Antigenic cooperation in viral populations: Transformation of functions of intra-host viral variants.

In this paper, we study intra-host viral adaptation by antigenic cooperation - a mechanism of immune escape that serves as an alternative to the standard mechanism of escape by continuous genomic diversification and allows to explain a number of experimental observations associated with the establishment of chronic infections by highly mutable viruses. Within this mechanism, the topology of a cross-immunoreactivity network forces intra-host viral variants to specialize for complementary roles and adapt to the host's immune response as a quasi-social ecosystem. Here we study dynamical changes in immune adaptation caused by evolutionary and epidemiological events. First, we show that the emergence of a viral variant with altered antigenic features may result in a rapid re-arrangement of the viral ecosystem and a change in the roles played by existing viral variants. In particular, it may push the population under immune escape by genomic diversification towards the stable state of adaptation by antigenic cooperation. Next, we study the effect of a viral transmission between two chronically infected hosts, which results in the merging of two intra-host viral populations in the state of stable immune-adapted equilibrium. In this case, we also describe how the newly formed viral population adapts to the host's environment by changing the functions of its members. The results are obtained analytically for minimal cross-immunoreactivity networks and numerically for larger populations.

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.

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.

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.

Inference of mutability landscapes of tumors from single cell sequencing data.

One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.

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.

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.

Inference of clonal selection in cancer populations using single-cell sequencing data.

SUMMARY: Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. AVAILABILITY AND IMPLEMENTATION: Its source code is available at https://github.com/compbel/SCIFIL.

Delineating Surface Epitopes of Lyme Disease Pathogen Targeted by Highly Protective Antibodies of New Zealand White Rabbits.

Lyme disease (LD), the most prevalent vector-borne illness in the United States and Europe, is caused by Borreliella burgdorferi No vaccine is available for humans. Dogmatically, B. burgdorferi can establish a persistent infection in the mammalian host (e.g., mice) due to a surface antigen, VlsE. This antigenically variable protein allows the spirochete to continually evade borreliacidal antibodies. However, our recent study has shown that the B. burgdorferi spirochete is effectively cleared by anti-B. burgdorferi antibodies of New Zealand White rabbits, despite the surface expression of VlsE. Besides homologous protection, the rabbit antibodies also cross-protect against heterologous B. burgdorferi spirochetes and significantly reduce the pathology of LD arthritis in persistently infected mice. Thus, this finding that NZW rabbits develop a unique repertoire of very potent antibodies targeting the protective surface epitopes, despite abundant VlsE, prompted us to identify the specificities of the protective rabbit antibodies and their respective targets. By applying subtractive reverse vaccinology, which involved the use of random peptide phage display libraries coupled with next-generation sequencing and our computational algorithms, repertoires of nonprotective (early) and protective (late) rabbit antibodies were identified and directly compared. Consequently, putative surface epitopes that are unique to the protective rabbit sera were mapped. Importantly, the relevance of newly identified protection-associated epitopes for their surface exposure has been strongly supported by prior empirical studies. This study is significant because it now allows us to systematically test the putative epitopes for their protective efficacy with an ultimate goal of selecting the most efficacious targets for development of a long-awaited LD vaccine.

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.

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.

Antigenic cooperation among intrahost HCV variants organized into a complex network of cross-immunoreactivity.

Hepatitis C virus (HCV) has the propensity to cause chronic infection. Continuous immune escape has been proposed as a mechanism of intrahost viral evolution contributing to HCV persistence. Although the pronounced genetic diversity of intrahost HCV populations supports this hypothesis, recent observations of long-term persistence of individual HCV variants, negative selection increase, and complex dynamics of viral subpopulations during infection as well as broad cross-immunoreactivity (CR) among variants are inconsistent with the immune-escape hypothesis. Here, we present a mathematical model of intrahost viral population dynamics under the condition of a complex CR network (CRN) of viral variants and examine the contribution of CR to establishing persistent HCV infection. The model suggests a mechanism of viral adaptation by antigenic cooperation (AC), with immune responses against one variant protecting other variants. AC reduces the capacity of the host's immune system to neutralize certain viral variants. CRN structure determines specific roles for each viral variant in host adaptation, with variants eliciting broad-CR antibodies facilitating persistence of other variants immunoreacting with these antibodies. The proposed mechanism is supported by empirical observations of intrahost HCV evolution. Interference with AC is a potential strategy for interruption and prevention of chronic HCV infection.

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.

Accurate Genetic Detection of Hepatitis C Virus Transmissions in Outbreak Settings.

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections are associated with unsafe injection practices, drug diversion, and other exposures to blood and are difficult to detect and investigate. Here, we developed and validated a simple approach for molecular detection of HCV transmissions in outbreak settings. We obtained sequences from the HCV hypervariable region 1 (HVR1), using end-point limiting-dilution (EPLD) technique, from 127 cases involved in 32 epidemiologically defined HCV outbreaks and 193 individuals with unrelated HCV strains. We compared several types of genetic distances and calculated a threshold, using minimal Hamming distances, that identifies transmission clusters in all tested outbreaks with 100% accuracy. The approach was also validated on sequences obtained using next-generation sequencing from HCV strains recovered from 239 individuals, and findings showed the same accuracy as that for EPLD. On average, the nucleotide diversity of the intrahost population was 6.2 times greater in the source case than in any incident case, allowing the correct detection of transmission direction in 8 outbreaks for which source cases were known. A simple and accurate distance-based approach developed here for detecting HCV transmissions streamlines molecular investigation of outbreaks, thus improving the public health capacity for rapid and effective control of hepatitis C.

Network Analysis of the Chronic Hepatitis C Virome Defines Hypervariable Region 1 Evolutionary Phenotypes in the Context of Humoral Immune Responses.

UNLABELLED: Hypervariable region 1 (HVR1) of hepatitis C virus (HCV) comprises the first 27 N-terminal amino acid residues of E2. It is classically seen as the most heterogeneous region of the HCV genome. In this study, we assessed HVR1 evolution by using ultradeep pyrosequencing for a cohort of treatment-naive, chronically infected patients over a short, 16-week period. Organization of the sequence set into connected components that represented single nucleotide substitution events revealed a network dominated by highly connected, centrally positioned master sequences. HVR1 phenotypes were observed to be under strong purifying (stationary) and strong positive (antigenic drift) selection pressures, which were coincident with advancing patient age and cirrhosis of the liver. It followed that stationary viromes were dominated by a single HVR1 variant surrounded by minor variants comprised from conservative single amino acid substitution events. We present evidence to suggest that neutralization antibody efficacy was diminished for stationary-virome HVR1 variants. Our results identify the HVR1 network structure during chronic infection as the preferential dominance of a single variant within a narrow sequence space. IMPORTANCE: HCV infection is often asymptomatic, and chronic infection is generally well established in advance of initial diagnosis and subsequent treatment. HVR1 can undergo rapid sequence evolution during acute infection, and the variant pool is typically seen to diverge away from ancestral sequences as infection progresses from the acute to the chronic phase. In this report, we describe HVR1 viromes in chronically infected patients that are defined by a dominant epitope located centrally within a narrow variant pool. Our findings suggest that weakened humoral immune activity, as a consequence of persistent chronic infection, allows for the acquisition and maintenance of host-specific adaptive mutations at HVR1 that reflect virus fitness.

Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling.

MOTIVATION: Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, providing an opportunity to implement large-scale molecular surveillance of viral diseases. However, despite improvements in technology, traditional protocols for NGS of large numbers of samples are still highly cost and labor intensive. One of the possible cost-effective alternatives is combinatorial pooling. Although a number of pooling strategies for consensus sequencing of DNA samples and detection of SNPs have been proposed, these strategies cannot be applied to sequencing of highly heterogeneous viral populations. RESULTS: We developed a cost-effective and reliable protocol for sequencing of viral samples, that combines NGS using barcoding and combinatorial pooling and a computational framework including algorithms for optimal virus-specific pools design and deconvolution of individual samples from sequenced pools. Evaluation of the framework on experimental and simulated data for hepatitis C virus showed that it substantially reduces the sequencing costs and allows deconvolution of viral populations with a high accuracy. AVAILABILITY AND IMPLEMENTATION: The source code and experimental data sets are available at http://alan.cs.gsu.edu/NGS/?q=content/pooling.

Nosocomial hepatitis C virus transmission from tampering with injectable anesthetic opioids.

UNLABELLED: The extent of provider-to-patient hepatitis C virus (HCV) transmission from diversion, self-injection, and substitution ("tampering") of anesthetic opioids is unknown. To quantify the contribution of opioid tampering to nosocomial HCV outbreaks, data from health care-related HCV outbreaks occurring in developed countries from 1990 to 2012 were collated, grouped, and compared. Tampering was associated with 17% (8 of 46) of outbreaks, but 53% (438 of 833) of cases. Of the tampering outbreaks, six (75%) involved fentanyl, five (63%) occurred in the United States, and one each in Australia, Israel, and Spain. Case counts ranged from 5 to 275 in the tampering outbreaks (mean, 54.8; median, 25), and 1-99 in the nontampering outbreaks (mean, 10.4; median, 5); between them, the difference in mean ranks of counts was significant (P < 0.01). To estimate HCV transmission risks from tampering, risk-assessment models were constructed, and these risks compared with those from surgery. HCV transmission risk from exposure to an opioid preparation tampered by a provider of unknown HCV infection status who is a person who injects drugs (PWID; 0.62%; standard error [SE] = 0.38%) exceeds 16,757 times the risk from surgery by a surgeon of unknown HCV infection status (0.000037%; SE = 0.000029%) and 135 times by an HCV-infected surgeon (0.0046%; SE = 0.0033%). To pose a 50% patient transmission risk, an infected surgeon may take 30 years, compared to <1 year for a PWID tamperer, and weeks or days for a PWID tamperer who intensifies access to opioids. CONCLUSION: Disproportionately, many cases of HCV infection from nosocomial outbreaks were attributable to provider tampering of anesthetic opioids. Transmission risk from tampering is substantially higher than from surgery.

Cryptic Hepatitis B and E in Patients With Acute Hepatitis of Unknown Etiology.

BACKGROUND: Up to 30% of acute viral hepatitis has no known etiology. To determine the disease etiology in patients with acute hepatitis of unknown etiology (HUE), serum specimens were obtained from 38 patients residing in the United Kingdom and Vietnam and from 26 healthy US blood donors. All specimens tested negative for known viral infections causing hepatitis, using commercially available serological and nucleic acid assays. METHODS: Specimens were processed by sequence-independent complementary DNA amplification and next-generation sequencing (NGS). Sufficient material for individual NGS libraries was obtained from 12 HUE cases and 26 blood donors; the remaining HUE cases were sequenced as a pool. Read mapping was done by targeted and de novo assembly. RESULTS: Sequences from hepatitis B virus (HBV) were detected in 7 individuals with HUE (58.3%) and the pooled library, and hepatitis E virus (HEV) was detected in 2 individuals with HUE (16.7%) and the pooled library. Both HEV-positive cases were coinfected with HBV. HBV sequences belonged to genotypes A, D, or G, and HEV sequences belonged to genotype 3. No known hepatotropic viruses were detected in the tested normal human sera. CONCLUSIONS: NGS-based detection of HBV and HEV infections is more sensitive than using commercially available assays. HBV and HEV may be cryptically associated with HUE.