Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data

RSV-GenoScan: An automated pipeline for whole-genome human respiratory syncytial virus (RSV) sequence analysis

BACKGROUND

Advances in high-throughput sequencing (HTS) technologies and reductions in sequencing costs have revolutionised the study of genomics and molecular biology by making whole-genome sequencing (WGS) accessible to many laboratories. However, the analysis of WGS data requires significant computational effort, which is the major drawback in implementing WGS as a routine laboratory technique.

OBJECTIVE

Automated pipelines have been developed to overcome this issue, but they do not exist for all organisms. This is the case for human respiratory syncytial virus (RSV), which is a leading cause of lower respiratory tract infections in infants, the elderly, and immunocompromised adults.

RESULTS

We present RSV-GenoScan, a fast and easy-to-use pipeline for WGS analysis of RSV generated by HTS on Illumina or Nanopore platforms. RSV-GenoScan automates the WGS analysis steps directly from the raw sequence data. The pipeline filters the sequence data, maps the reads to the RSV reference genomes, generates a consensus sequence, identifies the RSV subgroup, and lists amino acid mutations, insertions and deletions in the F and G viral genes. This enables the rapid identification of mutations in these coding genes that are known to confer resistance to monoclonal antibodies.

AVAILABILITY

RSV-GenoScan is freely available at https://github.com/AlexandreD-bio/RSV-GenoScan.

Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data

In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimate linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.

An amplicon-based protocol for whole-genome sequencing of human respiratory syncytial virus subgroup A

It is convenient to study complete genome sequences of human respiratory syncytial virus (hRSV) for ongoing genomic characterization and identification of highly transmissible or pathogenic variants. Whole genome sequencing of hRSV has been challenging from respiratory tract specimens with low viral loads. Herein, we describe an amplicon-based protocol for whole genome sequencing of hRSV subgroup A validated with 24 isolates from nasopharyngeal swabs and infected cell cultures, which showed cycle threshold (Ct) values ranging from 10 to 31, as determined by quantitative reverse-transcription polymerase chain reaction. MinION nanopore generated 3200 to 5400 reads per sample to sequence over 93% of the hRSV-A genome. Coverage of each contig ranged from 130× to 200×. Samples with Ct values of 20.9, 25.2, 27.1, 27.7, 28.2, 28.8, and 29.6 led to the sequencing of over 99.0% of the virus genome, indicating high genome coverage even at high Ct values. This protocol enables the identification of hRSV subgroup A genotypes, as primers were designed to target highly conserved regions. Consequently, it holds potential for application in molecular epidemiology and surveillance of this hRSV subgroup.

Evolutionary analysis of seasonal influenza A viruses in Pakistan 2020-2023

INTRODUCTION

Influenza A viruses cause global health concerns due to their high amino acid substitution rates. They are linked to yearly seasonal epidemics and occasional pandemics. This study focused on sequencing influenza A virus strains in Pakistan.

MATERIALS AND METHODS

We analyzed the genetic characteristics of influenza A(H1N1)pdm09 and A(H3N2) viruses circulating in Pakistan from January 2020 to January 2023. Whole genome sequences from influenza A (n = 126) virus isolates were amplified and sequenced by the Oxford Nanopore (MinION) platform.

RESULTS

The HA genes of influenza A(H1N1)pdm09 underwent amino acid substitutions at positions K54Q, A186T, Q189E, E224A, R259K, and K308R in sequenced samples. The HA genes of influenza A(H3N2) had amino acid substitutions at G53D, E83K, D104G, I140M, S205F, A212T, and K276R in the sequenced samples. Furthermore, the HA gene sequences of influenza A(H1N1)pdm09 in this study belonged to subclade 6B.1A.5a.2a. Similarly, the HA gene sequences of influenza A(H3N2) were classified under six subclades (3C.3a.1 and 3C.2a1b.2a [2, 2a.1, 2b, 2c, and 2a.3b]). Notably, amino acid substitutions in other gene segments of influenza A(H1N1)pdm09 and A(H3N2) were also found.

CONCLUSION

These findings indicate influenza A(H1N1)pdm09 and A(H3N2) viruses co-circulated during the 2020-2023 influenza season in Pakistan. Continued surveillance is crucial for real-time monitoring of possible high-virulence variation and their relevance to existing vaccine strains.

Genomic characterisation of respiratory syncytial virus: a novel system for whole genome sequencing and full-length G and F gene sequences

To advance our understanding of respiratory syncytial virus (RSV) impact through genomic surveillance, we describe two PCR-based sequencing systems, (i) RSVAB-WGS for generic whole-genome sequencing and (ii) RSVAB-GF, which targets major viral antigens, G and F, and is used as a complement for challenging cases with low viral load. These methods monitor RSV genetic diversity to inform molecular epidemiology, vaccine effectiveness and treatment strategies, contributing also to the standardisation of surveillance in a new era of vaccines.

Genomic characterization of respiratory syncytial virus genotypes circulating in the paediatric population of Sydney, NSW, Australia

Respiratory syncytial virus (RSV), or human orthopneumovirus, is a major cause of acute lower respiratory infection (ALRI), particularly in young children, causing significant morbidity and mortality. We used pathogen genomics to characterize the population structure and genetic signatures of RSV isolates circulating in children in New South Wales between 2016 and 2018 and to understand the evolutionary dynamics of these strains in the context of publicly available RSV genomes from the region and globally. Whole-genome phylogenetic analysis demonstrated the co-circulation of a few major RSV clades in the paediatric population from Sydney. The whole-genome-based genotypes A23 (RSV-A ON1-like genotype) and B6 (RSV-B BA9-like genotype) were the predominant RSV-A and RSV-B genotypes circulating during the study period, respectively. These genotypes were characterized with high levels of diversity of predicted N- and O-linked glycosylation patterns in both the G and F glycoproteins. Interestingly, a novel 72-nucleotide triplication in the sequence that corresponds to the C-terminal region of the G gene was identified in four of the A23 genotype sequenced in this study. Consistently, the population dynamics analysis demonstrated a continuous increase in the effective population size of A23 and B6 genotypes globally. Further investigations including functional mapping of mutations and identifying the impact of sequence changes on virus fitness are highly required. This study highlights the potential impact of an integrated approach that uses WG-based phylogeny and studying selective pressure events in understanding the emergence and dissemination of RSV genotypes.

Managing Viral Emerging Infectious Diseases via Current and Future Molecular Diagnostics

Emerging viral infectious diseases have been a constant threat to global public health in recent times. In managing these diseases, molecular diagnostics has played a critical role. Molecular diagnostics involves the use of various technologies to detect the genetic material of various pathogens, including viruses, in clinical samples. One of the most commonly used molecular diagnostics technologies for detecting viruses is polymerase chain reaction (PCR). PCR amplifies specific regions of the viral genetic material in a sample, making it easier to detect and identify viruses. PCR is particularly useful for detecting viruses that are present in low concentrations in clinical samples, such as blood or saliva. Another technology that is becoming increasingly popular for viral diagnostics is next-generation sequencing (NGS). NGS can sequence the entire genome of a virus present in a clinical sample, providing a wealth of information about the virus, including its genetic makeup, virulence factors, and potential to cause an outbreak. NGS can also help identify mutations and discover new pathogens that could affect the efficacy of antiviral drugs and vaccines. In addition to PCR and NGS, there are other molecular diagnostics technologies that are being developed to manage emerging viral infectious diseases. One of these is CRISPR-Cas, a genome editing technology that can be used to detect and cut specific regions of viral genetic material. CRISPR-Cas can be used to develop highly specific and sensitive viral diagnostic tests, as well as to develop new antiviral therapies. In conclusion, molecular diagnostics tools are critical for managing emerging viral infectious diseases. PCR and NGS are currently the most commonly used technologies for viral diagnostics, but new technologies such as CRISPR-Cas are emerging. These technologies can help identify viral outbreaks early, track the spread of viruses, and develop effective antiviral therapies and vaccines.

Estimating linkage disequilibrium and selection from allele frequency trajectories

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

Frequency matters: comparison of drug resistance mutation detection by Sanger and next-generation sequencing in HIV-1

BACKGROUND

Next-generation sequencing (NGS) is gradually replacing Sanger sequencing (SS) as the primary method for HIV genotypic resistance testing. However, there are limited systematic data on comparability of these methods in a clinical setting for the presence of low-abundance drug resistance mutations (DRMs) and their dependency on the variant-calling thresholds.

METHODS

To compare the HIV-DRMs detected by SS and NGS, we included participants enrolled in the Swiss HIV Cohort Study (SHCS) with SS and NGS sequences available with sample collection dates ≤7 days apart. We tested for the presence of HIV-DRMs and compared the agreement between SS and NGS at different variant-calling thresholds.

RESULTS

We included 594 pairs of SS and NGS from 527 SHCS participants. Males accounted for 80.5% of the participants, 76.3% were ART naive at sample collection and 78.1% of the sequences were subtype B. Overall, we observed a good agreement (Cohen's kappa >0.80) for HIV-DRMs for variant-calling thresholds ≥5%. We observed an increase in low-abundance HIV-DRMs detected at lower thresholds [28/417 (6.7%) at 10%-25% to 293/812 (36.1%) at 1%-2% threshold]. However, such low-abundance HIV-DRMs were overrepresented in ART-naive participants and were in most cases not detected in previously sampled sequences suggesting high sequencing error for thresholds <3%.

CONCLUSIONS

We found high concordance between SS and NGS but also a substantial number of low-abundance HIV-DRMs detected only by NGS at lower variant-calling thresholds. Our findings suggest that a substantial fraction of the low-abundance HIV-DRMs detected at thresholds <3% may represent sequencing errors and hence should not be overinterpreted in clinical practice.

Special Issue: "Evolution, Ecology and Diversity of Plant Virus"

The next-generation sequencing method was developed in the second half of the 2000s and marked the beginning of high-throughput sequencing (HTS) analyses of viral communities [...].

A combined approach of rolling-circle amplification-single site restriction endonuclease digestion followed by next generation sequencing to characterize the whole genome and intra-host variants of human Torque teno virus

Torque Teno Virus (TTV) was initially associated with post-transfusion hepatitis, but growing evidence of its ubiquity in humans is compatible to no apparent clinical significance. TTV is a small non-enveloped virus with a circular single-negative-stranded DNA genome, belonging to the Anelloviridae family. Currently, TTVs are divided in seven phylogenetic groups and are further classified into 21 species. Studies about diversity of TTV in different conditions are receiving increasing interest and in this sense, sequencing of whole genomes for better genetic characterization becomes even more important. Since its discovery in 1997, few TTV complete genomes have been reported worldwide. This is probably due, among other reasons, to the great genetic heterogeneity among TTV strains that prevents its amplification and sequencing by conventional PCR and cloning methods. In addition, although metagenomics approach is useful in these cases, it remains a challenging tool for viromic analysis. With the aim of contributing to the expansion of the TTV whole genomes dataset and to study intra-host variants, we employed a methodology that combined a rolling-circle amplification approach followed by EcoRI digestion, generating a DNA fragment of ∼4Kb consistent with TTV genome length which was sequenced by Illumina next generation sequencing. A genogroup 3 full-length consensus TTV genome was obtained and co-infection with other species (at least those with a single EcoRI cleavage site) was not identified. Additionally, bioinformatics analysis allowed to identify the spectrum of TTV intra-host variants which provides evidence of a complex evolution dynamics of these DNA circular viruses, similarly to what occurs with RNA viruses.

Identifying high-confidence variants in human cytomegalovirus genomes sequenced from clinical samples

Understanding the intrahost evolution of viral populations has implications in pathogenesis, diagnosis, and treatment and has recently made impressive advances from developments in high-throughput sequencing. However, the underlying analyses are very sensitive to sources of bias, error, and artefact in the data, and it is important that these are addressed adequately if robust conclusions are to be drawn. The key factors include (1) determining the number of viral strains present in the sample analysed; (2) monitoring the extent to which the data represent these strains and assessing the quality of these data; (3) dealing with the effects of cross-contamination; and (4) ensuring that the results are reproducible. We investigated these factors by generating sequence datasets, including biological and technical replicates, directly from clinical samples obtained from a small cohort of patients who had been infected congenitally with the herpesvirus human cytomegalovirus, with the aim of developing a strategy for identifying high-confidence intrahost variants. We found that such variants were few in number and typically present in low proportions and concluded that human cytomegalovirus exhibits a very low level of intrahost variability. In addition to clarifying the situation regarding human cytomegalovirus, our strategy has wider applicability to understanding the intrahost variability of other viruses.

Safety in Multi-Assembly via Paths Appearing in All Path Covers of a DAG

A multi-assembly problem asks to reconstruct multiple genomic sequences from mixed reads sequenced from all of them. Standard formulations of such problems model a solution as a path cover in a directed acyclic graph, namely a set of paths that together cover all vertices of the graph. Since multi-assembly problems admit multiple solutions in practice, we consider an approach commonly used in standard genome assembly: output only partial solutions (contigs, or safe paths), that appear in all path cover solutions. We study constrained path covers, a restriction on the path cover solution that incorporate practical constraints arising in multi-assembly problems. We give efficient algorithms finding all maximal safe paths for constrained path covers. We compute the safe paths of splicing graphs constructed from transcript annotations of different species. Our algorithms run in less than 15 seconds per species and report RNA contigs that are over 99% precise and are up to 8 times longer than unitigs. Moreover, RNA contigs cover over 70% of the transcripts and their coding sequences in most cases. With their increased length to unitigs, high precision, and fast construction time, maximal safe paths can provide a better base set of sequences for transcript assembly programs.

From Clinical Specimen to Whole Genome Sequencing of A(H3N2) Influenza Viruses: A Fast and Reliable High-Throughput Protocol

(1) Background: Over the last few years, there has been growing interest in the whole genome sequencing (WGS) of rapidly mutating pathogens, such as influenza viruses (IVs), which has led us to carry out in-depth studies on viral evolution in both research and diagnostic settings. We aimed at describing and determining the validity of a WGS protocol that can obtain the complete genome sequence of A(H3N2) IVs directly from clinical specimens. (2) Methods: RNA was extracted from 80 A(H3N2)-positive respiratory specimens. A one-step RT-PCR assay, based on the use of a single set of specific primers, was used to retro-transcribe and amplify the entire IV type A genome in a single reaction, thus avoiding additional enrichment approaches and host genome removal treatments. Purified DNA was quantified; genomic libraries were prepared and sequenced by using Illumina MiSeq platform. The obtained reads were evaluated for sequence quality and read-pair length. (3) Results: All of the study specimens were successfully amplified, and the purified DNA concentration proved to be suitable for NGS (at least 0.2 ng/µL). An acceptable coverage depth for all eight genes of influenza A(H3N2) virus was obtained for 90% (72/80) of the clinical samples with viral loads >105 genome copies/mL. The mean depth of sequencing ranged from 105 to 200 reads per position, with the majority of the mean depth values being above 103 reads per position. The total turnaround time per set of 20 samples was four working days, including sequence analysis. (4) Conclusions: This fast and reliable high-throughput sequencing protocol should be used for influenza surveillance and outbreak investigation.

Technologies for HIV-1 drug resistance testing: inventory and needs

PURPOSE OF REVIEW

HIV-1 drug resistance (HIV DR) testing is routinely performed by genotyping plasma viruses using Sanger population sequencing. Next-generation sequencing (NGS) is increasingly replacing standardized Sanger sequencing. This opens up new opportunities, but also brings challenges.

RECENT FINDINGS

The number of NGS applications and protocols for HIV DR testing is increasing. All of them are noninferior to Sanger sequencing when comparing NGS-derived consensus sequences to Sanger sequencing-derived sequences. In addition, NGS enables high-throughput sequencing of near full-length HIV-1 genomes and detection of low-abundance drug-resistant HIV-1 variants, although their clinical implications need further investigation. Several groups have defined remaining challenges in implementing NGS protocols for HIV-1 resistance testing. Some of them are already being addressed. One of the most important needs is quality management and consequently, if possible, standardization.

SUMMARY

The use of NGS technologies on HIV DR testing will allow unprecedented insights into genomic structures of virus populations that may be of immediate relevance to both clinical and research areas such as personalized antiretroviral treatment. Efforts continue to tackle the remaining challenges in NGS-based HIV DR testing.

Detection of Ancient Viruses and Long-Term Viral Evolution

The COVID-19 outbreak has reminded us of the importance of viral evolutionary studies as regards comprehending complex viral evolution and preventing future pandemics. A unique approach to understanding viral evolution is the use of ancient viral genomes. Ancient viruses are detectable in various archaeological remains, including ancient people's skeletons and mummified tissues. Those specimens have preserved ancient viral DNA and RNA, which have been vigorously analyzed in the last few decades thanks to the development of sequencing technologies. Reconstructed ancient pathogenic viral genomes have been utilized to estimate the past pandemics of pathogenic viruses within the ancient human population and long-term evolutionary events. Recent studies revealed the existence of non-pathogenic viral genomes in ancient people's bodies. These ancient non-pathogenic viruses might be informative for inferring their relationships with ancient people's diets and lifestyles. Here, we reviewed the past and ongoing studies on ancient pathogenic and non-pathogenic viruses and the usage of ancient viral genomes to understand their long-term viral evolution.

Clinical and biological consequences of respiratory syncytial virus genetic diversity

Respiratory syncytial virus (RSV) is one of the most common etiological agents of global acute respiratory tract infections with a disproportionate burden among infants, individuals over the age of 65, and immunocompromised populations. The two major subtypes of RSV (A and B) co-circulate with a predominance of either group during different epidemic seasons, with frequently emerging genotypes due to RSV's high genetic variability. Global surveillance systems have improved our understanding of seasonality, disease burden, and genomic evolution of RSV through genotyping by sequencing of attachment (G) glycoprotein. However, the integration of these systems into international infrastructures is in its infancy, resulting in a relatively low number (~2200) of publicly available RSV genomes. These limitations in surveillance hinder our ability to contextualize RSV evolution past current canonical attachment glycoprotein (G)-oriented understanding, thus resulting in gaps in understanding of how genetic diversity can play a role in clinical outcome, therapeutic efficacy, and the host immune response. Furthermore, utilizing emerging RSV genotype information from surveillance and testing the impact of viral evolution using molecular techniques allows us to establish causation between the clinical and biological consequences of arising genotypes, which subsequently aids in informed vaccine design and future vaccination strategy. In this review, we aim to discuss the findings from current molecular surveillance efforts and the gaps in knowledge surrounding the consequence of RSV genetic diversity on disease severity, therapeutic efficacy, and RSV-host interactions.

What sequencing technologies can teach us about innate immunity

For years, we have taken a reductionist approach to understanding gene regulation through the study of one gene in one cell at a time. While this approach has been fruitful it is laborious and fails to provide a global picture of what is occurring in complex situations involving tightly coordinated immune responses. The emergence of whole-genome techniques provides a system-level view of a response and can provide a plethora of information on events occurring in a cell from gene expression changes to splicing changes and chemical modifications. As with any technology, this often results in more questions than answers, but this wealth of knowledge is providing us with an unprecedented view of what occurs inside our cells during an immune response. In this review, we will discuss the current RNA-sequencing technologies and what they are helping us learn about the innate immune system.

Composition and Dynamics of H1N1 and H7N9 Influenza A Virus Quasispecies in a Co-infected Patient Analyzed by Single Molecule Sequencing Technology

A human co-infected with H1N1 and H7N9 subtypes influenza A virus (IAV) causes a complex infectious disease. The identification of molecular-level variations in composition and dynamics of IAV quasispecies will help to understand the pathogenesis and provide guidance for precision medicine treatment. In this study, using single-molecule real-time sequencing (SMRT) technology, we successfully acquired full-length IAV genomic sequences and quantified their genotypes abundance in serial samples from an 81-year-old male co-infected with H1N1 and H7N9 subtypes IAV. A total of 26 high diversity nucleotide loci was detected, in which the A-G base transversion was the most abundant substitution type (67 and 64%, in H1N1 and H7N9, respectively). Seven significant amino acid variations were detected, such as NA:H275Y and HA: R222K in H1N1 as well as PB2:E627K and NA: K432E in H7N9, which are related to viral drug-resistance or mammalian adaptation. Furtherly, we retrieved 25 H1N1 and 22 H7N9 genomic segment haplotypes from the eight samples based on combining high-diversity nucleotide loci, which provided a more concise overview of viral quasispecies composition and dynamics. Our approach promotes the popularization of viral quasispecies analysis in a complex infectious disease, which will boost the understanding of viral infections, pathogenesis, evolution, and precision medicine.

Reconstruction of evolving gene variants and fitness from short sequencing reads

Directed evolution can generate proteins with tailor-made activities. However, full-length genotypes, their frequencies and fitnesses are difficult to measure for evolving gene-length biomolecules using most high-throughput DNA sequencing methods, as short read lengths can lose mutation linkages in haplotypes. Here we present Evoracle, a machine learning method that accurately reconstructs full-length genotypes (R² = 0.94) and fitness using short-read data from directed evolution experiments, with substantial improvements over related methods. We validate Evoracle on phage-assisted continuous evolution (PACE) and phage-assisted non-continuous evolution (PANCE) of adenine base editors and OrthoRep evolution of drug-resistant enzymes. Evoracle retains strong performance (R² = 0.86) on data with complete linkage loss between neighboring nucleotides and large measurement noise, such as pooled Sanger sequencing data (~US$10 per timepoint), and broadens the accessibility of training machine learning models on gene variant fitnesses. Evoracle can also identify high-fitness variants, including low-frequency 'rising stars', well before they are identifiable from consensus mutations.

Nested PCR followed by NGS: Validation and application for HPV genotyping of Tunisian cervical samples

The most used methodologies for HPV genotyping in Tunisian studies are based on hybridization that are limited to a restricted number of HPV types and to a lack of specificity and sensitivity for same types. Recently, Next-Generation sequencing (NGS) technology has been efficiently used for HPV genotyping. In this work we designed and validated a sensitive genotyping method based on nested PCR followed by NGS. Eighty-six samples were tested for the validation of an HPV genotyping assay based on Nested-PCR followed by NGS. These include, 43 references plasmids and 43 positive HPV clinical cervical specimens previously evaluated with the conventional genotyping method: Reverse Line Hybridization (RLH). Results of genotyping using NGS were compared to those of RLH. The analytical sensitivity of the NGS assay was 1GE/μl per sample. The NGS allowed the detection of all HPV types presented in references plasmids. On the clinical samples, a total of 19 HPV types were detected versus 14 types using RLH. Besides the identification of more HPV types in multiple infection (6 types for NGS versus 4 for RLH), NGS allowed the identification of HPV types that were not detected by RLH. In addition, the NGS assay detected newly HPV types that were not described in Tunisia so far: HPV81, HPV43, HPV74, and HPV62. The high sensitivity and specificity of NGS for HPV genotyping in addition to the identification of new HPV types may justify the use of such technique to provide with high accuracy the profile of circulating types in epidemiological studies.

Epidemiological and genetic characterization of measles virus circulating strains at Marseille, France during 2017-2019 measles outbreak

OBJECTIVES

We attempted to establish a molecular investigation by Next Generation sequencing of the measles virus (MeV) strains circulating in Marseille-France during the last outbreak that occurred between 2017 and 2019.

METHODS

The circulating MeV were isolated from clinical samples using cell culture method and whole genomes were sequenced by Illumina Miseq Next Generation. Genotyping and comparative analyses were assessed by phylogenetic reconstructions. Clinical and epidemiological data from cases were also recorded.

RESULTS

A total of 110 MeV strains were isolated in cell culture. Our analysis based on whole genome sequences of 98 isolates confirmed that 93 strains belonged to the genotype D8 and 5 to the genotype B3. Phylogenetic analyses revealed 4 distinct MeV circulating clones in Marseille. Measles mostly occured in children < 5 years-old and in adults 30-50 years-old. Measles infection also occurred in 2 adequately vaccinated cases (2 doses). Among 63 measles cases of whom we had available clinical data informations, a total of 35 patients were hospitalized and 19 developed complications including one death case recorded.

CONCLUSIONS

Whole Genome Sequencing seems to be a useful tool for more refined genomic characterization of large measles outbreak. Vaccination strategies for measles eradication need to be re-evaluated in the current context.

Relevant SARS-CoV-2 Genome Variation through Six Months of Worldwide Monitoring

Real-time genome monitoring of the SARS-CoV-2 pandemic outbreak is of utmost importance for designing diagnostic tools, guiding antiviral treatment and vaccination strategies. In this study, we present an accurate method for temporal and geographical comparison of mutational events based on GISAID database genome sequencing. Among 42523 SARS-CoV-2 genomes analyzed, we found 23202 variants compared to the reference genome. The Ti/Tv (transition/transversion) ratio was used to filter out possible false-positive errors. Transition mutations generally occurred more frequently than transversions. Our clustering analysis revealed remarkable hotspot mutation patterns for SARS-CoV-2. Mutations were clustered based on how their frequencies changed over time according to each geographical location. We observed some clusters showing a clear variation in mutation frequency and continuously evolving in the world. However, many mutations appeared in specific periods without a clear pattern over time. Various important nonsynonymous mutations were observed, mainly in Oceania and Asia. More than half of these mutations were observed only once. Four hotspot mutations were found in all geographical locations at least once: T265I (NSP2), P314L (NSP12), D614G (S), and Q57H (ORF3a). The current analysis of SARS-CoV-2 genomes provides valuable information on the geographical and temporal mutational evolution of SARS-CoV-2.

Compartmentalized evolution of hepatitis B virus contributes differently to the prognosis of hepatocellular carcinoma

Serum hepatitis B virus (HBV) mutations can predict hepatocellular carcinoma (HCC) occurrence. We aimed to clarify if HBV evolves synchronously in the sera, adjacent liver and tumors and predict HCC prognosis equally. A total of 203 HBV-positive HCC patients with radical hepatectomy in Shanghai, China, during 2011-15 were enrolled in this prospective study. Quasispecies complexity (QC) in HBV core promoter region was assessed using clone-based sequencing. We performed RNA sequencing on tumors and paired adjacent tissues of another 15 HCC patients and analyzed it with three public data sets containing 127 samples. HBV QC was positively correlated to APOBEC3s' expression level (r = 0.28, P < 0.001), higher in the adjacent tissues than in the tumors (P = 6.50e-3), and higher in early tumors than in advanced tumors (P = 0.039). The evolutionary distance between the sera-derived HBV strains and the tumor-derived ones was significantly longer than that between the sera-derived ones and the adjacent tissue-derived ones (P < 0.001). Multivariate Cox regression analyses indicated that high HBV QC in the sera predicted an unfavorable overall survival (P = 0.002) and recurrence-free survival (RFS; P = 0.004) in HCC, whereas, in the tumors, it predicted a favorable RFS (P < 0.001). APOBECs-related HBV mutations, including G1764A, were more frequent in the sera than in the adjacent tissues. High-frequent A1762T/G1764A in the sera predicted an unfavorable RFS (P < 0.001), whereas, in the tumors, it predicted a favorable RFS (P = 0.035). In conclusion, HBV evolves more advanced in the sera than in the tumors. HBV QC and A1762T/G1764A in the sera and tumors have contrary prognostic effects in HCC.

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.

Going to extremes - a metagenomic journey into the dark matter of life

The Virus-X-Viral Metagenomics for Innovation Value-project was a scientific expedition to explore and exploit uncharted territory of genetic diversity in extreme natural environments such as geothermal hot springs and deep-sea ocean ecosystems. Specifically, the project was set to analyse and exploit viral metagenomes with the ultimate goal of developing new gene products with high innovation value for applications in biotechnology, pharmaceutical, medical, and the life science sectors. Viral gene pool analysis is also essential to obtain fundamental insight into ecosystem dynamics and to investigate how viruses influence the evolution of microbes and multicellular organisms. The Virus-X Consortium, established in 2016, included experts from eight European countries. The unique approach based on high throughput bioinformatics technologies combined with structural and functional studies resulted in the development of a biodiscovery pipeline of significant capacity and scale. The activities within the Virus-X consortium cover the entire range from bioprospecting and methods development in bioinformatics to protein production and characterisation, with the final goal of translating our results into new products for the bioeconomy. The significant impact the consortium made in all of these areas was possible due to the successful cooperation between expert teams that worked together to solve a complex scientific problem using state-of-the-art technologies as well as developing novel tools to explore the virosphere, widely considered as the last great frontier of life.

Virus Detection: A Review of the Current and Emerging Molecular and Immunological Methods

Viruses are ubiquitous in the environment. While many impart no deleterious effects on their hosts, several are major pathogens. This risk of pathogenicity, alongside the fact that many viruses can rapidly mutate highlights the need for suitable, rapid diagnostic measures. This review provides a critical analysis of widely used methods and examines their advantages and limitations. Currently, nucleic-acid detection and immunoassay methods are among the most popular means for quickly identifying viral infection directly from source. Nucleic acid-based detection generally offers high sensitivity, but can be time-consuming, costly, and require trained staff. The use of isothermal-based amplification systems for detection could aid in the reduction of results turnaround and equipment-associated costs, making them appealing for point-of-use applications, or when high volume/fast turnaround testing is required. Alternatively, immunoassays offer robustness and reduced costs. Furthermore, some immunoassay formats, such as those using lateral-flow technology, can generate results very rapidly. However, immunoassays typically cannot achieve comparable sensitivity to nucleic acid-based detection methods. Alongside these methods, the application of next-generation sequencing can provide highly specific results. In addition, the ability to sequence large numbers of viral genomes would provide researchers with enhanced information and assist in tracing infections.

MPL resolves genetic linkage in fitness inference from complex evolutionary histories

Genetic linkage causes the fate of new mutations in a population to be contingent on the genetic background on which they appear. This makes it challenging to identify how individual mutations affect fitness. To overcome this challenge, we developed marginal path likelihood (MPL), a method to infer selection from evolutionary histories that resolves genetic linkage. Validation on real and simulated data sets shows that MPL is fast and accurate, outperforming existing inference approaches. We found that resolving linkage is crucial for accurately quantifying selection in complex evolving populations, which we demonstrate through a quantitative analysis of intrahost HIV-1 evolution using multiple patient data sets. Linkage effects generated by variants that sweep rapidly through the population are particularly strong, extending far across the genome. Taken together, our results argue for the importance of resolving linkage in studies of natural selection.

Application of whole-genome sequencing for norovirus outbreak tracking and surveillance efforts in Orange County, CA

Noroviruses are the leading cause of acute gastroenteritis and foodborne illness in the United States. Traditional Sanger sequencing of short genomic regions (~300-600 bp) is the primary method for differentiation of this pathogen; however, whole-genome sequencing (WGS) offers a valuable approach to further characterize strains of this virus. The objective of this study was to investigate the ability of WGS compared to Sanger sequencing to differentiate norovirus strains and enhance outbreak investigation and surveillance efforts. WGS results for 41 norovirus-positive stool samples from 15 different outbreaks occurring from 2012 to 2019 in Orange County, CA, were analyzed for this study. All samples were genotyped with both WGS and Sanger sequencing based on the B-C region. WGS generated nearly full-length viral genome sequences (7029-7768 bp) with 4x to 35,378x coverage. Phylogenetic analysis of WGS data enabled differentiation of genotypically similar strains from separate outbreaks. Single nucleotide variation (SNV) analysis on a subset of strains revealed nucleotide variations (15-79 nt) among isolates from multiple outbreaks of GII.4 Sydney_2015[P31] and GII.17[P17]. Overall, the results demonstrated that coupling norovirus genotype identification with WGS enables enhanced genetic differentiation of strains and provides valuable information for outbreak investigation and surveillance efforts.

Low-Abundance Drug-Resistant HIV-1 Variants in Antiretroviral Drug-Naive Individuals: A Systematic Review of Detection Methods, Prevalence, and Clinical Impact

BACKGROUND

The presence of high-abundance drug-resistant HIV-1 jeopardizes success of antiretroviral therapy (ART). Despite numerous investigations, the clinical impact of low-abundance drug-resistant HIV-1 variants (LA-DRVs) at levels <15%-25% of the virus population in antiretroviral (ARV) drug-naive individuals remains controversial.

METHODS

We systematically reviewed 103 studies assessing prevalence, detection methods, technical and clinical detection cutoffs, and clinical significance of LA-DRVs in antiretroviral drug-naive adults.

RESULTS

In total, 14 919 ARV drug-naive individuals were included. Prevalence of LA-DRVs (ie, proportion of individuals harboring LA-DRVs) was 0%-100%. Technical detection cutoffs showed a 4 log range (0.001%-10%); 42/103 (40.8%) studies investigating the impact of LA-DRVs on ART; 25 studies included only individuals on first-line nonnucleoside reverse transcriptase inhibitor-based ART regimens. Eleven of those 25 studies (44.0%) reported a significantly association between preexisting LA-DRVs and risk of virological failure whereas 14/25 (56.0%) did not.

CONCLUSIONS

Comparability of the 103 studies is hampered by high heterogeneity of the studies' designs and use of different methods to detect LA-DRVs. Thus, evaluating clinical impact of LA-DRVs on first-line ART remains challenging. We, the WHO HIVResNet working group, defined central areas of future investigations to guide further efforts to implement ultrasensitive resistance testing in routine settings.

V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data

Motivation

High-throughput sequencing technologies are used increasingly not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations.

Results

To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape.

Availabilityand implementation

V-pipe is freely available at https://github.com/cbg-ethz/V-pipe.

Supplementary information

Supplementary data are available at Bioinformatics online.

Next-Generation Sequencing Analysis of the Within-Host Genetic Diversity of Influenza A(H1N1)pdm09 Viruses in the Upper and Lower Respiratory Tracts of Patients with Severe Influenza

The D222G/N substitution in the hemagglutinin (HA) protein of influenza A(H1N1)pdm09 virus has been reported to be associated with disease severity and mortality in numerous previous studies. In the present study, 75% of lower respiratory samples contained heterogeneous influenza populations that carried different amino acids at position 222 of the HA protein, whereas all upper respiratory samples only contained the wild-type 222D.

The influenza A(H1N1)pdm09 virus emerged in April 2009 with an unusual incidence of severe disease and mortality, and currently circulates as a seasonal influenza virus. Previous studies using consensus viral genome sequencing data have overlooked the viral genomic and phenotypic diversity. Next-generation sequencing (NGS) may instead be used to characterize viral populations in an unbiased manner and to measure within-host genetic diversity. In this study, we used NGS analysis to investigate the within-host genetic diversity of influenza A(H1N1)pdm09 virus in the upper and lower respiratory samples from nine patients who were admitted to the intensive care unit (ICU). A total of 47 amino acid substitution positions were found to differ between the upper and lower respiratory tract samples from all patients. However, the D222G/N substitution in hemagglutinin (HA) protein was the only amino acid substitution common to multiple patients. Furthermore, the substitution was detected only in the six samples from the lower respiratory tract. Therefore, it is important to investigate influenza A(H1N1)pdm09 virus populations using multiple paired samples from the upper and lower respiratory tract to avoid overlooking potentially important substitutions, especially in patients with severe disease.

IMPORTANCE The D222G/N substitution in the hemagglutinin (HA) protein of influenza A(H1N1)pdm09 virus has been reported to be associated with disease severity and mortality in numerous previous studies. In the present study, 75% of lower respiratory samples contained heterogeneous influenza populations that carried different amino acids at position 222 of the HA protein, whereas all upper respiratory samples only contained the wild-type 222D. These results suggest the influenza A(H1N1)pdm09 virus has diversified inside the host owing to differences in tissue specificity. In this study, the within-host genetic diversity of influenza A(H1N1)pdm09 virus was investigated for the first time using next-generation sequencing analysis of the viral whole-genome in samples extracted from the upper and lower respiratory tracts of patients with severe disease.

Stability of SARS-CoV-2 phylogenies

The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab-or protocol-specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein-coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation or recombination among viral lineages. We suggest how samples can be screened and problematic variants removed, and we plan to regularly inform the scientific community with our updated results as more SARS-CoV-2 genome sequences are shared (https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473 and https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480). We also develop tools for comparing and visualizing differences among very large phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.

PIQMEE: Bayesian Phylodynamic Method for Analysis of Large Data Sets with Duplicate Sequences

Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.

How Bank Vole-PUUV Interactions Influence the Eco-Evolutionary Processes Driving Nephropathia Epidemica Epidemiology-An Experimental and Genomic Approach

In Europe, Puumala virus (PUUV) is responsible for nephropathia epidemica (NE), a mild form of hemorrhagic fever with renal syndrome (HFRS). Despite the presence of its reservoir, the bank vole, on most of French territory, the geographic distribution of NE cases is heterogeneous and NE endemic and non-endemic areas have been reported. In this study we analyzed whether bank vole-PUUV interactions could partly shape these epidemiological differences. We performed crossed-experimental infections using wild bank voles from French endemic (Ardennes) and non-endemic (Loiret) areas and two French PUUV strains isolated from these areas. The serological response and dynamics of PUUV infection were compared between the four cross-infection combinations. Due to logistical constraints, this study was based on a small number of animals. Based on this experimental design, we saw a stronger serological response and presence of PUUV in excretory organs (bladder) in bank voles infected with the PUUV endemic strain. Moreover, the within-host viral diversity in excretory organs seemed to be higher than in other non-excretory organs for the NE endemic cross-infection but not for the NE non-endemic cross-infection. Despite the small number of rodents included, our results showed that genetically different PUUV strains and in a lesser extent their interaction with sympatric bank voles, could affect virus replication and diversity. This could impact PUUV excretion/transmission between rodents and to humans and in turn at least partly shape NE epidemiology in France.

A beginner's guide for FMDV quasispecies analysis: sub-consensus variant detection and haplotype reconstruction using next-generation sequencing

Deep sequencing of viral genomes is a powerful tool to study RNA virus complexity. However, the analysis of next-generation sequencing data might be challenging for researchers who have never approached the study of viral quasispecies by this methodology. In this work we present a suitable and affordable guide to explore the sub-consensus variability and to reconstruct viral quasispecies from Illumina sequencing data. The guide includes a complete analysis pipeline along with user-friendly descriptions of software and file formats. In addition, we assessed the feasibility of the workflow proposed by analyzing a set of foot-and-mouth disease viruses (FMDV) with different degrees of variability. This guide introduces the analysis of quasispecies of FMDV and other viruses through this kind of approach.

Detection and Genetic Characterization of Puumala Orthohantavirus S-Segment in Areas of France Non-Endemic for Nephropathia Epidemica

Puumala virus (PUUV) in Europe causes nephropathia epidemica (NE), a mild form of hemorrhagic fever with renal syndrome (HFRS). The incidence of NE is highly heterogeneous spatially, whereas the geographic distribution of the wild reservoir of PUUV, the bank vole, is essentially homogeneous. Our understanding of the processes driving this heterogeneity remains incomplete due to gaps in knowledge. Little is known about the current distribution and genetic variation of PUUV in the areas outside the well-identified zones of NE endemicity. We trapped bank voles in four forests in French regions in which NE is considered non-endemic, but sporadic NE cases have been reported recently. We tested bank voles for anti-PUUV IgG and characterized the S segment sequences of PUUV from seropositive animals. Phylogenetic analyses revealed specific amino-acid signatures and genetic differences between PUUV circulating in non-endemic and nearby NE-endemic areas. We also showed, in temporal surveys, that the amino-acid sequences of PUUV had undergone fewer recent changes in areas non-endemic for NE than in endemic areas. The evolutionary history of the current French PUUV clusters was investigated by phylogeographic approaches, and the results were considered in the context of the history of French forests. Our findings highlight the need to monitor the circulation and genetics of PUUV in a larger array of bank vole populations, to improve our understanding of the risk of NE.

Modern diagnostic technologies for HIV

Novel diagnostic technologies, including nanotechnology, microfluidics, -omics science, next-generation sequencing, genomics big data, and machine learning, could contribute to meeting the UNAIDS 95-95-95 targets to end the HIV epidemic by 2030. Novel technologies include multiplexed technologies (including biomarker-based point-of-care tests and molecular platform technologies), biomarker-based combination antibody and antigen technologies, dried-blood-spot testing, and self-testing. Although biomarker-based rapid tests, in particular antibody-based tests, have dominated HIV diagnostics since the development of the first HIV test in the mid-1980s, targets such as nucleic acids and genes are now used in nanomedicine, biosensors, microfluidics, and -omics to enable early diagnosis of HIV. These novel technologies show promise as they are associated with ease of use, high diagnostic accuracy, rapid detection, and the ability to detect HIV-specific markers. Additional clinical and implementation research is needed to generate evidence for use of novel technologies and a public health approach will be required to address clinical and operational challenges to optimise their global deployment.

Measurement error and variant-calling in deep Illumina sequencing of HIV

MOTIVATION

Next-generation deep sequencing of viral genomes, particularly on the Illumina platform, is increasingly applied in HIV research. Yet, there is no standard protocol or method used by the research community to account for measurement errors that arise during sample preparation and sequencing. Correctly calling high and low-frequency variants while controlling for erroneous variants is an important precursor to downstream interpretation, such as studying the emergence of HIV drug-resistance mutations, which in turn has clinical applications and can improve patient care.

RESULTS

We developed a new variant-calling pipeline, hivmmer, for Illumina sequences from HIV viral genomes. First, we validated hivmmer by comparing it to other variant-calling pipelines on real HIV plasmid datasets. We found that hivmmer achieves a lower rate of erroneous variants, and that all methods agree on the frequency of correctly called variants. Next, we compared the methods on an HIV plasmid dataset that was sequenced using Primer ID, an amplicon-tagging protocol, which is designed to reduce errors and amplification bias during library preparation. We show that the Primer ID consensus exhibits fewer erroneous variants compared to the variant-calling pipelines, and that hivmmer more closely approaches this low error rate compared to the other pipelines. The frequency estimates from the Primer ID consensus do not differ significantly from those of the variant-calling pipelines.

AVAILABILITY AND IMPLEMENTATION

hivmmer is freely available for non-commercial use from https://github.com/kantorlab/hivmmer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chromatyping: Reconstructing Nucleosome Profiles from NOMe Sequencing Data

Measuring nucleosome positioning in cells is crucial for the analysis of epigenetic gene regulation. Reconstruction of nucleosome profiles of individual cells or subpopulations of cells remains challenging because most genome-wide assays measure nucleosome positioning and DNA accessibility for thousands of cells using bulk sequencing. In this study we use characteristics of the NOMe (nucleosome occupancy and methylation)-sequencing assay to derive a new approach, called ChromaClique, for deconvolution of different nucleosome profiles (chromatypes) from cell subpopulations of one NOMe-seq measurement. ChromaClique uses a maximal clique enumeration algorithm on a newly defined NOMe read graph that is able to group reads according to their nucleosome profiles. We show that the edge probabilities of that graph can be efficiently computed using hidden Markov models. We demonstrate using simulated data that ChromaClique is more accurate than a related method and scales favorably, allowing genome-wide analyses of chromatypes in cell subpopulations.

Viral Diversity Based on Next-Generation Sequencing of HIV-1 Provides Precise Estimates of Infection Recency and Time Since Infection

BACKGROUND

Human immunodeficiency virus type 1 (HIV-1) genetic diversity increases over the course of infection and can be used to infer the time since infection and, consequently, infection recency, which are crucial for HIV-1 surveillance and the understanding of viral pathogenesis.

METHODS

We considered 313 HIV-infected individuals for whom reliable estimates of infection dates and next-generation sequencing (NGS)-derived nucleotide frequency data were available. Fractions of ambiguous nucleotides, obtained by population sequencing, were available for 207 samples. We assessed whether the average pairwise diversity calculated using NGS sequences provided a more exact prediction of the time since infection and classification of infection recency (<1 year after infection), compared with the fraction of ambiguous nucleotides.

RESULTS

NGS-derived average pairwise diversity classified an infection as recent with a sensitivity of 88% and a specificity of 85%. When considering only the 207 samples for which fractions of ambiguous nucleotides were available, the NGS-derived average pairwise diversity exhibited a higher sensitivity (90% vs 78%) and specificity (95% vs 67%) than the fraction of ambiguous nucleotides. Additionally, the average pairwise diversity could be used to estimate the time since infection with a mean absolute error of 0.84 years, compared with 1.03 years for the fraction of ambiguous nucleotides.

CONCLUSIONS

Viral diversity based on NGS data is more precise than that based on population sequencing in its ability to predict infection recency and provides an estimated time since infection that has a mean absolute error of <1 year.

Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings

Next-generation sequencing (NGS) technologies have revolutionized biological research by generating genomic data that were once unaffordable by traditional first-generation sequencing technologies. These sequencing methodologies provide an opportunity for in-depth analyses of host and pathogen genomes as they are able to sequence millions of templates at a time. However, these large datasets can only be efficiently explored using bioinformatics analyses requiring huge data storage and computational resources adapted for high-performance processing. High-performance computing allows for efficient handling of large data and tasks that may require multi-threading and prolonged computational times, which is not feasible with ordinary computers. However, high-performance computing resources are costly and therefore not always readily available in low-income settings. We describe the establishment of an affordable high-performance computing bioinformatics cluster consisting of 3 nodes, constructed using ordinary desktop computers and open-source software including Linux Fedora, SLURM Workload Manager, and the Conda package manager. For the analysis of large antibody sequence datasets and for complex viral phylodynamic analyses, the cluster out-performed desktop computers. This has demonstrated that it is possible to construct high-performance computing capacity capable of analyzing large NGS data from relatively low-cost hardware and entirely free (open-source) software, even in resource-limited settings. Such a cluster design has broad utility beyond bioinformatics to other studies that require high-performance computing.

Molecular variability and genetic structure of white spot syndrome virus strains from northwest Mexico based on the analysis of genomes

White spot syndrome virus (WSSV) has a ∼300 kb double-stranded DNA genome. It originated in China, spread rapidly through shrimp farms in Asia, and subsequently to America. This study determined complete genome sequences for nine historic WSSV strains isolated from Pacific white shrimp (Litopenaeus vannamei) captured in farm ponds in northwest Mexico (Sinaloa and Nayarit). Genomic DNA was captured by an amplification method using overlapping long-range PCR and sequencing by Ion Torrent-PGM. Complete genome sequences were assembled (length range 255-290 kb) and comparative genome analysis with WSSV strains revealed substantial deletions (3 and 10 kb in two regions) in seven strains, with two strains differing from the rest. Phylogenetic analysis identified that the WSSV strains from the northern area of the state of Sinaloa clustered with strains from China (LC1, LC10, DVI) and Korea (ACF2, ACF4), while those from the southern region of the state of Nayarit (AC1 and JP) differed from both of those and from strains found in Taiwan and Thailand. Our data offer insights into the diversity of the WSSV genome in one country and their divergent origin, suggest that it entered Mexico via multiple routes and that specific genome regions can accommodate substantial deletions without compromising viability.

De novo haplotype reconstruction in viral quasispecies using paired-end read guided path finding

Motivation

RNA virus populations contain different but genetically related strains, all infecting an individual host. Reconstruction of the viral haplotypes is a fundamental step to characterize the virus population, predict their viral phenotypes and finally provide important information for clinical treatment and prevention. Advances of the next-generation sequencing technologies open up new opportunities to assemble full-length haplotypes. However, error-prone short reads, high similarities between related strains, an unknown number of haplotypes pose computational challenges for reference-free haplotype reconstruction. There is still much room to improve the performance of existing haplotype assembly tools.

Results

In this work, we developed a de novo haplotype reconstruction tool named PEHaplo, which employs paired-end reads to distinguish highly similar strains for viral quasispecies data. It was applied on both simulated and real quasispecies data, and the results were benchmarked against several recently published de novo haplotype reconstruction tools. The comparison shows that PEHaplo outperforms the benchmarked tools in a comprehensive set of metrics.

Availability and implementation

The source code and the documentation of PEHaplo are available at https://github.com/chjiao/PEHaplo.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Needs for Developing Experiments on Reservoirs in Hantavirus Research: Accomplishments, Challenges and Promises for the Future

Due to their large geographic distribution and potential high mortality rates in human infections, hantaviruses constitute a worldwide threat to public health. As such, they have been the subject of a large array of clinical, virological and eco-evolutionary studies. Many experiments have been conducted in vitro or on animal models to identify the mechanisms leading to pathogenesis in humans and to develop treatments of hantavirus diseases. Experimental research has also been dedicated to the understanding of the relationship between hantaviruses and their reservoirs. However, these studies remain too scarce considering the diversity of hantavirus/reservoir pairs identified, and the wide range of issues that need to be addressed. In this review, we present a synthesis of the experimental studies that have been conducted on hantaviruses and their reservoirs. We aim at summarizing the knowledge gathered from this research, and to emphasize the gaps that need to be filled. Despite the many difficulties encountered to carry hantavirus experiments, we advocate for the need of such studies in the future, at the interface of evolutionary ecology and virology. They are critical to address emerging areas of research, including hantavirus evolution and the epidemiological consequences of individual variation in infection outcomes.

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.

DISSEQT-DIStribution-based modeling of SEQuence space Time dynamics

Rapidly evolving microbes are a challenge to model because of the volatile, complex, and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing, and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. The pipeline is openly available on GitHub (https://github.com/rasmushenningsson/DISSEQT.jl, accessed 23 June 2019) and Synapse (https://www.synapse.org/#!Synapse: syn11425758, accessed 23 June 2019), covering the entire workflow from read alignment to visualization of results. Our pipeline is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype-phenotype maps. We illustrate its utility and capacity with examples from evolving RNA virus populations, which present one of the highest degrees of genetic heterogeneity within a given population found in nature. Using our pipeline, we empirically reconstruct the evolutionary trajectories of evolving populations in sequence space and genotype-phenotype fitness landscapes. We show that while sequence space is vastly multidimensional, the relevant genetic space of evolving microbial populations is of intrinsically low dimension. In addition, evolutionary trajectories of these populations can be faithfully monitored to identify the key minority genotypes contributing most to evolution. Finally, we show that empirical fitness landscapes, when reconstructed to include minority variants, can predict phenotype from genotype with high accuracy.