing (HTS) for the analysis of viral populations. INFECTION GENETICS AND EVOLUTION 2020; 80:104208. [PMID: 32001386 DOI: 10.1016/j.meegid.2020.104208] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Marcos Pérez-Losada
- Computational Biology Institute, Milken Institute School of Public Health, George Washington University, Washington, DC, USA; CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão 4485-661, Portugal
| | - Miguel Arenas
- Department of Biochemistry, Genetics and Immunology, University of Vigo, 36310 Vigo, Spain; Biomedical Research Center (CINBIO), University of Vigo, 36310 Vigo, Spain.
| | - Juan Carlos Galán
- Microbiology Service, Hospital Ramón y Cajal, Madrid, Spain; CIBER in Epidemiology and Public Health, Spain.
| | - Mª Alma Bracho
- CIBER in Epidemiology and Public Health, Spain; Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Valencia, Spain.
| | - Julia Hillung
- Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Valencia, Spain; Institute for Integrative Systems Biology (I2SysBio), CSIC-University of Valencia, Valencia, Spain.
| | - Neris García-González
- Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Valencia, Spain; Institute for Integrative Systems Biology (I2SysBio), CSIC-University of Valencia, Valencia, Spain.
| | - Fernando González-Candelas
- CIBER in Epidemiology and Public Health, Spain; Joint Research Unit "Infection and Public Health" FISABIO-University of Valencia, Valencia, Spain; Institute for Integrative Systems Biology (I2SysBio), CSIC-University of Valencia, Valencia, Spain.
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