geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data

Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models

The mechanisms triggering the human immunodeficiency virus type I (HIV-1) to switch the coreceptor usage from CCR5 to CXCR4 during the course of infection are not entirely understood. While low CD4+ T cell counts are associated with CXCR4 usage, a predominance of CXCR4 usage with still high CD4+ T cell counts remains puzzling. Here, we explore the hypothesis that viral adaptation to the human leukocyte antigen (HLA) complex, especially to the HLA class II alleles, contributes to the coreceptor switch. To this end, we sequence the viral gag and env protein with corresponding HLA class I and II alleles of a new cohort of 312 treatment-naive, subtype C, chronically-infected HIV-1 patients from South Africa. To estimate HLA adaptation, we develop a novel computational approach using Bayesian generalized linear mixed models (GLMMs). Our model allows to consider the entire HLA repertoire without restricting the model to pre-learned HLA-polymorphisms. In addition, we correct for phylogenetic relatedness of the viruses within the model itself to account for founder effects. Using our model, we observe that CXCR4-using variants are more adapted than CCR5-using variants (p-value = 1.34e-2). Additionally, adapted CCR5-using variants have a significantly lower predicted false positive rate (FPR) by the geno2pheno[coreceptor] tool compared to the non-adapted CCR5-using variants (p-value = 2.21e-2), where a low FPR is associated with CXCR4 usage. Consequently, estimating HLA adaptation can be an asset in predicting not only coreceptor usage, but also an approaching coreceptor switch in CCR5-using variants. We propose the usage of Bayesian GLMMs for modeling virus-host adaptation in general.

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.

Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that involves learning a feature representation within a subspace of the reproducing kernel Hilbert space of the position-aware motif kernel function. The resulting model enables to directly interpret and evaluate prediction outcomes by providing a biologically and medically meaningful explanation without the need for additional post-hoc analysis. We show that our model is able to robustly learn on small datasets and reaches state-of-the-art performance on relevant healthcare prediction tasks. Our proposed method can be utilized on DNA and protein sequences. Furthermore, we show that the proposed method learns biologically meaningful concepts directly from data using an end-to-end learning scheme.

Phylogenetic and drug- and vaccine-resistance profiles of Hepatitis B Virus among children with HIV co-infection in Pakistan

INTRODUCTION

HIV-1 and hepatitis B virus (HBV) share common routes of transmission and therefore co-infection is common. In 2019, an HIV-1 outbreak that resulted in >1000 children being infected, predominantly through nosocomial transmission, occurred in Sindh, Pakistan. We conducted a phylogenetic and drug resistance analysis of the HBV Reverse Transcriptase (RT) gene in children with HIV-1 and HBV co-infection.

METHODOLOGY

Blood samples were collected from 321 children with HIV who were recruited as part of a study to investigate the HIV-1 outbreak. All samples were tested for HBV surface antigen (HBsAg) using an ELISA assay, and positive samples were used to amplify and sequence the HBV RT gene. The phylogenetic relationship between sequences was analyzed, and drug- and vaccine- resistance mutations in the RT gene were explored.

RESULTS

Of 321 samples, 23% (n = 75) were positive for HBsAg on ELISA. Phylogenetic analysis of the sequences revealed that 63.5% of HBV sequences were sub-genotype D1, while the rest were sub-genotype D2. Cluster analysis revealed grouping of sub-genotype D1 sequences exclusively with Pakistani sequences, while clustering of sub-genotypes D2 predominantly with global sequences. The 236Y mutation associated with resistance to tenofovir was observed in 2.8% of HBV sequences. Additionally, seven vaccine escape mutations were observed, the most common being 128 V.

CONCLUSION

Our study suggests ongoing transmission of HBV D1 and D2 sub-genotypes in the HIV-1 co-infected population, likely nosocomially, given common routes of HVB and HIV-1 transmission. The prevalence of major HBV drug- and vaccine-resistant mutations remains low. Surveillance for further transmissions and the possible emergence of major drug- or vaccine-resistant variants is required.

Evolution of SIVmac239 following serial passaging in humanized mice

Serial passage of SIVmac239 allows for greater understanding of the genetic changes necessary for cross-species transmission of primate lentiviruses into humans. Using humanized mice, we show that adaptive mutations continue to accumulate in SIVmac239 during four serial passages, with persistent CD4+ T cell decline and increases in plasma viral loads.

First report of computational protein-ligand docking to evaluate susceptibility to HIV integrase inhibitors in HIV-infected Iranian patients

Background

Iran has recently included integrase (INT) inhibitors (INTIs) in the first-line treatment regimen in human immunodeficiency virus (HIV)-infected patients. However, there is no bioinformatics data to elaborate the impact of resistance-associated mutations (RAMs) and naturally occurring polymorphisms (NOPs) on INTIs treatment outcome in Iranian patients.

Method

In this cross-sectional survey, 850 HIV-1-infected patients enrolled; of them, 78 samples had successful sequencing results for INT gene. Several analyses were performed including docking screening, genotypic resistance, secondary/tertiary structures, post-translational modification (PTM), immune epitopes, etc.

Result

The average docking energy (E value) of different samples with elvitegravir (EVG) and raltegravir (RAL) was more than other INTIs. Phylogenetic tree analysis and Stanford HIV Subtyping program revealed HIV-1 CRF35-AD was the predominant subtype (94.9%) in our cases; in any event, online subtyping tools confirmed A1 as the most frequent subtype. For the first time, CRF-01B and BF were identified as new subtypes in Iran. Decreased CD4 count was associated with several factors: poor or unstable adherence, naïve treatment, and drug user status.

Conclusion

As the first bioinformatic report on HIV-integrase from Iran, this study indicates that EVG and RAL are the optimal INTIs in first-line antiretroviral therapy (ART) in Iranian patients. Some conserved motifs and specific amino acids in INT-protein binding sites have characterized that mutation(s) in them may disrupt INT-drugs interaction and cause a significant loss in susceptibility to INTIs. Good adherence, treatment of naïve patients, and monitoring injection drug users are fundamental factors to control HIV infection in Iran effectively.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

New aspects of the Virus Life Cycle and Clinical Utility of Next Generation Sequencing based HIV-1 Resistance Testing in the Genomic, the Proviral and the Viral Reservoir of Peripheral Blood Mononuclear Cells

BACKGROUND

Typically, genotypic resistance testing is recommended at the start of antiretroviral therapy and is even mandatory in cases of virologic failure. The material of choice is plasma viral RNA. However, in patients with low viremia (viral load < 500 copies/ml), resistance testing by population-based sequencing is very difficult.

OBJECTIVE

Therefore, we aimed to investigate whether next generation sequencing (NGS) from proviral DNA and RNA could be an alternative.

MATERIAL AND METHODS

EDTA blood samples (n = 36) from routine clinical viral load testing were used for the study. Viral loads ranged from 96 to 390,000 copies/mL, with 100% of samples having low viremia. Distribution of subtypes A (n = 2), B (n = 16), C (n = 4), D (n = 2), G (1), CRF02 AG (n = 5), CRF01 AE (n = 5), undefined/mixed (n = 4). The extracted consensus sequences were uploaded to the Stanford HIV Drug Resistance Data Base and Geno2pheno for online analysis of drug resistance mutations and resistance factors.

RESULTS

A total of 2476 variants or drug resistance mutations (DRMs) were detected with Sanger sequencing, compared with 2892 variants with NGS. An average of 822/1008 variants were identified in plasma viral RNA by Sanger or NGS sequencing, 834/956 in cellular viral RNA, and 820/928 in cellular viral DNA.

CONCLUSIONS

Both methods are well suited for the detection of HIV substitutions or drug resistance mutations. Our results suggest that cellular RNA or cellular viral DNA is an informative alternative to plasma viral RNA for variant detection in patients with low viremia, as shown by the high correlation of variants in the different viral pools. And we show that by using UDS, a plus of two DRMs per patient becomes visible and that can make a big difference in the assessment of the expected resistance behavior of the virus.

Simultaneous determination of HCV genotype and NS5B resistance associated substitutions using dried serum spots from São Paulo state, Brazil

Hepatitis C virus (HCV) is responsible for more than 180 million infections worldwide, and about 80 % of infections are reported in Low and Middle-income countries (LMICs). Therapy is based on the administration of interferon (INF), ribavirin (RBV) or more recently Direct-Acting Antivirals (DAAs). However, amino acid substitutions associated with resistance (RAS) have been extensively described and can contribute to treatment failure, and diagnosis of RAS requires considerable infrastructure, not always locally available. Dried serum spots (DSS) sampling is an alternative specimen collection method, which embeds drops of serum onto filter paper to be transported by posting to a centralized laboratory. Here, we assessed feasibility of genotypic analysis of HCV from DSS in a cohort of 80 patients from São Paulo state Brazil. HCV RNA was detected on DSS specimens in 83 % of samples of HCV infected patients. HCV genotypes 1a, 1b, 2a, 2c and 3a were determined using the sequence of the palm domain of NS5B region, and RAS C316N/Y, Q309R and V321I were identified in HCV 1b samples. Concerning therapy outcome, 75 % of the patients who used INF +RBV as a previous protocol of treatment did not respond to DAAs, and 25 % were end-of-treatment responders. It suggests that therapy with INF plus RBV may contribute for non-response to a second therapeutic protocol with DAAs. One patient that presented RAS (V321I) was classified as non-responder, and combination of RAS C316N and Q309R does not necessarily imply in resistance to treatment in this cohort of patients. Data presented herein highlights the relevance of studying circulating variants for a better understanding of HCV variability and resistance to the therapy. Furthermore, the feasibility of carrying out genotyping and RAS phenotyping analysis by using DSS card for the potential of informing future treatment interventions could be relevant to overcome the limitations of processing samples in several location worldwide, especially in LMICs.

Detection of archived lamivudine-associated resistance mutations in virologically suppressed, lamivudine-experienced HIV-infected adults by different genotyping techniques (GEN-PRO study)

BACKGROUND

Previously selected lamivudine resistance-associated mutations (RAMs) may remain archived within the proviral HIV-DNA.

OBJECTIVES

To evaluate the ability of proviral DNA genotyping to detect lamivudine RAMs in HIV-1 virologically suppressed participants; the correlation between Sanger and next generation sequencing (NGS); and predictive factors for detection of lamivudine RAMs in proviral DNA.

METHODS

Cross-sectional study of participants on stable antiretroviral therapy and suppressed for ≥1 year. Analysis of proviral DNA was performed by Sanger sequencing in whole blood and by NGS in PBMCs.

RESULTS

We analysed samples from 102 subjects (52 with and 50 without lamivudine RAMs in historical plasma RNA-genotypes). Among participants with previous lamivudine resistance, Sanger sequencing detected RAMs in 26.9%. Detection rates significantly increased using NGS: 47.9%, 64.6%, 75% and 87.5% with the 20%, 10%, 5% and 1% thresholds, respectively. As for participants without historical lamivudine resistance, Sanger detected the RAMs in 1/49 (2%), and NGS (5% threshold) in 8/45 (17.8%). Multivariate models fitted to the whole population revealed that having a history of lamivudine resistance was a risk factor for detection of lamivudine RAMs by NGS. Among participants with historical lamivudine resistance, multivariate analysis showed that a longer time since HIV diagnosis was associated with persistence of archived mutations by NGS at thresholds of >10% [OR 1.10 (95% CI: 1.00-1.24)] and >5% [OR 1.16 (95% CI: 1.02-1.32)].

CONCLUSIONS

Proviral DNA Sanger sequencing does not detect the majority of historical lamivudine RAMs. NGS increases the sensitivity of detection at lower thresholds, although the relevance of these minority populations with lamivudine RAMs needs further evaluation.

Characterization of HIV-1 drug resistance among patients with failure of second-line combined antiretroviral therapy in central Ethiopia

BACKGROUND

As a consequence of the improved availability of combined antiretroviral therapy (cART) in resource-limited countries, an emergence of HIV drug resistance (HIVDR) has been observed. We assessed the prevalence and spectrum of HIVDR in patients with failure of second-line cART at two HIV clinics in central Ethiopia.

METHODS

HIV drug resistance was analysed in HIV-1-infected patients with virological failure of second-line cART using the geno2pheno application.

RESULTS

Among 714 patients receiving second-line cART, 44 (6.2%) fulfilled the criteria for treatment failure and 37 were eligible for study inclusion. Median age was 42 years [interquartile range (IQR): 20-45] and 62.2% were male. At initiation of first-line cART, 23 (62.2%) were WHO stage III, mean CD4 cell count was 170.6 (range: 16-496) cells/µL and median (IQR) HIV-1 viral load was 30 220 (7963-82 598) copies/mL. Most common second-line cART regimens at the time of failure were tenofovir disoproxil fumarate (TDF)-lamivudine (3TC)-ritonavir-boosted atazanavir (ATV/r) (19/37, 51.4%) and zidovudine (ZDV)-3TC-ATV/r (9/37, 24.3%). Genotypic HIV-1 resistance testing was successful in 35 (94.6%) participants. We found at least one resistance mutation in 80% of patients and 40% carried a protease inhibitor (PI)-associated mutation. Most common mutations were M184V (57.1%), Y188C (25.7%), M46I/L (25.7%) and V82A/M (25.7%). High-level resistance against the PI ATV (10/35, 28.6%) and lopinavir (LPV) (5/35, 14.3%) was reported. As expected, no resistance mutations conferring integrase inhibitor resistance were detected.

CONCLUSIONS

We found a high prevalence of resistance mutations, also against PIs (40%), as the national standard second-line cART components. Resistance testing before switching to second- or third-line cART is warranted.

Prevalence of Naturally Occurring Resistance Associated Substitutions in NS3/4A Protease Inhibitors in Iranian HCV/HIV Infected Patients

BACKGROUND

Hepatitis C virus (HCV) acts in host as a complicated mixture of related variants with the potency to genetically escape host immune responses. Direct acting antivirals (DAAs) have been approved for HCV treatment with shorter duration, better cure rates and lower side effects. However, naturally occurring resistance associated substitutions(RASs) make some obstacles to this antiviral therapy success.

OBJECTIVE

In this study, we aimed at determination of the naturally occurring NS3/4A RASs in HCV/human immunodeficiency virus (HIV)infected patients.

METHODS

A total of 120 DAA-naïve HCV-HIV co-infected patients were included. HCV NS3/4Agenome region was amplified with PCR and mutation analysis was performed by Sanger sequencing technique. The amino acid sequence diversity of the region wasanalyzed using geno2pheno HCV.

RESULTS

Phylogenetic analysis showed that 73 cases were infected by 3a and 47 subjects by subtype1a. The overall RASs among studied subjects wereobserved in 6 (5%) individuals from 120 studied cases who were infected with HCV 1a. V36M/L,Q80L,S122G/L,R155T/G,A156S,D168Y/N and S174A/N/T mutations were detected in this study.

CONCLUSION

Although the prevalence of RASs was totally low in this study, the presence of several cases of double and triple mutants among this population suggests prior evaluation of protease inhibitors related mutations before initiation of standard treatment and also investigation on a large population could be of high value.

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.

Drug Resistance Spread in 6 Metropolitan Regions, Germany, 2001-20181

We analyzed 1,397 HIV-1 pol sequences of antiretroviral therapy–naive patients in a total of 7 university hospitals in Bonn, Cologne, Frankfurt, Hamburg, Hannover, and Munich, Germany. Phylogenetic and network analysis elucidated numerous cases of shared drug resistance mutations among genetically linked patients; K103N was the most frequently shared mutation.

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.

Treatment outcomes and costs of a simplified antiviral treatment strategy for hepatitis C among monoinfected and HIV and/or hepatitis B virus-co-infected patients in Myanmar

Access to hepatitis C virus (HCV) testing and treatment is limited in Myanmar. We assessed an integrated HIV and viral hepatitis testing and HCV treatment strategy. Sofosbuvir/velpatasvir (SOF/VEL) ± weight-based ribavirin for 12 weeks was provided at three treatment sites in Myanmar and sustained virologic response (SVR) assessed at 12 weeks after treatment. Participants co-infected with HBV were treated concurrently with tenofovir. Cost estimates in 2018 USD were made at Yangon and Mandalay using standard micro-costing methods. 803 participants initiated SOF/VEL; 4.8% were lost to follow-up. SVR was achieved in 680/803 (84.6%) by intention-to-treat analysis. SVR amongst people who inject drugs (PWID) was 79.7% (381/497), but 92.5% among PWID on opioid substitution therapy (OST) (74/80), and 97.4% among non-PWID (298/306). Utilizing data from 492 participants, of whom 93% achieved SVR, the estimated average cost of treatment per patient initiated was $1030 (of which 54% were medication costs), with a production cost per successful outcome (SVR) of $1109 and real-world estimate of $1250. High SVR rates were achieved for non-PWID and PWID on OST. However, the estimated average cost of the intervention (under the assumption of no genotype testing and reduced real-world effectiveness) of $1250/patient is unaffordable for a national elimination strategy. Reductions in the cost of antivirals and linkage to social and behavioural health services including substance use disorder treatment to increase retention and adherence to treatment are critical to HCV elimination in this population.

Dry Panels Supporting External Quality Assessment Programs for Next Generation Sequencing-Based HIV Drug Resistance Testing

External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.

Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework

Next-generation sequencing (NGS) in HIV drug resistance (HIVDR) testing has the potential to improve both clinical and public health settings, however it challenges the normal operations of quality management systems to be more flexible due to its complexity, massive data generation, and rapidly evolving protocols. While guidelines for quality management in NGS data have previously been outlined, little guidance has been implemented for NGS-based HIVDR testing. This document summarizes quality control procedures for NGS-based HIVDR testing laboratories using a laboratory information systems (LIS) framework. Here, we focus in particular on the quality control measures applied on the final sequencing product aligned with the recommendations from the World Health Organization HIV Drug Resistance Laboratory Network.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

openPrimeR for multiplex amplification of highly diverse templates

To study the diversity of immune receptors and pathogens, multiplex PCR has become a central approach in research and diagnostics. However, insufficient primer design against highly diverse templates often prevents amplification and therefore limits the correct understanding of biological processes. Here, we present openPrimeR, an R-based tool for evaluating and designing multiplex PCR primers. openPrimeR provides a functional and intuitive interface and uses either a greedy algorithm or an integer linear program to compute the minimal set of primers that performs full target coverage. As proof of concept, we used openPrimeR to find optimal primer sets for the amplification of highly mutated immunoglobulins. Comprehensive analyses on specifically generated immunoglobulin variable gene segment libraries resulted in the composition of highly effective primer sets (oPR-IGHV, oPR-IGKV and oPR-IGLV) that demonstrated to be particularly suitable for the isolation of novel human antibodies.

Next-generation sequencing for the clinical management of hepatitis C virus infections: does one test fits all purposes?

While the prospect of viral cure is higher than ever for individuals infected with the hepatitis C virus (HCV) due to ground-breaking progress in antiviral treatment, success rates are still negatively influenced by HCV's high genetic variability. This genetic diversity is represented in the circulation of various genotypes and subtypes, mixed infections, recombinant forms and the presence of numerous drug resistant variants among infected individuals. Common misclassifications by commercial genotyping assays in combination with the limitations of currently used targeted population sequencing approaches have encouraged researchers to exploit alternative methods for the clinical management of HCV infections. Next-generation sequencing (NGS), a revolutionary and powerful tool with a variety of applications in clinical virology, can characterize viral diversity and depict viral dynamics in an ultra-wide and ultra-deep manner. The level of detail it provides makes it the method of choice for the diagnosis and clinical assessment of HCV infections. The sequence library provided by NGS is of a higher magnitude and sensitivity than data generated by conventional methods. Therefore, these technologies are helpful to guide clinical practice and at the same time highly valuable for epidemiological studies. The decreasing costs of NGS to determine genotypes, mixed infections, recombinant strains and drug resistant variants will soon make it feasible to employ NGS in clinical laboratories, to assist in the daily care of patients with HCV.

Signature of natural resistance in NS3 protease revealed by deep sequencing of HCV strains circulating in Iran

A tremendous upscale of screening and treatment strategies is required to achieve elimination of the hepatitis C virus (HCV) in Iran by 2030. Among treated patients, at least 5-10% is expected to experience treatment failure. To efficiently retreat cases with prior exposure to NS5A and NS5B drugs, knowledge on the natural prevalence of NS3 resistance is key. The NS3 region of 32 samples from sixteen Iranian HCV patients, among which 6 injecting drug users, was amplified and subjected to deep sequencing. Amplification and sequencing were successful in 29 samples. The reads were assembled to consensus sequences and showed that 6 patients were infected with HCV1a (37.5%), 7 with HCV1b (43.8%) and 3 with HCV3a (18.7%). Nucleotide identities were shared for >97% between intra-host sequences. Two patients were infected with natural resistant viruses, of which one solely comprising low frequency variants. Inferred phylogenies showed that Iranian sequences clustered together for HCV1a and HCV1b, while for HCV3a a potential recombination event was detected. We firstly report the use of deep sequencing for HCV in Iran, demonstrate the use of NS3 inhibitors as salvage therapy in case of retreatment and stress the importance for Iran to prioritize drug users for screening and treatment.

NS5A Gene Analysis by Next Generation Sequencing in HCV Nosocomial Transmission Clusters of HCV Genotype 1b Infected Patients

Background: The aim of the study was to investigate the intra-host variability through next-generation-sequencing (NGS) of the NS5A-gene in nosocomial transmission-clusters observed in two Italian hospitals among hepatitis C virus (HCV)-genotype-1b infected patients. Methods: HCV-sequencing was performed by Sanger-sequencing (NS3 + NS5A + NS5B) and by NGS (NS5A, MiSeq-Illumina) in 15 HCV-1b infected patients [five acute with onco-hematologic-disease and 10 (4/6 acute/chronic) with β-thalassemia]. Resistance-associated-substitutions (RAS) were analysed by Geno2pheno-algorithm. Nucleotide-sequence-variability (NSV, at 1%, 2%, 5%, 10% and 15% NGS-cutoffs) and Shannon entropy were estimated. Phylogenetic analysis was performed by Mega6-software and Bayesian-analysis. Results: Phylogenetic analysis showed five transmission-clusters: one involving four HCV-acute onco-hematologic-patients; one involving three HCV-chronic β-thalassemia-patients and three involving both HCV-acute and chronic β-thalassemia-patients. The NS5A-RAS Y93H was found in seven patients, distributed differently among chronic/acute patients involved in the same transmission-clusters, independently from the host-genetic IL-28-polymorphism. The intra-host NSV was higher in chronic-patients versus acute-patients, at all cutoffs analyzed (p < 0.05). Even though Shannon-entropy was higher in chronic-patients, significantly higher values were observed only in chronic β-thalassemia-patients versus acute β-thalassemia-patients (p = 0.01). Conclusions: In nosocomial HCV transmission-clusters, the intra-host HCV quasispecies divergence in patients with acute-infection was very low in comparison to that in chronic-infection. The NS5A-RAS Y93H was often transmitted and distributed differently within the same transmission-clusters, independently from the IL-28-polymorphism.

Hotspots of Transmission Driving the Local Human Immunodeficiency Virus Epidemic in the Cologne-Bonn Region, Germany

BACKGROUND

Geographical allocation of interventions focusing on hotspots of human immunodeficiency virus (HIV) transmission has the potential to improve efficiency. We used phylogeographic analyses to identify hotspots of the HIV transmission in Cologne-Bonn, Germany.

METHODS

We included 714 HIV-1 infected individuals, followed up at the University Hospitals Cologne and Bonn. Distance-based molecular network analyses were performed to infer putative relationships. Characteristics of genetically linked individuals and assortativity (shared characteristics) were analyzed. Geospatial diffusion (ie, viral gene flow) was evaluated using a Slatkin-Maddison approach. Geospatial dispersal was determined by calculating the average distance between the residences of linked individuals (centroids of 3-digit zip code).

RESULTS

In sum, 217/714 (30.4%) sequences had a putative genetic linkage, forming 77 clusters (size range: 2-8). Linked individuals were more likely to live in areas surrounding the city center (P = .043), <30 years of age (P = .009). and infected with HIV-1 subtype B (P = .002). Clustering individuals were nonassortative by area of residency (-.0026, P = .046). Geospatial analyses revealed a median distance between genetically linked individuals of 23.4 kilometers (km), lower than expected (P < .001). Slatkin-Maddison analyses revealed increased gene flow from central Cologne toward the surrounding areas (P < .001).

CONCLUSION

Phylogeographic analysis suggests that central Cologne may be a significant driver of the regional epidemic. Although clustering individuals lived closer than unlinked individuals, they were less likely to be linked to others from their same zip code. These results could help public health entities better understand transmission dynamics, facilitating allocation of resources to areas of greatest need.

Interpreting Viral Deep Sequencing Data with GLUE

Using deep sequencing technologies such as Illumina’s platform, it is possible to obtain reads from the viral RNA population revealing the viral genome diversity within a single host. A range of software tools and pipelines can transform raw deep sequencing reads into Sequence Alignment Mapping (SAM) files. We propose that interpretation tools should process these SAM files, directly translating individual reads to amino acids in order to extract statistics of interest such as the proportion of different amino acid residues at specific sites. This preserves per-read linkage between nucleotide variants at different positions within a codon location. The samReporter is a subsystem of the GLUE software toolkit which follows this direct read translation approach in its processing of SAM files. We test samReporter on a deep sequencing dataset obtained from a cohort of 241 UK HCV patients for whom prior treatment with direct-acting antivirals has failed; deep sequencing and resistance testing have been suggested to be of clinical use in this context. We compared the polymorphism interpretation results of the samReporter against an approach that does not preserve per-read linkage. We found that the samReporter was able to properly interpret the sequence data at resistance-associated locations in nine patients where the alternative approach was equivocal. In three cases, the samReporter confirmed that resistance or an atypical substitution was present at NS5A position 30. In three further cases, it confirmed that the sofosbuvir-resistant NS5B substitution S282T was absent. This suggests the direct read translation approach implemented is of value for interpreting viral deep sequencing data.

Profile of the VERSANT HCV genotype 2.0 assay

INTRODUCTION

Hepatitis C virus (HCV) is divided into 7 genotypes and 67 subtypes. HCV genotype studies reflect the viral transmission patterns as well as human migration routes. In a clinical setting, HCV genotype is a baseline predictor for the sustained virological response (SVR) in chronic hepatitis C patients treated with peginterferon or some direct acting antivirals (DAAs). The Versant HCV genotype 2.0 assay has been globally used for HCV genotyping over a decade. Areas covered: The assay is based on reverse hybridization principle. It is evolved from its former versions, and the accuracy and successful genotyping/subtyping rate are substantially improved. It shows an accuracy of 99-100% for genotypes 1-6. It can also reliably identify subtypes 1a and 1b. However, the assay does not allow a high resolution for many other subtypes. Reasons for indeterminate or inaccurate genotyping/subtyping results are discussed. Expert commentary: Genotyping helps to find the most efficacious and cost-effective treatment regimen. The rapid development of anti-HCV treatment regimens, however, is greatly simplifying laboratory tests. In the near future, the need for HCV genotyping and frequent serial on-treatment HCV RNA tests will decrease along with the wide use of the more potent and pan-genotypic DAA regimens.

Bioinformatic data processing pipelines in support of next-generation sequencing-based HIV drug resistance testing: the Winnipeg Consensus

INTRODUCTION

Next-generation sequencing (NGS) has several advantages over conventional Sanger sequencing for HIV drug resistance (HIVDR) genotyping, including detection and quantitation of low-abundance variants bearing drug resistance mutations (DRMs). However, the high HIV genomic diversity, unprecedented large volume of data, complexity of analysis and potential for error pose significant challenges for data processing. Several NGS analysis pipelines have been developed and used in HIVDR research; however, the absence of uniformity in data processing strategies results in lack of consistency and comparability of outputs from different pipelines. To fill this gap, an international symposium on bioinformatic strategies for NGS-based HIVDR testing was held in February 2018 in Winnipeg, Canada, convening laboratory scientists, bioinformaticians and clinicians involved in four recently developed, publicly available NGS HIVDR pipelines. The goal of this symposium was to establish a consensus on effective bioinformatic strategies for NGS data management and its use for HIVDR reporting.

DISCUSSION

Essential functionalities of an NGS HIVDR pipeline were divided into five analytic blocks: (1) NGS read quality control (QC)/quality assurance (QA); (2) NGS read alignment and reference mapping; (3) HIV variant calling and variant QC; (4) NGS HIVDR reporting; and (5) extended data applications and additional considerations for data management. The consensuses reached among the participants on all major aspects of these blocks are summarized here. They encompass not only recommended data management and analysis strategies, but also detailed bioinformatic approaches that help ensure accuracy of the derived HIVDR analysis outputs for both research and potential clinical use.

CONCLUSIONS

While NGS is being adopted more broadly in HIVDR testing laboratories, data processing is often a bottleneck hindering its generalized application. The proposed standardization of NGS read QC/QA, read alignment and reference mapping, variant calling and QC, HIVDR reporting and relevant data management strategies in this "Winnipeg Consensus" may serve as a starting guideline for NGS HIVDR data processing that informs the refinement of existing pipelines and those yet to be developed. Moreover, the bioinformatic strategies presented here may apply more broadly to NGS data analysis of microbes harbouring significant genomic diversity.