The influenza virus resource at the National Center for Biotechnology Information

Unusual A(H1N7) influenza A virus isolated from free-range domestic ducks in Bangladesh, 2023

In Bangladesh, free-range duck farms provide opportunities for the generation of novel influenza A viruses as evidenced by the emergence of an unusual A(H1N7) virus in 2023. Continued surveillance of such environments for the potential emergence of influenza A viruses with novel properties remains a priority.

Molecular surveillance of influenza A virus in Saudi Arabia: whole-genome sequencing and metagenomic approaches

Outbreaks of influenza A viruses are generally seasonal and cause annual epidemics worldwide. Due to their frequent reassortment and evolution, annual surveillance is of paramount importance to guide vaccine strategies. The aim of this study was to explore the molecular epidemiology of influenza A virus and nasopharyngeal microbiota composition in infected patients in Saudi Arabia. A total of 103 nasopharyngeal samples from 2015 and 12 samples from 2022 were collected from patients positive for influenza A. Sequencing of influenza A as well as metatranscriptomic analysis of the nasopharyngeal microbiota was conducted using Oxford Nanopore sequencing. Phylogenetic analysis of hemagglutinin, neuraminidase segments, and concatenated influenza A genomes was performed using MEGA7. Whole-genome sequencing analysis revealed changing clades of influenza A virus: from 6B.1 in 2015 to 5a.2a in 2022. One sample containing the antiviral resistance-mediating mutation S247N toward oseltamivir and zanamivir was found. Phylogenetic analysis showed the clustering of influenza A strains with the corresponding vaccine strains in each period, thus suggesting vaccine effectiveness. Principal component analysis and alpha diversity revealed the absence of a relationship between hospital admission status, age, or gender of infected patients and the nasopharyngeal microbial composition, except for the infecting clade 5a.2a. The opportunistic pathogens Staphylococcus aureus, Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis were the most common species detected. The molecular epidemiology appears to be changing in Saudi Arabia after the COVID-19 pandemic. Antiviral resistance should be carefully monitored in future studies. In addition, the disease severity of patients as well as the composition of the nasopharyngeal microbiota in patients infected with different clades should also be assessed.IMPORTANCEIn this work, we have found that the clade of influenza A virus circulating in Riyadh, KSA, has changed over the last few years from 6B.1 to 5a.2a. Influenza strains clustered with the corresponding vaccine strains in our population, thus emphasizing vaccine effectiveness. Metatranscriptomic analysis showed no correlation between the nasopharyngeal microbiome and the clinical and/or demographic characteristics of infected patients. This is except for the 5a.2a strains isolated post-COVID-19 pandemic. The influenza virus is among the continuously evolving viruses that can cause severe respiratory infections. Continuous surveillance of its molecular diversity and the monitoring of anti-viral-resistant strains are thus of vital importance. Furthermore, exploring potential microbial markers and/or dysbiosis of the nasopharyngeal microbiota during infection could assist in the better management of patients in severe cases.

Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City

Highly pathogenic avian influenza viruses of the H5N1 clade 2.3.4.4b were detected in North America in the winter of 2021/2022. These viruses have spread across the Americas, causing morbidity and mortality in both wild and domestic birds as well as some mammalian species, including cattle. Many surveillance programs for wildlife as well as commercial poultry operations have detected these viruses. In this study, we conducted surveillance of avian species in the urban environment in New York City. We detected highly pathogenic H5N1 viruses in six samples from four different bird species and performed whole-genome sequencing. Sequencing analysis showed the presence of multiple different genotypes. Our work highlights that the interface between animals and humans that may give rise to zoonotic infections or even pandemics is not limited to rural environments and commercial poultry operations but extends into the heart of our urban centers.IMPORTANCEWhile surveillance programs for avian influenza viruses are often focused on migratory routes and their associated stop-over locations or commercial poultry operations, many bird species-including migratory birds-frequent or live in urban green spaces and wetlands. This brings them into contact with a highly dense population of humans and pets, providing an extensive urban animal-human interface in which the general public may have little awareness of circulating infectious diseases. This study focuses on virus surveillance of this interface, combined with culturally responsive science education and community outreach.

Characterisation of novel influenza-derived HLA-B*18:01-restricted epitopes

Objectives

Seasonal influenza viruses cause roughly 650 000 deaths annually despite available vaccines. CD8+ T cells typically recognise influenza-derived peptides from internal structural and non-structural influenza proteins and are an attractive avenue for future vaccine design as they could reduce the severity of disease following infection with diverse influenza strains. CD8+ T cells recognise peptides presented by the highly polymorphic Human Leukocyte Antigens class I molecules (HLA-I). Each HLA-I variant has distinct peptide binding preferences, representing a significant obstacle for designing vaccines that elicit CD8+ T cell responses across broad populations. Consequently, the rational design of a CD8+ T cell-mediated vaccine would require the identification of highly immunogenic peptides restricted to a range of different HLA molecules.

Methods

Here, we assessed the immunogenicity of six recently published novel influenza-derived peptides identified by mass-spectrometry and predicted to bind to the prevalent HLA-B*18:01 molecule.

Results

Using CD8+ T cell activation assays and protein biochemistry, we showed that 3/6 of the novel peptides were immunogenic in several HLA-B*18:01+ individuals and confirmed their HLA-B*18:01 restriction. We subsequently compared CD8+ T cell responses towards the previously identified highly immunogenic HLA-B*18:01-restricted NP219 peptide. Using X-ray crystallography, we solved the first crystal structures of HLA-B*18:01 presenting immunogenic influenza-derived peptides. Finally, we dissected the first TCR repertoires specific for HLA-B*18:01 restricted pathogen-derived peptides, identifying private and restricted repertoires against each of the four peptides.

Conclusion

Overall the characterisation of these novel immunogenic peptides provides additional HLA-B*18:01-restricted vaccine targets derived from the Matrix protein 1 and potentially the non-structural protein and the RNA polymerase catalytic subunit of influenza viruses.

Seasonal antigenic prediction of influenza A H3N2 using machine learning

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City

Highly pathogenic avian influenza viruses of the H5N1 clade 2.3.4.4b arrived in North America in the winter of 2021/2022. These viruses have spread across the Americas causing morbidity and mortality in both wild and domestic birds as well as some mammalian species, including cattle. Many surveillance programs in wildlife as well as commercial poultry operations have detected these viruses. Here we conducted surveillance of avian species in the urban environment in New York City. We detected highly pathogenic H5N1 viruses in six samples from four different bird species and performed full genome sequencing. Sequence analysis showed the presence of multiple different genotypes. Our work highlights that the interface between animals and humans that may give rise to zoonotic infections or even pandemics is not limited to rural environments and commercial poultry operations but extends into the heart of our urban centers.

Pandemic Risk Assessment for Swine Influenza A Virus in Comparative In Vitro and In Vivo Models

Swine influenza A viruses pose a public health concern as novel and circulating strains occasionally spill over into human hosts, with the potential to cause disease. Crucial to preempting these events is the use of a threat assessment framework for human populations. However, established guidelines do not specify which animal models or in vitro substrates should be used. We completed an assessment of a contemporary swine influenza isolate, A/swine/GA/A27480/2019 (H1N2), using animal models and human cell substrates. Infection studies in vivo revealed high replicative ability and a pathogenic phenotype in the swine host, with replication corresponding to a complementary study performed in swine primary respiratory epithelial cells. However, replication was limited in human primary cell substrates. This contrasted with our findings in the Calu-3 cell line, which demonstrated a replication profile on par with the 2009 pandemic H1N1 virus. These data suggest that the selection of models is important for meaningful risk assessment.

Flu-CED: A comparative transcriptomics database of influenza virus-infected human and animal models

BACKGROUND

The continuing emergence of influenza virus has highlighted the value of public databases and related bioinformatic analysis tools in investigating transcriptomic change caused by different influenza virus infections in human and animal models.

METHODS

We collected a large amount of transcriptome research data related to influenza virus-infected human and animal models in public databases (GEO and ArrayExpress), and extracted and integrated array and metadata. The gene expression matrix was generated through strictly quality control, balance, standardization, batch correction, and gene annotation. We then analyzed gene expression in different species, virus, cells/tissues or after antibody/vaccine treatment and imported sample metadata and gene expression datasets into the database.

RESULTS

Overall, maintaining careful processing and quality control, we collected 8064 samples from 103 independent datasets, and constructed a comparative transcriptomics database of influenza virus named the Flu-CED database (Influenza comparative expression database, https://flu.com-med.org.cn/). Using integrated and processed transcriptomic data, we established a user-friendly website for realizing the integration, online retrieval, visualization, and exploration of gene expression of influenza virus infection in different species and the biological functions involved in differential genes. Flu-CED can quickly query single and multi-gene expression profiles, combining different experimental conditions for comparative transcriptome analysis, identifying differentially expressed genes (DEGs) between comparison groups, and conveniently finding DEGs.

CONCLUSION

Flu-CED provides data resources and tools for analyzing gene expression in human and animal models infected with influenza virus that can deepen our understanding of the mechanisms underlying disease occurrence and development, and enable prediction of key genes or therapeutic targets that can be used for medical research.

High-throughput investigation of genetic design constraints in domesticated Influenza A Virus for transient gene delivery

Replication-incompetent single cycle infectious Influenza A Virus (sciIAV) has demonstrated utility as a research and vaccination platform. Protein-based therapeutics are increasingly attractive due to their high selectivity and potent efficacy but still suffer from low bioavailability and high manufacturing cost. Transient RNA-mediated delivery is a safe alternative that allows for expression of protein-based therapeutics within the target cells or tissues but is limited by delivery efficiency. Here, we develop recombinant sciIAV as a platform for transient gene delivery in vivo and in vitro for therapeutic, research, and manufacturing applications (in vivo antimicrobial production, cell culture contamination clearance, and production of antiviral proteins in vitro). While adapting the system to deliver new protein cargo we discovered expression differences presumably resulting from genetic context effects. We applied a high-throughput screen to map these within the 3'-untranslated and coding regions of the hemagglutinin-encoding segment 4. This screen revealed permissible mutations in the 3'-UTR and depletion of RNA level motifs in the N-terminal coding region.

Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision

This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer architecture. ENBED uses a sub-quadratic implementation of attention to develop an efficient model capable of sequence-to-sequence transformations, generalizing previous genomic models with encoder-only or decoder-only architectures. We use Masked Language Modeling to pre-train the foundation model using reference genome sequences and apply it in the following downstream tasks: (1) identification of enhancers, promotors and splice sites, (2) recognition of sequences containing base call mismatches and insertion/deletion errors, an advantage over tokenization schemes involving multiple base pairs, which lose the ability to analyze with byte-level precision, (3) identification of biological function annotations of genomic sequences, and (4) generating mutations of the Influenza virus using the encoder-decoder architecture and validating them against real-world observations. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results.

RNA barcode segments for SARS-CoV-2 identification from HCoVs and SARSr-CoV-2 lineages

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for coronavirus disease 2019 (COVID-19), continues to evolve, giving rise to more variants and global reinfections. Previous research has demonstrated that barcode segments can effectively and cost-efficiently identify specific species within closely related populations. In this study, we designed and tested RNA barcode segments based on genetic evolutionary relationships to facilitate the efficient and accurate identification of SARS-CoV-2 from extensive virus samples, including human coronaviruses (HCoVs) and SARSr-CoV-2 lineages. Nucleotide sequences sourced from NCBI and GISAID were meticulously selected and curated to construct training sets, encompassing 1733 complete genome sequences of HCoVs and SARSr-CoV-2 lineages. Through genetic-level species testing, we validated the accuracy and reliability of the barcode segments for identifying SARS-CoV-2. Subsequently, 75 main and subordinate species-specific barcode segments for SARS-CoV-2, located in ORF1ab, S, E, ORF7a, and N coding sequences, were intercepted and screened based on single-nucleotide polymorphism sites and weighted scores. Post-testing, these segments exhibited high recall rates (nearly 100%), specificity (almost 30% at the nucleotide level), and precision (100%) performance on identification. They were eventually visualized using one and two-dimensional combined barcodes and deposited in an online database (http://virusbarcodedatabase.top/). The successful integration of barcoding technology in SARS-CoV-2 identification provides valuable insights for future studies involving complete genome sequence polymorphism analysis. Moreover, this cost-effective and efficient identification approach also provides valuable reference for future research endeavors related to virus surveillance.

PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network

Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.

VarEPS-Influ:an risk evaluation system of occurred and virtual variations of influenza virus genomes

Influenza viruses undergo frequent genomic mutations, leading to potential cross-species transmission, phenotypic changes, and challenges in diagnostic reagents and vaccines. Accurately evaluating and predicting the risk of such variations remain significant challenges. To address this, we developed the VarEPS-Influ database, an influenza virus variations risk evaluation system (VarEPS-Influ). This database employs a 'multi-dimensional evaluation of mutations' strategy, utilizing various tools to assess the physical and chemical properties, primary, secondary, and tertiary structures, receptor affinity, antibody binding capacity, antigen epitopes, and other aspects of the variation's impact. Additionally, we consider space-time distribution, host species distribution, pedigree analysis, and frequency of mutations to provide a comprehensive risk evaluation of mutations and viruses. The VarEPS-Influ database evaluates both observed variations and virtual variations (variations that have not yet occurred), thereby addressing the time-lag issue in risk predictions. Our current one-stop evaluation system for influenza virus genomic variation integrates 1065290 sequences from 224 927 Influenza A, B and C isolates retrieved from public resources. Researchers can freely access the data at https://nmdc.cn/influvar/.

Genetic, biological and epidemiological study on a cluster of H9N2 avian influenza virus infections among chickens, a pet cat, and humans at a backyard farm in Guangxi, China

During an investigation in October 2018, two people with diarrhoea, mild abdominal pain, and mild arthralgia symptoms in Guangxi, China, were identified as infected by H9N2 avian influenza virus (AIV). Four H9N2 AIVs were isolated from one of two patients, a pet cat, and a dead chicken (two respective isolates from its lung and kidney tissues) bred by the patients at a backyard farm. Epidemiological investigation indicated that the newly bought chicken died first, and clinical syndromes appeared subsequently in the two owners and one cat. Furthermore, the two individuals possessed high H9N2-specific hemagglutination inhibition and microneutralization antibodies. Shared nucleotide sequence identity (99.9% - 100%) for all genes was detected in the four H9N2 isolates, and hemagglutinin (HA) T138A located on the receptor binding domain (RBD), resulted from nucleotide polymorphisms that were exclusively found in the isolate from the female patient. Moreover, HA K137N on the RBD was found in isolates from these three host species. Importantly, these four H9N2 isolates presented an exclusive binding preference for the human-type receptor (α2-6-SA), and could replicate and cause pathological changes in mice. Phylogenetic analyses showed that these four isolates clustered together and belonged to clade C1.2, lineage Y280. In addition, H9N2 viruses of human origin are genetically divergent and interspersed with the widespread poultry-origin H9N2 AIVs. All these results indicate a high risk of H9N2 AIVs in public health, and effective prevention and control measures against H9N2 AIVs should be considered and performed for both animal and human health.

The CombE-IDMS Alternate Potency Method for H5N1 and H5N8 Cell-Based Vaccines

Assaying the potency of inactivated viral influenza vaccines is performed using single radial immunodiffusion, which is the globally accepted release method for potency. Under conditions of a rapidly emerging pandemic, such as the 2009 H1N1 influenza pandemic, a recognized obstacle in the delivery of vaccines to the public is the time needed for the distribution of calibrated SRID reagents (antisera and antigen standards) to vaccine manufacturers. Previously, we first described a novel streamlined MS-based assay, CombE-IDMS, which does not rely on antisera/antibodies or reference antigens, as a potential rapidly deployable alternate potency method through a comparison with SRID on adjuvanted seasonal quadrivalent vaccine cell-based (aQIVc) materials. In this report, we further demonstrate that the CombE-IDMS method can also be applied to measure the potency of pre-pandemic H5N1 and H5N8 monovalent vaccine materials, each subtype both unadjuvanted and adjuvanted, through a forced degradation study. Overall, CombE-IDMS results align with those of the gold standard SRID method on both H5N1 and H5N8 materials under conditions of thermal, pH, oxidative and freeze/thaw stress, lending further evidence for the CombE-IDMS method's suitability as an alternate assay for potency of both seasonal and pandemic influenza vaccines.

Emergence of a new genotype of clade 2.3.4.4b H5N1 highly pathogenic avian influenza A viruses in Bangladesh

Influenza virological surveillance was conducted in Bangladesh from January to December 2021 in live poultry markets (LPMs) and in Tanguar Haor, a wetland region where domestic ducks have frequent contact with migratory birds. The predominant viruses circulating in LPMs were low pathogenic avian influenza (LPAI) H9N2 and clade 2.3.2.1a highly pathogenic avian influenza (HPAI) H5N1 viruses. Additional LPAIs were found in both LPM (H4N6) and Tanguar Haor wetlands (H7N7). Genetic analyses of these LPAIs strongly suggested long-distance movement of viruses along the Central Asian migratory bird flyway. We also detected a novel clade 2.3.4.4b H5N1 virus from ducks in free-range farms in Tanguar Haor that was similar to viruses first detected in October 2020 in The Netherlands but with a different PB2. Identification of clade 2.3.4.4b HPAI H5N1 viruses in Tanguar Haor provides continued support of the role of migratory birds in transboundary movement of influenza A viruses (IAV), including HPAI viruses. Domestic ducks in free range farm in wetland areas, like Tangua Haor, serve as a conduit for the introduction of LPAI and HPAI viruses into Bangladesh. Clade 2.3.4.4b viruses have dominated in many regions of the world since mid-2021, and it remains to be seen if these viruses will replace the endemic clade 2.3.2.1a H5N1 viruses in Bangladesh.

Consequences of asymmetric mutational pressure for the dynamic of linear B-cell epitopes repertoire of influenza a virus neuraminidase rearrangement

Full-length nucleotide sequences of avian influenza A virus neuraminidase coding region (20,631 sequences) were analyzed and compared with those isolated from viruses infecting human and swine (63,750 sequences). If in fourfold degenerate sites there is asymmetric A-bias that may be more or less asymmetric depending on the type of neuraminidase and the host, than in twofold degenerate sites from third codon positions there is a strong asymmetric U-bias in coding regions of N4, N5, and N8 isolated from viruses infecting birds, as well as in those of N1 and N2 isolated from viruses infecting human, swine, and birds, while in coding regions of N9 isolated from birds, there is surprisingly strong C-bias, and in sequences of N3, N6, and N7 the usage of C is quite close to the level of U. Revealed stabilization of both U and C in twofold degenerate sites is the evidence of frequent changes in mutational pressure direction. Asymmetric mutational pressure was one of the sources of amino acid replacements that resulted in an equal percentage of sites with appeared and disappeared linear B-cell epitopes in N1, N2, N4, and N5 (33.62-35.33% vs. 32.41-36.45%, respectively), and controlled by the immune pressure it resulted in a stronger tendency to disappear for B-cell epitopes of N3, N6, N7, N8, and N9 of avian viruses (8.74-28.77% vs. 28.96-38.89%). The lack of correlation between nucleotide usages in fourfold and twofold degenerate sites for three nucleotides, except U, is a strong evidence of mutational pressure theory.

Navigating the Landscape: A Comprehensive Review of Current Virus Databases

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.

Understanding the divergent evolution and epidemiology of H3N8 influenza viruses in dogs and horses

Cross-species virus transmission events can lead to dire public health emergencies in the form of epidemics and pandemics. One example in animals is the emergence of the H3N8 equine influenza virus (EIV), first isolated in 1963 in Miami, FL, USA, after emerging among horses in South America. In the early 21st century, the American lineage of EIV diverged into two 'Florida' clades that persist today, while an EIV transferred to dogs around 1999 and gave rise to the H3N8 canine influenza virus (CIV), first reported in 2004. Here, we compare CIV in dogs and EIV in horses to reveal their host-specific evolution, to determine the sources and connections between significant outbreaks, and to gain insight into the factors controlling their different evolutionary fates. H3N8 CIV only circulated in North America, was geographically restricted after the first few years, and went extinct in 2016. Of the two EIV Florida clades, clade 1 circulates widely and shows frequent transfers between the USA and South America, Europe and elsewhere, while clade 2 was globally distributed early after it emerged, but since about 2018 has only been detected in Central Asia. Any potential zoonotic threat of these viruses to humans can only be determined with an understanding of its natural history and evolution. Our comparative analysis of these three viral lineages reveals distinct patterns and rates of sequence variation yet with similar overall evolution between clades, suggesting epidemiological intervention strategies for possible eradication of H3N8 EIV.

TFOFinder: Python program for identifying purine-only double-stranded stretches in the predicted secondary structure(s) of RNA targets

Nucleic acid probes are valuable tools in biology and chemistry and are indispensable for PCR amplification of DNA, RNA quantification and visualization, and downregulation of gene expression. Recently, triplex-forming oligonucleotides (TFO) have received increased attention due to their improved selectivity and sensitivity in recognizing purine-rich double-stranded RNA regions at physiological pH by incorporating backbone and base modifications. For example, triplex-forming peptide nucleic acid (PNA) oligomers have been used for imaging a structured RNA in cells and inhibiting influenza A replication. Although a handful of programs are available to identify triplex target sites (TTS) in DNA, none are available that find such regions in structured RNAs. Here, we describe TFOFinder, a Python program that facilitates the identification of intramolecular purine-only RNA duplexes that are amenable to forming parallel triple helices (pyrimidine/purine/pyrimidine) and the design of the corresponding TFO(s). We performed genome- and transcriptome-wide analyses of TTS in Drosophila melanogaster and found that only 0.3% (123) of total unique transcripts (35,642) show the potential of forming 12-purine long triplex forming sites that contain at least one guanine. Using minimization algorithms, we predicted the secondary structure(s) of these transcripts, and using TFOFinder, we found that 97 (79%) of the identified 123 transcripts are predicted to fold to form at least one TTS for parallel triple helix formation. The number of transcripts with potential purine TTS increases when the strict search conditions are relaxed by decreasing the length of the probe or by allowing up to two pyrimidine inversions or 1-nucleotide bulge in the target site. These results are encouraging for the use of modified triplex forming probes for live imaging of endogenous structured RNA targets, such as pre-miRNAs, and inhibition of target-specific translation and viral replication.

Generation of host-directed and virus-specific antivirals using targeted protein degradation promoted by small molecules and viral RNA mimics

Targeted protein degradation (TPD), as exemplified by proteolysis-targeting chimera (PROTAC), is an emerging drug discovery platform. PROTAC molecules, which typically contain a target protein ligand linked to an E3 ligase ligand, recruit a target protein to the E3 ligase to induce its ubiquitination and degradation. Here, we applied PROTAC approaches to develop broad-spectrum antivirals targeting key host factors for many viruses and virus-specific antivirals targeting unique viral proteins. For host-directed antivirals, we identified a small-molecule degrader, FM-74-103, that elicits selective degradation of human GSPT1, a translation termination factor. FM-74-103-mediated GSPT1 degradation inhibits both RNA and DNA viruses. Among virus-specific antivirals, we developed viral RNA oligonucleotide-based bifunctional molecules (Destroyers). As a proof of principle, RNA mimics of viral promoter sequences were used as heterobifunctional molecules to recruit and target influenza viral polymerase for degradation. This work highlights the broad utility of TPD to rationally design and develop next-generation antivirals.

Ecogeographic Drivers of the Spatial Spread of Highly Pathogenic Avian Influenza Outbreaks in Europe and the United States, 2016-Early 2022

H5Nx highly pathogenic avian influenza (HPAI) viruses of clade 2.3.4.4 have caused outbreaks in Europe among wild and domestic birds since 2016 and were introduced to North America via wild migratory birds in December 2021. We examined the spatiotemporal extent of HPAI viruses across continents and characterized ecological and environmental predictors of virus spread between geographic regions by constructing a Bayesian phylodynamic generalized linear model (phylodynamic-GLM). The findings demonstrate localized epidemics of H5Nx throughout Europe in the first several years of the epizootic, followed by a singular branching point where H5N1 viruses were introduced to North America, likely via stopover locations throughout the North Atlantic. Once in the United States (US), H5Nx viruses spread at a greater rate between US-based regions as compared to prior spread in Europe. We established that geographic proximity is a predictor of virus spread between regions, implying that intercontinental transport across the Atlantic Ocean is relatively rare. An increase in mean ambient temperature over time was predictive of reduced H5Nx virus spread, which may reflect the effect of climate change on declines in host species abundance, decreased persistence of the virus in the environment, or changes in migratory patterns due to ecological alterations. Our data provide new knowledge about the spread and directionality of H5Nx virus dispersal in Europe and the US during an actively evolving intercontinental outbreak, including predictors of virus movement between regions, which will contribute to surveillance and mitigation strategies as the outbreak unfolds, and in future instances of uncontained avian spread of HPAI viruses.

Remote Sensing and Ecological Variables Related to Influenza A Prevalence and Subtype Diversity in Wild Birds in the Lluta Wetland of Northern Chile

The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index-NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.

Spatial genetic structure of 2009 H1N1 pandemic influenza established as a result of interaction with human populations in mainland China

Identifying the spatial patterns of genetic structure of influenza A viruses is a key factor for understanding their spread and evolutionary dynamics. In this study, we used phylogenetic and Bayesian clustering analyses of genetic sequences of the A/H1N1pdm09 virus with district-level locations in mainland China to investigate the spatial genetic structure of the A/H1N1pdm09 virus across human population landscapes. Positive correlation between geographic and genetic distances indicates high degrees of genetic similarity among viruses within small geographic regions but broad-scale genetic differentiation, implying that local viral circulation was a more important driver in the formation of the spatial genetic structure of the A/H1N1pdm09 virus than even, countrywide viral mixing and gene flow. Geographic heterogeneity in the distribution of genetic subpopulations of A/H1N1pdm09 virus in mainland China indicates both local to local transmission as well as broad-range viral migration. This combination of both local and global structure suggests that both small-scale and large-scale population circulation in China is responsible for viral genetic structure. Our study provides implications for understanding the evolution and spread of A/H1N1pdm09 virus across the population landscape of mainland China, which can inform disease control strategies for future pandemics.

Influenza A viruses in gulls in landfills and freshwater habitats in Minnesota, United States

Introduction: The unpredictable evolution of avian influenza viruses (AIVs) presents an ongoing threat to agricultural production and public and wildlife health. Severe outbreaks of highly pathogenic H5N1 viruses in US poultry and wild birds since 2022 highlight the urgent need to understand the changing ecology of AIV. Surveillance of gulls in marine coastal environments has intensified in recent years to learn how their long-range pelagic movements potentially facilitate inter-hemispheric AIV movements. In contrast, little is known about inland gulls and their role in AIV spillover, maintenance, and long-range dissemination. Methods: To address this gap, we conducted active AIV surveillance in ring-billed gulls (Larus delawarensis) and Franklin's gulls (Leucophaeus pipixcan) in Minnesota's natural freshwater lakes during the summer breeding season and in landfills during fall migration (1,686 samples). Results: Whole-genome AIV sequences obtained from 40 individuals revealed three-lineage reassortants with a mix of genome segments from the avian Americas lineage, avian Eurasian lineage, and a global "Gull" lineage that diverged more than 50 years ago from the rest of the AIV global gene pool. No poultry viruses contained gull-adapted H13, NP, or NS genes, pointing to limited spillover. Geolocators traced gull migration routes across multiple North American flyways, explaining how inland gulls imported diverse AIV lineages from distant locations. Migration patterns were highly varied and deviated far from assumed "textbook" routes. Discussion: Viruses circulating in Minnesota gulls during the summer breeding season in freshwater environments reappeared in autumn landfills, evidence of AIV persistence in gulls between seasons and transmission between habitats. Going forward, wider adoption of technological advances in animal tracking devices and genetic sequencing is needed to expand AIV surveillance in understudied hosts and habitats.

Estimating linkage disequilibrium and selection from allele frequency trajectories

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

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R ₀ ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R ₀ values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R ₀ estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R ₀ estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R ₀ estimation and surveillance-based R ₀ estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

Designing multi-epitope mRNA construct as a universal influenza vaccine candidate for future epidemic/pandemic preparedness

Despite the availability of prevention and treatment strategies and advancing immunization approaches, the influenza virus remains a global threat that continues to plague humanity with unpredictable pandemics. Due to the unusual genetic variability and segmented genome, the reassortment between different strains of influenza is facilitated and the viruses continuously evolve and adapt to the host cell's immunity. This underlies the seasonal vaccine mismatches that decrease the vaccine efficacy and increase the risk of outbreaks. Thus, the development of a universal vaccine covering all the influenza A and B strains would reduce the pervasiveness of the influenza virus. In the current study, a potentially universal influenza multi-epitope vaccine was designed based on the experimentally tested conserved T cell and B cell epitopes of hemagglutinin (HA), neuraminidase (NA), nucleoprotein (NP), and matrix-2 proton channel (M2) of the virus. The immune simulation and molecular docking of the vaccine construct with TLR2, TLR3, and TLR4 elicited the favorable immunogenicity of the vaccine and the formation of stable complexes, respectively. Ultimately, based on the immunoinformatics analysis, the universal mRNA multi-epitope vaccine designed in this study might have a protection potential against the various subtypes of influenza A and B.

TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution

The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO.

Leveraging machine learning to assess market-level food safety and zoonotic disease risks in China

While many have advocated for widespread closure of Chinese wet and wholesale markets due to numerous zoonotic disease outbreaks (e.g., SARS) and food safety risks, this is impractical due to their central role in China's food system. This first-of-its-kind work offers a data science enabled approach to identify market-level risks. Using a massive, self-constructed dataset of food safety tests, market-level adulteration risk scores are created through machine learning techniques. Analysis shows that provinces with more high-risk markets also have more human cases of zoonotic flu, and specific markets associated with zoonotic disease have higher risk scores. Furthermore, it is shown that high-risk markets have management deficiencies (e.g., illegal wild animal sales), potentially indicating that increased and integrated regulation targeting high-risk markets could mitigate these risks.

A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

Affinity maturation (AM) of B cells through somatic hypermutations (SHMs) enables the immune system to evolve to recognize diverse pathogens. The accumulation of SHMs leads to the formation of clonal lineages of antibody-secreting b cells that have evolved from a common naïve B cell. Advances in high-throughput sequencing have enabled deep scans of B cell receptor repertoires, paving the way for reconstructing clonal trees. However, it is not clear if clonal trees, which capture microevolutionary time scales, can be reconstructed using traditional phylogenetic reconstruction methods with adequate accuracy. In fact, several clonal tree reconstruction methods have been developed to fix supposed shortcomings of phylogenetic methods. Nevertheless, no consensus has been reached regarding the relative accuracy of these methods, partially because evaluation is challenging. Benchmarking the performance of existing methods and developing better methods would both benefit from realistic models of clonal lineage evolution specifically designed for emulating B cell evolution. In this paper, we propose a model for modeling B cell clonal lineage evolution and use this model to benchmark several existing clonal tree reconstruction methods. Our model, designed to be extensible, has several features: by evolving the clonal tree and sequences simultaneously, it allows modeling selective pressure due to changes in affinity binding; it enables scalable simulations of large numbers of cells; it enables several rounds of infection by an evolving pathogen; and, it models building of memory. In addition, we also suggest a set of metrics for comparing clonal trees and measuring their properties. Our results show that while maximum likelihood phylogenetic reconstruction methods can fail to capture key features of clonal tree expansion if applied naively, a simple post-processing of their results, where short branches are contracted, leads to inferences that are better than alternative methods.

Surveillance of avian influenza viruses in live bird markets of Shandong province from 2013 to 2019

Avian influenza viruses (AIVs) seriously affect the poultry industry and pose a great threat to humans. Timely surveillance of AIVs is the basis for preparedness of the virus. This study reported the long-term surveillance of AIVs in the live bird market (LBM) of 16 cities in Shandong province from 2013 to 2019. A total of 29,895 samples were obtained and the overall positive rate of AIVs was 9.7%. The H9 was found to be the most predominant subtype in most of the time and contributed most to the monthly positve rate of AIVs as supported by the univariate and multivariate analysis, while H5 and H7 only circulated in some short periods. Then, the whole-genome sequences of 62 representative H9N2 viruses including one human isolate from a 7-year-old boy in were determined and they were genetically similar to each other with the median pairwise sequence identities ranging from 0.96 to 0.98 for all segments. The newly sequenced viruses were most similar to viruses isolated in chickens in mainland China, especially the provinces in Eastern China. Phylogenetic analysis showed that these newly sequenced H9N2 viruses belonged to the same clade for all segments except PB1. Nearly all of these viruses belonged to the G57 genotype which has dominated in China since 2010. Finally, several molecular markers associated with human adaptation, mammalian virulence, and drug resistance were identified in the newly sequenced H9N2 viruses. Overall, the study deepens our understanding of the epidemic and evolution of AIVs and provides a basis for effective control of AIVs in China.

Inferring Epistasis from Genetic Time-series Data

Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.

Genetic and Pathogenic Characterization of Avian Influenza Virus in Migratory Birds between 2015 and 2019 in Central China

Active surveillance of avian influenza virus (AIV) in wetlands and lakes is important for exploring the gene pool in wild birds. Through active surveillance from 2015 through 2019, 10,900 samples from wild birds in central China were collected, and 89 AIVs were isolated, including 2 subtypes of highly pathogenic AIV and 12 of low-pathogenic AIV; H9N2 and H6Ny were the dominant subtypes. Phylogenetic analysis of the isolates demonstrated that extensive intersubtype reassortments and frequent intercontinental gene exchange occurred in AIVs. AIV gene segments persistently circulated in several migration seasons, but interseasonal persistence of the whole genome was rare. The whole genomes of one H6N6 and polymerase basic 2 (PB2), polymerase acidic (PA), hemagglutinin (HA), neuraminidase (NA), M, and nonstructural (NS) genes of one H9N2 virus were found to be of poultry origin, suggesting a spillover of AIVs from poultry to wild birds. Importantly, one H9N2 virus only bound to human-type receptor, and one H1N1, four H6, and seven H9N2 viruses possessed dual receptor-binding capacity. Nineteen of 20 representative viruses tested could replicate in the lungs of mice without preadaptation, which poses a clear threat of infection in humans. Together, our study highlights the need for intensive AIV surveillance. IMPORTANCE Influenza virus surveillance in wild birds plays an important role in the early recognition and control of the virus. However, the AIV gene pool in wild birds in central China along the East Asian-Australasian flyway has not been well studied. Here, we conducted a 5-year AIV active surveillance in this region. Our data revealed the long-term circulation and prevalence of AIVs in wild birds in central China, and we observed that intercontinental gene exchange of AIVs is more frequent and continuous than previously thought. Spillover events from poultry to wild bird were observed in H6 and H9 viruses. In addition, in 20 representative viruses, 12 viruses could bind human-type receptors, and 19 viruses could replicate in mice without preadaption. Our work highlights the potential threat of wild bird AIVs to public health.

Designing sensitive viral diagnostics with machine learning

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus's genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT's designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus's variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.

Human pathogenic RNA viruses establish noncompeting lineages by occupying independent niches

Numerous pathogenic viruses are endemic in humans and cause a broad variety of diseases, but what is their potential for causing new pandemics? We show that most human pathogenic RNA viruses form multiple, cocirculating lineages with low turnover rates. These lineages appear to be largely noncompeting and occupy distinct epidemiological niches that are not regionally or seasonally defined, and their persistence appears to stem from limited outbreaks in small communities so that only a small fraction of the global susceptible population is infected at any time. However, due to globalization, interaction and competition between lineages might increase, potentially leading to increased diversification and pathogenicity. Thus, endemic viruses appear to merit global attention with respect to the prevention of future pandemics.

Many pathogenic viruses are endemic among human populations and can cause a broad variety of diseases, some potentially leading to devastating pandemics. How virus populations maintain diversity and what selective pressures drive population turnover is not thoroughly understood. We conducted a large-scale phylodynamic analysis of 27 human pathogenic RNA viruses spanning diverse life history traits, in search of unifying trends that shape virus evolution. For most virus species, we identify multiple, cocirculating lineages with low turnover rates. These lineages appear to be largely noncompeting and likely occupy semiindependent epidemiological niches that are not regionally or seasonally defined. Typically, intralineage mutational signatures are similar to interlineage signatures. The principal exception are members of the family Picornaviridae, for which mutations in capsid protein genes are primarily lineage defining. Interlineage turnover is slower than expected under a neutral model, whereas intralineage turnover is faster than the neutral expectation, further supporting the existence of independent niches. The persistence of virus lineages appears to stem from limited outbreaks within small communities, so that only a small fraction of the global susceptible population is infected at any time. As disparate communities become increasingly connected through globalization, interaction and competition between lineages might increase as well, which could result in changing selective pressures and increased diversification and/or pathogenicity. Thus, in addition to zoonotic events, ongoing surveillance of familiar, endemic viruses appears to merit global attention with respect to the prevention or mitigation of future pandemics.

Sheet, Surveillance, Strategy, Salvage and Shield in global biodefense system to protect the public health and tackle the incoming pandemics

The pandemic of COVID-19 challenges the global health system and raises our concerns on the next waves of other emerging infectious diseases. Considering the lessons from the failure of world's pandemic warning system against COVID-19, many scientists and politicians have mentioned different strategies to improve global biodefense system, among which Sheet, Surveillance, Strategy, Salvage and Shield (5S) are frequently discussed. Nevertheless, the current focus is mainly on the optimization and management of individual strategy, and there are limited attempts to combine the five strategies as an integral global biodefense system. Sheet represents the biosafety datasheet for biohazards in natural environment and human society, which helps our deeper understanding on the geographical pattern, transmission routes and infection mechanism of pathogens. Online surveillance and prognostication network is an environmental Surveillance tool for monitoring the outbreak of pandemic diseases and alarming the risks to take emergency actions, targeting aerosols, waters, soils and animals. Strategy is policies and legislations for social distancing, lockdown and personal protective equipment to block the spread of infectious diseases in communities. Clinical measures are Salvage on patients by innovating appropriate medicines and therapies. The ultimate defensive Shield is vaccine development to protect healthy crowds from infection. Fighting against COVID-19 and other emerging infectious diseases is a long rocky journey, requiring the common endeavors of scientists and politicians from all countries around the world. 5S in global biodefense system bring a ray of light to the current darkest and future road from environmental and geographical perspectives.

T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles

CD8⁺ T cells are a pivotal part of the immune response to viruses, playing a key role in disease outcome and providing long-lasting immunity to conserved pathogen epitopes. Understanding CD8⁺ T cell immunity in humans is complex due to CD8⁺ T cell restriction by highly polymorphic Human Leukocyte Antigen (HLA) proteins, requiring T cell epitopes to be defined for different HLA allotypes across different ethnicities. Here we evaluate strategies that have been developed to facilitate epitope identification and study immunogenic T cell responses. We describe an immunopeptidomics approach to sequence HLA-bound peptides presented on virus-infected cells by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Using antigen presenting cell lines that stably express the HLA alleles characteristic of Indigenous Australians, this approach has been successfully used to comprehensively identify influenza-specific CD8⁺ T cell epitopes restricted by HLA allotypes predominant in Indigenous Australians, including HLA-A*24:02 and HLA-A*11:01. This is an essential step in ensuring high vaccine coverage and efficacy in Indigenous populations globally, known to be at high risk from influenza disease and other respiratory infections.

Nuclear magnetic resonance reveals a two hairpin equilibrium near the 3'-splice site of influenza A segment 7 mRNA that can be shifted by oligonucleotides

Influenza A kills hundreds of thousands of people globally every year and has the potential to generate more severe pandemics. Influenza A's RNA genome and transcriptome provide many potential therapeutic targets. Here, nuclear magnetic resonance (NMR) experiments suggest that one such target could be a hairpin loop of 8 nucleotides in a pseudoknot that sequesters a 3' splice site in canonical pairs until a conformational change releases it into a dynamic 2 × 2-nt internal loop. NMR experiments reveal that the hairpin loop is dynamic and able to bind oligonucleotides as short as pentamers. A 3D NMR structure of the complex contains 4 and likely 5 bp between pentamer and loop. Moreover, a hairpin sequence was discovered that mimics the equilibrium of the influenza hairpin between its structure in the pseudoknot and upon release of the splice site. Oligonucleotide binding shifts the equilibrium completely to the hairpin secondary structure required for pseudoknot folding. The results suggest this hairpin can be used to screen for compounds that stabilize the pseudoknot and potentially reduce splicing.

Novel Low Pathogenic Avian Influenza H6N1 in Backyard Chicken in Easter Island (Rapa Nui), Chilean Polynesia

Little is known about the prevalence of avian influenza viruses (AIV) in wildlife and domestic animals in Polynesia. Here, we present the results of active AIV surveillance performed during two sampling seasons in 2019 on Easter Island (Rapa Nui). Tracheal and cloacal swabs as well as sera samples were obtained from domestic backyard poultry, while fresh faeces were collected from wild birds. In addition to detecting antibodies against AIV in 46% of the domestic chickens in backyard production systems tested, we isolated a novel low pathogenic H6N1 virus from a chicken. Phylogenetic analysis of all genetic segments revealed that the virus was closely related to AIV’s circulating in South America. Our analysis showed different geographical origins of the genetic segments, with the PA, HA, NA, NP, and MP gene segments coming from central Chile and the PB2, PB1, and NS being closely related to viruses isolated in Argentina. While the route of introduction can only be speculated, our analysis shows the persistence and independent evolution of this strain in the island since its putative introduction between 2015 and 2016. The results of this research are the first evidence of AIV circulation in domestic birds on a Polynesian island and increase our understanding of AIV ecology in region, warranting further surveillance on Rapa Nui and beyond.

A Coarse-Grained Model of Affinity Maturation Indicates the Importance of B-Cell Receptor Avidity in Epitope Subdominance

The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal in the design of vaccines against rapidly-mutating viruses. In the case of influenza, many bnAbs that target conserved epitopes on the stem of the hemagglutinin protein (HA) have been discovered. However, these antibodies are rare, are not boosted well upon reinfection, and often have low neutralization potency, compared to strain-specific antibodies directed to the HA head. Different hypotheses have been proposed to explain this phenomenon. We use a coarse-grained computational model of the germinal center reaction to investigate how B-cell receptor binding valency affects the growth and affinity maturation of competing B-cells. We find that receptors that are unable to bind antigen bivalently, and also those that do not bind antigen cooperatively, have significantly slower rates of growth, memory B-cell production, and, under certain conditions, rates of affinity maturation. The corresponding B-cells are predicted to be outcompeted by B-cells that bind bivalently and cooperatively. We use the model to explore strategies for a universal influenza vaccine, e.g., how to boost the concentrations of the slower growing cross-reactive antibodies directed to the stem. The results suggest that, upon natural reinfections subsequent to vaccination, the protectiveness of such vaccines would erode, possibly requiring regular boosts. Collectively, our results strongly support the importance of bivalent antibody binding in immunodominance, and suggest guidelines for developing a universal influenza vaccine.

Sequence Matching between Hemagglutinin and Neuraminidase through Sequence Analysis Using Machine Learning

To date, many experiments have revealed that the functional balance between hemagglutinin (HA) and neuraminidase (NA) plays a crucial role in viral mobility, production, and transmission. However, whether and how HA and NA maintain balance at the sequence level needs further investigation. Here, we applied principal component analysis and hierarchical clustering analysis on thousands of HA and NA sequences of A/H1N1 and A/H3N2. We discovered significant coevolution between HA and NA at the sequence level, which is closely related to the type of host species and virus epidemic years. Furthermore, we propose a sequence-to-sequence transformer model (S2STM), which mainly consists of an encoder and a decoder that adopts a multi-head attention mechanism for establishing the mapping relationship between HA and NA sequences. The training results reveal that the S2STM can effectively realize the “translation” from HA to NA or vice versa, thereby building a relationship network between them. Our work combines unsupervised and supervised machine learning methods to identify the sequence matching between HA and NA, which will advance our understanding of IAVs’ evolution and also provide a novel idea for sequence analysis methods.

Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens

Artificial mutagenesis and protein engineering have laid the foundation for antigenic characterization and universal vaccine design for influenza viruses. However, many methods used in this process require manual sequence editing and protein expression, limiting their efficiency and utility in high-throughput applications. More streamlined in silico tools allowing researchers to properly analyze and visualize influenza viral protein sequences with accurate nomenclature are necessary to improve antigen design and productivity. To address this need, we developed Librator, a system for analyzing and designing custom protein sequences of influenza virus hemagglutinin (HA) and neuraminidase (NA) glycoproteins. Within Librator's graphical interface, users can easily interrogate viral sequences and phylogenies, visualize antigen structures and conservation, mutate target residues and design custom antigens. Librator also provides optimized fragment design for Gibson Assembly of HA and NA expression constructs based on peptide conservation of all historical HA and NA sequences, ensuring fragments are reusable and compatible across related subtypes, thereby promoting reagent savings. Finally, the program facilitates single-cell immune profiling, epitope mapping of monoclonal antibodies and mosaic protein design. Using Librator-based antigen construction, we demonstrate that antigenicity can be readily transferred between HA molecules of H3, but not H1, lineage viruses. Altogether, Librator is a valuable tool for analyzing influenza virus HA and NA proteins and provides an efficient resource for optimizing recombinant influenza antigen synthesis.

DeLUCS: Deep learning for unsupervised clustering of DNA sequences

We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates "mimic" sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.

Distinct but connected avian influenza virus activities in wetlands and live poultry markets in Bangladesh, 2018-2019

From April 2018 to October 2019, we continued active surveillance for influenza viruses in Bangladeshi live poultry markets (LPMs) and in Tanguar Haor, a wetland region of Bangladesh where domestic ducks have frequent contact with migratory birds. The predominant virus subtypes circulating in the LPMs were low pathogenic avian influenza (LPAI) H9N2 and clade 2.3.2.1a highly pathogenic avian influenza (HPAI) H5N1 viruses of the H5N1-R1 genotype, like those found in previous years. Viruses of the H5N1-R2 genotype, which were previously reported as co-circulating with H5N1-R1 genotype viruses in LPM, were not detected. In addition to H9N2 viruses, which were primarily found in chicken and quail, H2N2, H3N8 and H11N3 LPAI viruses were detected in LPMs, exclusively in ducks. Viruses in domestic ducks and/or wild birds in Tanguar Haor were more diverse, with H1N1, H4N6, H7N1, H7N3, H7N4, H7N6, H8N4, H10N3, H10N4 and H11N3 detected. Phylogenetic analyses of these LPAI viruses suggested that some were new to Bangladesh (H2N2, H7N6, H8N4, H10N3 and H10N4), likely introduced by migratory birds of the Central Asian flyway. Our results show a complex dynamic of viral evolution and diversity in Bangladesh based on factors such as host populations and geography. The LPM environment was characterised by maintenance of viruses with demonstrated zoonotic potential and H5N1 genotype turnover. The wetland environment was characterised by greater viral gene pool diversity but a lower overall influenza virus detection rate. The genetic similarity of H11N3 viruses in both environments demonstrates that LPM and wetlands are connected despite their having distinct influenza ecologies.

Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction

The rapid mutations on hemagglutinin (HA) of influenza A virus (IAV) can lead to significant antigenic variance and consequent immune mismatch of vaccine strains. Thus, rapid antigenicity evaluation is highly desired. The subtype-specific antigenicity models have been widely used for common subtypes such as H1 and H3. However, the continuous emerging of new IAV subtypes requires the construction of universal antigenic prediction model which could be applied on multiple IAV subtypes, including the emerging or re-emerging ones. In this study, we presented Univ-Flu, series structure-based universal models for HA antigenicity prediction. Initially, the universal antigenic regions were derived on multiple subtypes. Then, a radial shell structure combined with amino acid indexes were introduced to generate the new three-dimensional structure based descriptors, which could characterize the comprehensive physical–chemical property changes between two HA variants within or across different subtypes. Further, by combining with Random Forest classifier and different training datasets, Univ-Flu could achieve high prediction performances on intra-subtype (average AUC of 0.939), inter-subtype (average AUC of 0.771), and universal-subtype (AUC of 0.978) prediction, through independent test. Results illustrated that the designed descriptor could provide accurate universal antigenic description. Finally, the application on high-throughput antigenic coverage prediction for circulating strains showed that the Univ-Flu could screen out virus strains with high cross-protective spectrum, which could provide in-silico reference for vaccine recommendation.

Evolution of influenza A virus in intensive and free-range swine farms in Spain

Swine harbor genetically diverse influenza A viruses (IAVs) with the capacity to host-switch to humans, causing global pandemics. Spain is the largest swine producer in Europe and has a mixed production system that includes 'white coat' pigs raised intensively in modern buildings and free-range Iberian pigs that interface differently with humans, wildlife, and other swine. Through active longitudinal IAV surveillance in nine Spanish provinces during 2015-9, we generated forty-seven complete or near-complete genome sequences from IAVs collected from swine in both systems. Genetically diverse IAVs were identified in intensively raised white pigs and free-range Iberian pigs, including new H3N1 reassortants. Both systems are dynamic environments for IAV evolution, but driven by different processes. IAVs in white pigs were genetically related to viruses found in swine raised intensively in other European countries, reflecting high rates of viral introduction following European trade routes. In contrast, IAVs in Iberian pigs have a genetic makeup shaped by frequent introductions of human IAVs, reflecting rearing practices with high rates of human contact. Transmission between white and Iberian pigs also occurred. In conclusion, Iberian swine with high rates of human contact harbor genetically diverse IAVs and potentially serve as intermediary hosts between white pigs and humans, presenting an understudied zoonotic risk that requires further investigation.