FluReassort: a database for the study of genomic reassortments among influenza viruses

Influenza virus reassortment patterns exhibit preference and continuity while uncovering cross-species transmission events

Genomic reassortment is a key driver of influenza virus evolution and a major factor in pandemic emergence, as reassorted strains can exhibit significantly altered antigenicity. However, due to technical and ethical constraints, research on reassortment patterns (RPs) has been limited, impeding effective surveillance and control strategies. To address this gap, we developed FluRPId, a framework for identifying RPs based on the genetic diversity of influenza viruses. FluRPId integrates principles of reassortment diversity maximization, dominance, and epidemiological likelihood to assess the credibility of detected reassortment events. Applying FluRPId, we constructed a comprehensive reassortment landscape of influenza viruses, encompassing widespread reassortment events with high credibility, which also include most previously reported reassortment events. Our analysis revealed that the NS gene frequently reassorts with PA and NA, while reassortment involving HA, NA, and NS occurs more frequently than expected. Furthermore, we identified specific loci combinations that exhibit strong linkage during reassortment, providing insights into segment association preferences. Additionally, extensive reassortment chains were observed across all subtypes, underscoring the continuity of reassortment in influenza virus evolution. Notably, we identified significant cross-species reassortment events and characterized host adaptation changes in cross-species-transmitted viruses. Our study provides the most comprehensive reassortment landscape of influenza viruses to date, uncovering key patterns, preferences, and evolutionary continuity. These findings bridge a critical gap in macro-scale reassortment studies and offer insights for future research and control efforts.

Transmission restriction and genomic evolution co-shape the genetic diversity patterns of influenza A virus

Influenza A virus (IAV) shows an extensive host range and rapid genomic variations, leading to continuous emergence of novel viruses with significant antigenic variations and the potential for cross-species transmission. This causes global pandemics and seasonal flu outbreaks, posing sustained threats worldwide. Thus, studying all IAVs' evolutionary patterns and underlying mechanisms is crucial for effective prevention and control. We developed FluTyping to identify IAV genotypes, to explore overall genetic diversity patterns and their restriction factors. FluTyping groups isolates based on genetic distance and phylogenetic relationships using whole genomes, enabling identification of each isolate's genotype. Three distinct genetic diversity patterns were observed: one genotype domination pattern comprising only H1N1 and H3N2 seasonal influenza subtypes, multi-genotypes co-circulation pattern including majority avian influenza subtypes and swine influenza H1N2, and hybrid-circulation pattern involving H7N9 and three H5 subtypes of influenza viruses. Furthermore, the IAVs in multi-genotypes co-circulation pattern showed region-specific dominant genotypes, implying the restriction of virus transmission is a key factor contributing to distinct genetic diversity patterns, and the genomic evolution underlying different patterns was more influenced by host-specific factors. In summary, a comprehensive picture of the evolutionary patterns of overall IAVs is provided by the FluTyping's identified genotypes, offering important theoretical foundations for future prevention and control of these viruses.

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

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.

Promising strategy for developing mRNA-based universal influenza virus vaccine for human population, poultry, and pigs- focus on the bigger picture

Since the first outbreak in the 19th century influenza virus has remained emergent owing to the huge pandemic potential. Only the pandemic of 1918 caused more deaths than any war in world history. Although two types of influenza- A (IAV) and B (IBV) cause epidemics annually, influenza A deserves more attention as its nature is much wilier. IAVs have a large animal reservoir and cause the infection manifestation not only in the human population but in poultry and domestic pigs as well. This many-sided characteristic of IAV along with the segmented genome gives rise to the antigenic drift and shift that allows evolving the new strains and new subtypes, respectively. As a result, the immune system of the body is unable to recognize them. Importantly, several highly pathogenic avian IAVs have already caused sporadic human infections with a high fatality rate (~60%). The current review discusses the promising strategy of using a potentially universal IAV mRNA vaccine based on conserved elements for humans, poultry, and pigs. This will better aid in averting the outbreaks in different susceptible species, thus, reduce the adverse impact on agriculture, and economics, and ultimately, prevent deadly pandemics in the human population.

Reassortment Network of Influenza A Virus

Influenza A virus (IAV) genomes are composed of eight single-stranded RNA segments. Genetic exchange through reassortment of the segmented genomes often endows IAVs with new genetic characteristics, which may affect transmissibility and pathogenicity of the viruses. However, a comprehensive understanding of the reassortment history of IAVs remains lacking. To this end, we assembled 40,296 whole-genome sequences of IAVs for analysis. Using a new clustering method based on Mean Pairwise Distances in the phylogenetic trees, we classified each segment of IAVs into clades. Correspondingly, reassortment events among IAVs were detected by checking the segment clade compositions of related genomes under specific environment factors and time period. We systematically identified 1,927 possible reassortment events of IAVs and constructed their reassortment network. Interestingly, minimum spanning tree of the reassortment network reproved that swine act as an intermediate host in the reassortment history of IAVs between avian species and humans. Moreover, reassortment patterns among related subtypes constructed in this study are consistent with previous studies. Taken together, our genome-wide reassortment analysis of all the IAVs offers an overview of the leaping evolution of the virus and a comprehensive network representing the relationships of IAVs.

Progress and Challenge in Computational Identification of Influenza Virus Reassortment

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