Ultra-deep sequencing for the analysis of viral populations

The dynamic variation position and predominant quasispecies of hepatitis B virus: Novel predictors of early hepatocarcinoma

To find the predictors of early HCC based on the dynamic changes of HBV quasispecies, this study utilizing the second-generation sequencing (NGS) and high-order multiplex droplet digital PCR (ddPCR) technology to examine the HBV quasispecies in serum of total 247 subjects recruited from high-incidence area of HCC. In the discovery stage, 15 non-synonymous Single Nucleotide Polymorphisms (SNPs) with higher variant proportion in HCC case group were founded (all P<0.05). Furthermore, the variant proportions in some of these SNPs were observed changing regularly within 5 years before the onset of HCC, and 5 of them located in HBX, 2 in HBS and 2 in HBC. The HBV predominant quasispecies and their consensus sequences were identified by genetic evolution analysis, in which the high HBS and HBC quasispecies heterogeneity were found associated with the forming of multifarious quasispecies clones, and the HBX gene had the highest proportion of predominant quasispecies (46.7 % in HBX vs 12.7 % and 13.8 % in HBS and HBC respectively) with the key variations (G1512A, A1630G, T1753C/G/A, A1762T and G1764A) determined. In the validation stage, we confirmed that the combined double mutations of G1512A+A1630G, A1762T+G1764A, and the combined triple mutations of T1753C/G/A + A1762T+G1764A, all expressed higher in early HCC cases when comparing with control group (all P<0.05). We also demonstrated the advantages of ddPCR using in multi-variations detection in large-sample for early HCC surveillance and screening. So we think that the dynamic of key HBV variation positions and their different combinations determined by quasispecies anlysis in this study can act as the novel predictors of early hepatocarcinoma and suitable to popularize and apply in HCC screening.

Ryegrass mottle virus complete genome determination and development of infectious cDNA by combining two methods- 3' RACE and RNA-Seq

Ryegrass mottle virus (RGMoV; genus: Sobemovirus) is a single-stranded positive RNA virus with a 30 nm viral particle size. It exhibits T = 3 symmetry with 180 coat protein (CP) subunits forming a viral structure. The RGMoV genome comprises five open reading frames that encode P1, Px, a membrane-anchored 3C-like serine protease, a viral genome-linked protein, P16, an RNA-dependent RNA polymerase, and CP. The RGMoV genome size varies, ranging from 4175 nt (MW411579.1) to 4253 nt (MW411579.1) in the deposited sequences. An earlier deposited RGMoV complete genome sequence of 4212 nt length (EF091714.1) was used to develop an infectious complementary DNA (icDNA) construct for in vitro gRNA transcription from the T7 promoter. However, viral infection was not induced when the transcribed gRNA was introduced into oat plants, indicating the potential absence of certain sequences in either the 5' or 3' untranslated regions (UTR) or both. The complete sequence of the 3' UTR was determined through 3' end RACE, while the 5' UTR was identified using high-throughput sequencing (HTS)-RNA-Seq to resolve the potential absences. Only the icDNA vector containing the newly identified UTR sequences proved infectious, resulting in typical viral infection symptoms and subsequent propagation of progeny viruses, exhibiting the ability to cause repeated infections in oat plants after at least one passage. The successful generation of icDNA highlighted the synergistic potential of utilizing both methods when a single approach failed. Furthermore, this study demonstrated the reliability of HTS as a method for determining the complete genome sequence of viral genomes.

SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications

Genetic sequencing is subject to many different types of errors, but most analyses treat the resultant sequences as if they are known without error. Next generation sequencing methods rely on significantly larger numbers of reads than previous sequencing methods in exchange for a loss of accuracy in each individual read. Still, the coverage of such machines is imperfect and leaves uncertainty in many of the base calls. In this work, we demonstrate that the uncertainty in sequencing techniques will affect downstream analysis and propose a straightforward method to propagate the uncertainty. Our method (which we have dubbed Sequence Uncertainty Propagation, or SUP) uses a probabilistic matrix representation of individual sequences which incorporates base quality scores as a measure of uncertainty that naturally lead to resampling and replication as a framework for uncertainty propagation. With the matrix representation, resampling possible base calls according to quality scores provides a bootstrap- or prior distribution-like first step towards genetic analysis. Analyses based on these re-sampled sequences will include a more complete evaluation of the error involved in such analyses. We demonstrate our resampling method on SARS-CoV-2 data. The resampling procedures add a linear computational cost to the analyses, but the large impact on the variance in downstream estimates makes it clear that ignoring this uncertainty may lead to overly confident conclusions. We show that SARS-CoV-2 lineage designations via Pangolin are much less certain than the bootstrap support reported by Pangolin would imply and the clock rate estimates for SARS-CoV-2 are much more variable than reported.

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 [...].

Next Generation Sequencing for the Analysis of Parvovirus B19 Genomic Diversity

Parvovirus B19 (B19V) is a ssDNA human virus, responsible for an ample range of clinical manifestations. Sequencing of B19V DNA from clinical samples is frequently reported in the literature to assign genotype (genotypes 1-3) and for finer molecular epidemiological tracing. The increasing availability of Next Generation Sequencing (NGS) with its depth of coverage potentially yields information on intrinsic sequence heterogeneity; however, integration of this information in analysis of sequence variation is not routinely obtained. The present work investigated genomic sequence heterogeneity within and between B19V isolates by application of NGS techniques, and by the development of a novel dedicated bioinformatic tool and analysis pipeline, yielding information on two newly defined parameters. The first, α-diversity, is a measure of the amount and distribution of position-specific, normalised Shannon Entropy, as a measure of intra-sample sequence heterogeneity. The second, σ-diversity, is a measure of the amount of inter-sample sequence heterogeneity, also incorporating information on α-diversity. Based on these indexes, further cluster analysis can be performed. A set of 24 high-titre viraemic samples was investigated. Of these, 23 samples were genotype 1 and one sample was genotype 2. Genotype 1 isolates showed low α-diversity values, with only a few samples showing distinct position-specific polymorphisms; a few genetically related clusters emerged when analysing inter-sample distances, correlated to the year of isolation; the single genotype 2 isolate showed the highest α-diversity, even if not presenting polymorphisms, and was an evident outlier when analysing inter-sample distance. In conclusion, NGS analysis and the bioinformatic tool and pipeline developed and used in the present work can be considered effective tools for investigating sequence diversity, an observable parameter that can be incorporated into the quasispecies theory framework to yield a better insight into viral evolution dynamics.

Quasispecies Fitness Partition to Characterize the Molecular Status of a Viral Population. Negative Effect of Early Ribavirin Discontinuation in a Chronically Infected HEV Patient

The changes occurring in viral quasispecies populations during infection have been monitored using diversity indices, nucleotide diversity, and several other indices to summarize the quasispecies structure in a single value. In this study, we present a method to partition quasispecies haplotypes into four fractions according to their fitness: the master haplotype, rare haplotypes at two levels (those present at <0.1%, and those at 0.1−1%), and a fourth fraction that we term emerging haplotypes, present at frequencies >1%, but less than that of the master haplotype. We propose that by determining the changes occurring in the volume of the four quasispecies fitness fractions together with those of the Hill number profile we will be able to visualize and analyze the molecular changes in the composition of a quasispecies with time. To develop this concept, we used three data sets: a technical clone of the complete SARS-CoV-2 spike gene, a subset of data previously used in a study of rare haplotypes, and data from a clinical follow-up study of a patient chronically infected with HEV and treated with ribavirin. The viral response to ribavirin mutagenic treatment was selection of a rich set of synonymous haplotypes. The mutation spectrum was very complex at the nucleotide level, but at the protein (phenotypic/functional) level the pattern differed, showing a highly prevalent master phenotype. We discuss the putative implications of this observation in relation to mutagenic antiviral treatment.

Low-frequency HIV-1 drug resistance mutations in antiretroviral naïve individuals in Botswana

BACKGROUND

Individuals living with human immunodeficiency virus (HIV) who experience virological failure (VF) after combination antiretroviral therapy (cART) initiation may have had low-frequency drug resistance mutations (DRMs) at cART initiation. There are no data on low-frequency DRMs among cART-naïve HIV-positive individuals in Botswana.

METHODS

We evaluated the prevalence of low-frequency DRMs among cART-naïve individuals previously sequenced using Sanger sequencing. The generated pol amplicons were sequenced by next-generation sequencing.

RESULTS

We observed low-frequency DRMs (detected at <20% in 33/103 (32%) of the successfully sequenced individuals, of whom four also had mutations detected at >20%. K65R was the most common low-frequency DRM detected in 8 individuals. Eighty-two of the 103 individuals had follow-up viral load data while on cART. Twenty-seven of the 82 individuals harbored low-frequency DRMs. Only 12 of 82 individuals experienced VF. The following low-frequency DRMs were observed in four individuals experiencing VF: K65R, K103N, V108I, and Y188C. No statistically significant difference was observed in the prevalence of low-frequency DRMs between individuals experiencing VF (4/12) and those not experiencing VF (23/70) (P = .97). However, individuals with non-nucleoside reverse transcriptase inhibitors-associated low-frequency DRMs were 2.68 times more likely to experience VF (odds ratio, 2.68; 95% confidential interval, 0.4-13.9) compared with those without (P = .22).

CONCLUSION

Next-generation sequencing was able to detect low-frequency DRMs in this cohort in Botswana, but these DRMs did not contribute significantly to VF.

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.

Evolution of within-host variants of the hepatitis C virus

INTRODUCTION

Comprehensive investigation of the within-host evolution of hepatitis C virus (HCV) variants has been difficult without high coverage deep sequencing data and bioinformatics tools to characterise these variants. With the advent of high throughput, long-read sequencing platforms such as Oxford Nanopore Technology (ONT), capturing within-host evolution of HCV using full genome sequences has become feasible. This study aimed to provide the proof of concept that within-host evolutionary analysis of HCV using near-full-length genomes, is achievable.

METHODS

Five treatment naïve subjects with chronic HCV infection were sampled longitudinally from 6 months to 5 years post-infection, with 3-5 sampling timepoints per subject. Near full-length sequences generated using the ONT platform encompassing within-host HCV variants were analysed using an in-house bioinformatic tool. A 200-sequence proxy alignment of the viral variants was made for each subject and timepoint, proportionately representing the observed within-host variants. This alignment was then used in a Bayesian evolutionary analysis using BEAST software suite (v1.8).

RESULTS

The estimated within-host substitution rates ranged between 0.89 and 6.19 × 10^-5 substitutions/site/day. At most timepoints, observed viral lineages were closely related to those from the immediately preceding timepoint, and genetic diversity bottlenecks were observed at intervals in both the acute and chronic phases of infection. The highest within-host mutation rates were observed in the Envelope-P7 and NS5 regions while the Core region was the most conserved.

CONCLUSION

This study demonstrates the feasibility of studying within-host evolution of near-full-length HCV genomes, using long-read sequencing platforms. When considered in conjunction with meta-data such as the host immune response, these methods may offer high resolution insights into immune escape (in vivo or in vitro) to inform vaccine design and to predict spontaneous clearance.

A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events

Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.

Instance-based error correction for short reads of disease-associated genes

BACKGROUND

Genomic reads from sequencing platforms contain random errors. Global correction algorithms have been developed, aiming to rectify all possible errors in the reads using generic genome-wide patterns. However, the non-uniform sequencing depths hinder the global approach to conduct effective error removal. As some genes may get under-corrected or over-corrected by the global approach, we conduct instance-based error correction for short reads of disease-associated genes or pathways. The paramount requirement is to ensure the relevant reads, instead of the whole genome, are error-free to provide significant benefits for single-nucleotide polymorphism (SNP) or variant calling studies on the specific genes.

RESULTS

To rectify possible errors in the short reads of disease-associated genes, our novel idea is to exploit local sequence features and statistics directly related to these genes. Extensive experiments are conducted in comparison with state-of-the-art methods on both simulated and real datasets of lung cancer associated genes (including single-end and paired-end reads). The results demonstrated the superiority of our method with the best performance on precision, recall and gain rate, as well as on sequence assembly results (e.g., N50, the length of contig and contig quality).

CONCLUSION

Instance-based strategy makes it possible to explore fine-grained patterns focusing on specific genes, providing high precision error correction and convincing gene sequence assembly. SNP case studies show that errors occurring at some traditional SNP areas can be accurately corrected, providing high precision and sensitivity for investigations on disease-causing point mutations.

Adaptation of Oxford Nanopore technology for hepatitis C whole genome sequencing and identification of within-host viral variants

Background

Hepatitis C (HCV) and many other RNA viruses exist as rapidly mutating quasi-species populations in a single infected host. High throughput characterization of full genome, within-host variants is still not possible despite advances in next generation sequencing. This limitation constrains viral genomic studies that depend on accurate identification of hemi-genome or whole genome, within-host variants, especially those occurring at low frequencies. With the advent of third generation long read sequencing technologies, including Oxford Nanopore Technology (ONT) and PacBio platforms, this problem is potentially surmountable. ONT is particularly attractive in this regard due to the portable nature of the MinION sequencer, which makes real-time sequencing in remote and resource-limited locations possible. However, this technology (termed here ‘nanopore sequencing’) has a comparatively high technical error rate. The present study aimed to assess the utility, accuracy and cost-effectiveness of nanopore sequencing for HCV genomes. We also introduce a new bioinformatics tool (Nano-Q) to differentiate within-host variants from nanopore sequencing.

Results

The Nanopore platform, when the coverage exceeded 300 reads, generated comparable consensus sequences to Illumina sequencing. Using HCV Envelope plasmids (~ 1800 nt) mixed in known proportions, the capacity of nanopore sequencing to reliably identify variants with an abundance as low as 0.1% was demonstrated, provided the autologous reference sequence was available to identify the matching reads. Successful pooling and nanopore sequencing of 52 samples from patients with HCV infection demonstrated its cost effectiveness (AUD$ 43 per sample with nanopore sequencing versus $100 with paired-end short read technology). The Nano-Q tool successfully separated between-host sequences, including those from the same subtype, by bulk sorting and phylogenetic clustering without an autologous reference sequence (using only a subtype-specific generic reference). The pipeline also identified within-host viral variants and their abundance when the parameters were appropriately adjusted.

Conclusion

Cost effective HCV whole genome sequencing and within-host variant identification without haplotype reconstruction are potential advantages of nanopore sequencing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07460-1.

MIPP-Seq: ultra-sensitive rapid detection and validation of low-frequency mosaic mutations

BACKGROUND

Mosaic mutations contribute to numerous human disorders. As such, the identification and precise quantification of mosaic mutations is essential for a wide range of research applications, clinical diagnoses, and early detection of cancers. Currently, the low-throughput nature of single allele assays (e.g., allele-specific ddPCR) commonly used for genotyping known mutations at very low alternate allelic fractions (AAFs) have limited the integration of low-level mosaic analyses into clinical and research applications. The growing importance of mosaic mutations requires a more rapid, low-cost solution for mutation detection and validation.

METHODS

To overcome these limitations, we developed Multiple Independent Primer PCR Sequencing (MIPP-Seq) which combines the power of ultra-deep sequencing and truly independent assays. The accuracy of MIPP-seq to quantifiable detect and measure extremely low allelic fractions was assessed using a combination of SNVs, insertions, and deletions at known allelic fractions in blood and brain derived DNA samples.

RESULTS

The Independent amplicon analyses of MIPP-Seq markedly reduce the impact of allelic dropout, amplification bias, PCR-induced, and sequencing artifacts. Using low DNA inputs of either 25 ng or 50 ng of DNA, MIPP-Seq provides sensitive and quantitative assessments of AAFs as low as 0.025% for SNVs, insertion, and deletions.

CONCLUSIONS

MIPP-Seq provides an ultra-sensitive, low-cost approach for detecting and validating known and novel mutations in a highly scalable system with broad utility spanning both research and clinical diagnostic testing applications. The scalability of MIPP-Seq allows for multiplexing mutations and samples, which dramatically reduce costs of variant validation when compared to methods like ddPCR. By leveraging the power of individual analyses of multiple unique and independent reactions, MIPP-Seq can validate and precisely quantitate extremely low AAFs across multiple tissues and mutational categories including both indels and SNVs. Furthermore, using Illumina sequencing technology, MIPP-seq provides a robust method for accurate detection of novel mutations at an extremely low AAF.

Illuminating an Ecological Blackbox: Using High Throughput Sequencing to Characterize the Plant Virome Across Scales

The ecology of plant viruses began to be explored at the end of the 19th century. Since then, major advances have revealed mechanisms of virus-host-vector interactions in various environments. These advances have been accelerated by new technlogies for virus detection and characterization, most recently including high throughput sequencing (HTS). HTS allows investigators, for the first time, to characterize all or nearly all viruses in a sample without a priori information about which viruses might be present. This powerful approach has spurred new investigation of the viral metagenome (virome). The rich virome datasets accumulated illuminate important ecological phenomena such as virus spread among host reservoirs (wild and domestic), effects of ecosystem simplification caused by human activities (and agriculture) on the biodiversity and the emergence of new viruses in crops. To be effective, however, HTS-based virome studies must successfully navigate challenges and pitfalls at each procedural step, from plant sampling to library preparation and bioinformatic analyses. This review summarizes major advances in plant virus ecology associated with technological developments, and then presents important considerations and best practices for HTS use in virome studies.

Streamlined Subpopulation, Subtype, and Recombination Analysis of HIV-1 Half-Genome Sequences Generated by High-Throughput Sequencing

The highly recombinogenic nature of human immunodeficiency virus type 1 (HIV-1) leads to recombination and emergence of quasispecies. It is important to reliably identify subpopulations to understand the complexity of a viral population for drug resistance surveillance and vaccine development. High-throughput sequencing (HTS) provides improved resolution over Sanger sequencing for the analysis of heterogeneous viral subpopulations. However, current methods of analysis of HTS reads are unable to fully address accurate population reconstruction. Hence, there is a dire need for a more sensitive, accurate, user-friendly, and cost-effective method to analyze viral quasispecies. For this purpose, we have improved the HIVE-hexahedron algorithm that we previously developed with in silico short sequences to analyze raw HTS short reads. The significance of this study is that our standalone algorithm enables a streamlined analysis of quasispecies, subtype, and recombination patterns from long HIV-1 genome regions without the need of additional sequence analysis tools. Distinct viral populations and recombination patterns identified by HIVE-hexahedron are further validated by comparison with sequences obtained by single genome sequencing (SGS).

High-throughput sequencing (HTS) has been widely used to characterize HIV-1 genome sequences. There are no algorithms currently that can directly determine genotype and quasispecies population using short HTS reads generated from long genome sequences without additional software. To establish a robust subpopulation, subtype, and recombination analysis workflow, we amplified the HIV-1 3′-half genome from plasma samples of 65 HIV-1-infected individuals and sequenced the entire amplicon (∼4,500 bp) by HTS. With direct analysis of raw reads using HIVE-hexahedron, we showed that 48% of samples harbored 2 to 13 subpopulations. We identified various subtypes (17 A1s, 4 Bs, 27 Cs, 6 CRF02_AGs, and 11 unique recombinant forms) and defined recombinant breakpoints of 10 recombinants. These results were validated with viral genome sequences generated by single genome sequencing (SGS) or the analysis of consensus sequence of the HTS reads. The HIVE-hexahedron workflow is more sensitive and accurate than just evaluating the consensus sequence and also more cost-effective than SGS.

IMPORTANCE The highly recombinogenic nature of human immunodeficiency virus type 1 (HIV-1) leads to recombination and emergence of quasispecies. It is important to reliably identify subpopulations to understand the complexity of a viral population for drug resistance surveillance and vaccine development. High-throughput sequencing (HTS) provides improved resolution over Sanger sequencing for the analysis of heterogeneous viral subpopulations. However, current methods of analysis of HTS reads are unable to fully address accurate population reconstruction. Hence, there is a dire need for a more sensitive, accurate, user-friendly, and cost-effective method to analyze viral quasispecies. For this purpose, we have improved the HIVE-hexahedron algorithm that we previously developed with in silico short sequences to analyze raw HTS short reads. The significance of this study is that our standalone algorithm enables a streamlined analysis of quasispecies, subtype, and recombination patterns from long HIV-1 genome regions without the need of additional sequence analysis tools. Distinct viral populations and recombination patterns identified by HIVE-hexahedron are further validated by comparison with sequences obtained by single genome sequencing (SGS).

Evolutionary trajectory of fish Piscine novirhabdovirus (=Viral Hemorrhagic Septicemia Virus) across its Laurentian Great Lakes history: Spatial and temporal diversification

Piscine novirhabdovirus = Viral Hemorrhagic Septicemia Virus (VHSV) first appeared in the Laurentian Great Lakes with large outbreaks from 2005 to 2006, as a new and novel RNA rhabdovirus subgenogroup (IVb) that killed >30 fish species. Interlude periods punctuated smaller more localized outbreaks in 2007, 2010, and 2017, although some fishes tested positive in the intervals. There have not been reports of outbreaks or positives from 2018, 2019, or 2020. Here, we employ a combined population genetics and phylogenetic approach to evaluate spatial and temporal evolutionary trajectory on its G-gene sequence variation, in comparison with whole-genome sequences (11,083 bp) from a subset of 44 individual isolates (including 40 newly sequenced ones). Our results show that IVb (N = 184 individual fish isolates) diversified into 36 G-gene haplotypes from 2003 to 2017, stemming from two originals ("a" and "b"). G-gene haplotypes "a" and "b" differed by just one synonymous single-nucleotide polymorphism (SNP) substitution, remained the most abundant until 2011, then disappeared. Group "a" descendants (14 haplotypes) remained most prevalent in the Upper and Central Great Lakes, with eight (51%) having nonsynonymous substitutions. Group "b" descendants primarily have occurred in the Lower Great Lakes, including 22 haplotypes, of which 15 (68%) contained nonsynonymous changes. Evolutionary patterns of the whole-genome sequences (which had 34 haplotypes among 44 isolates) appear congruent with those from the G-gene. Virus populations significantly diverged among the Upper, Central, and Lower Great Lakes, diversifying over time. Spatial divergence was apparent in the overall patterns of nucleotide substitutions, while amino acid changes increased temporally. VHSV-IVb thus significantly differentiated across its less than two decades in the Great Lakes, accompanied by declining outbreaks and virulence. Continuing diversification likely allowed the virus to persist at low levels in resident fish populations, and may facilitate its potential for further and future spread to new habitats and nonacclimated hosts.

Evaluation of haplotype callers for next-generation sequencing of viruses

Currently, the standard practice for assembling next-generation sequencing (NGS) reads of viral genomes is to summarize thousands of individual short reads into a single consensus sequence, thus confounding useful intra-host diversity information for molecular phylodynamic inference. It is hypothesized that a few viral strains may dominate the intra-host genetic diversity with a variety of lower frequency strains comprising the rest of the population. Several software tools currently exist to convert NGS sequence variants into haplotypes. Previous benchmarks of viral haplotype reconstruction programs used simulation scenarios that are useful from a mathematical perspective but do not reflect viral evolution and epidemiology. Here, we tested twelve NGS haplotype reconstruction methods using viral populations simulated under realistic evolutionary dynamics. We simulated coalescent-based populations that spanned known levels of viral genetic diversity, including mutation rates, sample size and effective population size, to test the limits of the haplotype reconstruction methods and to ensure coverage of predicted intra-host viral diversity levels (especially HIV-1). All twelve investigated haplotype callers showed variable performance and produced drastically different results that were mainly driven by differences in mutation rate and, to a lesser extent, in effective population size. Most methods were able to accurately reconstruct haplotypes when genetic diversity was low. However, under higher levels of diversity (e.g., those seen intra-host HIV-1 infections), haplotype reconstruction quality was highly variable and, on average, poor. All haplotype reconstruction tools, except QuasiRecomb and ShoRAH, greatly underestimated intra-host diversity and the true number of haplotypes. PredictHaplo outperformed, in regard to highest precision, recall, and lowest UniFrac distance values, the other haplotype reconstruction tools followed by CliqueSNV, which, given more computational time, may have outperformed PredictHaplo. Here, we present an extensive comparison of available viral haplotype reconstruction tools and provide insights for future improvements in haplotype reconstruction tools using both short-read and long-read technologies.

Transmission modes affect the population structure of potato virus Y in potato

Transmission is a crucial part of a viral life cycle and transmission mode can have an important impact on virus biology. It was demonstrated that transmission mode can influence the virulence and evolution of a virus; however, few empirical data are available to describe the direct underlying changes in virus population structure dynamics within the host. Potato virus Y (PVY) is an RNA virus and one of the most damaging pathogens of potato. It comprises several genetically variable strains that are transmitted between plants via different transmission modes. To investigate how transmission modes affect the within-plant viral population structure, we have used a deep sequencing approach to examine the changes in the genetic structure of populations (in leaves and tubers) of three PVY strains after successive passages by horizontal (aphid and mechanical) and vertical (via tubers) transmission modes. Nucleotide diversities of viral populations were significantly influenced by transmission modes; lineages transmitted by aphids were the least diverse, whereas lineages transmitted by tubers were the most diverse. Differences in nucleotide diversities of viral populations between leaves and tubers were transmission mode-dependent, with higher diversities in tubers than in leaves for aphid and mechanically transmitted lineages. Furthermore, aphid and tuber transmissions were shown to impose stronger genetic bottlenecks than mechanical transmission. To better understand the structure of virus populations within the host, transmission mode, movement of the virus within the host, and the number of replication cycles after transmission event need to be considered. Collectively, our results suggest a significant impact of virus transmission modes on the within-plant diversity of virus populations and provide quantitative fundamental data for understanding how transmission can shape virus diversity in the natural ecosystems, where different transmission modes are expected to affect virus population structure and consequently its evolution.

Within-Host Viral Diversity: A Window into Viral Evolution

The evolutionary dynamics of a virus can differ within hosts and across populations. Studies of within-host evolution provide an important link between experimental studies of virus evolution and large-scale phylodynamic analyses. They can determine the extent to which global processes are recapitulated on local scales and how accurately experimental infections model natural ones. They may also inform epidemiologic models of disease spread and reveal how host-level dynamics contribute to a virus's evolution at a larger scale. Over the last decade, advances in viral sequencing have enabled detailed studies of viral genetic diversity within hosts. I review how within-host diversity is sampled, measured, and expressed, and how comparative studies of viral diversity can be leveraged to elucidate a virus's evolutionary dynamics. These concepts are illustrated with detailed reviews of recent research on the within-host evolution of influenza virus, dengue virus, and cytomegalovirus.

The population structure of Rose rosette virus in the USA

Rose rosette virus (RRV) (genus Emaravirus) is the causal agent of the homonymous disease, the most destructive malady of roses in the USA. Although the importance of the disease is recognized, little sequence information and no full genomes are available for RRV, a multi-segmented RNA virus. To better understand the population structure of the virus we implemented a Hi-Plex PCR amplicon high-throughput sequencing approach to sequence all 7 segments and to quantify polymorphisms in 91 RRV isolates collected from 16 states in the USA. Analysis revealed insertion/deletion (indel) polymorphisms primarily in the 5' and 3' non-coding, but also within coding regions, including some resulting in changes of protein length. Phylogenetic analysis showed little geographical structuring, suggesting that topography does not have a strong influence on virus evolution. Overall, the virus populations were homogeneous, possibly because of regular movement of plants, the recent emergence of RRV and/or because the virus is under strong purification selection to preserve its integrity and biological functions.

Ecological Dynamics Impacting Bluetongue Virus Transmission in North America

Bluetongue virus (BTV) is an arbovirus transmitted to domestic and wild ruminants by certain species of Culicoides midges. The disease resulting from infection with BTV is economically important and can influence international trade and movement of livestock, the economics of livestock production, and animal welfare. Recent changes in the epidemiology of Culicoides-transmitted viruses, notably the emergence of exotic BTV genotypes in Europe, have demonstrated the devastating economic consequences of BTV epizootics and the complex nature of transmission across host-vector landscapes. Incursions of novel BTV serotypes into historically enzootic countries or regions, including the southeastern United States (US), Israel, Australia, and South America, have also occurred, suggesting diverse pathways for the transmission of these viruses. The abundance of BTV strains and multiple reassortant viruses circulating in Europe and the US in recent years demonstrates considerable genetic diversity of BTV strains and implies a history of reassortment events within the respective regions. While a great deal of emphasis is rightly placed on understanding the epidemiology and emergence of BTV beyond its natural ecosystem, the ecological contexts in which BTV maintains an enzootic cycle may also be of great significance. This review focuses on describing our current knowledge of ecological factors driving BTV transmission in North America. Information presented in this review can help inform future studies that may elucidate factors that are relevant to longstanding and emerging challenges associated with prevention of this disease.

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.

Laboratory Methods in Molecular Epidemiology: Viral Infections

Viruses, which are the most abundant biological entities on the planet, have been regarded as the "dark matter" of biology in the sense that despite their ubiquity and frequent presence in large numbers, their detection and analysis are not always straightforward. The majority of them are very small (falling under the limit of 0.5 μm), and collectively, they are extraordinarily diverse. In fact, the majority of the genetic diversity on the planet is found in the so-called virosphere, or the world of viruses. Furthermore, the most frequent viral agents of disease in humans display an RNA genome, and frequently evolve very fast, due to the fact that most of their polymerases are devoid of proofreading activity. Therefore, their detection, genetic characterization, and epidemiological surveillance are rather challenging. This review (part of the Curated Collection on Advances in Molecular Epidemiology of Infectious Diseases) describes many of the methods that, throughout the last few decades, have been used for viral detection and analysis. Despite the challenge of having to deal with high genetic diversity, the majority of these methods still depend on the amplification of viral genomic sequences, using sequence-specific or sequence-independent approaches, exploring thermal profiles or a single nucleic acid amplification temperature. Furthermore, viral populations, and especially those with RNA genomes, are not usually genetically uniform but encompass swarms of genetically related, though distinct, viral genomes known as viral quasispecies. Therefore, sequence analysis of viral amplicons needs to take this fact into consideration, as it constitutes a potential analytic problem. Possible technical approaches to deal with it are also described here. *This article is part of a curated collection.

Analysis of HBV X gene quasispecies characteristics by next-generation sequencing and cloning-based sequencing and its association with hepatocellular carcinoma progression

OBJECTIVES

This study aimed to describe the differences between next-generation sequencing (NGS) and cloning-based sequencing (CBS) in HBX quasispecies research and primitively investigate the relationship between the dominant HBX quasispecies and hepatocellular carcinoma (HCC).

METHODS

A total of 12 serum samples were collected. Serum hepatitis B virus (HBV) DNA was extracted, and the HBV X-region (HBX) was amplified by nested polymerase chain reaction (PCR). The PCR products were simultaneously tested with NGS and CBS to detect quasispecies of the HBX.

RESULTS

A total of 9348 eligible quasispecies sequences were obtained by NGS, which were much larger than the 98 of that by CBS. By the phylogenetic tree, the dominant quasispecies sequence of each sample could be found, although they had several nucleotides differences between the dominant quasispecies sequences found by CBS and NGS. By comparing the quasispecies heterogeneity, it was found that the quasispecies complexity value of HBV X-region obtained by NGS was higher than CBS (P < 0.05). The diversity values, including d, dS, dN, an d d N/ dS obtained by NGS were lower than by CBS (all of P < 0.01). The relativity of Spearman(rs) in d, dS, and dN were statistically significant (rs_ d = 0.865, P = 0.001; rs_ dS = 0.722, P = 0.014; and rs_ dN = 0.738, P = 0.011, respectively). There were 21 different bases between the HBX quasispecies of case A and control B.

CONCLUSION

The results of this can be used as guidance when researchers plan to choose a suitable method to study quasispecies, especially the HBV X gene quasispecies. Some high-risk mutations of HBX quasispecies were also found in this study and their relationship with HCC need deeper exploration.

Theoretical conditions for the coexistence of viral strains with differences in phenotypic traits: a bifurcation analysis

We investigate the dynamics of a wild-type viral strain which generates mutant strains differing in phenotypic properties for infectivity, virulence and mutation rates. We study, by means of a mathematical model and bifurcation analysis, conditions under which the wild-type and mutant viruses, which compete for the same host cells, can coexist. The coexistence conditions are formulated in terms of the basic reproductive numbers of the strains, a maximum value of the mutation rate and the virulence of the pathogens. The analysis reveals that parameter space can be divided into five regions, each with distinct dynamics, that are organized around degenerate Bogdanov-Takens and zero-Hopf bifurcations, the latter of which gives rise to a curve of transcritical bifurcations of periodic orbits. These results provide new insights into the conditions by which viral populations may contain multiple coexisting strains in a stable manner.

Measurements of intrahost viral diversity require an unbiased diversity metric

Viruses exist within hosts at large population sizes and are subject to high rates of mutation. As such, viral populations exhibit considerable sequence diversity. A variety of summary statistics have been developed which describe, in a single number, the extent of diversity in a viral population; such measurements allow the diversities of different populations to be compared, and the effect of evolutionary forces on a population to be assessed. Here we highlight statistical artefacts underlying some common measures of sequence diversity, whereby variation in the depth of genome sequencing may substantially affect the extent of diversity measured in a viral population, making comparisons of population diversity invalid. Specifically, naive estimation of sequence entropy provides a systematically biased metric, a lower read depth being expected to produce a lower estimate of diversity. The number of polymorphic loci per kilobase of genome is more unpredictably affected by read depth, giving potentially flawed results at lower sequencing depths. We show that the nucleotide diversity statistic π provides an unbiased estimate of diversity in the sense that the expected value of the statistic is equal to the correct value of the property being measured. Our results are of importance for studies interpreting genome sequence data; we describe how diversity may be assessed in viral populations in a fair and unbiased manner.

Full-Length Envelope Analyzer (FLEA): A tool for longitudinal analysis of viral amplicons

Next generation sequencing of viral populations has advanced our understanding of viral population dynamics, the development of drug resistance, and escape from host immune responses. Many applications require complete gene sequences, which can be impossible to reconstruct from short reads. HIV env, the protein of interest for HIV vaccine studies, is exceptionally challenging for long-read sequencing and analysis due to its length, high substitution rate, and extensive indel variation. While long-read sequencing is attractive in this setting, the analysis of such data is not well handled by existing methods. To address this, we introduce FLEA (Full-Length Envelope Analyzer), which performs end-to-end analysis and visualization of long-read sequencing data. FLEA consists of both a pipeline (optionally run on a high-performance cluster), and a client-side web application that provides interactive results. The pipeline transforms FASTQ reads into high-quality consensus sequences (HQCSs) and uses them to build a codon-aware multiple sequence alignment. The resulting alignment is then used to infer phylogenies, selection pressure, and evolutionary dynamics. The web application provides publication-quality plots and interactive visualizations, including an annotated viral alignment browser, time series plots of evolutionary dynamics, visualizations of gene-wide selective pressures (such as dN/dS) across time and across protein structure, and a phylogenetic tree browser. We demonstrate how FLEA may be used to process Pacific Biosciences HIV env data and describe recent examples of its use. Simulations show how FLEA dramatically reduces the error rate of this sequencing platform, providing an accurate portrait of complex and variable HIV env populations. A public instance of FLEA is hosted at http://flea.datamonkey.org. The Python source code for the FLEA pipeline can be found at https://github.com/veg/flea-pipeline. The client-side application is available at https://github.com/veg/flea-web-app. A live demo of the P018 results can be found at http://flea.murrell.group/view/P018.

Viral populations constantly evolve and diversify. In this article we introduce a method, FLEA, for reconstructing and visualizing the details of evolutionary changes. FLEA specifically processes data from sequencing platforms that generate reads that are long, but error-prone. To study the evolutionary dynamics of entire genes during viral infection, data is collected via long-read sequencing at discrete time points, allowing us to understand how the virus changes over time. However, the experimental and sequencing process is imperfect, so the resulting data contain not only real evolutionary changes, but also mutations and other genetic artifacts caused by sequencing errors. Our method corrects most of these errors by combining thousands of erroneous sequences into a much smaller number of unique consensus sequences that represent biologically meaningful variation. The resulting high-quality sequences are used for further analysis, such as building an evolutionary tree that tracks and interprets the genetic changes in the viral population over time. FLEA is open source, and is freely available online.

Dynamic interactions between RNA viruses and human hosts unravelled by a decade of next generation sequencing

BACKGROUND

Next generation sequencing (NGS) methods have significantly contributed to a paradigm shift in genomic research for nearly a decade now. These methods have been useful in studying the dynamic interactions between RNA viruses and human hosts.

SCOPE OF THE REVIEW

In this review, we summarise and discuss key applications of NGS in studying the host - pathogen interactions in RNA viral infections of humans with examples.

MAJOR CONCLUSIONS

Use of NGS to study globally relevant RNA viral infections have revolutionized our understanding of the within host and between host evolution of these viruses. These methods have also been useful in clinical decision-making and in guiding biomedical research on vaccine design.

GENERAL SIGNIFICANCE

NGS has been instrumental in viral genomic studies in resolving within-host viral genomic variants and the distribution of nucleotide polymorphisms along the full-length of viral genomes in a high throughput, cost effective manner. In the future, novel advances such as long read, single molecule sequencing of viral genomes and simultaneous sequencing of host and pathogens may become the standard of practice in research and clinical settings. This will also bring on new challenges in big data analysis.

Inter- and Intra-Host Nucleotide Variations in Hepatitis A Virus in Culture and Clinical Samples Detected by Next-Generation Sequencing

The accurate virus detection, strain discrimination, and source attribution of contaminated food items remains a persistent challenge because of the high mutation rates anticipated to occur in foodborne RNA viruses, such as hepatitis A virus (HAV). This has led to predictions of the existence of more than one sequence variant between the hosts (inter-host) or within an individual host (intra-host). However, there have been no reports of intra-host variants from an infected single individual, and little is known about the accuracy of the single nucleotide variations (SNVs) calling with various methods. In this study, the presence and identity of viral SNVs, either between HAV clinical specimens or among a series of samples derived from HAV clone1-infected FRhK4 cells, were determined following analyses of nucleotide sequences generated using next-generation sequencing (NGS) and pyrosequencing methods. The results demonstrate the co-existence of inter- and intra-host variants both in the clinical specimens and the cultured samples. The discovery and confirmation of multi-viral RNAs in an infected individual is dependent on the strain discrimination at the SNV level, and critical for successful outbreak traceback and source attribution investigations. The detection of SNVs in a time series of HAV infected FRhK4 cells improved our understanding on the mutation dynamics determined probably by different selective pressures. Additionally, it demonstrated that NGS could potentially provide a valuable investigative approach toward SNV detection and identification for other RNA viruses.

Mixed Mycobacterium tuberculosis-Strain Infections Are Associated With Poor Treatment Outcomes Among Patients With Newly Diagnosed Tuberculosis, Independent of Pretreatment Heteroresistance

Background

Heteroresistant Mycobacterium tuberculosis infections (defined as concomitant infection with drug-resistant and drug-susceptible strains) may explain the higher risk of poor tuberculosis treatment outcomes observed among patients with mixed-strain M. tuberculosis infections. We investigated the clinical effect of mixed-strain infections while controlling for pretreatment heteroresistance in a population-based sample of patients with tuberculosis starting first-line tuberculosis therapy in Botswana.

Methods

We performed 24-locus mycobacterial interspersed repetitive unit-variable number tandem-repeat analysis and targeted deep sequencing on baseline primary cultured isolates to detect mixed infections and heteroresistance, respectively. Drug-sensitive, micro-heteroresistant, macro-heteroresistant, and fixed-resistant infections were defined as infections in which the frequency of resistance was <0.1%, 0.1%-4%, 5%-94%, and ≥95%, respectively, in resistance-conferring domains of the inhA promoter, the katG gene, and the rpoB gene.

Results

Of the 260 patients with tuberculosis included in the study, 25 (9.6%) had mixed infections and 30 (11.5%) had poor treatment outcomes. Micro-heteroresistance, macro-heteroresistance, and fixed resistance were found among 11 (4.2%), 2 (0.8%), and 11 (4.2%), respectively, for isoniazid and 21 (8.1%), 0 (0%), and 10 (3.8%), respectively, for rifampicin. In multivariable analysis, mixed infections but not heteroresistant infections independently predicted poor treatment outcomes.

Conclusions

Among patients starting first-line tuberculosis therapy in Botswana, mixed infections were associated with poor tuberculosis treatment outcomes, independent of heteroresistance.

A novel framework for inferring parameters of transmission from viral sequence data

Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data

Motivation

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

Results

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

Contact

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

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic diversity of Hepatitis C Virus in Pakistan using Next Generation Sequencing

BACKGROUND

In Pakistan, HCV disease is considered a major public health issue with about 10-17 million people suffering with this infection and rate is increasing every day without any hindrance. The currently available Pyrosequencing approach used to analyze complex viral genomes as it can determine minor variants. It is crucial to understand viral evolution and quasispecies diversity in complex viral strains.

OBJECTIVES

To assess genetic diversity in patients with HCV using Next Generation Sequencing (NGS) and compare nucleotide diversity of genotype 3a with respect to other genotypes.

STUDY DESIGN

Intra-host viral diversity of HCV was determined using NGS from 13 chronically HCV infected individuals. NGS of three different regions (E2 (HVR1), NS3 and NS5B) of HCV-3a allowed for a comprehensive analysis of the viral population.

RESULT

Phylogenetic analysis of different HCV genes revealed great variability within the Pakistani population. The average nucleotide diversity for HVR1, NS3 and NS5B was 0.029, 0.011 and 0.010 respectively.

CONCLUSION

Our findings clearly indicate that patient-2 greater quasispecies heterogeneity than other patients of same genotype-3a using phylogenetic and one step network analyses. Initially phylogenetic analysis of these three genes showed that genotype 3a samples have greater genetic diversity. However, no significant difference was determined when nucleotide variability of genotype 3a compared with other genotypes (1a, 1b, 2a & 4a).

Sanger-based sequencing technology for yellow fever vaccine genetic quality control

Yellow Fever (YF) is an acute viral hemorrhagic disease prevalent mainly in Africa and Americas, with 20-60% fatality rate in severe forms. Currently, antiviral drugs for the infection are not available, reinforcing the importance of vaccination in resident populations and travelers. Manufactured in 7 different countries, the YF vaccine was first created in 1937 and two substrains are used for production, 17DD and 17D-204. The vaccine produced in Bio-Manguinhos/Brazil uses 17DD substrain and more than 160 million doses have been exported to over 74 countries. The World Health Organization (WHO) recommends that new seed- and working-lots should have the viral genome sequenced in order to check vaccine genetic stability. The aim of this study was to develop and standardize a Sanger-based sequencing protocol for the genetic monitoring of the Brazilian 17DD vaccine. We designed 54 oligos to access the complete YF vaccine genome by RT-PCR and sequencing approach. After protocol standardization, we tested 45 vaccine lots and the corresponding secondary and working seed lots. All 45 lots presented 100% nucleotide identity to each other and to the seed lots. We also detected 2 heterogeneous positions at nucleotides 4523 (C/T) and 6673 (C/T) that may indicate a quasispecies diversity of YF 17DD strain. When compared to the Brazilian GenBank sequence YFU17066, the Brazilian 17DD vaccine presented 6 silent mutations. By applying the sequencing methodology to two YF 17D-204 strains, we showed that our method can also be used to sequence different YF vaccine virus. In summary, we have developed a robust method for the genetic monitoring of YF vaccines, which has been successfully applied in Bio-Manguinhos since 2009 and could also be used by other manufacturers for YF17D-based vaccines. There were no genetic variation in the Brazilian tested lots, highlighting the safety, production consistency and, more importantly, the genetic stability of Bio-Manguinhos' YF vaccine in the last 3 decades.

RNA and DNA Sanger sequencing versus next-generation sequencing for HIV-1 drug resistance testing in treatment-naive patients

Background

Sanger sequencing of plasma RNA is the standard method for HIV-1 drug resistance testing in treatment-naive patients, but is limited by the non-detection of resistance-associated mutations (RAMs) with prevalence below approximately 20%.

Objectives

We compared RNA and DNA Sanger sequencing (RSS and DSS) with RNA next-generation sequencing (NGS) for RAM detection in HIV-1 reverse transcriptase (RT), protease (PR) and integrase (IN) genes.

Methods

Sanger sequencing was performed on RNA and DNA, following the recommendations of the French Agency for AIDS Research (ANRS). NGS was performed on RNA using the HIV-1 Drug Resistance Assay, v. 3.0 (Roche) on the 454 GS Junior sequencer. The IAS-USA list was used to identify RAMs. ANRS, Rega and Stanford algorithms were used for drug resistance interpretation.

Results

The study included 48 ART-naive patients. The number of patients with at least one major RAM was 3, 3, 4 and 8 when using RSS, DSS, NGS 20% and NGS 5%, respectively. Numerous minor mutations were detected in patients, especially in the protease gene. None of the methods detected any major mutation in the integrase gene. Overall, the mutation detection rate was similar between RSS and DSS, and higher with NGS 20%. Differences in drug resistance interpretation were found between algorithms. No impact of the minority RAMs detected by NGS was found on the short-term treatment outcome.

Conclusions

DSS does not clearly improve the detection of RAMs in ART-naive patients, as compared with RSS. NGS allows detection of additional minority RAMs; however, their clinical relevance requires further investigation.

Distinctive variation in the U3R region of the 5' Long Terminal Repeat from diverse HIV-1 strains

Functional mapping of the 5’LTR has shown that the U3 and the R regions (U3R) contain a cluster of regulatory elements involved in the control of HIV-1 transcription and expression. As the HIV-1 genome is characterized by extensive variability, here we aimed to describe mutations in the U3R from various HIV-1 clades and CRFs in order to highlight strain specific differences that may impact the biological properties of diverse HIV-1 strains. To achieve our purpose, the U3R sequence of plasma derived virus belonging to different clades (A1, B, C, D, F2) and recombinants (CRF02_AG, CRF01_AE and CRF22_01A1) was obtained using Illumina technology. Overall, the R region was very well conserved among and across different strains, while in the U3 region the average inter-strains nucleotide dissimilarity was up to 25%. The TAR hairpin displayed a strain-distinctive cluster of mutations affecting the bulge and the loop, but mostly the stem. Like in previous studies we found a TATAA motif in U3 promoter region from the majority of HIV-1 strains and a TAAAA motif in CRF01_AE; but also in LTRs from CRF22_01A1 isolates. Although LTRs from CRF22_01A1 specimens were assigned CRF01_AE, they contained two NF-kB sites instead of the single TFBS described in CRF01_AE. Also, as previously describe in clade C isolates, we found no C/EBP binding site directly upstream of the enhancer region in CRF22_01A1 specimens. In our study, one-third of CRF02_AG LTRs displayed three NF-kB sites which have been mainly described in clade C isolates. Overall, the number, location and binding patterns of potential regulatory elements found along the U3R might be specific to some HIV-1 strains such as clade F2, CRF02_AG, CRF01_AE and CRF22_01A1. These features may be worth consideration as they may be involved in distinctive regulation of HIV-1 transcription and replication by different and diverse infecting strains.

Zika Virus Infection Preferentially Counterbalances Human Peripheral Monocyte and/or NK Cell Activity

Zika virus (ZIKV) has reemerged in the population and caused unprecedented global outbreaks. Here, the transcriptomic consequences of ZIKV infection were studied systematically first in human peripheral blood CD14⁺ monocytes and monocyte-derived macrophages with high-density RNA sequencing. Analyses of the ZIKV genome revealed that the virus underwent genetic diversification, and differential mRNA abundance was found in host cells during infection. Notably, there was a significant change in the cellular response, with cross talk between monocytes and natural killer (NK) cells as one of the highly identified pathways. Immunophenotyping of peripheral blood from ZIKV-infected patients further confirmed the activation of NK cells during acute infection. ZIKV infection in peripheral blood cells isolated from healthy donors led to the induction of gamma interferon (IFN-γ) and CD107a-two key markers of NK cell function. Depletion of CD14⁺ monocytes from peripheral blood resulted in a reduction of these markers and reduced priming of NK cells during infection. This was complemented by the immunoproteomic changes observed. Mechanistically, ZIKV infection preferentially counterbalances monocyte and/or NK cell activity, with implications for targeted cytokine immunotherapies. IMPORTANCE ZIKV reemerged in recent years, causing outbreaks in many parts of the world. Alarmingly, ZIKV infection has been associated with neurological complications such as Guillain-Barré syndrome (GBS) in adults and congenital fetal growth-associated anomalies in newborns. Host peripheral immune cells are one of the first to interact with the virus upon successful transmission from an infected female Aedes mosquito. However, little is known about the role of these immune cells during infection. In this work, the immune responses of monocytes, known target cells of ZIKV infection, were investigated by high-density transcriptomics. The analysis saw a robust immune response being elicited. Importantly, it also divulged that monocytes prime NK cell activities during virus infection. Removal of monocytes during the infection changed the immune milieu, which in turn reduced NK cell stimulation. This study provides valuable insights into the pathobiology of the virus and allows for the possibility of designing novel targeted therapeutics.

A penalized regression approach to haplotype reconstruction of viral populations arising in early HIV/SIV infection

Motivation

Next generation sequencing (NGS) has been increasingly applied to characterize viral evolution during HIV and SIV infections. In particular, NGS datasets sampled during the initial months of infection are characterized by relatively low levels of diversity as well as convergent evolution at multiple loci dispersed across the viral genome. Consequently, fully characterizing viral evolution from NGS datasets requires haplotype reconstruction across large regions of the viral genome. Existing haplotype reconstruction algorithms have not been developed with the particular characteristics of early HIV/SIV infection in mind, raising the possibility that better performance could be achieved through a specifically designed algorithm.

Results

Here, we introduce a haplotype reconstruction algorithm, RegressHaplo, specifically designed for low diversity and convergent evolution regimes. The algorithm uses a penalized regression that balances a data fitting term with a penalty term that encourages solutions with few haplotypes. The regression covariates are a large set of potential haplotypes and fitting the regression is made computationally feasible by the low diversity setting. Using simulated and in vivo datasets, we compare RegressHaplo to PredictHaplo and QuRe, two existing haplotype reconstruction algorithms. RegressHaplo performs better than these algorithms on simulated datasets with relatively low diversity levels. We suggest RegressHaplo as a novel tool for the investigation of early infection HIV/SIV datasets and, more generally, low diversity viral NGS datasets.

Contact

sr286@georgetown.edu.

Availability and Implementation

https://github.com/SLeviyang/RegressHaplo.

SeekDeep: single-base resolution de novo clustering for amplicon deep sequencing

PCR amplicon deep sequencing continues to transform the investigation of genetic diversity in viral, bacterial, and eukaryotic populations. In eukaryotic populations such as Plasmodium falciparum infections, it is important to discriminate sequences differing by a single nucleotide polymorphism. In bacterial populations, single-base resolution can provide improved resolution towards species and strains. Here, we introduce the SeekDeep suite built around the qluster algorithm, which is capable of accurately building de novo clusters representing true, biological local haplotypes differing by just a single base. It outperforms current software, particularly at low frequencies and at low input read depths, whether resolving single-base differences or traditional OTUs. SeekDeep is open source and works with all major sequencing technologies, making it broadly useful in a wide variety of applications of amplicon deep sequencing to extract accurate and maximal biologic information.

Diverse genetic error modes constrain large-scale bio-based production

A transition toward sustainable bio-based chemical production is important for green growth. However, productivity and yield frequently decrease as large-scale microbial fermentation progresses, commonly ascribed to phenotypic variation. Yet, given the high metabolic burden and toxicities, evolutionary processes may also constrain bio-based production. We experimentally simulate large-scale fermentation with mevalonic acid-producing Escherichia coli. By tracking growth rate and production, we uncover how populations fully sacrifice production to gain fitness within 70 generations. Using ultra-deep (>1000×) time-lapse sequencing of the pathway populations, we identify multiple recurring intra-pathway genetic error modes. This genetic heterogeneity is only detected using deep-sequencing and new population-level bioinformatics, suggesting that the problem is underestimated. A quantitative model explains the population dynamics based on enrichment of spontaneous mutant cells. We validate our model by tuning production load and escape rate of the production host and apply multiple orthogonal strategies for postponing genetically driven production declines.

The declining performance of scale-up bioreactor cultures is commonly attributed to phenotypic and physical heterogeneities. Here, the authors reveal multiple recurring intra-pathway error modes that limit engineered E. coli mevalonic acid production over time- and industrial-scale fermentations.

Separation and assembly of deep sequencing data into discrete sub-population genomes

Sequence heterogeneity is a common characteristic of RNA viruses that is often referred to as sub-populations or quasispecies. Traditional techniques used for assembly of short sequence reads produced by deep sequencing, such as de-novo assemblers, ignore the underlying diversity. Here, we introduce a novel algorithm that simultaneously assembles discrete sequences of multiple genomes present in populations. Using in silico data we were able to detect populations at as low as 0.1% frequency with complete global genome reconstruction and in a single sample detected 16 resolved sequences with no mismatches. We also applied the algorithm to high throughput sequencing data obtained for viruses present in sewage samples and successfully detected multiple sub-populations and recombination events in these diverse mixtures. High sensitivity of the algorithm also enables genomic analysis of heterogeneous pathogen genomes from patient samples and accurate detection of intra-host diversity, enabling not just basic research in personalized medicine but also accurate diagnostics and monitoring drug therapies, which are critical in clinical and regulatory decision-making process.

How viral genetic variants and genotypes influence disease and treatment outcome of chronic hepatitis B. Time for an individualised approach?

Chronic hepatitis B virus (HBV) infection remains a global problem. Several HBV genotypes exist with different biology and geographical prevalence. Whilst the future aim of HBV treatment remains viral eradication, current treatment strategies aim to suppress the virus and prevent the progression of liver disease. Current strategies also involve identification of patients for treatment, namely those at risk of progressive liver disease. Identification of HBV genotype, HBV mutants and other predictive factors allow for tailoured treatments, and risk-surveillance pathways, such as hepatocellular cancer screening. In the future, these factors may enable stratification not only of treatment decisions, but also of patients at risk of higher relapse rates when current therapies are discontinued. Newer technologies, such as next-generation sequencing, to assess drug-resistant or immune escape variants and quasi-species heterogeneity in patients, may allow for more information-based treatment decisions between the clinician and the patient. This article serves to discuss how HBV genotypes and genetic variants impact not only upon the disease course and outcomes, but also current treatment strategies. Adopting a personalised genotypic approach may play a role in future strategies to combat the disease. Herein, we discuss new technologies that may allow more informed decision-making for response guided therapy in the battle against HBV.

On the effective depth of viral sequence data

Genome sequence data are of great value in describing evolutionary processes in viral populations. However, in such studies, the extent to which data accurately describes the viral population is a matter of importance. Multiple factors may influence the accuracy of a dataset, including the quantity and nature of the sample collected, and the subsequent steps in viral processing. To investigate this phenomenon, we sequenced replica datasets spanning a range of viruses, and in which the point at which samples were split was different in each case, from a dataset in which independent samples were collected from a single patient to another in which all processing steps up to sequencing were applied to a single sample before splitting the sample and sequencing each replicate. We conclude that neither a high read depth nor a high template number in a sample guarantee the precision of a dataset. Measures of consistency calculated from within a single biological sample may also be insufficient; distortion of the composition of a population by the experimental procedure or genuine within-host diversity between samples may each affect the results. Where it is possible, data from replicate samples should be collected to validate the consistency of short-read sequence data.

Ultra-Deep Sequencing Analysis on HIV Drug-Resistance-Associated Mutations Among HIV-Infected Individuals: First Report from the Philippines

A sharp increase in the number of people living with HIV has been documented in the Philippines. In response, the government has instituted antiretroviral therapy (ART) nationwide through HIV treatment hubs. However, no data presently exist on the status of ART drug-resistance-associated mutations (DRMs). In this study, we aim at analyzing DRM profiles in the Philippines and at providing comprehensive data on DRMs to guide treatment decisions and prevent viral failures. We conducted a cross-sectional study in 119 volunteers who tested positive for HIV from more than 8,000 participants screened for HIV across the nation through the 2013 Integrated HIV Behavioral and Serologic Surveillance (IHBSS) program. Amplicons were generated from plasma RNA by using primers designed to analyze diverse HIV-1 isolates targeting the reverse transcriptase region and sequenced on a 454 ultra-deep sequencing (UDS) platform to assess DRMs. DRMs were defined by using the Stanford HIV drug resistance database, and we found only 2 from 110 evaluable individuals with major HIV variants (>20% prevalence) that were highly resistant to the non-nucleoside reverse transcriptase inhibitor (NNRTI: efavirenz and nevirapine). However, a larger fraction of individuals harbored minority drug-resistant HIV variants (0.5%-20% prevalence) and they were highly resistant to NNRTI nevirapine (89/110), rilpivirine (5/110), and efavirenz (49/110). This study is the first report on the presence of HIV drug resistance in the Philippines and demonstrates the utility of UDS in assisting the detection of HIV minor variants. Monitoring for ART-DRMs will assist in improving HIV management strategies in curtailing the evolving epidemic in the Philippines.

Identification of novel microRNAs in the sheep heart and their regulation in heart failure

Study of microRNA (miRNAs) using sheep models is limited due to lack of miRNA information. We therefore investigated oar-miRNAs and their regulation in an ovine model of heart failure (HF). Left ventricular (LV) tissue was collected from normal (Cont), HF (LV pacing @ ~220bpm for 13-days) and HF-recovery sheep (HF-R, 26-days after pacing cessation). MiRNA expression was profiled using next-generation sequencing (NGS) and miRNA array, and validated by stem-loop qPCR. Detected sequences were mapped against the ovine genome (Oar v4.0) and aligned with known miRNAs (miRBase v21). A total of 36,438,340 raw reads were obtained with a peak distribution of 18-23 nt. Of these, 637 miRNAs were detected by NGS and mapped to the ovine genome. With cut-off at 10 counts, 275 novel miRNAs were identified (with 186 showing 100% alignment and 89 showing 70-99% alignment with human/mouse and/or rat miRNAs, respectively), and 78 known oar-miRNAs. Cardiac-enriched miRNA-1, -133a, -208a/b and -499 were highly expressed in the LV. With HF induction, miRNA-133b-3p, -208b-3p, -125a-5p, -125b-5p, -126-3p, -21-5p, -210-3p, -29a-3p, -320a and -494-3p were significantly up-regulated relative to Cont and tended to return to normal levels following HF-recovery. This study has expanded the sheep miRNA database, and demonstrated HF-induced regulation of miRNAs.

The Number of Target Molecules of the Amplification Step Limits Accuracy and Sensitivity in Ultradeep-Sequencing Viral Population Studies

The invention of next-generation sequencing (NGS) techniques marked the coming of a new era in the detection of the genetic diversity of intrahost viral populations. A good understanding of the genetic structure of these populations requires, first, the ability to identify the different isolates or variants and, second, the ability to accurately quantify them. However, the initial amplification step of NGS studies can impose potential quantitative biases, modifying the variant relative frequencies. In particular, the number of target molecules (NTM) used during the amplification step is vastly overlooked although of primary importance, as it sets the limit of the accuracy and sensitivity of the sequencing procedure. In the present article, we investigated quantitative biases in an NGS study of populations of a multipartite single-stranded DNA (ssDNA) virus at different steps of the procedure. We studied 20 independent populations of the ssDNA virus faba bean necrotic stunt virus (FBNSV) in two host plants, Vicia faba and Medicago truncatula FBNSV is a multipartite virus composed of eight genomic segments, whose specific and host-dependent relative frequencies are defined as the "genome formula." Our results show a significant distortion of the FBNSV genome formula after the amplification and sequencing steps. We also quantified the genetic bottleneck occurring at the amplification step by documenting the NTM of two genomic segments of FBNSV. We argue that the NTM must be documented and carefully considered when determining the sensitivity and accuracy of data from NGS studies.IMPORTANCE The advent of next-generation sequencing (NGS) techniques now enables study of the genetic diversity of viral populations. A good understanding of the genetic structure of these populations first requires the ability to identify the different isolates or variants and second requires the ability to accurately quantify them. Prior to sequencing, viral genomes need to be amplified, a step that potentially imposes quantitative biases and modifies the viral population structure. In particular, the number of target molecules (NTM) used during the amplification step is of primary importance, as it sets the limit of the accuracy and sensitivity of the sequencing procedure. In this work, we used 20 replicated populations of the multipartite faba bean necrotic stunt virus (FBNSV) to estimate the various limitations of ultradeep-sequencing studies performed on intrahost viral populations. We report quantitative biases during rolling-circle amplification and the NTM of two genomic segments of FBNSV.

Deep sequencing in the management of hepatitis virus infections

The hepatitis viruses represent a major public health problem worldwide. Procedures for characterization of the genomic composition of their populations, accurate diagnosis, identification of multiple infections, and information on inhibitor-escape mutants for treatment decisions are needed. Deep sequencing methodologies are extremely useful for these viruses since they replicate as complex and dynamic quasispecies swarms whose complexity and mutant composition are biologically relevant traits. Population complexity is a major challenge for disease prevention and control, but also an opportunity to distinguish among related but phenotypically distinct variants that might anticipate disease progression and treatment outcome. Detailed characterization of mutant spectra should permit choosing better treatment options, given the increasing number of new antiviral inhibitors available. In the present review we briefly summarize our experience on the use of deep sequencing for the management of hepatitis virus infections, particularly for hepatitis B and C viruses, and outline some possible new applications of deep sequencing for these important human pathogens.

Genotyping assay for differentiation of wild-type and vaccine viruses in subjects immunized with live attenuated influenza vaccine

Live attenuated influenza vaccines (LAIVs) are considered as safe and effective tool to control influenza in different age groups, especially in young children. An important part of the LAIV safety evaluation is the detection of vaccine virus replication in the nasopharynx of the vaccinees, with special attention to a potential virus transmission to the unvaccinated close contacts. Conducting LAIV clinical trials in some geographical regions with year-round circulation of influenza viruses warrants the development of robust and reliable tools for differentiating vaccine viruses from wild-type influenza viruses in nasal pharyngeal wash (NPW) specimens of vaccinated subjects. Here we report the development of genotyping assay for the detection of wild-type and vaccine-type influenza virus genes in NPW specimens of young children immunized with Russian-backbone seasonal trivalent LAIV using Sanger sequencing from newly designed universal primers. The new primer set allowed amplification and sequencing of short fragments of viral genes in NPW specimens and appeared to be more sensitive than conventional real-time RT-PCR protocols routinely used for the detection and typing/subtyping of influenza virus in humans. Furthermore, the new assay is capable of defining the origin of wild-type influenza virus through BLAST search with the generated sequences of viral genes fragments.