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Akiyama MJ, Khudyakov Y, Ramachandran S, Riback LR, Ackerman M, Nyakowa M, Arthur L, Lizcano J, Walker J, Cherutich P, Kurth A. Widespread hepatitis C virus transmission network among people who inject drugs in Kenya. Int J Infect Dis 2024; 147:107215. [PMID: 39182826 DOI: 10.1016/j.ijid.2024.107215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/29/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVES Hepatitis C virus (HCV) disproportionately affects people who inject drugs (PWID) worldwide. Despite carrying a high HCV burden, little is known about transmission dynamics in low- and middle-income countries. METHODS We recruited PWID from Nairobi and coastal cities of Mombasa, Kilifi, and Malindi in Kenya at needle and syringe programs. Next-generation sequencing data from HCV hypervariable region 1 were analyzed using Global Hepatitis Outbreak and Surveillance Technology to identify transmission clusters. RESULTS HCV strains belonged to genotype 1a (n = 64, 46.0%) and 4a (n = 72, 51.8%) and were mixed HCV/1a/4a (n = 3, 2.2%). HCV/1a was dominant (61.2%) in Nairobi, whereas HCV/4a was dominant in Malindi (85.7%) and Kilifi (60.9%), and both genotypes were evenly identified in Mombasa (45.3% for HCV/1a and 50.9% for HCV/4a). Global Hepatitis Outbreak and Surveillance Technology identified 11 transmission clusters involving 90 cases. Strains in the two largest clusters (n = 38 predominantly HCV/4a and n = 32 HCV/1a) were sampled from all four cities. CONCLUSIONS Transmission clusters involving 64.7% of cases indicate an effective sampling of major HCV strains circulating among PWID. Large clusters involving 77.8% of strains from Nairobi and Coast suggest successful introduction of two ancestral HCV/1a and HCV/4a strains to PWID, with widely spread progeny. The disruption of the country-wide transmission network is essential for HCV elimination.
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Affiliation(s)
- Matthew J Akiyama
- Department of Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, United States.
| | - Yury Khudyakov
- Division of Viral Hepatitis, Centers for Disease Control, Atlanta, United States
| | - Sumathi Ramachandran
- Division of Viral Hepatitis, Centers for Disease Control, Atlanta, United States
| | - Lindsey R Riback
- Department of Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, United States
| | - Maxwell Ackerman
- Department of Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, United States
| | - Mercy Nyakowa
- Kenya Ministry of Health, National AIDS&STI Control Program (NASCOP), Nairobi, Kenya
| | - Leonard Arthur
- Division of Viral Hepatitis, Centers for Disease Control, Atlanta, United States
| | | | | | - Peter Cherutich
- Kenya Ministry of Health, National AIDS&STI Control Program (NASCOP), Nairobi, Kenya
| | - Ann Kurth
- University of Bristol, Bristol, United Kingdom
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2
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Pedro M, Seanna P, Honoria G, Renee H, Chunki F, Ben E. HCV prevalence and phylogenetic characteristics in a cross-sectional, community study of young people who inject drugs in New York City: Opportunity for and threats to HCV elimination. Health Sci Rep 2024; 7:e2211. [PMID: 38957862 PMCID: PMC11217018 DOI: 10.1002/hsr2.2211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/08/2024] [Accepted: 06/04/2024] [Indexed: 07/04/2024] Open
Abstract
Background and Aims In the United States, the opioid epidemic has led many young people who use opioids to initiate injection drug use, putting them at risk for hepatitis C virus (HCV) infection. However, community surveys to monitor HCV prevalence among young people who inject drugs (YPWID) are rare. Methods As part of Staying Safe (Ssafe), a trial to evaluate an HCV-prevention intervention, a community-recruited sample of 439 young people who use opioids (ages 18-30) in New York City (NYC) were screened from 2018 to 2021. Screening procedures included a brief verbal questionnaire, a visual check for injection marks, onsite urine drug testing, rapid HCV antibody (Ab) testing, and dried blood spot (DBS) collection. DBS specimens were sent to a laboratory for HCV RNA testing and phylogenetic analysis to identify genetic linkages among HCV RNA-positive specimens. Multivariable logistic regression was used to assess associations between HCV status (Ab and RNA) and demographics and drug use patterns. Results Among the 330 participants who reported injecting drugs (past 6 months), 33% (n = 110) tested HCV Ab-positive, 58% of whom (n = 64) had HCV RNA-positive DBS specimens, indicating active infection. In multivariable analysis, visible injection marks (AOR = 3.02; p < 0.001), older age (AOR = 1.38; p < 0.05), and female gender (AOR = 1.69; p = 0.052) were associated with HCV Ab-positive status. Visible injection marks were also associated with HCV RNA-positive status (AOR = 5.24; p < 0.01). Twenty-five percent of RNA-positive specimens (14/57) were genetically linked. Conclusion The relatively low prevalence of active infection suggests the potential impact of treatment-as-prevention in reducing HCV prevalence among YPWID. Targeted community serosurveys could help identify actively infected YPWID for treatment, thereby reducing HCV incidence and future transmissions.
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Affiliation(s)
| | - Pratt Seanna
- CUNY Graduate School of Public Health and Health PolicyNew York CityNew YorkUSA
| | - Guarino Honoria
- CUNY Graduate School of Public Health and Health PolicyNew York CityNew YorkUSA
| | - Hallack Renee
- NYS Department of HealthWadsworth CenterAlbanyNew YorkUSA
| | - Fong Chunki
- CUNY Graduate School of Public Health and Health PolicyNew York CityNew YorkUSA
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3
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Tully DC, Power KA, Sarette J, Stopka TJ, Friedmann PD, Korthuis PT, Cooper H, Young AM, Seal DW, Westergaard RP, Allen TM. Validation of dried blood spots for capturing hepatitis C virus diversity for genomic surveillance. J Viral Hepat 2024; 31:266-270. [PMID: 38366329 PMCID: PMC11023755 DOI: 10.1111/jvh.13924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 12/14/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024]
Abstract
Dried blood spots (DBS) have emerged as a promising alternative to traditional venous blood for hepatitis C virus (HCV) testing. However, their capacity to accurately reflect the genetic diversity of HCV remains poorly understood. We employed deep sequencing and advanced phylogenetic analyses on paired plasma and DBS samples from two common subtypes to evaluate the suitability of DBS for genomic surveillance. Results demonstrated that DBS captured equivalent viral diversity compared to plasma with no phylogenetic discordance observed. The ability of DBS to accurately reflect the profile of viral genetic diversity suggests it may be a promising avenue for future surveillance efforts to curb HCV outbreaks.
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Affiliation(s)
- Damien C. Tully
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Center for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Karen A. Power
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jacklyn Sarette
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Thomas J. Stopka
- Tufts University School of Medicine, Department of Public Health and Community Medicine, 136 Harrison Avenue, Boston, MA, 02111, USA
| | - Peter D. Friedmann
- Baystate Medical Center—University of Massachusetts, Office of Research, UMass Chan Medical School - Baystate, 3601 Main Street, 3rd Floor, Springfield, MA, 01199, USA
| | - P. Todd Korthuis
- Oregon Health & Science University, 3270 Southwest Pavilion Loop OHSU Physicians Pavilion, Suite 350, Portland, OR, 97239, USA
| | - Hannah Cooper
- Rollins School of Public Health, Emory University, Grace Crum Rollins Building 1518 Clifton Road, Atlanta, GA, 30322, USA
| | - April M. Young
- University of Kentucky, 760 Press Avenue Suite 280, Lexington, KY, 40536, USA
| | - David W. Seal
- Tulane University, School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2210, New Orleans, LA, 70112, USA
| | - Ryan P. Westergaard
- University of Wisconsin-Madison, 1685 Highland Avenue, 5th Floor, Madison, WI, 53705-2281, USA
| | - Todd M. Allen
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
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4
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Cintron R, Whitmer SLM, Moscoso E, Campbell EM, Kelly R, Talundzic E, Mobley M, Chiu KW, Shedroff E, Shankar A, Montgomery JM, Klena JD, Switzer WM. HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations. Viruses 2023; 15:2208. [PMID: 38005885 PMCID: PMC10675615 DOI: 10.3390/v15112208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.
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Affiliation(s)
- Roxana Cintron
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
| | - Shannon L. M. Whitmer
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Evan Moscoso
- General Dynamics Information Technology, Atlanta, GA 30329, USA; (E.M.); (R.K.)
| | - Ellsworth M. Campbell
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
| | - Reagan Kelly
- General Dynamics Information Technology, Atlanta, GA 30329, USA; (E.M.); (R.K.)
| | - Emir Talundzic
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Melissa Mobley
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Kuo Wei Chiu
- General Dynamics Information Technology, Atlanta, GA 30329, USA; (E.M.); (R.K.)
| | - Elizabeth Shedroff
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Anupama Shankar
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
| | - Joel M. Montgomery
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - John D. Klena
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - William M. Switzer
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
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5
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Tully DC, Power KA, Sarette J, Stopka TJ, Friedmann PD, Korthuis PT, Cooper H, Young AM, Seal DW, Westergaard RP, Allen TM. Validation of Dried Blood Spots for Capturing Hepatitis C Virus Diversity for Genomic Surveillance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.06.23292160. [PMID: 37461565 PMCID: PMC10350139 DOI: 10.1101/2023.07.06.23292160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Dried blood spots (DBS) have emerged as a promising alternative to traditional venous blood for HCV testing. However, their capacity to accurately reflect the genetic diversity of HCV remains poorly understood. We employed deep sequencing and advanced phylogenetic analyses on paired plasma and DBS samples to evaluate the suitability of DBS for genomic surveillance. Results demonstrated that DBS captured equivalent viral diversity compared to plasma with no phylogenetic discordance observed. The ability of DBS to accurately reflect the profile of viral genetic diversity suggests it may be a promising avenue for future surveillance efforts to curb HCV outbreaks.
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Affiliation(s)
- Damien C. Tully
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Center for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Karen A. Power
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jacklyn Sarette
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Thomas J. Stopka
- Tufts University School of Medicine Public Health and Community Medicine, 136 Harrison Avenue, Boston, MA, 02111, USA
| | - Peter D. Friedmann
- Baystate Medical Center—University of Massachusetts, Office of Research, UMass Chan Medical School - Baystate, 3601 Main Street, 3rd Floor, Springfield, MA, 01199, USA
| | - P. Todd Korthuis
- Oregon Health & Science University, 3270 Southwest Pavilion Loop OHSU Physicians Pavilion, Suite 350, Portland, OR, 97239, USA
| | - Hannah Cooper
- Rollins School of Public Health, Emory University, Grace Crum Rollins Building 1518 Clifton Road, Atlanta, GA, 30322, USA
| | - April M. Young
- University of Kentucky, 760 Press Avenue Suite 280, Lexington, KY, 40536, USA
| | - David W. Seal
- Tulane University, 1440 Canal Street, Suite 2210, New Orleans, LA, 70112, USA
| | - Ryan P. Westergaard
- University of Wisconsin-Madison, 1685 Highland Avenue, 5th Floor, Madison, WI, 53705-2281, USA
| | - Todd M. Allen
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
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6
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Senchyna F, Singh R. Dynamic Epidemiological Networks: A Data Representation Framework for Modeling and Tracking of SARS-CoV-2 Variants. J Comput Biol 2023; 30:446-468. [PMID: 37098217 DOI: 10.1089/cmb.2022.0469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023] Open
Abstract
The large-scale real-time sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has allowed for rapid identification of concerning variants through phylogenetic analysis. However, the nature of phylogenetic reconstruction is typically static, in that the relationships between taxonomic units, once defined, are not subject to alterations. Furthermore, most phylogenetic methods are intrinsically batch mode in nature, requiring the presence of the entire data set. Finally, the emphasis of phylogenetics is on relating taxonomical units. These characteristics complicate the application of classical phylogenetics methods to represent relationships in molecular data collected from rapidly evolving strains of an etiological agent, such as SARS-CoV-2, since the molecular landscape is updated continuously as samples are collected. In such settings, variant definitions are subject to epistemological constraints and may change as data accumulate. Furthermore, representing within-variant molecular relationships may be as important as representing between variant relationships. This article describes a novel data representation framework called dynamic epidemiological networks (DENs) along with algorithms that underpin its construction to address these issues. The proposed representation is applied to study the molecular development underlying the spread of the COVID-19 (coronavirus disease 2019) pandemic in two countries: Israel and Portugal spanning a 2-year period from February 2020 to April 2022. The results demonstrate how this framework could be used to provide a multiscale representation of the data by capturing molecular relationships between samples as well as those between variants, automatically identifying the emergence of high frequency variants (lineages), including variants of concern such as Alpha and Delta, and tracking their growth. Additionally, we show how analyzing the evolution of the DEN can help identify changes in the viral population that could not be readily inferred from phylogenetic analysis.
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Affiliation(s)
- Fiona Senchyna
- Department of Computer Science, San Francisco State University, San Francisco, California, USA
| | - Rahul Singh
- Department of Computer Science, San Francisco State University, San Francisco, California, USA
- Center for Discovery and Innovation in Parasitic Diseases, University of California, San Diego, California, USA
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7
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Skums P, Mohebbi F, Tsyvina V, Baykal PI, Nemira A, Ramachandran S, Khudyakov Y. SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework. Cell Syst 2022; 13:844-856.e4. [PMID: 36265470 PMCID: PMC9590096 DOI: 10.1016/j.cels.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 01/26/2023]
Abstract
Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.
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Affiliation(s)
- Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.
| | - Fatemeh Mohebbi
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vyacheslav Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Pelin Icer Baykal
- Department of Biosystems Science & Engineering, ETH Zurich, Basel, Switzerland
| | - Alina Nemira
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Sumathi Ramachandran
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Yury Khudyakov
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
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8
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Sahibzada KI, Ganova-Raeva L, Dimitrova Z, Ramachandran S, Lin Y, Longmire G, Arthur L, Xia GL, Khudyakov Y, Khan I, Sadaf S. Hepatitis C virus transmission cluster among injection drug users in Pakistan. PLoS One 2022; 17:e0270910. [PMID: 35839216 PMCID: PMC9286280 DOI: 10.1371/journal.pone.0270910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 06/21/2022] [Indexed: 11/19/2022] Open
Abstract
Hepatitis C virus (HCV) infections are public health problem across the globe, particularly in developing countries. Pakistan has the second highest prevalence of HCV infection worldwide. Limited data exist from Pakistan about persons who inject drugs (PWID) and are at significant risk of exposure to HCV infection and transmission. Serum specimens (n = 110) collected from PWID residing in four provinces were tested for molecular markers of HCV infection. Next generation sequencing (NGS) of the hypervariable region (HVR1) of HCV and Global Hepatitis Outbreak and Surveillance Technology (GHOST) were used to determine HCV genotype, genetic heterogeneity, and construct transmission networks. Among tested specimens, 47.3% were found anti-HCV positive and 34.6% were HCV RNA-positive and belonged to four genotypes, with 3a most prevalent followed by 1a, 1b and 4a. Variants sampled from five cases formed phylogenetic cluster and a transmission network. One case harbored infection with two different genotypes. High prevalence of infections and presence of various genotypes indicate frequent introduction and transmission of HCV among PWID in Pakistan. Identification of a transmission cluster across three provinces, involving 20% of all cases, suggests the existence of a countrywide transmission network among PWIDs. Understanding the structure of this network should assist in devising effective public health strategies to eliminate HCV infection in Pakistan.
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Affiliation(s)
- Kashif Iqbal Sahibzada
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Lilia Ganova-Raeva
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Zoya Dimitrova
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Sumathi Ramachandran
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Yulin Lin
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Garrett Longmire
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Leonard Arthur
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Guo-liang Xia
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Yury Khudyakov
- Division of Viral Hepatitis, Center of Disease Control and Prevention, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of America
| | - Idrees Khan
- University of Peshawar, Peshawar, KPK, Pakistan
| | - Saima Sadaf
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Pakistan
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9
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Gussler JW, Campo DS, Dimitrova Z, Skums P, Khudyakov Y. Primary case inference in viral outbreaks through analysis of intra-host variant population. BMC Bioinformatics 2022; 23:62. [PMID: 35135469 PMCID: PMC8822801 DOI: 10.1186/s12859-022-04585-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/25/2022] [Indexed: 11/21/2022] Open
Abstract
Background Investigation of outbreaks to identify the primary case is crucial for the interruption and prevention of transmission of infectious diseases. These individuals may have a higher risk of participating in near future transmission events when compared to the other patients in the outbreak, so directing more transmission prevention resources towards these individuals is a priority. Although the genetic characterization of intra-host viral populations can aid the identification of transmission clusters, it is not trivial to determine the directionality of transmissions during outbreaks, owing to complexity of viral evolution. Here, we present a new computational framework, PYCIVO: primary case inference in viral outbreaks. This framework expands upon our earlier work in development of QUENTIN, which builds a probabilistic disease transmission tree based on simulation of evolution of intra-host hepatitis C virus (HCV) variants between cases involved in direct transmission during an outbreak. PYCIVO improves upon QUENTIN by also adding a custom heterogeneity index and identifying the scenario when the primary case may have not been sampled. Results These approaches were validated using a set of 105 sequence samples from 11 distinct HCV transmission clusters identified during outbreak investigations, in which the primary case was epidemiologically verified. Both models can detect the correct primary case in 9 out of 11 transmission clusters (81.8%). However, while QUENTIN issues erroneous predictions on the remaining 2 transmission clusters, PYCIVO issues a null output for these clusters, giving it an effective prediction accuracy of 100%. To further evaluate accuracy of the inference, we created 10 modified transmission clusters in which the primary case had been removed. In this scenario, PYCIVO was able to correctly identify that there was no primary case in 8/10 (80%) of these modified clusters. This model was validated with HCV; however, this approach may be applicable to other microbial pathogens. Conclusions PYCIVO improves upon QUENTIN by also implementing a custom heterogeneity index which empowers PYCIVO to make the important ‘No primary case’ prediction. One or more samples, possibly including the primary case, may have not been sampled, and this designation is meant to account for these scenarios.
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Affiliation(s)
- J Walker Gussler
- Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, USA.,Department of Computer Science, Georgia State University, 1 Park Place NE, Atlanta, GA, 30303, USA
| | - David S Campo
- Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, USA.
| | - Zoya Dimitrova
- Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, 1 Park Place NE, Atlanta, GA, 30303, USA
| | - Yury Khudyakov
- Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, USA
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10
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Campbell EM, Boyles A, Shankar A, Kim J, Knyazev S, Cintron R, Switzer WM. MicrobeTrace: Retooling molecular epidemiology for rapid public health response. PLoS Comput Biol 2021; 17:e1009300. [PMID: 34492010 PMCID: PMC8491948 DOI: 10.1371/journal.pcbi.1009300] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 10/05/2021] [Accepted: 07/23/2021] [Indexed: 12/12/2022] Open
Abstract
Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. MicrobeTrace is a web-based, client-side, JavaScript application (https://microbetrace.cdc.gov) that runs in Chromium-based browsers and remains fully operational without an internet connection. Using publicly available data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. Users can email microbetrace@cdc.gov for support. The source code is available at https://github.com/cdcgov/microbetrace. Rapid advances in the fields of data science and bioinformatics have significantly improved molecular epidemiology tools used in public health and have led to major changes in the way outbreak investigation and pathogen transmission studies are conducted. However, the need for specialized computer skills often impedes the use of many of these tools in the public heath domain. We bridge this knowledge gap by development of an intuitive, standalone tool called MicrobeTrace to securely integrate, visualize and explore pathogen epidemiologic data. MicrobeTrace is an easy to use browser-based tool which can effectively merge contact tracing and/or microbial genomic data with demographic or behavioral information, resulting in elegant and informative networks as well as multiple customizable visualizations. MicrobeTrace can be used offline, with analyses being performed locally in the field, ensuring secure and confidential use of personally identifiable information (PII). We provide real world examples of how MicrobeTrace has been used in public health, including COVID outbreak investigations.
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Affiliation(s)
- Ellsworth M Campbell
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Anthony Boyles
- Northrup Grumman, Atlanta, Georgia, United States of America
| | - Anupama Shankar
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jay Kim
- Northrup Grumman, Atlanta, Georgia, United States of America
| | - Sergey Knyazev
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.,Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America.,Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America
| | - Roxana Cintron
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - William M Switzer
- Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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11
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Next-generation sequencing studies on the E1-HVR1 region of hepatitis C virus (HCV) from non-high-risk HCV patients living in Punjab and Khyber Pakhtunkhwa, Pakistan. Arch Virol 2021; 166:3049-3059. [PMID: 34448937 DOI: 10.1007/s00705-021-05203-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/17/2021] [Indexed: 11/09/2022]
Abstract
The incidence rate of hepatitis C virus (HCV) infection in Pakistan is very high. In this study, we evaluated the genetic heterogeneity of HCV hypervariable region 1 (HVR1) from the HCV-infected Pakistani population and compare the isolated genotypes with representative sequences from internationally diverse geographic regions. We also investigated potential transmission events in non-high-risk HCV patients. Next-generation sequencing (NGS) data from the E1-HVR1 region from 30 HCV patients were used for phylogenetic analysis. Reference sequences were retrieved from the Los Alamos HCV and GenBank databases. NGS data were analyzed to examine HCV HVR1 sequence diversity and identify transmission links among HCV-infected individuals using Global Hepatitis Outbreak and Surveillance Technology (GHOST). Phylogenetic analysis showed the predominance of HCV genotype 3a (86.6%), followed by 1a (6.6%), 1b (3.3%), and 3b (3.3%). NGS of HVR1 displayed significant genetic heterogeneity of HCV populations within each patient. The average nucleotide sequence diversity for HVR1 was 0.055. JR781281 was found to be the most diverse (0.14) of the specimens. Phylogenetic analysis demonstrated that all HCV specimens sequenced in this study were more similar to each other and showed variations from the representative sequences. The GHOST results suggested genetic relatedness between two (6.6%) HCV cases, possibly defining an incipient outbreak in a non-high-risk population. We urge rigorous countrywide investigation of outbreaks to identify transmission clusters and their sources to incorporate preventive measures for disease control.
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12
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Tully DC, Hahn JA, Bean DJ, Evans JL, Morris MD, Page K, Allen TM. Identification of Genetically Related HCV Infections Among Self-Described Injecting Partnerships. Clin Infect Dis 2021; 74:993-1003. [PMID: 34448809 DOI: 10.1093/cid/ciab596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The current opioid epidemic across the United States has fueled a surge in the rate of new hepatitis C virus (HCV) infections among young persons who inject drugs (PWIDs). Paramount to interrupting transmission is targeting these high-risk populations and understanding the underlying network structures facilitating transmission within these communities. METHODS Deep sequencing data were obtained for 52 participants from 32 injecting partnerships enrolled in the U-Find-Out (UFO) Partner Study, which is a prospective study of self-described injecting dyad partnerships from a large community-based study of HCV infection in young adult PWIDs from San Francisco. Phylogenetically linked transmission events were identified using traditional genetic-distance measures and viral deep sequence phylogenies reconstructed to determine the statistical support of inferences and the direction of transmission within partnerships. RESULTS Using deep sequencing data, we found that 12 of 32 partnerships were genetically similar and clustered. Three additional phylogenetic clusters were found describing novel putative transmission links outside of the injecting relationship. Transmission direction was inferred correctly for 5 partnerships with the incorrect transmission direction inferred in more than 50% of cases. Notably, we observed that phylogenetic linkage was most often associated with a lower number of network partners and involvement in a sexual relationship. CONCLUSIONS Deep sequencing of HCV among self-described injecting partnerships demonstrates that the majority of transmission events originate from outside of the injecting partnership. Furthermore, these findings caution that phylogenetic methods may be unable to routinely infer the direction of transmission among PWIDs especially when transmission events occur in rapid succession within high-risk networks.
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Affiliation(s)
- Damien C Tully
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.,Center for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Judith A Hahn
- Department of Medicine, University of California, San Francisco, California, USA
| | - David J Bean
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jennifer L Evans
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Meghan D Morris
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Kimberly Page
- Department of Internal Medicine, University of New Mexico Health Center, Albuquerque, New Mexico, USA
| | - Todd M Allen
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
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13
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Lehnertz NB, Wang X, Garfin J, Taylor J, Zipprich J, VonBank B, Martin K, Eikmeier D, Medus C, Wiedinmyer B, Bernu C, Plumb M, Pung K, Honein MA, Carter R, MacCannell D, Smith KE, Como-Sabetti K, Ehresmann K, Danila R, Lynfield R. Transmission Dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 in High-Density Settings, Minnesota, USA, March-June 2020. Emerg Infect Dis 2021; 27:2052-2063. [PMID: 34138695 PMCID: PMC8314815 DOI: 10.3201/eid2708.204838] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Coronavirus disease has disproportionately affected persons in congregate settings and high-density workplaces. To determine more about the transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in these settings, we performed whole-genome sequencing and phylogenetic analysis on 319 (14.4%) samples from 2,222 SARS-CoV-2-positive persons associated with 8 outbreaks in Minnesota, USA, during March-June 2020. Sequencing indicated that virus spread in 3 long-term care facilities and 2 correctional facilities was associated with a single genetic sequence and that in a fourth long-term care facility, outbreak cases were associated with 2 distinct sequences. In contrast, cases associated with outbreaks in 2 meat-processing plants were associated with multiple SARS-CoV-2 sequences. These results suggest that a single introduction of SARS-CoV-2 into a facility can result in a widespread outbreak. Early identification and cohorting (segregating) of virus-positive persons in these settings, along with continued vigilance with infection prevention and control measures, is imperative.
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14
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Hochstatter KR, Tully DC, Power KA, Koepke R, Akhtar WZ, Prieve AF, Whyte T, Bean DJ, Seal DW, Allen TM, Westergaard RP. Hepatitis C Virus Transmission Clusters in Public Health and Correctional Settings, Wisconsin, USA, 2016-2017 1. Emerg Infect Dis 2021; 27:480-489. [PMID: 33496239 PMCID: PMC7853590 DOI: 10.3201/eid2702.202957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
Abstract
Ending the hepatitis C virus (HCV) epidemic requires stopping transmission among networks of persons who inject drugs. Identifying transmission networks by using genomic epidemiology may inform community responses that can quickly interrupt transmission. We retrospectively identified HCV RNA–positive specimens corresponding to 459 persons in settings that use the state laboratory, including correctional facilities and syringe services programs, in Wisconsin, USA, during 2016–2017. We conducted next-generation sequencing of HCV and analyzed it for phylogenetic linkage by using the Centers for Disease Control and Prevention Global Hepatitis Outbreak Surveillance Technology platform. Analysis showed that 126 persons were linked across 42 clusters. Phylogenetic clustering was higher in rural communities and associated with female sex and younger age among rural residents. These data highlight that HCV transmission could be reduced by expanding molecular-based surveillance strategies to rural communities affected by the opioid crisis.
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15
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Akiyama MJ, Lipsey D, Ganova-Raeva L, Punkova LT, Agyemang L, Sue A, Ramachandran S, Khudyakov Y, Litwin AH. A Phylogenetic Analysis of Hepatitis C Virus Transmission, Relapse, and Reinfection Among People Who Inject Drugs Receiving Opioid Agonist Therapy. J Infect Dis 2021; 222:488-498. [PMID: 32150621 DOI: 10.1093/infdis/jiaa100] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/03/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Understanding hepatitis C virus (HCV) transmission among people who inject drugs (PWID) is essential for HCV elimination. We aimed to differentiate reinfections from treatment failures and to identify transmission linkages and associated factors in a cohort of PWID receiving opioid agonist therapy (OAT). METHODS We analyzed baseline and follow-up specimens from 150 PWID from 3 OAT clinics in the Bronx, New York. Next-generation sequencing data from the hypervariable region 1 of HCV were analyzed using Global Hepatitis Outbreak and Surveillance Technology. RESULTS There were 3 transmission linkages between study participants. Sustained virologic response (SVR) was not achieved in 9 participants: 7 had follow-up specimens with similar sequences to baseline, and 2 died. In 4 additional participants, SVR was achieved but the participants were viremic at later follow-up: 2 were reinfected with different strains, 1 had a late treatment failure, and 1 was transiently viremic 17 months after treatment. All transmission linkages were from the same OAT clinic and involved spousal or common-law partnerships. CONCLUSION This study highlights the use of next-generation sequencing as an important tool for identifying viral transmission and to help distinguish relapse and reinfection among PWID. Results reinforce the need for harm reduction interventions among couples and those who report ongoing risk factors after SVR.
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Affiliation(s)
| | - Daniel Lipsey
- Montefiore Medical Center/Albert Einstein College of Medicine
| | | | - Lili T Punkova
- Centers for Disease Control, Division of Viral Hepatitis
| | - Linda Agyemang
- Montefiore Medical Center/Albert Einstein College of Medicine
| | - Amanda Sue
- Centers for Disease Control, Division of Viral Hepatitis
| | | | - Yury Khudyakov
- Centers for Disease Control, Division of Viral Hepatitis
| | - Alain H Litwin
- Prisma Health, University of South Carolina School of Medicine, Clemson University School of Health Research
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16
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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: 29] [Impact Index Per Article: 9.7] [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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17
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Icer Baykal PB, Lara J, Khudyakov Y, Zelikovsky A, Skums P. Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections. Virus Evol 2020; 7:veaa103. [PMID: 33505710 PMCID: PMC7816669 DOI: 10.1093/ve/veaa103] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Detection of incident hepatitis C virus (HCV) infections is crucial for identification of outbreaks and development of public health interventions. However, there is no single diagnostic assay for distinguishing recent and persistent HCV infections. HCV exists in each infected host as a heterogeneous population of genomic variants, whose evolutionary dynamics remain incompletely understood. Genetic analysis of such viral populations can be applied to the detection of incident HCV infections and used to understand intra-host viral evolution. We studied intra-host HCV populations sampled using next-generation sequencing from 98 recently and 256 persistently infected individuals. Genetic structure of the populations was evaluated using 245,878 viral sequences from these individuals and a set of selected features measuring their diversity, topological structure, complexity, strength of selection, epistasis, evolutionary dynamics, and physico-chemical properties. Distributions of the viral population features differ significantly between recent and persistent infections. A general increase in viral genetic diversity from recent to persistent infections is frequently accompanied by decline in genomic complexity and increase in structuredness of the HCV population, likely reflecting a high level of intra-host adaptation at later stages of infection. Using these findings, we developed a machine learning classifier for the infection staging, which yielded a detection accuracy of 95.22 per cent, thus providing a higher accuracy than other genomic-based models. The detection of a strong association between several HCV genetic factors and stages of infection suggests that intra-host HCV population develops in a complex but regular and predictable manner in the course of infection. The proposed models may serve as a foundation of cyber-molecular assays for staging infection, which could potentially complement and/or substitute standard laboratory assays.
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Affiliation(s)
- Pelin B Icer Baykal
- Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA 30302, USA
| | - James Lara
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA 30329, USA
| | - Yury Khudyakov
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA 30329, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA 30302, USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, 25 Park Place, Atlanta, GA 30302, USA
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18
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Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences. BMC Bioinformatics 2020; 21:482. [PMID: 33375937 PMCID: PMC7772912 DOI: 10.1186/s12859-020-03811-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 10/13/2020] [Indexed: 12/09/2022] Open
Abstract
Background In molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods. Results The convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data. Conclusions CHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.
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19
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Campo DS, Gussler JW, Sue A, Skums P, Khudyakov Y. Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches. PLoS One 2020; 15:e0243622. [PMID: 33284864 PMCID: PMC7721465 DOI: 10.1371/journal.pone.0243622] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), and noninfectious health conditions. Spatiotemporal identification of PWID communities is essential for developing efficient and cost-effective public health interventions for reducing morbidity and mortality associated with injection-drug use (IDU). Reported ODDs are a strong indicator of the extent of IDU in different geographic regions. However, ODD quantification can take time, with delays in ODD reporting occurring due to a range of factors including death investigation and drug testing. This delayed ODD reporting may affect efficient early interventions for infectious diseases. We present a novel model, Dynamic Overdose Vulnerability Estimator (DOVE), for assessment and spatiotemporal mapping of ODDs in different U.S. jurisdictions. Using Google® Web-search volumes (i.e., the fraction of all searches that include certain words), we identified a strong association between the reported ODD rates and drug-related search terms for 2004–2017. A machine learning model (Extremely Random Forest) was developed to produce yearly ODD estimates at state and county levels, as well as monthly estimates at state level. Regarding the total number of ODDs per year, DOVE’s error was only 3.52% (Median Absolute Error, MAE) in the United States for 2005–2017. DOVE estimated 66,463 ODDs out of the reported 70,237 (94.48%) during 2017. For that year, the MAE of the individual ODD rates was 4.43%, 7.34%, and 12.75% among yearly estimates for states, yearly estimates for counties, and monthly estimates for states, respectively. These results indicate suitability of the DOVE ODD estimates for dynamic IDU assessment in most states, which may alert for possible increased morbidity and mortality associated with IDU. ODD estimates produced by DOVE offer an opportunity for a spatiotemporal ODD mapping. Timely identification of potential mortality trends among PWID might assist in developing efficient ODD prevention and HBV, HCV, and HIV infection elimination programs by targeting public health interventions to the most vulnerable PWID communities.
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Affiliation(s)
- David S. Campo
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
- * E-mail:
| | - Joseph W. Gussler
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
- Georgia State University, Atlanta, Georgia, United States of America
| | - Amanda Sue
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Pavel Skums
- Georgia State University, Atlanta, Georgia, United States of America
| | - Yury Khudyakov
- Division of Viral Hepatitis, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
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20
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Skums P, Kirpich A, Baykal PI, Zelikovsky A, Chowell G. Global transmission network of SARS-CoV-2: from outbreak to pandemic. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.03.22.20041145. [PMID: 32511620 PMCID: PMC7276047 DOI: 10.1101/2020.03.22.20041145] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Background The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is straining health systems around the world. Although the Chinese government implemented a number of severe restrictions on people's movement in an attempt to contain its local and international spread, the virus had already reached many areas of the world in part due to its potent transmissibility and the fact that a substantial fraction of infected individuals develop little or no symptoms at all. Following its emergence, the virus started to generate sustained transmission in neighboring countries in Asia, Western Europe, Australia, Canada and the United States, and finally in South America and Africa. As the virus continues its global spread, a clear and evidence-based understanding of properties and dynamics of the global transmission network of SARS-CoV-2 is essential to design and put in place efficient and globally coordinated interventions. Methods We employ molecular surveillance data of SARS-CoV-2 epidemics for inference and comprehensive analysis of its global transmission network before the pandemic declaration. Our goal was to characterize the spatial-temporal transmission pathways that led to the establishment of the pandemic. We exploited a network-based approach specifically tailored to emerging outbreak settings. Specifically, it traces the accumulation of mutations in viral genomic variants via mutation trees, which are then used to infer transmission networks, revealing an up-to-date picture of the spread of SARS-CoV-2 between and within countries and geographic regions. Results and Conclusions The analysis suggest multiple introductions of SARS-CoV-2 into the majority of world regions by means of heterogeneous transmission pathways. The transmission network is scale-free, with a few genomic variants responsible for the majority of possible transmissions. The network structure is in line with the available temporal information represented by sample collection times and suggest the expected sampling time difference of few days between potential transmission pairs. The inferred network structural properties, transmission clusters and pathways and virus introduction routes emphasize the extent of the global epidemiological linkage and demonstrate the importance of internationally coordinated public health measures.
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Affiliation(s)
- Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | | | - Pelin Icer Baykal
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA
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21
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Armstrong GL, MacCannell DR, Taylor J, Carleton HA, Neuhaus EB, Bradbury RS, Posey JE, Gwinn M. Pathogen Genomics in Public Health. N Engl J Med 2019; 381:2569-2580. [PMID: 31881145 PMCID: PMC7008580 DOI: 10.1056/nejmsr1813907] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" through more effective investigations of outbreaks of foodborne illnesses, better-targeted tuberculosis control, and more timely and granular influenza surveillance to inform the selection of vaccine strains. In this article, we describe how public health agencies have been adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given the ongoing development in sequencing and sequencing-related technologies.
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Affiliation(s)
- Gregory L Armstrong
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - Duncan R MacCannell
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - Jill Taylor
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - Heather A Carleton
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - Elizabeth B Neuhaus
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - Richard S Bradbury
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - James E Posey
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
| | - Marta Gwinn
- From the National Center for Emerging and Zoonotic Infectious Diseases (G.L.A., D.R.M., H.A.C.), the National Center for Immunization and Respiratory Diseases (E.B.N.), the Center for Global Health (R.S.B.), and the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (J.E.P.), Centers for Disease Control and Prevention, and CFOL International (M.G.) - all in Atlanta; and the Wadsworth Center, New York State Department of Health, Albany (J.T.)
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22
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Ganova-Raeva L, Dimitrova Z, Alexiev I, Punkova L, Sue A, Xia GL, Gancheva A, Dimitrova R, Kostadinova A, Golkocheva-Markova E, Khudyakov Y. HCV transmission in high-risk communities in Bulgaria. PLoS One 2019; 14:e0212350. [PMID: 30835739 PMCID: PMC6400337 DOI: 10.1371/journal.pone.0212350] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/31/2019] [Indexed: 01/16/2023] Open
Abstract
Background The rate of HIV infection in Bulgaria is low. However, the rate of HCV-HIV-coinfection and HCV infection is high, especially among high-risk communities. The molecular epidemiology of those infections has not been studied before. Methods Consensus Sanger sequences of HVR1 and NS5B from 125 cases of HIV/HCV coinfections, collected during 2010–2014 in 15 different Bulgarian cities, were used for preliminary phylogenetic evaluation. Next-generation sequencing (NGS) data of the hypervariable region 1 (HVR1) analyzed via the Global Hepatitis Outbreak and Surveillance Technology (GHOST) were used to evaluate genetic heterogeneity and possible transmission linkages. Links between pairs that were below and above the established genetic distance threshold, indicative of transmission, were further examined by generating k-step networks. Results Preliminary genetic analyses showed predominance of HCV genotype 1a (54%), followed by 1b (20.8%), 2a (1.4%), 3a (22.3%) and 4a (1.4%), indicating ongoing transmission of many HCV strains of different genotypes. NGS of HVR1 from 72 cases showed significant genetic heterogeneity of intra-host HCV populations, with 5 cases being infected with 2 different genotypes or subtypes and 6 cases being infected with 2 strains of same subtype. GHOST revealed 8 transmission clusters involving 30 cases (41.7%), indicating a high rate of transmission. Four transmission clusters were found in Sofia, three in Plovdiv, and one in Peshtera. The main risk factor for the clusters was injection drug use. Close genetic proximity among HCV strains from the 3 Sofia clusters, and between HCV strains from Peshtera and one of the two Plovdiv clusters confirms a long and extensive transmission history of these strains in Bulgaria. Conclusions Identification of several HCV genotypes and many HCV strains suggests a frequent introduction of HCV to the studied high-risk communities. GHOST detected a broad transmission network, which sustains circulation of several HCV strains since their early introduction in the 3 cities. This is the first report on the molecular epidemiology of HIV/HCV coinfections in Bulgaria.
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Affiliation(s)
- Lilia Ganova-Raeva
- Centers for Disease Control and Prevention, Division of Viral Hepatitis, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of Ameirca
- * E-mail:
| | - Zoya Dimitrova
- Centers for Disease Control and Prevention, Division of Viral Hepatitis, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of Ameirca
| | - Ivailo Alexiev
- National Reference Confirmatory Laboratory for HIV, National Center for Infectious and Parasitic Diseases, Sofia, Bulgaria
| | - Lili Punkova
- Centers for Disease Control and Prevention, Division of Viral Hepatitis, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of Ameirca
| | - Amanda Sue
- Centers for Disease Control and Prevention, Division of Viral Hepatitis, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of Ameirca
| | - Guo-liang Xia
- Centers for Disease Control and Prevention, Division of Viral Hepatitis, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of Ameirca
| | - Anna Gancheva
- National Reference Confirmatory Laboratory for HIV, National Center for Infectious and Parasitic Diseases, Sofia, Bulgaria
| | - Reneta Dimitrova
- National Reference Confirmatory Laboratory for HIV, National Center for Infectious and Parasitic Diseases, Sofia, Bulgaria
| | - Asya Kostadinova
- National Reference Confirmatory Laboratory for HIV, National Center for Infectious and Parasitic Diseases, Sofia, Bulgaria
| | - Elitsa Golkocheva-Markova
- National Reference Laboratory of Hepatitis, National Center for Infectious and Parasitic Diseases, Sofia, Bulgaria
| | - Yury Khudyakov
- Centers for Disease Control and Prevention, Division of Viral Hepatitis, Molecular Epidemiology and Bioinformatics, Atlanta, GA, United States of Ameirca
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Ward JW, Hinman AR. What Is Needed to Eliminate Hepatitis B Virus and Hepatitis C Virus as Global Health Threats. Gastroenterology 2019; 156:297-310. [PMID: 30391470 DOI: 10.1053/j.gastro.2018.10.048] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 10/23/2018] [Accepted: 10/30/2018] [Indexed: 02/06/2023]
Abstract
Hepatitis B virus (HBV) and hepatitis C virus (HCV) cause 1.3 million deaths annually. To prevent more than 7 million deaths by 2030, the World Health Organization set goals to eliminate HBV and HCV, defined as a 90% reduction in new infections and a 65% reduction in deaths, and prevent more than 7 million related deaths by 2030. Elimination of HBV and HCV is feasible because of characteristics of the viruses, reliable diagnostic tools, and available cost-effective or cost-saving interventions. Broad implementation of infant immunization against HBV, blood safety, and infection-control programs have greatly reduced the burden of HBV and HCV infections. To achieve elimination, priorities include implementation of HBV vaccine-based strategies to prevent perinatal transmission, safe injection practices and HCV treatment for persons who inject drugs, and testing and treatment for HBV- and HCV-infected persons. With sufficient capacity, HBV and HCV elimination programs can meet their goals.
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Affiliation(s)
- John W Ward
- The Task Force for Global Health, Decatur, Georgia; Centers for Disease Control and Prevention, Atlanta, Georgia.
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24
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Nguyen DB, Bixler D, Patel PR. Transmission of hepatitis C virus in the dialysis setting and strategies for its prevention. Semin Dial 2018; 32:127-134. [PMID: 30569604 DOI: 10.1111/sdi.12761] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Hepatitis C virus (HCV) infection is more common among hemodialysis patients than the general population and transmission of HCV in dialysis clinics has been reported. In the context of the increased morbidity and mortality associated with HCV infection in the end stage renal disease population, it is important that dialysis clinics have processes in place for ensuring recommended infection control practices, including Standard Precautions, through regular audits and training of the staff. This review will summarize the epidemiology of HCV infection and risk factors for HCV transmission among hemodialysis patients. In addition, the proper protocols are required to investigate suspected cases of HCV transmission in dialysis facilities and recommendations for prevention of HCV transmission in will be reviewed.
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Affiliation(s)
- Duc B Nguyen
- Centers for Diseases Control and Prevention, Atlanta, Georgia
| | - Danae Bixler
- Centers for Diseases Control and Prevention, Atlanta, Georgia
| | - Priti R Patel
- Centers for Diseases Control and Prevention, Atlanta, Georgia
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25
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Ramachandran S, Thai H, Forbi JC, Galang RR, Dimitrova Z, Xia GL, Lin Y, Punkova LT, Pontones PR, Gentry J, Blosser SJ, Lovchik J, Switzer WM, Teshale E, Peters P, Ward J, Khudyakov Y. A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015. EBioMedicine 2018; 37:374-381. [PMID: 30448155 PMCID: PMC6284413 DOI: 10.1016/j.ebiom.2018.10.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/02/2018] [Indexed: 12/27/2022] Open
Abstract
Background A high prevalence (92.3%) of hepatitis C virus (HCV) co-infection among HIV patients identified during a large HIV outbreak associated with injection of oxymorphone in Indiana prompted genetic analysis of HCV strains. Methods Molecular epidemiological analysis of HCV-positive samples included genotyping, sampling intra-host HVR1 variants by next-generation sequencing (NGS) and constructing transmission networks using Global Hepatitis Outbreak and Surveillance Technology (GHOST). Findings Results from the 492 samples indicate predominance of HCV genotypes 1a (72.2%) and 3a (20.4%), and existence of 2 major endemic NS5B clusters involving 49.8% of the sequenced strains. Among 76 HIV co-infected patients, 60.5% segregated into 2 endemic clusters. NGS analyses of 281 cases identified 826,917 unique HVR1 sequences and 51 cases of mixed subtype/genotype infections. GHOST mapped 23 transmission clusters. One large cluster (n = 130) included 50 cases infected with ≥2 subtypes/genotypes and 43 cases co-infected with HIV. Rapid strain replacement and superinfection with different strains were found among 7 of 12 cases who were followed up. Interpretation GHOST enabled mapping of HCV transmission networks among persons who inject drugs (PWID). Findings of numerous transmission clusters, mixed-genotype infections and rapid succession of infections with different HCV strains indicate a high rate of HCV spread. Co-localization of HIV co-infected patients in the major HCV clusters suggests that HIV dissemination was enabled by existing HCV transmission networks that likely perpetuated HCV in the community for years. Identification of transmission networks is an important step to guiding efficient public health interventions for preventing and interrupting HCV and HIV transmission among PWID. Fund US Centers for Disease Control and Prevention, and US state and local public health departments.
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Affiliation(s)
- Sumathi Ramachandran
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA.
| | - Hong Thai
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Joseph C Forbi
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Romeo Regi Galang
- Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Zoya Dimitrova
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Guo-Liang Xia
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Yulin Lin
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Lili T Punkova
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | | | | | | | | | - William M Switzer
- Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, USA
| | - Eyasu Teshale
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Philip Peters
- Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, USA
| | - John Ward
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
| | - Yury Khudyakov
- Centers for Disease Control and Prevention, Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral hepatitis, USA
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26
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Abstract
BACKGROUND Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. RESULTS The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of "chordless" cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. CONCLUSIONS Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users.
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27
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Tsyvina V, Campo DS, Sims S, Zelikovsky A, Khudyakov Y, Skums P. Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants. BMC Bioinformatics 2018; 19:360. [PMID: 30343669 PMCID: PMC6196405 DOI: 10.1186/s12859-018-2333-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Background Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. Results In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj Conclusion The proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.
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Affiliation(s)
- Viachaslau Tsyvina
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.
| | - David S Campo
- Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
| | - Seth Sims
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.,Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
| | - Alex Zelikovsky
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA
| | - Yury Khudyakov
- Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
| | - Pavel Skums
- Computer Science Department, Georgia State University, 25 Park Place NE, Atlanta, 30303, GA, USA.,Molecular Epidemiology and Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Cliffton Road, Atlanta, 30333, GA, USA
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28
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Zhang X, Liu S, Dong Q, Xin Y, Xuan S. The Genetics of Clinical Liver Diseases: Insight into the TM6SF2 E167K Variant. J Clin Transl Hepatol 2018; 6:326-331. [PMID: 30271746 PMCID: PMC6160302 DOI: 10.14218/jcth.2018.00022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 07/22/2018] [Accepted: 08/15/2018] [Indexed: 12/12/2022] Open
Abstract
The transmembrane 6 superfamily member 2 (TM6SF2) gene E167K variant (rs58542926) was identified by exome-wide association study as a nonsynonymous single nucleotide polymorphism associated with nonalcoholic fatty liver disease. The TM6SF2 E167K variant features a C-to-T substitution at nucleotide 499, encoding a glutamate with lysine change at codon 167 (E167K). TM6SF2 is markedly expressed in the liver, small intestine and kidney, and has been proposed as an important risk factor for diseases associated with lipid metabolism. Subsequently, multifunctional studies of the TM6SF2 E167K variant have been carried out in a spectrum of liver diseases, such as nonalcoholic fatty liver disease, nonalcoholic steatohepatitis, fibrosis, cirrhosis, and viral hepatitis. This review summarizes the research status of the TM6SF2 E167K variant in different liver diseases and specific populations, and discusses the potential mechanisms of the TM6SF2 E167K variant's role in the progression of various liver diseases.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Gastroenterology, Taishan Medical University, Taian, China
- Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, China
| | - Shousheng Liu
- Digestive Disease Key Laboratory of Qingdao, Qingdao, China
- Central Laboratories, Qingdao Municipal Hospital, Qingdao, China
| | - Quanjiang Dong
- Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, China
- Central Laboratories, Qingdao Municipal Hospital, Qingdao, China
| | - Yongning Xin
- Department of Gastroenterology, Taishan Medical University, Taian, China
- Department of Infectious Disease, Qingdao Municipal Hospital, Qingdao, China
- Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, China
- Digestive Disease Key Laboratory of Qingdao, Qingdao, China
- *Correspondence to: Shiying Xuan, Department of Gastroenterology, Qingdao Municipal Hospital, 1 Jiaozhou Road, Qingdao, Shandong 266011, China. Tel: +86-532-88905508, Fax: +86-532-88905293, E-mail: ; Yongning Xin, Department of Infectious Disease, Qingdao Municipal Hospital, 1 Jiaozhou Road, Qingdao, Shandong 266011, China. Tel: +86-532-82789463, Fax: +86-532-85968434, E-mail:
| | - Shiying Xuan
- Department of Gastroenterology, Taishan Medical University, Taian, China
- Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, China
- Digestive Disease Key Laboratory of Qingdao, Qingdao, China
- *Correspondence to: Shiying Xuan, Department of Gastroenterology, Qingdao Municipal Hospital, 1 Jiaozhou Road, Qingdao, Shandong 266011, China. Tel: +86-532-88905508, Fax: +86-532-88905293, E-mail: ; Yongning Xin, Department of Infectious Disease, Qingdao Municipal Hospital, 1 Jiaozhou Road, Qingdao, Shandong 266011, China. Tel: +86-532-82789463, Fax: +86-532-85968434, E-mail:
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29
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Campo DS, Khudyakov Y. Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2018; 63:204-215. [PMID: 29860098 PMCID: PMC6103852 DOI: 10.1016/j.meegid.2018.05.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 05/18/2018] [Accepted: 05/28/2018] [Indexed: 01/20/2023]
Abstract
Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection could effectively interrupt HCV transmission. PHI targeting high-risk populations, e.g., people who inject drugs (PWID), are most efficient but there is a lack of tools for prioritizing individuals within a high-risk community. Here, we present Intelligent Network DisRuption Analysis (INDRA), a targeted strategy for efficient interruption of hepatitis C transmissions.Using a large HCV transmission network among PWID in Indiana as an example, we compare effectiveness of random and targeted strategies in reducing the rate of HCV transmission in two settings: (1) long-established and (2) rapidly spreading infections (outbreak). Identification of high centrality for the network nodes co-infected with HIV or > 1 HCV subtype indicates that the network structure properly represents the underlying contacts among PWID relevant to the transmission of these infections. Changes in the network's global efficiency (GE) were used as a measure of the PHI effects. In setting 1, simulation experiments showed that a 50% GE reduction can be achieved by removing 11.2 times less nodes using targeted vs random strategies. A greater effect of targeted strategies on GE was consistently observed when networks were simulated: (1) with a varying degree of errors in node sampling and link assignment, and (2) at different levels of transmission reduction at affected nodes. In simulations considering a 10% removal of infected nodes, targeted strategies were ~2.8 times more effective than random in reducing incidence. Peer-education intervention (PEI) was modeled as a probabilistic distribution of actionable knowledge of safe injection practices from the affected node to adjacent nodes in the network. Addition of PEI to the models resulted in a 2-3 times greater reduction in incidence than from direct PHI alone. In setting 2, however, random direct PHI were ~3.2 times more effective in reducing incidence at the simulated conditions. Nevertheless, addition of PEI resulted in a ~1.7-fold greater efficiency of targeted PHI. In conclusion, targeted PHI facilitated by INDRA outperforms random strategies in decreasing circulation of long-established infections. Network-based PEI may amplify effects of PHI on incidence reduction in both settings.
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Affiliation(s)
- David S Campo
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta 30333, GA, USA.
| | - Yury Khudyakov
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta 30333, GA, USA
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30
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Lara J, Teka MA, Sims S, Xia GL, Ramachandran S, Khudyakov Y. HCV adaptation to HIV coinfection. INFECTION GENETICS AND EVOLUTION 2018; 65:216-225. [PMID: 30075255 DOI: 10.1016/j.meegid.2018.07.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 07/25/2018] [Accepted: 07/30/2018] [Indexed: 02/07/2023]
Abstract
Human immunodeficiency virus (HIV) infection is rising as a leading cause of morbidity and mortality among hepatitis C virus (HCV)-infected patients. Both viruses interact in co-infected hosts, which may affect their intra-host evolution, potentially leading to differing genetic composition of viral populations in co-infected (CIP) and mono-infected (MIP) patients. Here, we investigate genetic differences between intra-host variants of the HCV hypervariable region 1 (HVR1) sampled from CIP and MIP. Nucleotide (nt) sequences of intra-host HCV HVR1 variants (N = 28,622) obtained from CIP (N = 112) and MIP (n = 176) were represented using 148 physical-chemical (PhyChem) indexes of DNA nt dimers. Significant (p < .0001) differences in the means and frequency distributions of 7 PhyChem properties were found between HVR1 variants from both groups. Linear projection analysis of 29 PhyChem features extracted from such PhyChem properties showed that the CIP and MIP HVR1 variants have a distinct distribution in the modeled 2D-space, with only ~1.3% of PhyChem profiles (N = 6782), shared by all HVR1 variants, being found in both groups. Probabilistic neural network (PNN) and naïve Bayesian (NB) classifiers trained on the PhyChem features accurately classified HVR1 variants by the group in cross-validation experiments (AUROC ≥ 0.96). Similarly, both models showed a high accuracy (AUROC ≥ 0.95) when evaluated on a test dataset of HVR1 sequences obtained from 10 patients, data from whom were not used for model building. Both models performed at the expected lower accuracy on randomly labeled datasets in cross-validation experiments (AUROC = 0.50). The random-label trained PNN showed a similar drop in accuracy on the test dataset (AUROC = 0.48), indicating that the detected associations were unlikely due to random correlations. Marked differences in genetic composition of HCV HVR1 variants sampled from CIP and MIP suggest differing intra-host HCV evolution in the presence of HIV infection. PhyChem features identified here may be used for detection of HIV infection from intra-host HCV variants alone in co-infected patients, thus facilitating monitoring for HIV introduction to high-risk populations with high HCV prevalence.
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Affiliation(s)
- James Lara
- Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, United States.
| | - Mahder A Teka
- Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, United States
| | - Seth Sims
- Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, United States
| | - Guo-Liang Xia
- Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, United States
| | - Sumathi Ramachandran
- Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, United States
| | - Yury Khudyakov
- Centers for Disease Control, 1600 Clifton Road, Atlanta, GA 30333, United States
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