A quantitative genotype algorithm reflecting H5N1 Avian influenza niches

Rural populations facilitated early SARS-CoV-2 evolution and transmission in Missouri, USA

In the United States, rural populations comprise 60 million individuals and suffered from high COVID-19 disease burdens. Despite this, surveillance efforts are biased toward urban centers. Consequently, how rurally circulating SARS-CoV-2 viruses contribute toward emerging variants remains poorly understood. In this study, we aim to investigate the role of rural communities in the evolution and transmission of SARS-CoV-2 during the early pandemic. We collected 544 urban and 435 rural COVID-19-positive respiratory specimens from an overall vaccine-naïve population in Southwest Missouri between July and December 2020. Genomic analyses revealed 53 SARS-CoV-2 Pango lineages in our study samples, with 14 of these lineages identified only in rural samples. Phylodynamic analyses showed that frequent bi-directional diffusions occurred between rural and urban communities in Southwest Missouri, and that four out of seven Missouri rural-origin lineages spread globally. Further analyses revealed that the nucleocapsid protein (N):R203K/G204R paired substitutions, which were detected disproportionately across multiple Pango lineages, were more associated with urban than rural sequences. Positive selection was detected at N:204 among rural samples but was not evident in urban samples, suggesting that viruses may encounter distinct selection pressures in rural versus urban communities. This study demonstrates that rural communities may be a crucial source of SARS-CoV-2 evolution and transmission, highlighting the need to expand surveillance and resources to rural populations for COVID-19 mitigation.

Progress and Challenge in Computational Identification of Influenza Virus Reassortment

Genomic reassortment is an important evolutionary mechanism for influenza viruses. In this process, the novel viruses acquire new characteristics by the exchange of the intact gene segments among multiple influenza virus genomes, which may cause flu endemics and epidemics within or even across hosts. Due to the safety and ethical limitations of the experimental studies on influenza virus reassortment, numerous computational researches on the influenza virus reassortment have been done with the explosion of the influenza virus genomic data. A great amount of computational methods and bioinformatics databases were developed to facilitate the identification of influenza virus reassortments. In this review, we summarized the progress and challenge of the bioinformatics research on influenza virus reassortment, which can guide the researchers to investigate the influenza virus reassortment events reasonably and provide valuable insight to develop the related computational identification tools.

New approaches for unravelling reassortment pathways

BACKGROUND

Every year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment.

RESULTS

The current work describes the implementation of two approaches for robustly identifying reassortment events. The algorithms rest on the idea of significance of difference between phylogenetic trees or phylogenetic tree sets, and subtree pruning and regrafting operations, which mimic the effect of reassortment on tree topologies. The first method is based on a maximum likelihood (ML) framework (MLreassort) and the second implements a Bayesian approach (Breassort) for reassortment detection. We focus on reassortment events that are found by both methods. We test both methods on a simulated dataset and on a small collection of real viral data isolated in Hong Kong in 1999.

CONCLUSIONS

The nature of segmented viral genomes present many challenges with respect to disease. The algorithms developed here can effectively identify reassortment events in small viral datasets and can be applied not only to influenza but also to other segmented viruses. Owing to computational demands of comparing tree topologies, further development in this area is necessary to allow their application to larger datasets.

Prospective of Genomics in Revealing Transmission, Reassortment and Evolution of Wildlife-Borne Avian Influenza A (H5N1) Viruses

The outbreak of highly pathogenic avian influenza (HPAI) H5N1 disease has led to significant loss of poultry and wild life and case fatality rates in humans of 60%. Wild birds are natural hosts for all avian influenza virus subtypes and over120 bird species have been reported with evidence of H5N1 infection. Influenza A viruses possess a segmented RNA genome and are characterized by frequently occurring genetic reassortment events, which play a very important role in virus evolution and the spread of novel gene constellations in immunologically naïve human and animal populations. Phylogenetic analysis of whole genome or sub-genomic sequences is a standard means for delineating genetic variation, novel reassortment events, and surveillance to trace the global transmission pathways. In this paper, special emphasis is given to the transmission and circulation of H5N1 among wild life populations, and to the reassortment events that are associated with inter-host transmission of the H5N1 viruses when they infect different hosts, such as birds, pigs and humans. In addition, we review the inter-subtype reassortment of the viral segments encoding inner proteins between the H5N1 viruses and viruses of other subtypes, such as H9N2 and H6N1. Finally, we highlight the usefulness of genomic sequences in molecular epidemiological analysis of HPAI H5N1 and the technical limitations in existing analytical methods that hinder them from playing a greater role in virological research.

Nephele: genotyping via complete composition vectors and MapReduce

BACKGROUND

Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.

RESULTS

Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours.

CONCLUSIONS

We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.

RAIphy: phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles

BACKGROUND

Computational analysis of metagenomes requires the taxonomical assignment of the genome contigs assembled from DNA reads of environmental samples. Because of the diverse nature of microbiomes, the length of the assemblies obtained can vary between a few hundred bp to a few hundred Kbp. Current taxonomic classification algorithms provide accurate classification for long contigs or for short fragments from organisms that have close relatives with annotated genomes. These are significant limitations for metagenome analysis because of the complexity of microbiomes and the paucity of existing annotated genomes.

RESULTS

We propose a robust taxonomic classification method, RAIphy, that uses a novel sequence similarity metric with iterative refinement of taxonomic models and functions effectively without these limitations. We have tested RAIphy with synthetic metagenomics data ranging between 100 bp to 50 Kbp. Within a sequence read range of 100 bp-1000 bp, the sensitivity of RAIphy ranges between 38%-81% outperforming the currently popular composition-based methods for reads in this range. Comparison with computationally more intensive sequence similarity methods shows that RAIphy performs competitively while being significantly faster. The sensitivity-specificity characteristics for relatively longer contigs were compared with the PhyloPythia and TACOA algorithms. RAIphy performs better than these algorithms at varying clade-levels. For an acid mine drainage (AMD) metagenome, RAIphy was able to taxonomically bin the sequence read set more accurately than the currently available methods, Phymm and MEGAN, and more accurately in two out of three tests than the much more computationally intensive method, PhymmBL.

CONCLUSIONS

With the introduction of the relative abundance index metric and an iterative classification method, we propose a taxonomic classification algorithm that performs competitively for a large range of DNA contig lengths assembled from metagenome data. Because of its speed, simplicity, and accuracy RAIphy can be successfully used in the binning process for a broad range of metagenomic data obtained from environmental samples.

GiRaF: robust, computational identification of influenza reassortments via graph mining

Reassortments in the influenza virus—a process where strains exchange genetic segments—have been implicated in two out of three pandemics of the 20th century as well as the 2009 H1N1 outbreak. While advances in sequencing have led to an explosion in the number of whole-genome sequences that are available, an understanding of the rate and distribution of reassortments and their role in viral evolution is still lacking. An important factor in this is the paucity of automated tools for confident identification of reassortments from sequence data due to the challenges of analyzing large, uncertain viral phylogenies. We describe here a novel computational method, called GiRaF (Graph-incompatibility-based Reassortment Finder), that robustly identifies reassortments in a fully automated fashion while accounting for uncertainties in the inferred phylogenies. The algorithms behind GiRaF search large collections of Markov chain Monte Carlo (MCMC)-sampled trees for groups of incompatible splits using a fast biclique enumeration algorithm coupled with several statistical tests to identify sets of taxa with differential phylogenetic placement. GiRaF correctly finds known reassortments in human, avian, and swine influenza populations, including the evolutionary events that led to the recent ‘swine flu’ outbreak. GiRaF also identifies several previously unreported reassortments via whole-genome studies to catalog events in H5N1 and swine influenza isolates.

Intra-host diversities of the receptor-binding domain of stork faeces-derived avian H5N1 viruses and its significance as predicted by molecular dynamic simulation

Virus evolution facilitates the emergence of viruses with unpredictable impacts on human health. This study investigated intra-host variations of the receptor-binding domain (RBD) of the haemagglutinin (HA) gene of the avian H5N1 viruses obtained from the 2004 and 2005 epidemics. The results showed that the mutation frequency of the RBD ranged from 0.3 to 0.6 %. The mutations generated one consensus and several minor populations. The consensus population of the 2004 epidemic was transmitted to the 2005 outbreak with increased frequency (39 and 45 %, respectively). Molecular dynamics simulation was applied to predict the significance of the variants. The results revealed that the consensus sequence (E218K/V248I) interacted unstably with sialic acid (SA) with an α2,6 linkage (SAα2,6Gal). Although the mutated K140R/E218K/V248I and Y191C/E218K/V248I sequences decreased the HA binding capacity to α2,3-linked SA, they were shown to bind α2,6-linked SA with increased affinity. Moreover, the substitutions at aa 140 and 191 were positive-selection sites. These data suggest that the K140R and Y191C mutations may represent a step towards human adaptation of the avian H5N1 virus.

Antigenic and genetic diversity of highly pathogenic avian influenza A (H5N1) viruses isolated in Egypt

Highly pathogenic avian influenza A virus (H5N1) has diverged antigenically and genetically since its initial detection in Asia in 1997. Viruses belonging to clade 2.2 in particular have been reported in numerous countries with the majority occurring in Egypt. Previous reports identified antigenic similarities between viruses belonging to clade 2.2. However, poultry and human viruses isolated in northern Egypt during 2007 and 2008 were found to be antigenically distinct from other clade 2.2 viruses from this country. Genetic analysis of the hemagglutinin revealed a high degree of nucleotide and amino acid divergence. The antigenic changes in Egyptian viruses isolated during 2007-08 necessitated that two of these strains be considered as potential H5N1 pre-pandemic vaccine candidates.

CD4+ T cell epitope discovery and rational vaccine design

T cell epitope-driven vaccine design employs bioinformatic algorithms to identify potential targets of vaccines against infectious diseases or cancer. Potential epitopes can be identified with major histocompatibility complex (MHC)-binding algorithms, and the ability to bind to MHC class I or class II indicates a predominantly CD4(+) or CD8(+) T cell response. Furthermore, an epitope-based vaccine can circumvent evolutionary events favoring immune escape present in native proteins from pathogens. It can also focus on only the most relevant epitopes (i.e. conserved and promiscuous) recognized by the majority of the target population. Mounting evidence points to the critical role of CD4(+) T cells in natural antigen encounter and active immunization. In this paper the need for CD4(+) T cell help in vaccine development, the selection of CD4(+) T cell epitopes for an epitope-based vaccine, and how the approach can be used to induce a protective effect are reviewed.

Spatiotemporal structure of molecular evolution of H5N1 highly pathogenic avian influenza viruses in Vietnam

BACKGROUND

Vietnam is one of the countries most affected by outbreaks of H5N1 highly pathogenic avian influenza viruses. First identified in Vietnam in poultry in 2001 and in humans in 2004, the virus has since caused 111 cases and 56 deaths in humans. In 2003/2004 H5N1 outbreaks, nearly the entire poultry population of Vietnam was culled. Our earlier study (Wan et al., 2008, PLoS ONE, 3(10): e3462) demonstrated that there have been at least six independent H5N1 introductions into Vietnam and there were nine newly emerged reassortants from 2001 to 2007 in Vietnam. H5N1 viruses in Vietnam cluster distinctly around Hanoi and Ho Chi Minh City. However, the nature of the relationship between genetic divergence and geographic patterns is still unclear.

METHODOLOGY/PRINCIPAL FINDINGS

In this study, we hypothesized that genetic distances between H5N1 viruses in Vietnam are correlated with geographic distances, as the result of distinct population and environment patterns along Vietnam's long north to south longitudinal extent. Based on this hypothesis, we combined spatial statistical methods with genetic analytic techniques and explicitly used geographic space to explore genetic evolution of H5N1 highly pathogenic avian influenza viruses at the sub-national scale in Vietnam. Our dataset consisted of 125 influenza viruses (with whole genome sets) isolated in Vietnam from 2003 to 2007. Our results document the significant effect of space and time on genetic evolution and the rise of two regional centers of genetic mixing by 2007. These findings give insight into processes underlying viral evolution and suggest that genetic differentiation is associated with the distance between concentrations of human and poultry populations around Hanoi and Ho Chi Minh City.

CONCLUSIONS/SIGNIFICANCE

The results show that genetic evolution of H5N1 viruses in Vietnamese domestic poultry is highly correlated with the location and spread of those viruses in geographic space. This correlation varies by scale, time, and gene, though a classic isolation by distance pattern is observed. This study is the first to characterize the geographic structure of influenza viral evolution at the sub-national scale in Vietnam and can shed light on how H5N1 HPAIVs evolve in certain geographic settings.

Phylogenetic designation of enterovirus 71 genotypes and subgenotypes using complete genome sequences

Human enterovirus 71 (EV-71) is genotyped for molecular epidemiological investigation mainly using the two structural genes, VP1 and VP4. Based on these, EV-71 is divided into three genotypes, A, B and C, and within the genotypes B and C, there are further subgenotypes, B1-B5 and C1-C5. Classification using these genes is useful but gives incomplete phylogenetic information. In the present study, the phylogenetic relationships amongst all the known EV-71 and human enterovirus A (HEV-A) isolates with complete genome sequences were examined. A different tree topology involving EV-71 isolates of subgenotypes, C4 and B5 was obtained in comparison to that drawn using VP1. The nucleotide sequence divergence of the C4 isolates was 18.11% (17-20%) when compared to other isolates of subgenotype C. However, this positions the C4 isolates within the cut-off divergence value of 17-22% used to designate the virus genotypes. Hence, it is proposed here that C4 should be designated as a new genotype D. In addition, the subgenotype B5 isolates had an average nucleotide divergence of only 6.14% (4-8%) when compared to other subgenotype B4 isolates. This places the B5 isolates within the subgenotype B4. It is proposed here that the B5 isolates to be redesignated as B4. With the newly proposed genotype D and inclusion of subgenotype B5 within B4, the average nucleotide divergence between genotypes was 18.99% (17-22%). Inter- and intra-subgenotype average divergences were 12.02% (10-14%) and 3.92% (1-10%), respectively. A phylogenetic tree built using the full genome sequences is robust as it takes into consideration changes in the sequences of both the structural and non-structural genes. Similar nucleotide similarities, however, were obtained if only VP1 and 3D RNA polymerase genes were used. Furthermore, addition of 3D RNA polymerase sequences will also show recombination events. Hence, in the absence of full genome sequences, it is proposed here that a combination of VP1 and 3D RNA polymerase gene sequences be used for initial genotyping of EV-71 isolates.

Evolution of highly pathogenic H5N1 avian influenza viruses in Vietnam between 2001 and 2007

Highly pathogenic avian influenza (HPAI) H5N1 viruses have caused dramatic economic losses to the poultry industry of Vietnam and continue to pose a serious threat to public health. As of June 2008, Vietnam had reported nearly one third of worldwide laboratory confirmed human H5N1 infections. To better understand the emergence, spread and evolution of H5N1 in Vietnam we studied over 300 H5N1 avian influenza viruses isolated from Vietnam since their first detection in 2001. Our phylogenetic analyses indicated that six genetically distinct H5N1 viruses were introduced into Vietnam during the past seven years. The H5N1 lineage that evolved following the introduction in 2003 of the A/duck/Hong Kong/821/2002-like viruses, with clade 1 hemagglutinin (HA), continued to predominate in southern Vietnam as of May 2007. A virus with a clade 2.3.4 HA newly introduced into northern Vietnam in 2007, reassorted with pre-existing clade 1 viruses, resulting in the emergence of novel genotypes with neuraminidase (NA) and/or internal gene segments from clade 1 viruses. A total of nine distinct genotypes have been present in Vietnam since 2001, including five that were circulating in 2007. At least four of these genotypes appear to have originated in Vietnam and represent novel H5N1 viruses not reported elsewhere. Geographic and temporal analyses of H5N1 infection dynamics in poultry suggest that the majority of viruses containing new genes were first detected in northern Vietnam and subsequently spread to southern Vietnam after reassorting with pre-existing local viruses in northern Vietnam. Although the routes of entry and spread of H5N1 in Vietnam remain speculative, enhanced poultry import controls and virologic surveillance efforts may help curb the entry and spread of new HPAI viral genes.

Genotypic diversity of H5N1 highly pathogenic avian influenza viruses

Besides enormous economic losses to the poultry industry, recent H5N1 highly pathogenic avian influenza viruses (HPAIVs) originating in eastern Asia have posed serious threats to public health. Up to April 17, 2008, 381 human cases had been confirmed with a mortality of more than 60 %. Here, we attempt to identify potential progenitor genes for H5N1 HPAIVs since their first recognition in 1996; most were detected in the Eurasian landmass before 1996. Combinations among these progenitor genes generated at least 21 reassortants (named H5N1 progenitor reassortant, H5N1-PR1-21). H5N1-PR1 includes A/Goose/Guangdong/1/1996(H5N1). Only reassortants H5N1-PR2 and H5N1-PR7 were associated with confirmed human cases: H5N1-PR2 in the Hong Kong H5N1 outbreak in 1997 and H5N1-PR7 in laboratory confirmed human cases since 2003. H5N1-PR7 also contains a majority of the H5N1 viruses causing avian influenza outbreaks in birds, including the first wave of genotype Z, Qinghai-like and Fujian-like virus lineages. Among the 21 reassortants identified, 13 are first reported here. This study illustrates evolutionary patterns of H5N1 HPAIVs, which may be useful toward pandemic preparedness as well as avian influenza prevention and control.