Genetic diversity among pandemic 2009 influenza viruses isolated from a transmission chain

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling

BACKGROUND

Pan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.

RESULTS

Here, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage's utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage's capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.

CONCLUSIONS

Entourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

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

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

Community spread and late season increased incidence of oseltamivir-resistant influenza A(H1N1) viruses in Norway 2016

Background

Antiviral resistance in Norwegian influenza viruses is rare. Only one A(H1N1)pdm09 virus from May 2015 had been found resistant to oseltamivir since the introduction of these viruses in 2009.

Objectives

Surveillance of antiviral resistance is part of the Norwegian surveillance system, to rapidly detect the development of antiviral‐resistant viruses and spread in the community. We describe the spread of oseltamivir‐resistant A(H1N1)pdm09 viruses in Norway in the 2016‐17 season, found as part of the routine surveillance.

Methods

Influenza H1N1 viruses were analysed for antiviral resistance by pyrosequencing, neuraminidase susceptibility assay and by Sanger sequencing of the HA and NA genes.

Results

During the 2015‐16 influenza season, 3% of all A(H1N1)pdm09 viruses screened for resistance in Norway were resistant to oseltamivir, possessing the H275Y substitution in the neuraminidase protein. In comparison, the overall frequency in Europe was 0.87%. Out of these, 37% (n = 10) were reported from Norway. Most cases in Norway were not related to antiviral treatment, and the cases were from several different locations of southern Norway. Genetic analysis revealed that resistant virus emerged independently on several occasions and that there was some spread of oseltamivir‐resistant influenza A(H1N1)6B.1 viruses in the community, characterised by a N370S substitution in the haemagglutinin and T48I in the neuraminidase.

Conclusions

Our findings emphasise the importance of antiviral resistance surveillance in the community, not only in immunocompromised patients or other patients undergoing antiviral treatment.

Application of deep sequencing methods for inferring viral population diversity

The first deep sequencing method was announced in 2005. Due to an increasing number of sequencing data and a reduction in the costs of each sequencing dataset, this innovative technique was soon applied to genetic investigations of viral genome diversity in various viruses, particularly RNA viruses. These deep sequencing findings documented viral epidemiology and evolution and provided high-resolution data on the genetic changes in viral populations. Here, we review deep sequencing platforms that have been applied in viral quasispecies studies. Further, we discuss recent deep sequencing studies on viral inter- and intrahost evolution, drug resistance, and humoral immune selection, especially in emerging and re-emerging viruses. Deep sequencing methods are becoming the standard for providing comprehensive results of viral population diversity, and their applications are discussed.

Seasonal Genetic Drift of Human Influenza A Virus Quasispecies Revealed by Deep Sequencing

After a pandemic wave in 2009 following their introduction in the human population, the H1N1pdm09 viruses replaced the previously circulating, pre-pandemic H1N1 virus and, along with H3N2 viruses, are now responsible for the seasonal influenza type A epidemics. So far, the evolutionary potential of influenza viruses has been mainly documented by consensus sequencing data. However, like other RNA viruses, influenza A viruses exist as a population of diverse, albeit related, viruses, or quasispecies. Interest in this quasispecies nature has increased with the development of next generation sequencing (NGS) technologies that allow a more in-depth study of the genetic variability. NGS deep sequencing methodologies were applied to determine the whole genome genetic heterogeneity of the three categories of influenza A viruses that circulated in humans between 2007 and 2012 in France, directly from clinical respiratory specimens. Mutation frequencies and single nucleotide polymorphisms were used for comparisons to address the level of natural intrinsic heterogeneity of influenza A viruses. Clear differences in single nucleotide polymorphism profiles between seasons for a given subtype also revealed the constant genetic drift that human influenza A virus quasispecies undergo.

Understanding the complex evolution of rapidly mutating viruses with deep sequencing: Beyond the analysis of viral diversity

With the advent of affordable deep sequencing technologies, detection of low frequency variants within genetically diverse viral populations can now be achieved with unprecedented depth and efficiency. The high-resolution data provided by next generation sequencing technologies is currently recognised as the gold standard in estimation of viral diversity. In the analysis of rapidly mutating viruses, longitudinal deep sequencing datasets from viral genomes during individual infection episodes, as well as at the epidemiological level during outbreaks, now allow for more sophisticated analyses such as statistical estimates of the impact of complex mutation patterns on the evolution of the viral populations both within and between hosts. These analyses are revealing more accurate descriptions of the evolutionary dynamics that underpin the rapid adaptation of these viruses to the host response, and to drug therapies. This review assesses recent developments in methods and provide informative research examples using deep sequencing data generated from rapidly mutating viruses infecting humans, particularly hepatitis C virus (HCV), human immunodeficiency virus (HIV), Ebola virus and influenza virus, to understand the evolution of viral genomes and to explore the relationship between viral mutations and the host adaptive immune response. Finally, we discuss limitations in current technologies, and future directions that take advantage of publically available large deep sequencing datasets.

Quantifying influenza virus diversity and transmission in humans

Influenza A virus is characterized by high genetic diversity. However, most of what is known about influenza evolution has come from consensus sequences sampled at the epidemiological scale that only represent the dominant virus lineage within each infected host. Less is known about the extent of within-host virus diversity and what proportion of this diversity is transmitted between individuals. To characterize virus variants that achieve sustainable transmission in new hosts, we examined within-host virus genetic diversity in household donor-recipient pairs from the first wave of the 2009 H1N1 pandemic when seasonal H3N2 was co-circulating. Although the same variants were found in multiple members of the community, the relative frequencies of variants fluctuated, with patterns of genetic variation more similar within than between households. We estimated the effective population size of influenza A virus across donor-recipient pairs to be approximately 100-200 contributing members, which enabled the transmission of multiple lineages, including antigenic variants.

Interplay between influenza A virus and host factors: targets for antiviral intervention

Influenza A viruses (IAVs) pose a major public health threat worldwide. Recent experience with the 2013 H7N9 outbreak in China and the 2009 "swine flu" pandemic have shown that antiviral vaccines and drugs fall short of controlling the spread of disease in a timely and effective manner. Major problems include rapid emergence of drug-resistant influenza virus strains and the slow process of vaccine production. With the threat of a highly pathogenic H5N1 bird-flu pandemic looming large, it is crucial to develop novel ways of combating influenza A viruses. Targeting the host factors critical for influenza A virus replication has shown promise as a strategy to develop novel antiviral molecules with broad-spectrum protection. In this review, we summarize the role of currently identified host factors that play a critical role in the influenza A virus life cycle and discuss the most promising candidates for anti-influenza therapeutics.

Evaluation of Unbiased Next-Generation Sequencing of RNA (RNA-seq) as a Diagnostic Method in Influenza Virus-Positive Respiratory Samples

Unbiased nontargeted metagenomic RNA sequencing (UMERS) has the advantage to detect known as well as unknown pathogens and, thus, can significantly improve the detection of viral, bacterial, parasitic, and fungal sequences in public health settings. In particular, conventional diagnostic methods successfully identify the putative pathogenic agent in only 30% to 40% of respiratory specimens from patients with acute respiratory illness. Here, we applied UMERS to 24 diagnostic respiratory specimens (bronchoalveolar lavage [BAL] fluid, sputum samples, and a swab) from patients with seasonal influenza infection and 5 BAL fluid samples from patients with pneumonia that tested negative for influenza to validate RNA sequencing as an unbiased diagnostic tool in comparison to conventional diagnostic methods. In addition to our comparison to PCR, we evaluated the potential to retrieve comprehensive influenza virus genomic information and the capability to detect known superinfecting pathogens. Compared to quantitative real-time PCR for influenza viral sequences, UMERS detected influenza viral sequences in 18 of 24 samples. Complete influenza virus genomes could be assembled from 8 samples. Furthermore, in 3 of 24 influenza-positive samples, additional viral pathogens could be detected, and 2 of 24 samples showed a significantly increased abundance of individual bacterial species known to cause superinfections during an influenza virus infection. Thus, analysis of respiratory samples from known or suspected influenza patients by UMERS provides valuable information that is relevant for clinical investigation.

Mammalian adaptation of influenza A(H7N9) virus is limited by a narrow genetic bottleneck

Human infection with avian influenza A(H7N9) virus is associated mainly with the exposure to infected poultry. The factors that allow interspecies transmission but limit human-to-human transmission are unknown. Here we show that A/Anhui/1/2013(H7N9) influenza virus infection of chickens (natural hosts) is asymptomatic and that it generates a high genetic diversity. In contrast, diversity is tightly restricted in infected ferrets, limiting further adaptation to a fully transmissible form. Airborne transmission in ferrets is accompanied by the mutations in PB1, NP and NA genes that reduce viral polymerase and neuraminidase activity. Therefore, while A(H7N9) virus can infect mammals, further adaptation appears to incur a fitness cost. Our results reveal that a tight genetic bottleneck during avian-to-mammalian transmission is a limiting factor in A(H7N9) influenza virus adaptation to mammals. This previously unrecognized biological mechanism limiting species jumps provides a measure of adaptive potential and may serve as a risk assessment tool for pandemic preparedness.

The Hippo effector YAP promotes resistance to RAF- and MEK-targeted cancer therapies

Resistance to RAF- and MEK-targeted therapy is a major clinical challenge. RAF and MEK inhibitors are initially but only transiently effective in some but not all patients with BRAF gene mutation and are largely ineffective in those with RAS gene mutation because of resistance. Through a genetic screen in BRAF-mutant tumor cells, we show that the Hippo pathway effector YAP (encoded by YAP1) acts as a parallel survival input to promote resistance to RAF and MEK inhibitor therapy. Combined YAP and RAF or MEK inhibition was synthetically lethal not only in several BRAF-mutant tumor types but also in RAS-mutant tumors. Increased YAP in tumors harboring BRAF V600E was a biomarker of worse initial response to RAF and MEK inhibition in patients, establishing the clinical relevance of our findings. Our data identify YAP as a new mechanism of resistance to RAF- and MEK-targeted therapy. The findings unveil the synthetic lethality of combined suppression of YAP and RAF or MEK as a promising strategy to enhance treatment response and patient survival.

Deep sequencing: becoming a critical tool in clinical virology

Population (Sanger) sequencing has been the standard method in basic and clinical DNA sequencing for almost 40 years; however, next-generation (deep) sequencing methodologies are now revolutionizing the field of genomics, and clinical virology is no exception. Deep sequencing is highly efficient, producing an enormous amount of information at low cost in a relatively short period of time. High-throughput sequencing techniques have enabled significant contributions to multiples areas in virology, including virus discovery and metagenomics (viromes), molecular epidemiology, pathogenesis, and studies of how viruses to escape the host immune system and antiviral pressures. In addition, new and more affordable deep sequencing-based assays are now being implemented in clinical laboratories. Here, we review the use of the current deep sequencing platforms in virology, focusing on three of the most studied viruses: human immunodeficiency virus (HIV), hepatitis C virus (HCV), and influenza virus.

Metadata beyond the sequence enables the phylodynamic inference of bovine viral diarrhea virus type 1a isolates from Western Canada

Bovine viral diarrhea virus (BVDV) has been recognized as an important pathogen of livestock in Canada. The high mutation rate of this virus leads to a great degree of diversity between isolates resulting in the ability to infer precise evolutionary relationships. Many studies have attempted to elucidate the regional and global evolution of BVDV, but so far few have applied Bayesian methods to this end. We aimed to describe the molecular epidemiology and phylodynamics of BVDV 1a isolates in Western Canada using 5'UTR and E1-E2 sequence data, collection dates and locations. Sequences were obtained from isolates submitted to a diagnostic laboratory in Saskatoon, Canada. Path sampling and stepping stone model testing were employed to identify the model that best fit the data. We found that these Western Canadian isolates share a most recent common ancestor dated near 1909. Furthermore, the E1-E2 region shows a median substitution rate about ten times greater than the 5'UTR. It was also noted that caution should be exercised when inferring phylogenetic relationships using the 5'UTR alone, as it becomes difficult to resolve relationships within major clades. Phylogeographic and population size fluctuation estimates require more thorough sampling than was performed here to be reliable. We have found that there are significant gains to be made by utilizing a Bayesian analysis and by incorporating additional types of data beyond the sequence. These include the estimation of most common recent ancestor dates and the precise inference of transmission routes. Future work will expand upon these findings by more thoroughly sampling BVDV isolates spatially and temporally and further refining the Bayesian model employed here.