Natural Selection on the Influenza Virus Genome

First identification of canine adenovirus 1 in mink and bioinformatics analysis of its 100 K protein

Introduction

Animal trade favors the spreading of emerging canine adenovirus 1 (CAdV-1) in mink. Because the 100K protein is not exposed to the viral surface at any stage, it can be used to differentiate the vaccine from wild virus infection. However, no related research has been conducted. This study aimed to find evidence of CAdV-1 in mink and predict the character of the 100K protein in the current circulating CAdV-1 strain of mink.

Method

In this experiment, the identification of CAdV-1, the phylogenetic tree, homology, and bioinformatics analysis of 100K were conducted.

Results

The results showed that the CAdV-1 was identified in the mink and that its Fiber was located in a separate branch. It was closely related to strains isolated from Norwegian Arctic fox and Red fox. 100K was located in a separate branch, which had the closest genetic relationship with skunks, porcupines, raccoons, and hedgehogs and a far genetic relationship with the strains in dogs. 100K protein is an unstable and hydrophobic protein. It had evidence of selective pressure and recombination, 1 glycosylation site, 48 phosphorylation sites, 60 dominant B cell epitopes, and 9 peptides of MHC-I and MHC-II. Its subcellular localization was mainly in the endoplasmic reticulum and mitochondria. The binding sites of 100K proteins were DBP proteins and 33K proteins.

Discussion

The stains in the mink were different from fox. The exploration of its genomic characteristics will provide us with a deeper understanding of the prevention of canine adenovirus.

Retrospective immunogenicity analysis of seasonal flu H3N2 vaccines recommended in the past ten years using immunized animal sera

BACKGROUND

Influenza A (H3N2) virus (A/H3N2) has complex antigenic evolution, resulting in frequent vaccine strain updates. We aimed to evaluate the protective effect of the vaccine strains on the circulating strains from past ten years and provide a basis for finding a broader and more efficient A/H3N2 vaccine strain.

METHODS

Eighty-four representative circulating A/H3N2 strains were selected from 65,791 deposited sequences in 2011-2020 and pseudotyped viruses were constructed with the VSV vector. We immunized guinea pigs with DNA vaccine containing the A/H3N2 components of the vaccine strains from 2011 to 2021 and tested neutralizing antibody against the pseudotyped viruses. We used a hierarchical clustering method to classify the eighty-four representative strains into different antigenic clusters. We also immunized animals with monovalent vaccine stock of the vaccine strains for the 2020-2021 and 2021-2022 seasons and tested neutralizing antibody against the pseudotyped viruses.

FINDINGS

The vaccine strains PE/09, VI/11 and TE/12 induced higher levels of neutralizing antibody against representative strains circulating in recommended year and the year immediately prior whereas vaccine strains HK/14, HK/19 and CA/20 induced poor neutralization against all representative strains. The representative strains were divided into five antigenic clusters (AgV), which were not identical to gene clades. The AgV5 strains were most difficult to be protected among the five clusters. Compared with single-dose immunization, three doses of monovalent vaccine stock (HK/19 or CA/20) could induce stronger and broader neutralizing antibodies against strains in each of the antigenic clusters.

INTERPRETATION

The protective effect of vaccine strains indicated that the accurate selection of A/H3N2 vaccine strains must remain a top priority. By increasing the frequency of immunization, stronger and broader neutralizing antibodies against strains in all antigenic clusters were induced, which provides direction for a new immunization strategy.

FUNDING

This work was supported by a grant from National Key R&D Program of China (No. 2021YFC2301700).

Genome-Wide Reassortment Analysis of Influenza A H7N9 Viruses Circulating in China during 2013-2019

Reassortment with the H9N2 virus gave rise to the zoonotic H7N9 avian influenza virus (AIV), which caused more than five outbreak waves in humans, with high mortality. The frequent exchange of genomic segments between H7N9 and H9N2 has been well-documented. However, the reassortment patterns have not been described and are not yet fully understood. Here, we used phylogenetic analyses to investigate the patterns of intersubtype and intrasubtype/intralineage reassortment across the eight viral segments. The H7N9 virus and its progeny frequently exchanged internal genes with the H9N2 virus but rarely with the other AIV subtypes. Before beginning the intrasubtype/intralineage reassortment analyses, five Yangtze River Delta (YRD A-E) and two Pearl River Delta (PRD A-B) clusters were divided according to the HA gene phylogeny. The seven reset segment genes were also nomenclatured consistently. As revealed by the tanglegram results, high intralineage reassortment rates were determined in waves 2–3 and 5. Additionally, the clusters of PB2 c05 and M c02 were the most dominant in wave 5, which could have contributed to the onset of the largest H7N9 outbreak in 2016–2017. Meanwhile, a portion of the YRD-C cluster (HP H7N9) inherited their PB2, PA, and M segments from the co-circulating YRD-E (LP H7N9) cluster during wave 5. Untanglegram results revealed that the reassortment rate between HA and NA was lower than HA with any of the other six segments. A multidimensional scaling plot revealed a robust genetic linkage between the PB2 and PA genes, indicating that they may share a co-evolutionary history. Furthermore, we observed relatively more robust positive selection pressure on HA, NA, M2, and NS1 proteins. Our findings demonstrate that frequent reassortment, particular reassorted patterns, and adaptive mutations shaped the H7N9 viral genetic diversity and evolution. Increased surveillance is required immediately to better understand the current state of the HP H7N9 AIV.

Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data

There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. To find such a broadly protective immunization strategy, it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenzalike evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method, we are able to infer single as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for the design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. In the future, this approach may be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.

The Missing Expression Level-Evolutionary Rate Anticorrelation in Viruses Does Not Support Protein Function as a Main Constraint on Sequence Evolution

One of the central goals in molecular evolutionary biology is to determine the sources of variation in the rate of sequence evolution among proteins. Gene expression level is widely accepted as the primary determinant of protein evolutionary rate, because it scales with the extent of selective constraints imposed on a protein, leading to the well-known negative correlation between expression level and protein evolutionary rate (the E-R anticorrelation). Selective constraints have been hypothesized to entail the maintenance of protein function, the avoidance of cytotoxicity caused by protein misfolding or nonspecific protein-protein interactions, or both. However, empirical tests evaluating the relative importance of these hypotheses remain scarce, likely due to the nontrivial difficulties in distinguishing the effect of a deleterious mutation on a protein's function versus its cytotoxicity. We realized that examining the sequence evolution of viral proteins could overcome this hurdle. It is because purifying selection against mutations in a viral protein that result in cytotoxicity per se is likely relaxed, whereas purifying selection against mutations that impair viral protein function persists. Multiple analyses of SARS-CoV-2 and nine other virus species revealed a complete absence of any E-R anticorrelation. As a control, the E-R anticorrelation does exist in human endogenous retroviruses where purifying selection against cytotoxicity is present. Taken together, these observations do not support the maintenance of protein function as the main constraint on protein sequence evolution in cellular organisms.

Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data

Genetic mutations play a central role in evolution. For a significantly beneficial mutation, a one-time mutation event suffices for the species to prosper and predominate through the process called “monophyletic selective sweep.” However, existing methods that rely on counting the number of mutation events to detect selection are unable to find such a mutation in selective sweep. We here introduce a method to detect mutations at the single amino acid/nucleotide level that could be responsible for monophyletic selective sweep evolution. The method identifies a genetic signature associated with selective sweep using the population genetic test statistic Tajima’s D. We applied the algorithm to ebolavirus, influenza A virus, and severe acute respiratory syndrome coronavirus 2 to identify known biologically significant mutations and unrecognized mutations associated with potential selective sweep. The method can detect beneficial mutations, possibly leading to discovery of previously unknown biological functions and mechanisms related to those mutations.

IMPORTANCE In biology, research on evolution is important to understand the significance of genetic mutation. When there is a significantly beneficial mutation, a population of species with the mutation prospers and predominates, in a process called “selective sweep.” However, there are few methods that can find such a mutation causing selective sweep from genetic data. We here introduce a novel method to detect such mutations. Applying the method to the genomes of ebolavirus, influenza viruses, and the novel coronavirus, we detected known biologically significant mutations and identified mutations the importance of which is previously unrecognized. The method can deepen our understanding of molecular and evolutionary biology.

Time-series analyses of directional sequence changes in SARS-CoV-2 genomes and an efficient search method for candidates for advantageous mutations for growth in human cells

We first conducted time-series analysis of mono- and dinucleotide composition for over 10,000 SARS-CoV-2 genomes, as well as over 1500 Zaire ebolavirus genomes, and found clear time-series changes in the compositions on a monthly basis, which should reflect viral adaptations for efficient growth in human cells. We next developed a sequence alignment free method that extensively searches for advantageous mutations and rank them in an increase level for their intrapopulation frequency. Time-series analysis of occurrences of oligonucleotides of diverse lengths for SARS-CoV-2 genomes revealed seven distinctive mutations that rapidly expanded their intrapopulation frequency and are thought to be candidates of advantageous mutations for the efficient growth in human cells.

•

Time-series change in mono- and dinucleotide compositions in SARS-CoV-2 genome

•

The time-series change found for SARS-CoV-2 differed from that of Zaire ebolavirus.

•

Sequence alignment-free method to search advantageous mutation in viral genomes

•

Seven mutations of SARS-CoV-2 rapidly expanding their population frequency

•

A method other than phylogenetic tree construction for viral evolutionary study

Monitoring the performance of foot-and-mouth disease vaccines prepared against local strains in the face of antigenic evolution in the field

National programs for foot-and-mouth disease (FMD) eradication includes the use of vaccination; Turkey which is endemic to FMD virus (FMDV) (except for the Thrace region) and there is a risk of incursion of exotic strains from eastern borders. In 2015, a devastating outbreak was caused by the A/ASIA/G-VII (G-VII) lineage, which led to the inclusion of a new vaccine strain (A/TUR/15) derived from this lineage in 3 months. Although most of the cattle population in Turkey was then immunized with A/TUR/15 (vaccine coverage: 92.8%), the G-VII lineage continued to cause outbreaks in the field despite the evidence of protection observed with A/TUR/15 in in vivo and in vitro tests. When G-VII field strains were examined, changes in their genomes were detected. As the lineage appeared to be evolving, an unconventional vaccination strategy was adapted which changed the vaccine strain with new variants of G-VII according to antigenic evolution. To assess the suitability of candidate vaccine strains derived from the variants of the G-VII lineage, three viral candidates were assessed (A/TUR/15, A/TUR/16 and A/TUR/17) by in vitro virus neutralization tests for r₁ vaccine matching and in vivo heterologous challenge tests. Although all three vaccine strains were antigenically well matched with each other and other G-VII field viruses, due to continues outbreaks the vaccine strain was changed three times in 20 months from A/TUR/15 (Dec 2015) to A/TUR/16 (Dec 2016) and then to A/TUR/17 (Aug 2017). With this strategy serotype A has not been observed in the field since January 2018. This study highlights the importance of adapting the vaccine strains according to antigenic evolution as this could be a valuable combat strategy in endemic countries, rather than using well-known vaccine strain and relying only on the relationship coefficient (r₁ ) value.

Structures and functions linked to genome-wide adaptation of human influenza A viruses

Human influenza A viruses elicit short-term respiratory infections with considerable mortality and morbidity. While H3N2 viruses circulate for more than 50 years, the recent introduction of pH1N1 viruses presents an excellent opportunity for a comparative analysis of the genome-wide evolutionary forces acting on both subtypes. Here, we inferred patches of sites relevant for adaptation, i.e. being under positive selection, on eleven viral protein structures, from all available data since 1968 and correlated these with known functional properties. Overall, pH1N1 have more patches than H3N2 viruses, especially in the viral polymerase complex, while antigenic evolution is more apparent for H3N2 viruses. In both subtypes, NS1 has the highest patch and patch site frequency, indicating that NS1-mediated viral attenuation of host inflammatory responses is a continuously intensifying process, elevated even in the longtime-circulating subtype H3N2. We confirmed the resistance-causing effects of two pH1N1 changes against oseltamivir in NA activity assays, demonstrating the value of the resource for discovering functionally relevant changes. Our results represent an atlas of protein regions and sites with links to host adaptation, antiviral drug resistance and immune evasion for both subtypes for further study.

Full-length genome and molecular characterization of dengue virus serotype 2 isolated from an imported patient from Myanmar

BACKGROUND

Dengue is the most common mosquito-borne infection worldwide and a serious threat to global public health. Sporadic dengue virus serotype 2 (DENV-2) imported cases from Myanmar have been documented almost every year in Yunnan Province of China since 2005. However, the complete genome sequences of DENV-2 isolates imported from Myanmar are not available.

METHODS

The full-length genome of the DENV-2 strain (YNPE2), isolated from an imported case from Myanmar in 2013, was identified by the next-generation sequencing. The extreme ends of the viral genome were validated by 5'/3' RACE and Sanger sequencing. Furthermore, phylogenetic, recombination and selection pressure analyses were conducted for the molecular characterization of YNPE2 strain.

RESULTS

Whole-genome sequencing revealed that the full-length sequence of YNPE2 strain was 10,724 bases, with an open reading frame encoding for 3391 amino acids. The YNPE2 strain had 99.0% nucleotide identity and 99.8% amino acid identity with two closely related strains, ThD2_0078_01 strain (DQ181797) and DENV-2/TH/BID-V2157/200 strain (FJ639832). The phylogenetic analysis suggested that the YNPE2 strain belonged to Asian I genotype and was likely derived from Thailand strain (DQ181797). Moreover, selection pressure analysis revealed two amino acid sites of the NS4B and NS5 proteins, with important evidence of positive selection.

CONCLUSION

This study revealed the first complete genome sequence and molecular characterization of a DENV-2 strain (YNPE2) isolated from an imported case from Myanmar, thus providing a valuable reference genome source for future surveillance, epidemiology and vaccine development of DENV-2 virus in Yunnan, China.

The role of natural selection in shaping genetic variation in a promising Chagas disease drug target: Trypanosoma cruzi trans-sialidase

Rational drug design creates innovative therapeutics based on knowledge of the biological target to provide more effective and responsible therapeutics. Chagas disease, endemic throughout Latin America, is caused by Trypanosoma cruzi, a protozoan parasite. Current therapeutics are problematic with widespread calls for new approaches. Researchers are using rational drug design for Chagas disease and one target receiving considerable attention is the T. cruzi trans-sialidase protein (TcTS). In T. cruzi, trans-sialidase catalyzes the transfer of sialic acid from a mammalian host to coat the parasite surface membrane and avoid immuno-detection. However, the role of TcTS in pathology variance among and within genetic variants of the parasite is not well understood despite numerous studies. Previous studies reported the crystalline structure of TcTS and the TS protein structure in other trypanosomes where the enzyme is often inactive. However, no study has examined the role of natural selection in genetic variation in TcTS. To understand the role of natural selection in TcTS DNA sequence and protein variation, we examined a 471 bp portion of the TcTS gene from 48 T. cruzi samples isolated from insect vectors. Because there may be multiple parasite genotypes infecting one insect and there are multiple copies of TcTS per parasite genome, all 48 sequences had multiple polymorphic bases. To resolve these polymorphisms, we examined cloned sequences from two insect vectors. The data are analyzed to understand the role of natural selection in shaping genetic variation in TcTS and interpreted in light of the possible role of TcTS as a drug target. The analysis highlights negative or purifying selection on three amino acids previously shown to be important in TcTS transfer activity. One amino acid in particular, Tyr342, is a strong candidate for a drug target because it is under negative selection and amino acid substitutions inactivate TcTS transfer activity. AUTHOR SUMMARY: Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted to humans and other mammals primarily by Triatomine insects. Being endemic in many South and Central American countries and affecting millions of people the need for new more effective and safe therapies is evident. Here, we examine genetic variation and natural selection on DNA (471 bp) and amino acid (157 aa) sequence data of the T. cruzi trans-sialdiase (TcTS) protein, often suggested as a candidate for rational drug design. In our surveyed region of the protein there were five amino acid residues that have been shown to be integral to the function of TcTS. We found that three were under strong negative selection making them ideal candidates for drug design; however, one was under balancing selection and should be avoided as a drug target. Our study provides new information into identifying potential targets for a new Chagas drug.

Molecular footprints of selective pressure in the neuraminidase gene of currently circulating human influenza subtypes and lineages

Influenza neuraminidase (NA) is under selective pressure (SP) of both host immune system and drug use. Here, we assembled large datasets of NA sequences of worldwide circulating viruses to estimate the global and site-specific SP acting on all current subtypes/lineages of human influenza NA. An overall negative SP of similar magnitude and a prevalence of negatively selected sites were observed for all subtypes/lineages. Positively selected sites varied according to the subtype/lineage, including N1-NA sites 247 and 275, N2-NA sites 148 and 151, and B/Victoria-NA site 395 associated with drug-resistance or reduced susceptibility. These results evidenced a potential role of positive selection in the low-level spread of A(H1N1)pdm09-H275Y drug-resistant viruses, and alerted for a potential higher risk of spread of a synergistic A(H1N1)pdm09 drug-resistant variant (H275Y/S247N). The positive selection detected at N2-NA sites 148 and 151 was probably an artefact from cell-culture. Overall mapping revealed six potential new druggable regions.

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

One Year Genome Evolution of Lausannevirus in Allopatric versus Sympatric Conditions

Amoeba-resisting microorganisms raised a great interest during the last decade. Among them, some large DNA viruses present huge genomes up to 2.5 Mb long, exceeding the size of small bacterial genomes. The rate of genome evolution in terms of mutation, deletion, and gene acquisition in these genomes is yet unknown. Given the suspected high plasticity of viral genomes, the microevolution of the 346 kb genome of Lausannevirus, a member of Megavirales, was studied. Hence, Lausannevirus was co-cultured within the amoeba Acanthamoeba castellanii over one year. Despite a low number of mutations, the virus showed a genome reduction of 3.7% after 12 months. Lausannevirus genome evolution in sympatric conditions was investigated by its co-culture with Estrella lausannensis, an obligate intracellular bacterium, in the amoeba A. castellanii during one year. Cultures were split every 3 months. Genome sequencing revealed that in these conditions both, Lausannevirus and E. lausannensis, show stable genome, presenting no major rearrangement. In fact, after one year they acquired from 2 to 7 and from 4 to 10 mutations per culture for Lausannevirus and E. lausannensis, respectively. Interestingly, different mutations in the endonuclease encoding genes of Lausannevirus were observed in different subcultures, highlighting the importance of this gene product in the replication of Lausannevirus. Conversely, mutations in E. lausannensis were mainly located in a gene encoding for a phosphoenolpyruvate–protein phosphotransferase (PtsI), implicated in sugar metabolism. Moreover, in our conditions and with our analyses we detected no horizontal gene transfer during one year of co-culture.

Characterization of the neuraminidase genes from human influenza A viruses circulating in Iran from 2010 to 2015

BACKGROUND

Characterization of influenza viruses is critical for detection of new emerging variants. Herein, we analyzed the genetic diversity and drug susceptibility of the neuraminidase gene (NAs) expressed by influenza A/H1N1pdm09 and A/H3N2 viruses circulating in Iran from 2010 to 2015.

METHODS

We genetically analyzed the NAs of 38 influenza A/H1N1pdm09 and 35 A/H3N2 isolates.

RESULTS

The Iranian A/H1N1pdm09 viruses belonged to seven genogroups/subgenogroups, with the dominant groups being genogroups 6B and 6C. The A/H3N2 isolates fell into six gneogroups/subgenogroups, with the dominant genogroups being 3C and 3C.2a. The most common mutations detected among the A/H1N1pdm09 viruses included N44S, V106I, N200S, and N248D. All H1N1pdm09 viruses were genetically susceptible to the NAIs. However, one A/H1N1pdm09 virus from the 2013-2014 season possessed an NA-S247N mutation, which reduces the susceptibility to oseltamivir. In case of H3N2, none of the analyzed Iranian strains carried a substitution that might affect its susceptibility to NAIs.

CONCLUSION

The ongoing evolution of influenza viruses and the detect of influenza viruses with reduced susceptibility to NAIs warrants continuous monitoring of the circulating strains.

Baculovirus Molecular Evolution via Gene Turnover and Recurrent Positive Selection of Key Genes

Hosts and viruses are locked in an evolutionary arms race. Hosts are constantly evolving to suppress virulence and replication, while viruses, which are reliant on host machinery for survival and reproduction, develop counterstrategies to escape this immune defense. Viruses must also adapt to novel conditions while establishing themselves in a host species. Both processes provide strong selection for viral adaptation. Understanding adaptive evolution in insect viruses can help us to better understand adaptive evolution in general and is important due to the use of these viruses as biocontrol agents and for protecting ecologically or economically important species from outbreaks. Here we examine the molecular evolution of baculoviruses and nudiviruses, a group of insect-infecting viruses with key roles in biocontrol. We looked for signatures of selection between genomes of baculoviruses infecting a range of species and within a population of baculoviruses. Both analyses found only a few strong signatures of positive selection, primarily in replication- and transcription-associated genes and several structural protein genes. In both analyses, we detected a conserved complex of genes, including the helicase gene, showing consistently high levels of adaptive evolution, suggesting that they may be key in antagonistic coevolution to escape host suppression. These genes are integral to the baculovirus life cycle and may be good focal genes for developing baculoviruses as effective biocontrol agents or for targeting baculoviruses infecting ecologically relevant species. Recombination and complex genomes make evolution in these double-stranded DNA viruses more efficient than that in smaller RNA viruses with error-prone replication, as seen via signatures of selection in specific genes within a population of baculoviruses.

IMPORTANCE Most viral evolutionary studies focus on RNA viruses. While these viruses cause many human and animal diseases, such studies leave us with a lesser understanding of how DNA viruses adapt to hosts and how the host responds to these pathogens. In this paper, we focus on the evolution of baculoviruses, a group of insect-infecting DNA viruses, many of which have been used in biocontrol. We find that most of the genome is under purifying selection, with only a few key genes evolving adaptively. Our results provide a glimpse into how DNA viruses differ from RNA viruses in their evolutionary dynamics and identify genes that are key to DNA virus adaptation, improving our understanding of how this group of pathogens evolves.

Evolution and Vaccination of Influenza Virus

In this study, we present an application paradigm in which an unsupervised machine learning approach is applied to the high-dimensional influenza genetic sequences to investigate whether vaccine is a driving force to the evolution of influenza virus. We first used a visualization approach to visualize the evolutionary paths of vaccine-controlled and non-vaccine-controlled influenza viruses in a low-dimensional space. We then quantified the evolutionary differences between their evolutionary trajectories through the use of within- and between-scatter matrices computation to provide the statistical confidence to support the visualization results. We used the influenza surface Hemagglutinin (HA) gene for this study as the HA gene is the major target of the immune system. The visualization is achieved without using any clustering methods or prior information about the influenza sequences. Our results clearly showed that the evolutionary trajectories between vaccine-controlled and non-vaccine-controlled influenza viruses are different and vaccine as an evolution driving force cannot be completely eliminated.

Evolution of the neuraminidase gene of seasonal influenza A and B viruses in Thailand between 2010 and 2015

The neuraminidase inhibitors (NAIs) oseltamivir and zanamivir are commonly used for the treatment and control of influenza A and B virus infection. However, the emergence of new influenza virus strains with reduced susceptibility to NAIs may appear with the use of these antivirals or even naturally. We therefore screened the neuraminidase (NA) sequences of seasonal influenza virus A(H1N1), A(H1N1)pdm09, A(H3N2), and influenza B virus strains identified in Thailand for the presence of substitutions previously reported to reduce susceptibility to NAIs. We initially examined oseltamivir resistance (characterized by the H275Y mutation in the NA gene) in 485 A(H1N1)pdm09 strains circulating in Thailand and found that 0.82% (4/485) had this substitution. To further evaluate the evolution of the NA gene, we also randomly selected 98 A(H1N1)pdm09, 158 A(H3N2), and 69 influenza B virus strains for NA gene amplification and sequencing, which revealed various amino acid mutations in the active site of the NA protein previously shown to be associated with reduced susceptibility to NAIs. Phylogenetic analysis of the influenza virus strains from this study and elsewhere around the world, together with the estimations of nucleotide substitution rates and selection pressure, and the predictions of B-cell epitopes and N-linked glycosylation sites all provided evidence for the ongoing evolution of NA. The overall rates of NA evolution for influenza A viruses were higher than for influenza B virus at the nucleotide level, although influenza B virus possessed more genealogical diversity than that of influenza A viruses. The continual surveillance of the antigenic changes associated with the NA protein will not only contribute to the influenza virus database but may also provide a better understanding of selection pressure exerted by antiviral use.

Genotype-Specific Evolution of Hepatitis E Virus

Hepatitis E virus (HEV) is the most common cause of acute viral hepatitis globally. HEV comprises four genotypes with different geographic distributions and host ranges. We utilize this natural case-control study for investigating the evolution of zoonotic viruses compared to single-host viruses, using 244 near-full-length HEV genomes. Genome-wide estimates of the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS ratio) located a region of overlapping reading frames, which is subject to positive selection in genotypes 3 and 4. The open reading frames (ORFs) involved have functions related to host-pathogen interaction, so genotype-specific evolution of these regions may reflect their fitness. Bayesian inference of evolutionary rates shows that genotypes 3 and 4 have significantly higher rates than genotype 1 across all ORFs. Reconstruction of the phylogenies of zoonotic genotypes demonstrates significant intermingling of isolates between hosts. We speculate that the genotype-specific differences may result from cyclical adaptation to different hosts in genotypes 3 and 4.IMPORTANCE Hepatitis E virus (HEV) is increasingly recognized as a pathogen that affects both the developing and the developed world. While most often clinically mild, HEV can be severe or fatal in certain demographics, such as expectant mothers. Like many other viral pathogens, HEV has been classified into several distinct genotypes. We show that most of the HEV genome is evolutionarily constrained. One locus of positive selection is unusual in that it encodes two distinct protein products. We are the first to detect positive selection in this overlap region. Genotype 1, which infects humans only, appears to be evolving differently from genotypes 3 and 4, which infect multiple species, possibly because genotypes 3 and 4 are unable to achieve the same fitness due to repeated host jumps.

An optimistic protein assembly from sequence reads salvaged an uncharacterized segment of mouse picobirnavirus

Advances in Next Generation Sequencing technologies have enabled the generation of millions of sequences from microorganisms. However, distinguishing the sequence of a novel species from sequencing errors remains a technical challenge when the novel species is highly divergent from the closest known species. To solve such a problem, we developed a new method called Optimistic Protein Assembly from Reads (OPAR). This method is based on the assumption that protein sequences could be more conserved than the nucleotide sequences encoding them. By taking advantage of metagenomics, bioinformatics and conventional Sanger sequencing, our method successfully identified all coding regions of the mouse picobirnavirus for the first time. The salvaged sequences indicated that segment 1 of this virus was more divergent from its homologues in other Picobirnaviridae species than segment 2. For this reason, only segment 2 of mouse picobirnavirus has been detected in previous studies. OPAR web tool is available at http://bioinformatics.czc.hokudai.ac.jp/opar/.

Analyses of Evolutionary Characteristics of the Hemagglutinin-Esterase Gene of Influenza C Virus during a Period of 68 Years Reveals Evolutionary Patterns Different from Influenza A and B Viruses

Infections with the influenza C virus causing respiratory symptoms are common, particularly among children. Since isolation and detection of the virus are rarely performed, compared with influenza A and B viruses, the small number of available sequences of the virus makes it difficult to analyze its evolutionary dynamics. Recently, we reported the full genome sequence of 102 strains of the virus. Here, we exploited the data to elucidate the evolutionary characteristics and phylodynamics of the virus compared with influenza A and B viruses. Along with our data, we obtained public sequence data of the hemagglutinin-esterase gene of the virus; the dataset consists of 218 unique sequences of the virus collected from 14 countries between 1947 and 2014. Informatics analyses revealed that (1) multiple lineages have been circulating globally; (2) there have been weak and infrequent selective bottlenecks; (3) the evolutionary rate is low because of weak positive selection and a low capability to induce mutations; and (4) there is no significant positive selection although a few mutations affecting its antigenicity have been induced. The unique evolutionary dynamics of the influenza C virus must be shaped by multiple factors, including virological, immunological, and epidemiological characteristics.

Re-emergence of H3N2 strains carrying potential neutralizing mutations at the N-linked glycosylation site at the hemagglutinin head, post the 2009 H1N1 pandemic

Background

Seasonally prevalent H1N1 and H3N2 influenza A viruses have evolved by antigenic drift; this evolution has resulted in the acquisition of asparagine (N)-linked glycosylation sites (NGSs) in the globular head of hemagglutinin (HA), thereby affecting the antigenic and receptor-binding properties, as well as virulence. An epidemiological survey indicated that although the traditional seasonal H1N1 strain had disappeared, H3N2 became predominant again in the seasons (2010–11 and 2011–12) immediately following the H1N1 pandemic of 2009. Interestingly, although the 2009 pandemic H1N1 strain (H1N1pdm09) lacks additional NGSs, clinically isolated H3N2 strains obtained during these seasons gained N (Asn) residues at positions 45 and 144 of HA that forms additional NGSs.

Methods

To investigate whether these NGSs are associated with re-emergence of H3N2 within the subtype, we tested the effect of amino acid substitutions on neutralizing activity by using the antisera raised against H3N2 strains with or without additional NGSs. Furthermore, because the N residue at position 144 of HA was identified as the site of mismatch between the vaccine and epidemic strains of 2011–2012, we generated mutant viruses by reverse genetics and tested the functional importance of this particular NGS for antibody-mediated neutralization by intranasal inoculation of mice.

Results

The results indicated that amino acid substitution at residue 144 significantly affected neutralization activity, acting as an escape mutation.

Conclusions

Our data suggest that the newly acquired NGSs in the HA globular head may play an important role in the re-emergence of endemic seasonal H3N2 strain by aiding the escape from humoral immunity.

Sequence amplification via cell passaging creates spurious signals of positive adaptation in influenza virus H3N2 hemagglutinin

Clinical influenza A virus isolates are frequently not sequenced directly. Instead, a majority of these isolates (~70% in 2015) are first subjected to passaging for amplification, most commonly in non-human cell culture. Here, we find that this passaging leaves distinct signals of adaptation, which can confound evolutionary analyses of the viral sequences. We find distinct patterns of adaptation to Madin-Darby (MDCK) and monkey cell culture absent from unpassaged hemagglutinin sequences. These patterns also dominate pooled datasets not separated by passaging type, and they increase in proportion to the number of passages performed. By contrast, MDCK-SIAT1 passaged sequences seem mostly (but not entirely) free of passaging adaptations. Contrary to previous studies, we find that using only internal branches of influenza virus phylogenetic trees is insufficient to correct for passaging artifacts. These artifacts can only be safely avoided by excluding passaged sequences entirely from subsequent analysis. We conclude that future influenza virus evolutionary analyses should appropriately control for potentially confounding effects of passaging adaptations.

Genome Hotspots for Nucleotide Substitutions and the Evolution of Influenza A (H1N1) Human Strains

In recent years a number of studies have brought attention to the role of positive selection during the evolution of antigenic escape by influenza strains. Particularly, the identification of positively selected sites within antigenic domains of viral surface proteins has been used to suggest that the evolution of viral–host receptor binding specificity is driven by selection. Here we show that, following the 1918 outbreak, the antigenic sites of the hemagglutinin (HA) viral surface protein and the stalk region of neuraminidase became substitution hotspots. The hotspots show similar patterns of nucleotide substitution bias at synonymous and nonsynonymous sites. Such bias imposes directionality in amino acid replacements that can influence signals of selection at antigenic sites. Our results suggest that the high accumulation of substitutions within the antigenic sites of HA can explain not only cases of antigenic escape by antigenic drift but also lead to occasional episodes of viral extinction.

Dynamic reassortments and genetic heterogeneity of the human-infecting influenza A (H7N9) virus

Influenza A (H7N9) virus has been causing human infections in China since February 2013, raising serious concerns of potential pandemics. Previous studies demonstrate that human infection is directly linked to live animal markets, and that the internal genes of the virus are derived from H9N2 viruses circulating in the Yangtze River Delta area in Eastern China. Here following analysis of 109 viruses, we show a much higher genetic heterogeneity of the H7N9 viruses than previously reported, with a total of 27 newly designated genotypes. Phylogenetic and genealogical inferences reveal that genotypes G0 and G2.6 dominantly co-circulate within poultry, with most human isolates belonging to the genotype G0. G0 viruses are also responsible for the inter- and intra-province transmissions, leading to the genesis of novel genotypes. These observations suggest the province-specific H9N2 virus gene pools increase the genetic diversity of H7N9 via dynamic reassortments and also imply that G0 has not gained overwhelming fitness and the virus continues to undergo reassortment.

Ebolavirus evolves in human to minimize the detection by immune cells by accumulating adaptive mutations

The current outbreak of Zaire ebolavirus (EBOV) lasted longer than the previous outbreaks and there is as yet no proven treatment or vaccine available. Understanding host immune pressure and associated EBOV immune evasion that drive the evolution of EBOV is vital for diagnosis as well as designing a highly effective vaccine. The aim of this study was to deduce adaptive selection pressure acting on each amino acid sites of EBOV responsible for the recent 2014 outbreak. Multiple statistical methods employed in the study include SLAC, FEL, REL, IFEL, FUBAR and MEME. Results show that a total of 11 amino acid sites from sGP and ssGP, and 14 sites from NP, VP40, VP24 and L proteins were inferred as positively and negatively selected, respectively. Overall, the function of 11 out of 25 amino acid sites under selection pressure exactly found to be involved in T cell and B-cell epitopes. We identified that the EBOV had evolved through purifying selection pressure, which is a predictor that is known to aid the virus to adapt better to the human host and subsequently reduce the efficiency of existing immunity. Furthermore, computational RNA structure prediction showed that the three synonymous nucleotide mutations in NP gene altered the RNA secondary structure and optimal base-pairing energy, implicating a possible effect on genome replication. Here, we have provided evidence that the EBOV strains involved in the recent 2014 outbreak have evolved to minimize the detection by T and B cells by accumulating adaptive mutations to increase the survival fitness.

Determination of antigenicity-altering patches on the major surface protein of human influenza A/H3N2 viruses

Human influenza viruses are rapidly evolving RNA viruses that cause short-term respiratory infections with substantial morbidity and mortality in annual epidemics. Uncovering the general principles of viral coevolution with human hosts is important for pathogen surveillance and vaccine design. Protein regions are an appropriate model for the interactions between two macromolecules, but the currently used epitope definition for the major antigen of influenza viruses, namely hemagglutinin, is very broad. Here, we combined genetic, evolutionary, antigenic, and structural information to determine the most relevant regions of the hemagglutinin of human influenza A/H3N2 viruses for interaction with human immunoglobulins. We estimated the antigenic weights of amino acid changes at individual sites from hemagglutination inhibition data using antigenic tree inference followed by spatial clustering of antigenicity-altering protein sites on the protein structure. This approach determined six relevant areas (patches) for antigenic variation that had a key role in the past antigenic evolution of the viruses. Previous transitions between successive predominating antigenic types of H3N2 viruses always included amino acid changes in either the first or second antigenic patch. Interestingly, there was only partial overlap between the antigenic patches and the patches under strong positive selection. Therefore, besides alterations of antigenicity, other interactions with the host may shape the evolution of human influenza A/H3N2 viruses.

Coordinated Evolution of Influenza A Surface Proteins

The surface proteins hemagglutinin (HA) and neuraminidase (NA) of human influenza A virus evolve under selection pressures to escape adaptive immune responses and antiviral drug treatments. In addition to these external selection pressures, some mutations in HA are known to affect the adaptive landscape of NA, and vice versa, because these two proteins are physiologically interlinked. However, the extent to which evolution of one protein affects the evolution of the other one is unknown. Here we develop a novel phylogenetic method for detecting the signatures of such genetic interactions between mutations in different genes – that is, inter-gene epistasis. Using this method, we show that influenza surface proteins evolve in a coordinated way, with mutations in HA affecting subsequent spread of mutations in NA and vice versa, at many sites. Of particular interest is our finding that the oseltamivir-resistance mutations in NA in subtype H1N1 were likely facilitated by prior mutations in HA. Our results illustrate that the adaptive landscape of a viral protein is remarkably sensitive to its genomic context and, more generally, that the evolution of any single protein must be understood within the context of the entire evolving genome.

The fitness of an organism depends on the coordinated function of many genes. Thus, how a mutation in one gene affects fitness often depends on what mutations are present in other genes. This dependence is called “genetic interaction” or “epistasis”. The prevalence and type of such interactions are not well understood. Epistasis can be inferred from time-series sequencing data when a mutation in one gene is observed to facilitate the spread of a mutation in another gene. However, the situation is much more complicated when new combinations of genes are formed by processes such as recombination or reassortment. In such cases, deducing the time and order of genetic changes is difficult. Here, we devise a method to infer pairs of mutations in different genes which closely follow one another in the presence of reassortment. We apply it to evolution of two surface proteins of influenza A virus, hemagglutinin and neuraminidase, which are important targets for the human immune system and drugs. We show that mutations in one of these proteins are often facilitated by prior mutations, or compensated by subsequent mutations, in the other protein. In particular, drug-resistance mutations in neuraminidase were likely made possible by prior mutation in hemagglutinin. Knowledge of such interactions is necessary to fully understand and predict evolution.

K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.

Selecting vaccine strains for H3N2 human influenza A virus

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenicity was unknown. Here the antigenic model for estimating the degree of antigenic difference (antigenic distance) between amino acid sequences of HA1 was integrated into the process of selecting vaccine strains for H3N2 human influenza A virus. When the effectiveness of a potential vaccine strain for a target season was evaluated retrospectively using the average antigenic distance between the strain and the epidemic viruses sampled in the target season, the most effective vaccine strain was identified mostly in the season one year before the target season (pre-target season). Effectiveness of actual vaccines appeared to be lower than that of the strains randomly chosen in the pre-target season on average. It was recommended to replace the vaccine strain for every target season with the strain having the smallest average antigenic distance to the others in the pre-target season. The procedure of selecting vaccine strains for future epidemic seasons described in the present study was implemented in the influenza virus forecasting system (INFLUCAST) (http://www.nsc.nagoya-cu.ac.jp/~yossuzuk/influcast.html).

Inferring natural selection signals in Plasmodium vivax-encoded proteins having a potential role in merozoite invasion

Detecting natural selection signals in Plasmodium parasites antigens might be used for identifying potential new vaccine candidates. Fifty-nine Plasmodium vivax-Sal-I genes encoding proteins having a potential role in invasion were used as query for identifying them in recent P. vivax strain genome sequences and two closely-related Plasmodium species. Several measures of DNA sequence variation were then calculated and selection signatures were detected by using different approaches. Our results may be used for determining which genes expressed during P. vivax merozoite stage could be prioritised for further population genetics or functional studies for designing a P. vivax vaccine which would avoid allele-specific immune responses.

Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin

We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution. We consider three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), Immune Epitope Database (IEDB) epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for one of the functions of hemagglutinin-to bind sialic acid). Individually, these quantities explain approximately 15% of the variation in site-wise dN/dS. In combination, solvent accessibility and proximity explain 32% of the variation in dN/dS; incorporating IEDB epitope sites into the model adds only an additional 2 percentage points. Thus, while solvent accessibility and proximity perform largely as independent predictors of evolutionary variation, they each overlap with the epitope-sites predictor. Furthermore, we find that the historical H3 epitope sites, which date back to the 1980s and 1990s, only partially overlap with the experimental sites from the IEDB, and display similar overlap in predictive power when combined with solvent accessibility and proximity. We also find that sites with dN/dS > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or IEDB epitope sites, but only by proximity to the receptor-binding region. In summary, a simple geometric model of HA evolution outperforms a model based on epitope sites. These results suggest that either the available epitope sites do not accurately represent the true influenza antigenic sites or that host immune bias may be less important for influenza evolution than commonly thought.

The relationship between dN/dS and scaled selection coefficients

Numerous computational methods exist to assess the mode and strength of natural selection in protein-coding sequences, yet how distinct methods relate to one another remains largely unknown. Here, we elucidate the relationship between two widely used phylogenetic modeling frameworks: dN/dS models and mutation-selection (MutSel) models. We derive a mathematical relationship between dN/dS and scaled selection coefficients, the focal parameters of MutSel models, and use this relationship to gain deeper insight into the behaviors, limitations, and applicabilities of these two modeling frameworks. We prove that, if all synonymous changes are neutral, standard MutSel models correspond to dN/dS ≤ 1. However, if synonymous codons differ in fitness, dN/dS can take on arbitrarily high values even if all selection is purifying. Thus, the MutSel modeling framework cannot necessarily accommodate positive, diversifying selection, while dN/dS cannot distinguish between purifying selection on synonymous codons and positive selection on amino acids. We further propose a new benchmarking strategy of dN/dS inferences against MutSel simulations and demonstrate that the widely used Goldman-Yang-style dN/dS models yield substantially biased dN/dS estimates on realistic sequence data. In contrast, the less frequently used Muse-Gaut-style models display much less bias. Strikingly, the least-biased and most precise dN/dS estimates are never found in the models with the best fit to the data, measured through both AIC and BIC scores. Thus, selecting models based on goodness-of-fit criteria can yield poor parameter estimates if the models considered do not precisely correspond to the underlying mechanism that generated the data. In conclusion, establishing mathematical links among modeling frameworks represents a novel, powerful strategy to pinpoint previously unrecognized model limitations and strengths.

A genotype network reveals homoplastic cycles of convergent evolution in influenza A (H3N2) haemagglutinin

Networks of evolving genotypes can be constructed from the worldwide time-resolved genotyping of pathogens like influenza viruses. Such genotype networks are graphs where neighbouring vertices (viral strains) differ in a single nucleotide or amino acid. A rich trove of network analysis methods can help understand the evolutionary dynamics reflected in the structure of these networks. Here, I analyse a genotype network comprising hundreds of influenza A (H3N2) haemagglutinin genes. The network is rife with cycles that reflect non-random parallel or convergent (homoplastic) evolution. These cycles also show patterns of sequence change characteristic for strong and local evolutionary constraints, positive selection and mutation-limited evolution. Such cycles would not be visible on a phylogenetic tree, illustrating that genotype network analysis can complement phylogenetic analyses. The network also shows a distinct modular or community structure that reflects temporal more than spatial proximity of viral strains, where lowly connected bridge strains connect different modules. These and other organizational patterns illustrate that genotype networks can help us study evolution in action at an unprecedented level of resolution.

Adaptive evolution of a novel avian-origin influenza A/H7N9 virus

In China, the recent outbreak of novel influenza A/H7N9 virus has been assumed to be severe, and it may possibly turn brutal in the near future. In order to develop highly protective vaccines and drugs for the A/H7N9 virus, it is critical to find out the selection pressure of each amino acid site. In the present study, six different statistical methods consisting of four independent codon-based maximum likelihood (CML) methods, one hierarchical Bayesian (HB) method and one branch-site (BS) method, were employed to determine if each amino acid site of A/H7N9 virus is under natural selection pressure. Functions for both positively and negatively selected sites were inferred by annotating these sites with experimentally verified amino acid sites. Comprehensively, the single amino acid site 627 of PB2 protein was inferred as positively selected and it function was identified as a T-cell epitope (TCE). Among the 26 negatively selected amino acid sites of PB2, PB1, PA, HA, NP, NA, M1 and NS2 proteins, only 16 amino acid sites were identified to be involved in TCEs. In addition, 7 amino acid sites including, 608 and 609 of PA, 480 of NP, and 24, 25, 109 and 205 of M1, were identified to be involved in both B-cell epitopes (BCEs) and TCEs. Conversely, the function of positions 62 of PA, and, 43 and 113 of HA was unknown. In conclusion, the seven amino acid sites engaged in both BCEs and TCEs were identified as highly suitable targets, as these sites will be predicted to play a principal role in inducing strong humoral and cellular immune responses against A/H7N9 virus.

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on nine non-homologous viral protein structures and from variation in homologous variants of those proteins, where they were available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1-0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than the more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

Pathogen evolution and the immunological niche

Host immunity is a major driver of pathogen evolution and thus a major determinant of pathogen diversity. Explanations for pathogen diversity traditionally assume simple interactions between pathogens and the immune system, a view encapsulated by the susceptible-infected-recovered (SIR) model. However, there is growing evidence that the complexity of many host-pathogen interactions is dynamically important. This revised perspective requires broadening the definition of a pathogen's immunological phenotype, or what can be thought of as its immunological niche. After reviewing evidence that interactions between pathogens and host immunity drive much of pathogen evolution, I introduce the concept of a pathogen's immunological phenotype. Models that depart from the SIR paradigm demonstrate the utility of this perspective and show that it is particularly useful in understanding vaccine-induced evolution. This paper highlights questions in immunology, evolution, and ecology that must be answered to advance theories of pathogen diversity.

Predictability of antigenic evolution for H3N2 human influenza A virus

Influenza A virus continues to pose a threat to public health. Since this virus can evolve escape mutants rapidly, it is desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately, they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. Here a theoretical method was introduced to predict the antigenic evolution of H3N2 human influenza A virus by evaluating de novo mutations through estimating the antigenic distance. The antigenic distance defined with the hemagglutination inhibition (HI) titer was estimated with antigenic models taking into account the volume, isoelectric point, relative solvent accessibility, and distances from receptor-binding sites (RBS) and N-linked glycosylation sites (NGS) for amino acids in hemagglutinin 1 (HA1). When the best model with the optimized parameter values was used to predict the antigenic evolution for the dominant strains, the prediction accuracy was relatively low. However, there appeared to be an overall tendency that the amino acid sites with larger potential net effect on antigenicity were more likely to evolve and the amino acid changes with larger potential effect were more likely to take place, suggesting that natural selection may operate to enhance the antigenic evolution of H3N2 human influenza A virus.

Loss and gain of N-linked glycosylation sites in globular head and stem of HA found in A/H3N2 flu fatal and severe cases during 2013 Tunisia flu seasonal survey

Glycosylation on the globular head of the hemagglutinin (HA) protein of influenza virus acts as an important target for recognition and destruction of virus by innate immune proteins of the collectin family. In the current study, we have characterized the dynamic amino acid changes at N-linked glycosylation sites of full length sequences of HA genes of 5 A/H3N2 Tunisian strains isolates from mild, severe, and fatal cases. Compared to the reference strain, A/Perth/16/2009 substitutions in potential N-glycosylation sites were observed in 5 HA genes at five different positions (45, 124, 128, 144, and 145) generating the losses and gains of N-linked glycosylation sites. Also the mutation N145S was presented in the receptor-binding site of all segments analyzed. Point mutations in several positions in the gene encoding the H3 of Tunisian strains were shown to ablate a glycan attachment site and also loss of a potential glycosylation site. The relation between these mutations and virulence of influenza A/H3N2 virus needed to be verified in the further experiments.

Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains

BACKGROUND

With the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed "Batch-Learning Self-Organizing Map (BLSOM)" which can characterize very many, even millions of, genomic sequences on one plane. Influenza virus is one of zoonotic viruses and shows clear host tropism. Important issues for bioinformatics studies of influenza viruses are prediction of genomic sequence changes in the near future and surveillance of potentially hazardous strains.

METHODS

To characterize sequence changes in influenza virus genomes after invasion into humans from other animal hosts, we applied BLSOMs to analyses of mono-, di-, tri-, and tetranucleotide compositions in all genome sequences of influenza A and B viruses and found clear host-dependent clustering (self-organization) of the sequences.

RESULTS

Viruses isolated from humans and birds differed in mononucleotide composition from each other. In addition, host-dependent oligonucleotide compositions that could not be explained with the host-dependent mononucleotide composition were revealed by oligonucleotide BLSOMs. Retrospective time-dependent directional changes of mono- and oligonucleotide compositions, which were visualized for human strains on BLSOMs, could provide predictive information about sequence changes in newly invaded viruses from other animal hosts (e.g. the swine-derived pandemic H1N1/09).

CONCLUSIONS

Basing on the host-dependent oligonucleotide composition, we proposed a strategy for prediction of directional changes of virus sequences and for surveillance of potentially hazardous strains when introduced into human populations from non-human sources. Millions of genomic sequences from infectious microbes and viruses have become available because of their medical and social importance, and BLSOM can characterize the big data and support efficient knowledge discovery.

Detection of positive selection eliminating effects of structural constraints in hemagglutinin of H3N2 human influenza A virus

In the evolutionary studies of proteins, the average effect of natural selection operating on amino acid mutations may be examined by comparing the numbers of synonymous (dS) and nonsynonymous (dN) substitutions that have accumulated during the same time period. In this method, destabilizing mutations occurring across protein molecules may interfere with detection of natural selection, particularly positive selection, operating on other mutations. Here an attempt to detect positive selection eliminating effects of structural constraints is demonstrated using hemagglutinin (HA) of H3N2 human influenza A virus as an example. Compatible and incompatible amino acids were inferred at each site from the computational analysis of three-dimensional structure using the thermodynamic stability as an indicator, and natural selection was examined by comparing dS and dN among compatible amino acids. In the analysis of 2701 nucleotide sequences for the entire coding region of HA, the new method identified twice as many positively selected amino acid sites as the ordinary method (16 and 4 sites in the former method without and with correction for multiple testing, respectively, and 8 and 2 sites in the latter method). Positively selected sites were involved in epitopes, receptor-binding pocket, epistasis, and stabilization, which appeared to be biologically reasonable. Nevertheless, there still appeared to be several problems, which may largely render this method conservative. It may be effective to analyze many densely sampled sequences in this method.

Cross-species comparison of site-specific evolutionary-rate variation in influenza haemagglutinin

We investigate the causes of site-specific evolutionary-rate variation in influenza haemagglutinin (HA) between human and avian influenza, for subtypes H1, H3, and H5. By calculating the evolutionary-rate ratio, ω = dN/dS as a function of a residue's solvent accessibility in the three-dimensional protein structure, we show that solvent accessibility has a significant but relatively modest effect on site-specific rate variation. By comparing rates within HA subtypes among host species, we derive an upper limit to the amount of variation that can be explained by structural constraints of any kind. Protein structure explains only 20-40% of the variation in ω. Finally, by comparing ω at sites near the sialic-acid-binding region to ω at other sites, we show that ω near the sialic-acid-binding region is significantly elevated in both human and avian influenza, with the exception of avian H5. We conclude that protein structure, HA subtype, and host biology all impose distinct selection pressures on sites in influenza HA.

A new look at an old virus: patterns of mutation accumulation in the human H1N1 influenza virus since 1918

BACKGROUND

The H1N1 influenza A virus has been circulating in the human population for over 95 years, first manifesting itself in the pandemic of 1917-1918. Initial mortality was extremely high, but dropped exponentially over time. Influenza viruses have high mutation rates, and H1N1 has undergone significant genetic changes since 1918. The exact nature of H1N1 mutation accumulation over time has not been fully explored.

METHODS

We have made a comprehensive historical analysis of mutational changes within H1N1 by examining over 4100 fully-sequenced H1N1 genomes. This has allowed us to examine the genetic changes arising within H1N1 from 1918 to the present.

RESULTS

We document multiple extinction events, including the previously known extinction of the human H1N1 lineage in the 1950s, and an apparent second extinction of the human H1N1 lineage in 2009. These extinctions appear to be due to a continuous accumulation of mutations. At the time of its disappearance in 2009, the human H1N1 lineage had accumulated over 1400 point mutations (more than 10% of the genome), including approximately 330 non-synonymous changes (7.4% of all codons). The accumulation of both point mutations and non-synonymous amino acid changes occurred at constant rates (μ = 14.4 and 2.4 new mutations/year, respectively), and mutations accumulated uniformly across the entire influenza genome. We observed a continuous erosion over time of codon-specificity in H1N1, including a shift away from host (human, swine, and bird [duck]) codon preference patterns.

CONCLUSIONS

While there have been numerous adaptations within the H1N1 genome, most of the genetic changes we document here appear to be non-adaptive, and much of the change appears to be degenerative. We suggest H1N1 has been undergoing natural genetic attenuation, and that significant attenuation may even occur during a single pandemic. This process may play a role in natural pandemic cessation and has apparently contributed to the exponential decline in mortality rates over time, as seen in all major human influenza strains. These findings may be relevant to the development of strategies for managing influenza pandemics and strain evolution.

Evolution of viral proteins originated de novo by overprinting

New protein-coding genes can originate either through modification of existing genes or de novo. Recently, the importance of de novo origination has been recognized in eukaryotes, although eukaryotic genes originated de novo are relatively rare and difficult to identify. In contrast, viruses contain many de novo genes, namely those in which an existing gene has been “overprinted” by a new open reading frame, a process that generates a new protein-coding gene overlapping the ancestral gene. We analyzed the evolution of 12 experimentally validated viral genes that originated de novo and estimated their relative ages. We found that young de novo genes have a different codon usage from the rest of the genome. They evolve rapidly and are under positive or weak purifying selection. Thus, young de novo genes might have strain-specific functions, or no function, and would be difficult to detect using current genome annotation methods that rely on the sequence signature of purifying selection. In contrast to young de novo genes, older de novo genes have a codon usage that is similar to the rest of the genome. They evolve slowly and are under stronger purifying selection. Some of the oldest de novo genes evolve under stronger selection pressure than the ancestral gene they overlap, suggesting an evolutionary tug of war between the ancestral and the de novo gene.

Positive selection for gains of N-linked glycosylation sites in hemagglutinin during evolution of H3N2 human influenza A virus

The number of N-linked glycosylation sites in the globular head of hemagglutinin (HA) has increased during evolution of H3N2 human influenza A virus. Here natural selection operating on the gains of N-linked glycosylation sites was examined by using the single-site analysis and the single-substitution analysis. In the single-site analysis, positive selection was not inferred at the amino acid sites where the substitutions generating N-linked glycosylation sites were observed, but was detected at antigenic sites. In contrast, in the single-substitution analysis, positive selection was detected for the amino acid substitutions generating N-linked glycosylation sites. The single-site analysis and the single-substitution analysis appeared to be suitable for detecting recurrent and episodic natural selection, respectively. The gains of N-linked glycosylation sites were likely to be positively selected for the function of shielding antigenic sites from immune responses. At the antigenic sites, positive selection appeared to have operated not only on the radical substitution but also on the conservative substitution in terms of the charge of amino acids, suggesting that the antigenic drift is not a by-product of the evolution of receptor binding avidity in HA of human H3N2 virus.

Subtype- and antigenic site-specific differences in biophysical influences on evolution of influenza virus hemagglutinin

Background

Influenza virus undergoes rapid evolution by both antigenic shift and antigenic drift. Antibodies, particularly those binding near the receptor-binding site of hemagglutinin (HA) or the neuraminidase (NA) active site, are thought to be the primary defense against influenza infection, and mutations in antibody binding sites can reduce or eliminate antibody binding. The binding of antibodies to their cognate antigens is governed by such biophysical properties of the interacting surfaces as shape, non-polar and polar surface area, and charge.

Methods

To understand forces shaping evolution of influenza virus, we have examined HA sequences of human influenza A and B viruses, assigning each amino acid values reflecting total accessible surface area, non-polar and polar surface area, and net charge due to the side chain. Changes in each of these values between neighboring sequences were calculated for each residue and mapped onto the crystal structures.

Results

Areas of HA showing the highest frequency of pairwise changes agreed well with previously identified antigenic sites in H3 and H1 HAs, and allowed us to propose more detailed antigenic maps and novel antigenic sites for H1 and influenza B HA. Changes in biophysical properties differed between HAs of different subtypes, and between different antigenic sites of the same HA. For H1, statistically significant differences in several biophysical quantities compared to residues lying outside antigenic sites were seen for some antigenic sites but not others. Influenza B antigenic sites all show statistically significant differences in biophysical quantities for all antigenic sites, whereas no statistically significant differences in biophysical quantities were seen for any antigenic site is seen for H3. In many cases, residues previously shown to be under positive selection at the genetic level also undergo rapid change in biophysical properties.

Conclusions

The biophysical consequences of amino acid changes introduced by antigenic drift vary from subtype to subtype, and between different antigenic sites. This suggests that the significance of antibody binding in selecting new variants may also be variable for different antigenic sites and influenza subtypes.