Differentiation of acute from chronic hepatitis C virus infection by nonstructural 5B deep sequencing: a population-level tool for incidence estimation

Characterization of Acute HCV Infection and Transmission Networks in People Who Currently Inject Drugs in Catalonia: Usefulness of Dried Blood Spots

BACKGROUND AND AIMS

Accurate identification of recent HCV infections is critical for tracing the extent and mechanisms of ongoing transmission. We aimed to validate dried blood spot (DBS) samples for the assessment of Hepatitis C virus (HCV) genetic diversity and to determine epidemiological parameters including incidence, determinants of acute infection, and phylogenetic clustering in people who inject drugs (PWID).

APPROACH AND RESULTS

HCV nonstructural protein 5B next-generation sequencing was performed from plasma and/or DBS in 220 viremic PWID from the HepCdetect II study. No significant differences were found in consensus sequences or Shannon entropy (SE) intrahost diversity estimate between paired plasma/DBS specimens. SE values were used to identify acute infections with 93.3% sensitivity (95% CI, 0.81-1.06) and 95.0% specificity (95% CI, 0.88-1.02) in a set of well-defined controls. An acute HCV infection (either primary infection or reinfection) was detected in 13.5% of viremic participants and was associated with age ≤30 years (OR, 8.09), injecting less than daily (OR, 4.35), ≤5 years of injected drug use (OR, 3.43), sharing cocaine snorting straws (OR, 2.89), and being unaware of their HCV status (OR, 3.62). Annualized HCV incidence was estimated between 31 and 59/100 person-years. On phylogenetic analysis, 46.8% of viremic cases were part of a transmission pair or cluster; age ≤30 years (OR, 6.16), acute infection (OR, 5.73), and infection with subtype 1a (OR, 4.78) were independently associated with this condition.

CONCLUSIONS

The results obtained from plasma and DBS characterize PWID with acute infection and those involved in ongoing HCV transmission and allow estimating incidence from cross-sectional data. This information is critical for the design and assessment of targeted harm reduction programs and test-and-treat interventions and to facilitate monitoring of HCV elimination in this key population.

Quantitative measures of within-host viral genetic diversity

The genetic diversity of virus populations within their hosts is known to influence disease progression, treatment outcome, drug resistance, cell tropism, and transmission risk, and the study of dynamic changes of genetic heterogeneity can provide insights into the evolution of viruses. Several measures to quantify within-host genetic diversity capturing different aspects of diversity patterns in a sample or population are used, based on incidence, relative frequencies, pairwise distances, or phylogenetic trees. Here, we review and compare several of these measures.

Intra-host evolutionary dynamics of the hepatitis C virus among people who inject drugs

Most individuals chronically infected with hepatitis C virus (HCV) are asymptomatic during the initial stages of infection and therefore the precise timing of infection is often unknown. Retrospective estimation of infection duration would improve existing surveillance data and help guide treatment. While intra-host viral diversity quantifications such as Shannon entropy have previously been utilized for estimating duration of infection, these studies characterize the viral population from only a relatively short segment of the HCV genome. In this study intra-host diversities were examined across the HCV genome in order to identify the region most reflective of time and the degree to which these estimates are influenced by high-risk activities including those associated with HCV acquisition. Shannon diversities were calculated for all regions of HCV from 78 longitudinally sampled individuals with known seroconversion timeframes. While the region of the HCV genome most accurately reflecting time resided within the NS3 gene, the gene region with the highest capacity to differentiate acute from chronic infections was identified within the NS5b region. Multivariate models predicting duration of infection from viral diversity significantly improved upon incorporation of variables associated with recent public, unsupervised drug use. These results could assist the development of strategic population treatment guidelines for high-risk individuals infected with HCV and offer insights into variables associated with a likelihood of transmission.

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.

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.

Genomic variability of within-host hepatitis C variants in acute infection

Interactions between the host immune system and the viral variants determine persistence of hepatitis C virus (HCV) infection after the acute phase of infection. This study describes the genetic variability of within-host HCV viral variants in acute infection and correlates it with host- and virus-related traits and infection outcome. Next generation sequence data (Illumina, MiSeq platform) of viral genomes from 116 incident acute infections (within 180 days of infection) were analysed to determine all the single nucleotide polymorphism (SNP) frequencies above a threshold of 0.1%. The variability of the SNPs for the full open reading frame of the genome as well as for each protein coding region were compared using mean standardized Shannon entropy (SE) values calculated separately for synonymous and nonsynonymous mutations. The envelope glycoproteins regions (E1 and E2) had the highest SE values (indicating greater variability) followed by the NS5B region. Nonsynonymous mutations rather than synonymous mutations were the main contributors to genomic variability in acute infection. The mean difference of Shannon entropy was also compared between subjects after categorizing the samples according to host and virus-related traits. Host IFNL3 allele CC polymorphism at rs12979860 (vs others) and viral genotype 1a (vs 3a) were associated with higher genomic variability across the viral open reading frame. Time since infection, host gender or continent of origin was not associated with the viral genomic variability. Viral genomic variability did not predict spontaneous clearance.

Contextualizing Canada's hepatitis C virus epidemic

In 2016, Canada signed on to the World Health Organization (WHO) 2030 hepatitis C virus (HCV) disease elimination targets. Most of Canada's HCV disease burden is among five disproportionately affected population groups: 1) Baby boomers, who are at increased risk of dying from decompensated cirrhosis and hepatocellular carcinoma and for whom one-time screening should be recommended to identify those undiagnosed; 2) People who inject drugs (PWID), whose mortality risks include HCV infection, HCV acquisition risks and co-morbid conditions. While HCV infection in PWID can be effectively cured with direct-acting antivirals, premature deaths from acquisition risks, now exacerbated by Canada's opioid crisis, will need to be addressed to achieve the full benefits of curative treatment. PWID require syndemic-based solutions (harm reduction, addictions and mental health support, and management of co-infections, including HIV); 3) Indigenous populations who will require wellness-based health promotion, prevention, care and treatment designed by Indigenous people to address their underlying health disparities; 4) Immigrants who will require culturally designed and linguistically appropriate services to enhance screening and engagement into care; and (5) For those incarcerated because of drug-related crimes, decriminalization and better access to harm reduction could help reduce the impact of HCV infections and premature mortality. A comprehensive prevention, care and treatment framework is needed for Canada's vulnerable populations, including those co-infected with HIV, if we are to achieve the WHO HCV elimination targets by 2030. The aim of this review is to describe the HCV epidemic in the Canadian context.

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.

Development and validation of a real-time, reverse transcription PCR assay for rapid and low-cost genotyping of hepatitis C virus genotypes 1a, 1b, 2, and 3a

Hepatitis C virus (HCV) infection affects millions of people and leads to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Treatment regimen selection requires HCV genotype (Gt) and Gt 1 subtype determination. Use of a laboratory developed, reverse transcription (RT)-PCR assay was explored as a low-cost, high-throughput screening approach for the major HCV genotypes and subtypes in North America. A commercial line probe assay (LiPA) was used for comparison. Sequencing and/or an alternative PCR assay were used for discordant analyses. Testing of 155 clinical samples revealed that a paired, duplex real-time RT-PCR assay that targets Gts 1a and 3a in one reaction and Gts 1b and 2 in another had 95% overall sensitivity and individual Gt sensitivity and specificity of 98-100% and 85-98%, respectively. The RT-PCR assay detected mixed HCV Gts in clinical and spiked samples and no false-positive reactions occurred with rare Gts 3b, 4, 5, or 6. Implementation of the RT-PCR assay, with some reflex LiPA testing, would cost only a small portion of the cost of using LiPA alone, and can also save 1.5h of hands-on time. The use of a laboratory developed RT-PCR assay for HCV genotyping has the potential to reduce cost and labour burdens in high-volume testing settings.

Deep sequencing increases hepatitis C virus phylogenetic cluster detection compared to Sanger sequencing

Effective surveillance and treatment strategies are required to control the hepatitis C virus (HCV) epidemic. Phylogenetic analyses are powerful tools for reconstructing the evolutionary history of viral outbreaks and identifying transmission clusters. These studies often rely on Sanger sequencing which typically generates a single consensus sequence for each infected individual. For rapidly mutating viruses such as HCV, consensus sequencing underestimates the complexity of the viral quasispecies population and could therefore generate different phylogenetic tree topologies. Although deep sequencing provides a more detailed quasispecies characterization, in-depth phylogenetic analyses are challenging due to dataset complexity and computational limitations. Here, we apply deep sequencing to a characterized population to assess its ability to identify phylogenetic clusters compared with consensus Sanger sequencing. For deep sequencing, a sample specific threshold determined by the 50th percentile of the patristic distance distribution for all variants within each individual was used to identify clusters. Among seven patristic distance thresholds tested for the Sanger sequence phylogeny ranging from 0.005-0.06, a threshold of 0.03 was found to provide the maximum balance between positive agreement (samples in a cluster) and negative agreement (samples not in a cluster) relative to the deep sequencing dataset. From 77 HCV seroconverters, 10 individuals were identified in phylogenetic clusters using both methods. Deep sequencing analysis identified an additional 4 individuals and excluded 8 other individuals relative to Sanger sequencing. The application of this deep sequencing approach could be a more effective tool to understand onward HCV transmission dynamics compared with Sanger sequencing, since the incorporation of minority sequence variants improves the discrimination of phylogenetically linked clusters.

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.

Multiregion deep sequencing of hepatitis C virus: An improved approach for genetic relatedness studies

Hepatitis C virus (HCV) is a major public health problem that affects more than 180 million people worldwide. Identification of HCV transmission networks is of critical importance for disease control. HCV related cases are often difficult to identify due to the characteristic long incubation period and lack of symptoms during the acute phase of the disease, making it challenging to link related cases to a common source of infection. Additionally, HCV transmission chains are difficult to trace back since viral variants from epidemiologically linked cases are genetically related but rarely identical. Genetic relatedness studies primarily rely on information obtained from the rapidly evolving HCV hypervariable region 1 (HVR1). However, in some instances, the rapid divergence of this region can lead to loss of genetic links between related isolates, which represents an important challenge for outbreak investigations and genetic relatedness studies. Sequencing of multiple and longer sub-genomic regions has been proposed as an alternative to overcome the limitations imposed by the rapid molecular evolution of the HCV HVR1. Additionally, conventional molecular approaches required to characterize the HCV intra-host genetic variation are laborious, time-consuming, and expensive while providing limited information about the composition of the viral population. Next generation sequencing (NGS) approaches enormously facilitate the characterization of the HCV intra-host population by detecting rare variants at much lower frequencies. Thus, NGS approaches using multiple sub-genomic regions should improve the characterization of the HCV intra-host population. Here, we explore the usefulness of multiregion sequencing using a NGS platform for genetic relatedness studies among HCV cases.