Accurate Genetic Detection of Hepatitis C Virus Transmissions in Outbreak Settings

Widespread hepatitis C virus transmission network among people who inject drugs in Kenya

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 and towns 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%), 4a (n = 72, 51.8%) and 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 sites.

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 clustered strains from Nairobi and Coast suggest successful introduction of two ancestral HCV/1a and HCV/4a strains to PWID and the existence of a widespread transmission network in the country. The disruption of this network is essential for HCV elimination.

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

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.

Validation of dried blood spots for capturing hepatitis C virus diversity for genomic surveillance

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.

Reconstruction of Viral Variants via Monte Carlo Clustering

Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate these methods on four benchmarks: two SARS-CoV-2 interhost data sets and two HIV intrahost data sets. A parallelized version of our tool is scalable to very large data sets. We show that both entropy and Hamming distance-based MC clusterings discern the meaningful information from sequencing data. The proposed clustering methods consistently converge to similar clusterings across different runs. Finally, we show that MC clustering improves reconstruction of intrahost viral population from sequencing data.

The distribution of hepatitis C viral genotypes shifted among chronic hepatitis C patients in Yunnan, China, between 2008-2018

Object

The hepatitis C virus (HCV) is prevalent across China, with a distinctive genotypic distribution that varies by geographical region and mode of transmission. Yunnan is one such geographical region wherein the local population continues to experience a high level of HCV infection, severely straining public health resources. This high prevalence is likely due to the increased incidence of intravenous drug use in that region, as Yunnan is a major point of entry for illegal heroin into China.

Methods

We investigated 510 individuals with chronic HCV infections in Yunnan Province from 2008 through 2018. Using reverse transcription PCR and Sanger sequencing to amplify and sequence samples. Bayesian analyses was performed to estimate the common ancestors and Bayesian skyline plot to estimate the effective viral population size. Molecular network was conducted to explore the characteristics of HCV transmission.

Results

We successfully amplified and sequenced a total of 503 viral samples and genotyped each as either 3b (37.6%), 3a (21.9%), 1b (19.3%), 2a (10.5%), HCV-6 (10.1%), or 1a (0.6%). Over this 11-year period, we observed that the proportion of 3a and 3b subtypes markedly increased and, concomitantly, that the proportion of 1b and 2a subtypes decreased. We also performed Bayesian analyses to estimate the common ancestors of the four major subtypes, 1b, 2a, 3a, and 3b. Finally, we determined that our Bayesian skyline plot and transmission network data correlated well with the changes we observed in the proportions of HCV subtypes over time.

Conclusions

Taken together, our results indicate that the prevalence of HCV 3a and 3b subtypes is rapidly increasing in Yunnan, thus demonstrating a steadily growing public health requirement to implement more stringent preventative and therapeutic measures to curb the spread of the virus.

Validation of Dried Blood Spots for Capturing Hepatitis C Virus Diversity for Genomic Surveillance

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.

Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data

Understanding the patterns of viral disease transmissions helps establish public health policies and aids in controlling and ending a disease outbreak. Classical methods for studying disease transmission dynamics that rely on epidemiological data, such as times of sample collection and duration of exposure intervals, struggle to provide desired insight due to limited informativeness of such data. A more precise characterization of disease transmissions may be acquired from sequencing data that reveal genetic distance between viral genomes in patient samples. Indeed, genetic distance between viral strains present in hosts contains valuable information about transmission history, thus motivating the design of methods that rely on genomic data to reconstruct a directed disease transmission network, detect transmission clusters, and identify significant network nodes (e.g., super-spreaders). In this article, we present a novel end-to-end framework for the analysis of viral transmissions utilizing viral genomic (sequencing) data. The proposed framework groups infected hosts into transmission clusters based on the reconstructed viral strains infecting them; the genetic distance between a pair of hosts is calculated using Earth Mover's Distance, and further used to infer transmission direction between the hosts. To quantify the significance of a host in the transmission network, the importance score is calculated by a graph convolutional autoencoder. The viral transmission network is represented by a directed minimum spanning tree utilizing the Edmond's algorithm modified to incorporate constraints on the importance scores of the hosts. The proposed framework outperforms state-of-the-art techniques for the analysis of viral transmission dynamics in several experiments on semiexperimental as well as experimental data.

Polyvalent immunization elicits a synergistic broadly neutralizing immune response to hypervariable region 1 variants of hepatitis C virus

A hepatitis C virus (HCV) vaccine is urgently needed. Vaccine development has been hindered by HCV's genetic diversity, particularly within the immunodominant hypervariable region 1 (HVR1). Here, we developed a strategy to elicit broadly neutralizing antibodies to HVR1, which had previously been considered infeasible. We first applied a unique information theory-based measure of genetic distance to evaluate phenotypic relatedness between HVR1 variants. These distances were used to model the structure of HVR1's sequence space, which was found to have five major clusters. Variants from each cluster were used to immunize mice individually, and as a pentavalent mixture. Sera obtained following immunization neutralized every variant in a diverse HCVpp panel (n = 10), including those resistant to monovalent immunization, and at higher mean titers (1/ID50 = 435) than a glycoprotein E2 (1/ID50 = 205) vaccine. This synergistic immune response offers a unique approach to overcoming antigenic variability and may be applicable to other highly mutable viruses.

Dynamic Epidemiological Networks: A Data Representation Framework for Modeling and Tracking of SARS-CoV-2 Variants

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.

Hepatitis C virus transmission cluster among injection drug users in Pakistan

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.

Primary case inference in viral outbreaks through analysis of intra-host variant population

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.

Scale-Free Spanning Trees and Their Application in Genomic Epidemiology

We study the algorithmic problem of finding the most “scale-free-like” spanning tree of a connected graph. This problem is motivated by the fundamental problem of genomic epidemiology: given viral genomes sampled from infected individuals, reconstruct the transmission network (“who infected whom”). We use two possible objective functions for this problem and introduce the corresponding algorithmic problems termedm-SF (-scale free) ands-SF Spanning Tree problems. We prove that those problems are APX- and NP-hard, respectively, even in the classes of cubic and bipartite graphs. We propose two integer linear programming (ILP) formulations for thes-SF Spanning Tree problem, and experimentally assess its performance using simulated and experimental data. In particular, we demonstrate that the ILP-based approach allows for accurate reconstruction of transmission histories of several hepatitis C outbreaks.

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

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.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient’s treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Updated Pathway to Micro-elimination of Hepatitis C Virus in the Hemodialysis Population

Chronic hepatitis C virus (HCV) infection continues to be transmitted to hemodialysis (HD) patients within HD facilities globally. The goal of the World Health Organization to micro-eliminate HCV infection from the HD population by the year 2030 is not on target to be achieved. Obstacles to eliminate HCV in HD settings remain daunting due to a complex system created by a confluence of guidelines, legislation, regulation, and economics. HCV prevalence remains high and seroconversion continues among the HD patient population globally as a result of the HD procedure. Preventive strategies that effectively prevent HCV transmission, treatment-as-prevention, and rapid referral to treatment balanced with kidney transplant candidacy should be added to the current universal precautions approach. A safer system must be designed before HCV transmission can be halted and eliminated from the HD population.

Hepatitis C Virus Transmission Clusters in Public Health and Correctional Settings, Wisconsin, USA, 2016-20171

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.

Outbreak of hepatitis B and hepatitis C virus infections associated with a cardiology clinic, West Virginia, 2012-2014

OBJECTIVE

To stop transmission of hepatitis B virus (HBV) and hepatitis C virus (HCV) infections in association with myocardial perfusion imaging (MPI) at a cardiology clinic.

DESIGN

Outbreak investigation and quasispecies analysis of HCV hypervariable region 1 genome.

SETTING

Outpatient cardiology clinic.

PATIENTS

Patients undergoing MPI.

METHODS

Case patients met definitions for HBV or HCV infection. Cases were identified through surveillance registry cross-matching against clinic records and serological screening. Observations of clinic practices were performed.

RESULTS

During 2012-2014, 7 cases of HCV and 4 cases of HBV occurred in 4 distinct clusters among patients at a cardiology clinic. Among 3 case patients with HCV infection who had MPI on June 25, 2014, 2 had 98.48% genetic identity of HCV RNA. Among 4 case patients with HCV infection who had MPI on March 13, 2014, 3 had 96.96%-99.24% molecular identity of HCV RNA. Also, 2 clusters of 2 patients each with HBV infection had MPI on March 7, 2012, and December 4, 2014. Clinic staff reused saline vials for >1 patient. No infection control breaches were identified at the compounding pharmacy that supplied the clinic. Patients seen in clinic through March 27, 2015, were encouraged to seek testing for HBV, HCV, and human immunodeficiency virus. The clinic switched to all single-dose medications and single-use intravenous flushes on March 27, 2015, and no further cases were identified.

CONCLUSIONS

This prolonged healthcare-associated outbreak of HBV and HCV was most likely related to breaches in injection safety. Providers should follow injection safety guidelines in all practice settings.

A Phylogenetic Analysis of Hepatitis C Virus Transmission, Relapse, and Reinfection Among People Who Inject Drugs Receiving Opioid Agonist Therapy

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.

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era

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.

Quantitative differences between intra-host HCV populations from persons with recently established and persistent infections

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.

Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences

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.

Analysis of heterogeneous genomic samples using image normalization and machine learning

BACKGROUND

Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures.

RESULTS

We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.

CONCLUSIONS

Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

Using earth mover's distance for viral outbreak investigations

Background

RNA viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants, which allows them to evade the host’s immune system and makes them particularly dangerous. Viral outbreaks pose a significant threat for public health, and, in order to deal with it, it is critical to infer transmission clusters, i.e., decide whether two viral samples belong to the same outbreak. Next-generation sequencing (NGS) can significantly help in tackling outbreak-related problems. While NGS data is first obtained as short reads, existing methods rely on assembled sequences. This requires reconstruction of the entire viral population, which is complicated, error-prone and time-consuming.

Results

The experimental validation using sequencing data from HCV outbreaks shows that the proposed algorithm can successfully identify genetic relatedness between viral populations, infer transmission direction, transmission clusters and outbreak sources, as well as decide whether the source is present in the sequenced outbreak sample and identify it.

Conclusions

Introduced algorithm allows to cluster genetically related samples, infer transmission directions and predict sources of outbreaks. Validation on experimental data demonstrated that algorithm is able to reconstruct various transmission characteristics. Advantage of the method is the ability to bypass cumbersome read assembly, thus eliminating the chance to introduce new errors, and saving processing time by allowing to use raw NGS reads.

Shared HCV Transmission Networks Among HIV-1-Positive and HIV-1-Negative Men Having Sex With Men by Ultradeep Sequencing

OBJECTIVE

Several studies reported hepatitis C virus (HCV) transmission networks among men having sex with men (MSM) in Europe and the spread of HCV strains from HIV-HCV coinfected toward HCV monoinfected MSM. We aimed to investigate HCV transmission dynamics among HIV-positive and HIV-negative MSM by ultradeep sequencing (UDS).

DESIGN AND METHODS

NS5B fragment (388 bp) was sequenced from virus of 50 HIV-positive and 18 HIV-negative patients diagnosed with recent HCV infection. UDS data were analyzed by Geneious (version 10.3.2). Phylogenetic trees were constructed by FastTree (version 2.1) and submitted to ClusterPicker (version 1.2.3) for transmission chain detection at different thresholds of maximum genetic distance (MGD) (3% for Sanger, 3% and 4.5% for UDS).

RESULTS

Ten, 17, and 18 HCV transmission chains were identified by Sanger at 3%, UDS at 3% and at 4.5% of MGD, respectively. Of 68 subjects enrolled, 38 (55.9%), 38 (55.9%), and 43 (65.3%) individuals were involved in transmission networks found by Sanger at 3%, UDS at 3%, and at 4.5% of MGD, respectively. Mixed transmission chains including HIV-positive and HIV-negative subjects were detected for 8/10 chains by Sanger at 3%, for 9/17 by UDS at 3%, and for 10/18 by UDS at 4.5% of MGD. Overall, the number of HIV-negative individuals clustering with HIV-positive ones was 9/18 by Sanger, 9/18 by UDS at 3%, and 10/18 by UDS at 4.5% of MGD.

CONCLUSIONS

HIV-positive and HIV-negative MSM shared HCV transmission networks, which emphasizes the need for HCV surveillance and prevention measures in these communities regardless of the HIV status.

Rapid and Sensitive Detection of Hepatitis C Virus in Clinical Blood Samples Using Reverse Transcriptase Polymerase Spiral Reaction

This study established a new polymerase spiral reaction (PSR) that combines with reverse transcription reactions for HCV detection targeting 5'UTR gene. To avoid cross-contamination of aerosols, an isothermal amplification tube (IAT), as a separate containment control, was used to judge the result. After optimizing the RT-PSR reaction system, its effectiveness and specificity were tested against 15 different virus strains which included 8 that were HCV positive and 7 as non-HCV controls. The results showed that the RT-PSR assay effectively detected all 8 HCV strains, and no false positives were found among the 7 non-HCV strains. The detection limit of our RT-PSR assay is comparable to the real-time RT-PCR, but is more sensitive than the RT-LAMP. The established RT-PSR assay was further evaluated for detection of HCV in clinical blood samples, and the resulting 80.25% detection rate demonstrated better or similar effectiveness compared to the RT-LAMP (79.63%) and real-time RT-PCR (80.25%). Overall, the results showed that the RT-PSR assay offers high specificity and sensitivity for HCV detection with great potential for screening HCV in clinical blood samples.

Global transmission network of SARS-CoV-2: from outbreak to pandemic

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.

HCV transmission in high-risk communities in Bulgaria

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.

A latent class approach to identify multi-risk profiles associated with phylogenetic clustering of recent hepatitis C virus infection in Australia and New Zealand from 2004 to 2015

INTRODUCTION

Over the last two decades, the incidence of hepatitis C virus (HCV) co-infection among men who have sex with men (MSM) living with HIV began increasing in post-industrialized countries. Little is known about transmission of acute or recent HCV, in particular among MSM living with HIV co-infection, which creates uncertainty about potential for reinfection after HCV treatment. Using phylogenetic methods, clinical, epidemiological and molecular data can be combined to better understand transmission patterns. These insights may help identify strategies to reduce reinfection risk, enhancing effectiveness of HCV treatment as prevention strategies. The aim of this study was to identify multi-risk profiles and factors associated with phylogenetic pairs and clusters among people with recent HCV infection.

METHODS

Data and specimens from five studies of recent HCV in Australia and New Zealand (2004 to 2015) were used. HCV Core-E2 sequences were used to infer maximum likelihood trees. Clusters were identified using 90% bootstrap and 5% genetic distance threshold. Multivariate logistic regression and latent class analyses were performed.

RESULTS

Among 237 participants with Core-E2 sequences, 47% were in a pair/cluster. Among HIV/HCV co-infected participants, 60% (74/123) were in a pair/cluster, compared to 30% (34/114) with HCV mono-infection (p < 0.001). HIV/HCV co-infection (vs. HCV mono-infection; adjusted odds ratio (AOR), 2.37, 95% confidence interval (CI), 1.45, 5.15) was independently associated with phylogenetic clustering. Latent class analysis identified three distinct risk profiles: (1) people who inject drugs, (2) HIV-positive gay and bisexual men (GBM) with low probability of injecting drug use (IDU) and (3) GBM with IDU & sexual risk behaviour. Class 2 (vs. Class 1, AOR 3.40; 95% CI, 1.52, 7.60), was independently associated with phylogenetic clustering. Many clusters displayed homogeneous characteristics, such as containing individuals exclusively from one city, individuals all with HIV/HCV co-infection or individuals sharing the same route of acquisition of HCV.

CONCLUSIONS

Clusters containing individuals with specific characteristics suggest that HCV transmission occurs through discrete networks, particularly among HIV/HCV co-infected individuals. The greater proportion of clustering found among HIV/HCV co-infected participants highlights the need to provide broad direct-acting antiviral access encouraging rapid uptake in this population and ongoing monitoring of the phylogeny.

Insights From Deep Sequencing of the HBV Genome-Unique, Tiny, and Misunderstood

Hepatitis B virus (HBV) is a unique, tiny, partially double-stranded, reverse-transcribing DNA virus with proteins encoded by multiple overlapping reading frames. The substitution rate is surprisingly high for a DNA virus, but lower than that of other reverse transcribing organisms. More than 260 million people worldwide have chronic HBV infection, which causes 0.8 million deaths a year. Because of the high burden of disease, international health agencies have set the goal of eliminating HBV infection by 2030. Nonetheless, the intriguing HBV genome has not been well characterized. We summarize data on the HBV genome structure and replication cycle, explain and quantify diversity within and among infected individuals, and discuss advances that can be offered by application of next-generation sequencing technology. In-depth HBV genome analyses could increase our understanding of disease pathogenesis and allow us to better predict patient outcomes, optimize treatment, and develop new therapeutics.

Transmission of hepatitis C virus in the dialysis setting and strategies for its prevention

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.

A large HCV transmission network enabled a fast-growing HIV outbreak in rural Indiana, 2015

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.

Automated quality control for a molecular surveillance system

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.

Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants

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.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data

Motivation

Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use.

Results

The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN.

Contact

pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.edu or yek0@cdc.gov.

Supplementary information

Supplementary data are available at Bioinformatics online.

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions

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.

A large healthcare-associated outbreak of hepatitis C virus genotype 1a in a clinic in Korea

BACKGROUND

In November 2015, reuse of needles and syringes in conjunction with an increase in cases of HCV at a clinic in Korea was reported and investigated by public health authorities. Patients who received injections at the clinic from the first time this infection control breach may have occurred in 2008 through 2015 when the practice was stopped were offered screening for HCV and other blood-borne pathogens such as HIV, HTLV, HBV, syphilis, and malaria.

OBJECTIVES

The aim of this study was to assess whether an outbreak of hepatitis C had occurred among the potentially exposed clinic patients due to this infection control breach.

STUDY DESIGN

We performed hepatitis C viral RNA load tests and genotyping using plasma from hepatitis C antibody-positive individuals who had visited the clinic between May 2008 and November 2015. We analyzed the core-E2 and NS5B regions of the virus from RNA-positive samples by constructing a phylogenetic tree based on maximum likelihood analysis. To identify transmission risk factors and epidemiological relationships among the patients, we reviewed their medical records, assessed staff infection control practices and performed environmental inspection of the clinic. Environmental samples from medication room surfaces and medication vial contents were tested for HCV RNA.

RESULTS AND CONCLUSIONS

Among the 1721 patients tested, 96 were IgG-positive and 70 were viral RNA-positive. Among the 61 patients whose viral loads were greater than the detection limit, 41 (67.2%) were classified as genotype 1a, 1 (1.6%) as genotype 1b, 18 (29.5%) as genotype 1, and one (1.6%) as genotype 2. After sequencing, 12 genotype 1 cases were further classified as genotype 1a (11) or 1b (1). The sequences of the core-E2 and NS5B regions of 45 patients formed a monophyletic cluster distinct from genotype 1a. The hepatitis C virus sequences from patients and environmental specimens were well-matched in the partial E1 gene region. We detected genotype 1a RNA in environmental specimens, indicating a healthcare-associated outbreak caused by reuse of syringes and contaminated multi-dose vials. Our molecular epidemiological investigation of hepatitis C genotype 1a, rare in Korea, will aid investigations of infection sources during future pathogen outbreaks.

Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center

Molecular characterization of early hepatitis C virus (HCV) infection remains rare. Ten out of 78 patients of a hematology/oncology center were found to be HCV RNA positive two to four months after hospitalization. Only two of the ten patients were anti-HCV positive. HCV hypervariable region 1 (HVR1) was amplified in seven patients (including one anti-HCV positive) and analyzed by next generation sequencing (NGS). Genetic variants were reconstructed by Shorah and an empirically established 0.5% variant frequency cut-off was implemented. These sequences were compared by phylogenetic and diversity analyses. Ten unrelated blood donors with newly acquired HCV infection detected at the time of donation (HCV RNA positive and anti-HCV negative) served as controls. One to seven HVR1 variants were found in each patient. Sequences intermixed phylogenetically with no evidence of clustering in individual patients. These sequences were more similar to each other (similarity 95.4% to 100.0%) than to those of controls (similarity 64.8% to 82.6%). An identical predominant variant was present in four patients, whereas other closely related variants dominated in the remaining three patients. In five patients the HCV population was limited to a single variant or one predominant variant and minor variants of less than 10% frequency. In conclusion, NGS analysis of a cluster of HCV infections acquired in the hospital setting revealed the presence of low diversity, very closely related variants in all patients, suggesting an early-stage infection with the same virus. NGS combined with phylogenetic analysis and classical epidemiological analysis could help in tracking of HCV outbreaks.

Inference of genetic relatedness between viral quasispecies from sequencing data

BACKGROUND

RNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations.

RESULTS

We present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters' structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks.

CONCLUSIONS

All algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.

GHOST: global hepatitis outbreak and surveillance technology

Background

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Effective HCV outbreak investigation requires comprehensive surveillance and robust case investigation. We previously developed and validated a methodology for the rapid and cost-effective identification of HCV transmission clusters. Global Hepatitis Outbreak and Surveillance Technology (GHOST) is a cloud-based system enabling users, regardless of computational expertise, to analyze and visualize transmission clusters in an independent, accurate and reproducible way.

Results

We present and explore performance of several GHOST implemented algorithms using next-generation sequencing data experimentally obtained from hypervariable region 1 of genetically related and unrelated HCV strains. GHOST processes data from an entire MiSeq run in approximately 3 h. A panel of seven specimens was used for preparation of six repeats of MiSeq libraries. Testing sequence data from these libraries by GHOST showed a consistent transmission linkage detection, testifying to high reproducibility of the system. Lack of linkage among genetically unrelated HCV strains and constant detection of genetic linkage between HCV strains from known transmission pairs and from follow-up specimens at different levels of MiSeq-read sampling indicate high specificity and sensitivity of GHOST in accurate detection of HCV transmission.

Conclusions

GHOST enables automatic extraction of timely and relevant public health information suitable for guiding effective intervention measures. It is designed as a virtual diagnostic system intended for use in molecular surveillance and outbreak investigations rather than in research. The system produces accurate and reproducible information on HCV transmission clusters for all users, irrespective of their level of bioinformatics expertise. Improvement in molecular detection capacity will contribute to increasing the rate of transmission detection, thus providing opportunity for rapid, accurate and effective response to outbreaks of hepatitis C. Although GHOST was originally developed for hepatitis C surveillance, its modular structure is readily applicable to other infectious diseases. Worldwide availability of GHOST for the detection of HCV transmissions will foster deeper involvement of public health researchers and practitioners in hepatitis C outbreak investigation.

Transmissibility of intra-host hepatitis C virus variants

Background

Intra-host hepatitis C virus (HCV) populations are genetically heterogeneous and organized in subpopulations. With the exception of blood transfusions, transmission of HCV occurs via a small number of genetic variants, the effect of which is frequently described as a bottleneck. Stochasticity of transmission associated with the bottleneck is usually used to explain genetic differences among HCV populations identified in the source and recipient cases, which may be further exacerbated by intra-host HCV evolution and differential biological capacity of HCV variants to successfully establish a population in a new host.

Results

Transmissibility was formulated as a property that can be measured from experimental Ultra-Deep Sequencing (UDS) data. The UDS data were obtained from one large hepatitis C outbreak involving an epidemiologically defined source and 18 recipient cases. k-Step networks of HCV variants were constructed and used to identify a potential association between transmissibility and network centrality of individual HCV variants from the source. An additional dataset obtained from nine other HCV outbreaks with known directionality of transmission was used for validation.

Transmissibility was not found to be dependent on high frequency of variants in the source, supporting the earlier observations of transmission of minority variants. Among all tested measures of centrality, the highest correlation of transmissibility was found with Hamming centrality (r = 0.720; p = 1.57 E-71). Correlation between genetic distances and differences in transmissibility among HCV variants from the source was found to be 0.3276 (Mantel Test, p = 9.99 E-5), indicating association between genetic proximity and transmissibility. A strong correlation ranging from 0.565–0.947 was observed between Hamming centrality and transmissibility in 7 of the 9 additional transmission clusters (p < 0.05).

Conclusions

Transmission is not an exclusively stochastic process. Transmissibility, as formally measured in this study, is associated with certain biological properties that also define location of variants in the genetic space occupied by the HCV strain from the source. The measure may also be applicable to other highly heterogeneous viruses. Besides improving accuracy of outbreak investigations, this finding helps with the understanding of molecular mechanisms contributing to establishment of chronic HCV infection.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis—where individuals are sampled sooner post-infection—rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.

Many pathogens evolve so rapidly that they accumulate genetic differences within a host before becoming transmitted to the next host. Consequently, clusters of sampled infections with nearly identical genomes may reveal outbreaks of recent or ongoing transmissions. There is rapidly growing interest in using model-free genetic clustering methods to guide public health responses to epidemics in near real-time, including HIV, Ebola virus and tuberculosis. However, we show that current methods are relatively ineffective at detecting transmission outbreaks; instead, they are predominantly influenced by how infections are sampled from the population. We describe a fundamentally new approach to genetic clustering that is based on modelling changes in transmission rates during the spread of the epidemic. We use simulated and real pathogen sequence data sets to demonstrate that this model-based approach is substantially more effective for detecting transmission outbreaks, and remains fast enough for real-time applications to large sequence databases.

Efficient detection of viral transmissions with Next-Generation Sequencing data

BACKGROUND

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains; helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. However, HCV exists as a population of numerous variants in each infected individual and it has been observed that minority variants in the source are often the ones responsible for transmission, a situation that precludes the use of a single sequence per individual because many such transmissions would be missed. The use of Next-Generation Sequencing immensely increases the sensitivity of transmission detection but brings a considerable computational challenge because all sequences need to be compared among all pairs of samples.

METHODS

We developed a three-step strategy that filters pairs of samples according to different criteria: (i) a k-mer bloom filter, (ii) a Levenhstein filter and (iii) a filter of identical sequences. We applied these three filters on a set of samples that cover the spectrum of genetic relationships among HCV cases, from being part of the same transmission cluster, to belonging to different subtypes.

RESULTS

Our three-step filtering strategy rapidly removes 85.1% of all the pairwise sample comparisons and 91.0% of all pairwise sequence comparisons, accurately establishing which pairs of HCV samples are below the relatedness threshold.

CONCLUSIONS

We present a fast and efficient three-step filtering strategy that removes most sequence comparisons and accurately establishes transmission links of any threshold-based method. This highly efficient workflow will allow a faster response and molecular detection capacity, improving the rate of detection of viral transmissions with molecular data.

Understanding the complex evolution of rapidly mutating viruses with deep sequencing: Beyond the analysis of viral diversity

With the advent of affordable deep sequencing technologies, detection of low frequency variants within genetically diverse viral populations can now be achieved with unprecedented depth and efficiency. The high-resolution data provided by next generation sequencing technologies is currently recognised as the gold standard in estimation of viral diversity. In the analysis of rapidly mutating viruses, longitudinal deep sequencing datasets from viral genomes during individual infection episodes, as well as at the epidemiological level during outbreaks, now allow for more sophisticated analyses such as statistical estimates of the impact of complex mutation patterns on the evolution of the viral populations both within and between hosts. These analyses are revealing more accurate descriptions of the evolutionary dynamics that underpin the rapid adaptation of these viruses to the host response, and to drug therapies. This review assesses recent developments in methods and provide informative research examples using deep sequencing data generated from rapidly mutating viruses infecting humans, particularly hepatitis C virus (HCV), human immunodeficiency virus (HIV), Ebola virus and influenza virus, to understand the evolution of viral genomes and to explore the relationship between viral mutations and the host adaptive immune response. Finally, we discuss limitations in current technologies, and future directions that take advantage of publically available large deep sequencing datasets.

Increased Mitochondrial Genetic Diversity in Persons Infected With Hepatitis C Virus

BACKGROUND & AIMS

The host genetic environment contributes significantly to the outcomes of hepatitis C virus (HCV) infection and therapy response, but little is known about any effects of HCV infection on the host beyond any changes related to adaptive immune responses. HCV persistence is associated strongly with mitochondrial dysfunction, with liver mitochondrial DNA (mtDNA) genetic diversity linked to disease progression.

METHODS

We evaluated the genetic diversity of 2 mtDNA genomic regions (hypervariable segments 1 and 2) obtained from sera of 116 persons using next-generation sequencing.

RESULTS

Results were as follows: (1) the average diversity among cases with seronegative acute HCV infection was 4.2 times higher than among uninfected controls; (2) the diversity level among cases with chronic HCV infection was 96.1 times higher than among uninfected controls; and (3) the diversity was 23.1 times higher among chronic than acute cases. In 2 patients who were followed up during combined interferon and ribavirin therapy, mtDNA nucleotide diversity decreased dramatically after the completion of therapy in both patients: by 100% in patient A after 54 days and by 70.51% in patient B after 76 days.

CONCLUSIONS

HCV infection strongly affects mtDNA genetic diversity. A rapid decrease in mtDNA genetic diversity observed after therapy-induced HCV clearance suggests that the effect is reversible, emphasizing dynamic genetic relationships between HCV and mitochondria. The level of mtDNA nucleotide diversity can be used to discriminate recent from past infections, which should facilitate the detection of recent transmission events and thus help identify modes of transmission.

Next-Generation Sequencing Reveals Frequent Opportunities for Exposure to Hepatitis C Virus in Ghana

Globally, hepatitis C Virus (HCV) infection is responsible for a large proportion of persons with liver disease, including cancer. The infection is highly prevalent in sub-Saharan Africa. West Africa was identified as a geographic origin of two HCV genotypes. However, little is known about the genetic composition of HCV populations in many countries of the region. Using conventional and next-generation sequencing (NGS), we identified and genetically characterized 65 HCV strains circulating among HCV-positive blood donors in Kumasi, Ghana. Phylogenetic analysis using consensus sequences derived from 3 genomic regions of the HCV genome, 5'-untranslated region, hypervariable region 1 (HVR1) and NS5B gene, consistently classified the HCV variants (n = 65) into genotypes 1 (HCV-1, 15%) and genotype 2 (HCV-2, 85%). The Ghanaian and West African HCV-2 NS5B sequences were found completely intermixed in the phylogenetic tree, indicating a substantial genetic heterogeneity of HCV-2 in Ghana. Analysis of HVR1 sequences from intra-host HCV variants obtained by NGS showed that three donors were infected with >1 HCV strain, including infections with 2 genotypes. Two other donors share an HCV strain, indicating HCV transmission between them. The HCV-2 strain sampled from one donor was replaced with another HCV-2 strain after only 2 months of observation, indicating rapid strain switching. Bayesian analysis estimated that the HCV-2 strains in Ghana were expanding since the 16^th century. The blood donors in Kumasi, Ghana, are infected with a very heterogeneous HCV population of HCV-1 and HCV-2, with HCV-2 being prevalent. The detection of three cases of co- or super-infections and transmission linkage between 2 cases suggests frequent opportunities for HCV exposure among the blood donors and is consistent with the reported high HCV prevalence. The conditions for effective HCV-2 transmission existed for ~ 3–4 centuries, indicating a long epidemic history of HCV-2 in Ghana.