OpenFluDB, a database for human and animal influenza virus

Molecular epidemiology and genetic evolution of avian influenza H5N1 subtype in Nigeria, 2006 to 2021

Nigeria recorded one of the earliest outbreaks of the Highly Pathogenic Avian Influenza (HPAI) virus H5N1 in 2006, which spread to other African countries. In 2023, 18 countries reported outbreaks of H5N1 in poultry, with human cases documented in Egypt, Nigeria, and Djibouti. There is limited information on the molecular epidemiology of HPAI H5N1 in Nigeria. We determined the molecular epidemiology and genetic evolution of the virus from 2006 to 2021. We investigated the trend and geographical distribution across Nigeria. The evolutionary history of 61 full-length genomes was performed from 13 countries worldwide, and compared with sequences obtained from the early outbreaks in Nigeria up to 2021. MEGA 11 was used to determine the phylogenetic relationships of H5N1 strains, which revealed close ancestry between sequences in Nigeria and those from other African countries. Clade classification was performed using the subspecies classification tool for Bacterial and Viral Bioinformatics Research Center (BV-BRC) version 3.35.5. H5N1 Clade 2.2 was observed in 2006, with 2.3.2, 2.3.2.1f clades observed afterwards and 2.3.4.4b in 2021. Our findings underscore the need for genomics surveillance to track antigenic variation and clades switching to monitor the epidemiological of the virus and safeguard human and animal health.Impacts Specific variations in the hemagglutinin (HA) and neuraminidase (NA) genes of Avian influenza virus are consistent in different geographical regions. H5N1 Clade 2.2 was reported in 2006, with 2.3.2, 2.3.2.1f afterwards and 2.3.4.4b in 2021. Nigeria is an epicentre for avian influenza with three major migratory routes for wild birds transversing the country. It is plausible that the Avian influenza in Northern Nigeria may be linked to wild bird sanctuaries in the region.

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.

Phylogenetic analysis and docking study of neuraminidase gene of influenza A/H1N1 viruses circulating in Iran from 2010 to 2019

Influenza A viruses (H1N1) have been consistently one of the most evolving viruses that escape from vaccine-induced immunity. Although there has been a rapid rise in human influenza virus knowledge since the 2009 pandemic, the molecular information about Iranian strains is still inadequate. The aim of this study was to analyze the neuraminidase (NA) segment of the Iranian isolates in terms of phylogenetic, antiviral resistance, and vaccine efficiency. Ninety-three NA sequences collected among 1758 nasopharyngeal swab samples during the 2015-2016 influenza season were sequenced and submitted to NCBI. Moreover, all the submitted Iranian influenza H1N1 NA sequences since 2010 till 2019 were included in the study. Software including MEGA-X, MODELLER, UCSF ChimeraX, Auto-Dock 4.2, and other online tools were used to analyze the phylogenetic relationship, vaccine efficiency, and binding affinity to sialic acid of the selected NA proteins. Moreover, the information about antiviral drug resistance mutations of NA were gathered and compared to the Iranian NA segments to check the presence of antiviral drug-resistant strains. The phylogenetic study showed that most Iranian NA sequences (between 2015 and 2016) were located in a single clade and following years were located in its subclade by 3 major mutations (G77R/K, V81A, and J188T). Resistant mutations in drug targets of NA including I117M, D151E, I223V, and S247N were ascertained in 10 isolates during the 2015-2016 flu seasons. Investigation of vaccination effect revealed that Iranian isolates in 2017 and 2018 were best matched to A/Brisbane/02/2018 (H1N1), and in 2019 to A/Guangdong-Maonan/SWL1536/2019 (H1N1). Furthermore, we performed an in-silico analysis of NA enzymatic activity of all Iranian sequences by assessment of enzyme stability, ligand affinity, and active site availability. Overall, the enzyme activity of four Iranian strains (AUG84119, AUG84157, AUG84095, and AUG84100) was assumed as the maximum enzyme activity. This study highlighted the evolutionary trend of influenza A virus/H1N1 circulating in Iran, which provides a preliminary viewpoint for a better comprehension of new emerging strains' virulence and thus, more appropriate monitoring of influenza virus A/H1N1 during each outbreak season.

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.

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.

Peering into Avian Influenza A(H5N8) for a Framework towards Pandemic Preparedness

2014 marked the first emergence of avian influenza A(H5N8) in Jeonbuk Province, South Korea, which then quickly spread worldwide. In the midst of the 2020-2021 H5N8 outbreak, it spread to domestic poultry and wild waterfowl shorebirds, leading to the first human infection in Astrakhan Oblast, Russia. Despite being clinically asymptomatic and without direct human-to-human transmission, the World Health Organization stressed the need for continued risk assessment given the nature of Influenza to reassort and generate novel strains. Given its promiscuity and easy cross to humans, the urgency to understand the mechanisms of possible species jumping to avert disastrous pandemics is increasing. Addressing the epidemiology of H5N8, its mechanisms of species jumping and its implications, mutational and reassortment libraries can potentially be built, allowing them to be tested on various models complemented with deep-sequencing and automation. With knowledge on mutational patterns, cellular pathways, drug resistance mechanisms and effects of host proteins, we can be better prepared against H5N8 and other influenza A viruses.

Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users

The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss) creates, maintains and disseminates a portfolio of reliable and state-of-the-art bioinformatics services and resources for the storage, analysis and interpretation of biological data. Through Expasy (https://www.expasy.org), the Swiss Bioinformatics Resource Portal, the scientific community worldwide, freely accesses more than 160 SIB resources supporting a wide range of life science and biomedical research areas. In 2020, Expasy was redesigned through a user-centric approach, known as User-Centred Design (UCD), whose aim is to create user interfaces that are easy-to-use, efficient and targeting the intended community. This approach, widely used in other fields such as marketing, e-commerce, and design of mobile applications, is still scarcely explored in bioinformatics. In total, around 50 people were actively involved, including internal stakeholders and end-users. In addition to an optimised interface that meets users' needs and expectations, the new version of Expasy provides an up-to-date and accurate description of high-quality resources based on a standardised ontology, allowing to connect functionally-related resources.

A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic

Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

Molecular epidemiology of the hemagglutinin gene of prevalent influenza virus A/H1N1/pdm09 among patient in Iran

In 2015, the influenza virus A/H1N1/pdm09 strain outbreak became prevalent throughout the different provinces of Iran. There are relatively limited complete genetic sequences available for this virus from Asian countries. Diagnosis and virological surveillance of influenza is essential for detecting novel genetic variants causing epidemic potential. This study describes the genetic properties of HA genome of influenza A/H1N1 pdm09 viruses circulating in Iran during the 2015/2016 season. In order to investigate the genetic pattern of influenza A/H1N1 pdm09, a total of 1758 nasopharyngeal swabs were screened by real-time RT-PCR. Of those, 510 cases were found to be positive for A/H1N1/pdm09 virus. Evolution of the approximately 100 positive specimens with high virus load was conducted via genomic phylogeny. Phylogenetic analysis of the HA genes of the A/H1N1pdm09 viruses revealed the circulation of clade 6B1, characterized by amino acid substitutions S84N, S162N and I216T, where position 162 became glycosylated. The N-glycosylation of HA protein is post or co-translational modification that affect the evolution of influenza viruses. For influenza A(H1N1) pdm09 viruses, we found more mutations in the antigenic sites than in the stem region. The results of this study confirmed the necessity of constant regular antigenic and molecular surveillance of circulating seasonal influenza viruses.

INSaFLU: an automated open web-based bioinformatics suite "from-reads" for influenza whole-genome-sequencing-based surveillance

BACKGROUND

A new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data.

RESULTS

We developed and implemented INSaFLU ("INSide the FLU"), which is the first influenza-oriented bioinformatics free web-based suite that deals with primary NGS data (reads) towards the automatic generation of the output data that are actually the core first-line "genetic requests" for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants' annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform multi-step software intensive analyses in a user-friendly manner without previous advanced training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. This platform additionally flags samples as "putative mixed infections" if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional "consensus-based" influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission.

CONCLUSIONS

In summary, INSaFLU supplies public health laboratories and influenza researchers with an open "one size fits all" framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus. INSaFLU can be accessed through https://insaflu.insa.pt .

Data distribution in public veterinary service: health and safety challenges push for context-aware systems

Background

Today’s globalised and interconnected world is characterized by intertwined and quickly evolving relationships between animals, humans and their environment and by an escalating number of accessible data for public health. The public veterinary services must exploit new modeling and decision strategies to face these changes. The organization and control of data flows have become crucial to effectively evaluate the evolution and safety concerns of a given situation in the territory. This paper discusses what is needed to develop modern strategies to optimize data distribution to the stakeholders.

Main text

If traditionally the system manager and knowledge engineer have been concerned with the increase of speed of data flow and the improvement of data quality, nowadays they need to worry about data overflow as well. To avoid this risk an information system should be capable of selecting the data which need to be shown to the human operator. In this perspective, two aspects need to be distinguished: data classification vs data distribution. Data classification is the problem of organizing data depending on what they refer to and on the way they are obtained; data distribution is the problem of selecting which data is accessible to which stakeholder. Data classification can be established and implemented via ontological analysis and formal logic but we claim that a context-based selection of data should be integrated in the data distribution application. Data distribution should provide these new features: (a) the organization of situation types distinguishing at least ordinary vs extraordinary scenarios (contextualization of scenarios); (b) the possibility to focus on the data that are really important in a given scenario (data contextualization by scenarios); and (c) the classification of which data is relevant to which stakeholder (data contextualization by users).

Short conclusion

Public veterinary services, to efficaciously and efficiently manage the information needed for today’s health and safety challenges, should contextualize and filter the continuous and growing flow of data by setting suitable frameworks to classify data, users’ roles and possible situations.

viruSITE-integrated database for viral genomics

Viruses are the most abundant biological entities and the reservoir of most of the genetic diversity in the Earth's biosphere. Viral genomes are very diverse, generally short in length and compared to other organisms carry only few genes. viruSITE is a novel database which brings together high-value information compiled from various resources. viruSITE covers the whole universe of viruses and focuses on viral genomes, genes and proteins. The database contains information on virus taxonomy, host range, genome features, sequential relatedness as well as the properties and functions of viral genes and proteins. All entries in the database are linked to numerous information resources. The above-mentioned features make viruSITE a comprehensive knowledge hub in the field of viral genomics.

The web interface of the database was designed so as to offer an easy-to-navigate, intuitive and user-friendly environment. It provides sophisticated text searching and a taxonomy-based browsing system. viruSITE also allows for an alternative approach based on sequence search. A proprietary genome browser generates a graphical representation of viral genomes. In addition to retrieving and visualising data, users can perform comparative genomics analyses using a variety of tools.

Database URL: http://www.virusite.org/

3DFlu: database of sequence and structural variability of the influenza hemagglutinin at population scale

The influenza virus type A (IVA) is an important pathogen which is able to cause annual epidemics and even pandemics. This fact is the consequence of the antigenic shifts and drifts capabilities of IVA, caused by the high mutation rate and the reassortment capabilities of the virus. The hemagglutinin (HA) protein constitutes the main IVA antigen and has a crucial role in the infection mechanism, being responsible for the recognition of host-specific sialic acid derivatives. Despite the relative abundance of HA sequence and serological studies, comparative structure-based analysis of HA are less investigated. The 3DFlu database contains well annotated HA representatives: 1192 models and 263 crystallographic structures. The relations between these proteins are defined using different metrics and are visualized as a network in the provided web interface. Moreover structural and sequence comparison of the proteins can be explored. Metadata information (e.g. protein identifier, IVA strain, year and location of infection) can enhance the exploration of the presented data. With our database researchers gain a useful tool for the exploration of high quality HA models, viewing and comparing changes in the HA viral subtypes at several information levels (sequence, structure, ESP). The complete and integrated view of those relations might be useful to determine the efficiency of transmission, pathogenicity and for the investigation of evolutionary tendencies of the influenza virus.

Database URL: http://nucleus3d.cent.uw.edu.pl/influenza

Clade-level Spatial Modelling of HPAI H5N1 Dynamics in the Mekong Region Reveals New Patterns and Associations with Agro-Ecological Factors

The highly pathogenic avian influenza (HPAI) H5N1 virus has been circulating in Asia since 2003 and diversified into several genetic lineages, or clades. Although the spatial distribution of its outbreaks was extensively studied, differences in clades were never previously taken into account. We developed models to quantify associations over time and space between different HPAI H5N1 viruses from clade 1, 2.3.4 and 2.3.2 and agro-ecological factors. We found that the distribution of clades in the Mekong region from 2004 to 2013 was strongly regionalised, defining specific epidemiological zones, or epizones. Clade 1 became entrenched in the Mekong Delta and was not supplanted by newer clades, in association with a relatively higher presence of domestic ducks. In contrast, two new clades were introduced (2.3.4 and 2.3.2) in northern Viet Nam and were associated with higher chicken density and more intensive chicken production systems. We suggest that differences in poultry production systems in these different epizones may explain these associations, along with differences in introduction pressure from neighbouring countries. The different distribution patterns found at the clade level would not be otherwise apparent through analysis treating all outbreaks equally, which requires improved linking of disease outbreak records and genetic sequence data.

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.

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses

FluKB is a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. FluKB was built using a modular framework allowing the implementation of analytical workflows and includes standard search tools, such as keyword search and sequence similarity queries, as well as advanced tools for the analysis of sequence variability. The advanced analytical tools for vaccine discovery include visual mapping of T- and B-cell vaccine targets and assessment of neutralizing antibody coverage. FluKB supports the discovery of vaccine targets and the analysis of viral diversity and its implications for vaccine discovery as well as potential T-cell breadth and antibody cross neutralization involving multiple strains. FluKB is representation of a new generation of databases that integrates data, analytical tools, and analytical workflows that enable comprehensive analysis and automatic generation of analysis reports.

MapMyFlu: visualizing spatio-temporal relationships between related influenza sequences

Understanding the molecular dynamics of viral spreading is crucial for anticipating the epidemiological implications of disease outbreaks. In the case of influenza, reassortments or point mutations affect the adaption to new hosts or resistance to anti-viral drugs and can determine whether a new strain will result in a pandemic infection or a less severe progression. To this end, tools integrating molecular information with epidemiological parameters are important to understand how molecular characteristics reflect in the infection dynamics. We present a new web tool, MapMyFlu, which allows to spatially and temporally display influenza viruses related to a query sequence on a Google Map based on BLAST results against the NCBI Influenza Database. Temporal and geographical trends appear clearly and may help in reconstructing the evolutionary history of a particular sequence. The tool is accessible through a web server, hence without the need for local installation. The website has an intuitive design and provides an easy-to-use service, and is available at http://mapmyflu.ipmb.uni-heidelberg.de

Emergence of a novel cluster of influenza A(H5N1) virus clade 2.2.1.2 with putative human health impact in Egypt, 2014/15

A distinct cluster of highly pathogenic avian influenzaviruses of subtype A(H5N1) has been found to emergewithin clade 2.2.1.2 in poultry in Egypt since summer2014 and appears to have quickly become predominant.Viruses of this cluster may be associated withincreased incidence of human influenza A(H5N1) infectionsin Egypt over the last months.

Unraveling the web of viroinformatics: computational tools and databases in virus research

The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain--viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article.

The hemagglutinin of the influenza A(H1N1)pdm09 is mutating towards stability

The last influenza A pandemic provided an excellent opportunity to study the adaptation of the influenza A(H1N1)pdm09 virus to the human host. Particularly, due to the availability of sequences taken from isolates since the beginning of the pandemic until date, we could monitor amino acid changes that occurred in the hemagglutinin (HA) as the virus spread worldwide and became the dominant H1N1 strain. HA is crucial to viral infection because it binds to sialidated cell-receptors and mediates fusion of cell and viral membranes; because antibodies that bind to HA may block virus entry to the cell, this protein is subjected to high selective pressure. Multiple alignment analysis of sequences of the HA from isolates taken since 2009 to date allowed us to find amino acid changes that were positively selected as the pandemic progressed. We found nine changes that became prevalent: HA1 subunits D104N, K166Q, S188T, S206T, A259T, and K285E; and HA2 subunits E47K, S124N, and E172K. Most of these changes were located in areas involved in inter- and intrachain interactions, while only two (K166Q and S188T) were located in known antigenic sites. We conclude that selective pressure on HA was aimed to improve its functionality and hence virus fitness, rather than at avoidance of immune recognition.

The EMPRES-i genetic module: a novel tool linking epidemiological outbreak information and genetic characteristics of influenza viruses

Combining epidemiological information, genetic characterization and geomapping in the analysis of influenza can contribute to a better understanding and description of influenza epidemiology and ecology, including possible virus reassortment events. Furthermore, integration of information such as agroecological farming system characteristics can provide new knowledge on risk factors of influenza emergence and spread. Integrating viral characteristics into an animal disease information system is therefore expected to provide a unique tool to trace-and-track particular virus strains; generate clade distributions and spatiotemporal clusters; screen for distribution of viruses with specific molecular markers; identify potential risk factors; and analyze or map viral characteristics related to vaccines used for control and/or prevention. For this purpose, a genetic module was developed within EMPRES-i (FAO’s global animal disease information system) linking epidemiological information from influenza events with virus characteristics and enabling combined analysis. An algorithm was developed to act as the interface between EMPRES-i disease event data and publicly available influenza virus sequences in OpenfluDB. This algorithm automatically computes potential links between outbreak event and sequences, which are subsequently manually validated by experts. Subsequently, other virus characteristics such as antiviral resistance can then be associated to outbreak data. To visualize such characteristics on a geographic map, shape files with virus characteristics to overlay on other EMPRES-i map layers (e.g. animal densities) can be generated. The genetic module allows export of associated epidemiological and sequence data for further analysis. FAO has made this tool available for scientists and policy makers. Contributions are expected from users to improve and validate the number of linked influenza events and isolate information as well as the quality of information. Possibilities to interconnect with other influenza sequence databases or to expand the genetic module to other viral diseases (e.g. foot and mouth disease) are being explored.

Database OpenfluDB URL:http://openflu.vital-it.ch

Database EMPRES-i URL:http://EMPRES-i.fao.org/

Analysis of adaptation mutants in the hemagglutinin of the influenza A(H1N1)pdm09 virus

Hemagglutinin is the major surface glycoprotein of influenza viruses. It participates in the initial steps of viral infection through receptor binding and membrane fusion events. The influenza pandemic of 2009 provided a unique scenario to study virus evolution. We performed molecular dynamics simulations with four hemagglutinin variants that appeared throughout the 2009 influenza A (H1N1) pandemic. We found that variant 1 (S143G, S185T) likely arose to avoid immune recognition. Variant 2 (A134T), and variant 3 (D222E, P297S) had an increased binding affinity for the receptor. Finally, variant 4 (E374K) altered hemagglutinin stability in the vicinity of the fusion peptide. Variants 1 and 4 have become increasingly predominant, while variants 2 and 3 declined as the pandemic progressed. Our results show some of the different strategies that the influenza virus uses to adapt to the human host and provide an example of how selective pressure drives antigenic drift in viral proteins.

Characterization of the 2012 highly pathogenic avian influenza H7N3 virus isolated from poultry in an outbreak in Mexico: pathobiology and vaccine protection

In June of 2012, an H7N3 highly pathogenic avian influenza (HPAI) virus was identified as the cause of a severe disease outbreak in commercial laying chicken farms in Mexico. The purpose of this study was to characterize the Mexican 2012 H7N3 HPAI virus (A/chicken/Jalisco/CPA1/2012) and determine the protection against the virus conferred by different H7 inactivated vaccines in chickens. Both adult and young chickens intranasally inoculated with the virus became infected and died at between 2 and 4 days postinoculation (p.i.). High virus titers and viral replication in many tissues were demonstrated at 2 days p.i. in infected birds. The virus from Jalisco, Mexico, had high sequence similarity of greater than 97% to the sequences of wild bird viruses from North America in all eight gene segments. The hemagglutinin gene of the virus contained a 24-nucleotide insert at the hemagglutinin cleavage site which had 100% sequence identity to chicken 28S rRNA, suggesting that the insert was the result of nonhomologous recombination with the host genome. For vaccine protection studies, both U.S. H7 low-pathogenic avian influenza (LPAI) viruses and a 2006 Mexican H7 LPAI virus were tested as antigens in experimental oil emulsion vaccines and injected into chickens 3 weeks prior to challenge. All H7 vaccines tested provided ≥90% protection against clinical disease after challenge and decreased the number of birds shedding virus and the titers of virus shed. This study demonstrates the pathological consequences of the infection of chickens with the 2012 Mexican lineage H7N3 HPAI virus and provides support for effective programs of vaccination against this virus in poultry.

Differential Contribution of Transcription Factors to Arabidopsis thaliana Defense Against Spodoptera littoralis

In response to insect herbivory, Arabidopsis plants activate the synthesis of the phytohormone jasmonate-isoleucine, which binds to a complex consisting of the receptor COI1 and JAZ repressors. Upon proteasome-mediated JAZ degradation, basic helix-loop-helix transcription factors (TFs) MYC2, MYC3, and MYC4 become activated and this results in the expression of defense genes. Although the jasmonate (JA) pathway is known to be essential for the massive transcriptional reprogramming that follows herbivory, there is however little information on other TFs that are required for defense against herbivores and whether they contribute significantly to JA-dependent defense gene expression. By transcriptome profiling, we identified 41 TFs that were induced in response to herbivory by the generalist Spodoptera littoralis. Among them, nine genes, including WRKY18, WRKY40, ANAC019, ANAC055, ZAT10, ZAT12, AZF2, ERF13, and RRTF1, were found to play a significant role in resistance to S. littoralis herbivory. Compared to the triple mutant myc234 that is as sensitive as coi1-1 to herbivory, knockout lines of these nine TFs were only partially more sensitive to S. littoralis but, however, some displayed distinct gene expression changes at the whole-genome level. Data thus reveal that MYC2, MYC3, and MYC4 are master regulators of Arabidopsis resistance to a generalist herbivore and identify new genes involved in insect defense.

ViralZone: recent updates to the virus knowledge resource

ViralZone (http://viralzone.expasy.org) is a knowledge repository that allows users to learn about viruses including their virion structure, replication cycle and host-virus interactions. The information is divided into viral fact sheets that describe virion shape, molecular biology and epidemiology for each viral genus, with links to the corresponding annotated proteomes of UniProtKB. Each viral genus page contains detailed illustrations, text and PubMed references. This new update provides a linked view of viral molecular biology through 133 new viral ontology pages that describe common steps of viral replication cycles shared by several viral genera. This viral cell-cycle ontology is also represented in UniProtKB in the form of annotated keywords. In this way, users can navigate from the description of a replication-cycle event, to the viral genus concerned, and the associated UniProtKB protein records.

Influenza research database: an integrated bioinformatics resource for influenza research and surveillance

Please cite this paper as: Squires et al. (2012) Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and Other Respiratory Viruses 6(6), 404–416.

Background  The recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics.

Design  The Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly‐accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user‐friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in‐protected ‘workbench’ spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature.

Results  To demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross‐protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks.

Conclusions  The IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics.

Requirements and design of the PROSPER protocol for implementation of information infrastructures supporting pandemic response: a Nominal Group study

BACKGROUND

Advanced technical systems and analytic methods promise to provide policy makers with information to help them recognize the consequences of alternative courses of action during pandemics. Evaluations still show that response programs are insufficiently supported by information systems. This paper sets out to derive a protocol for implementation of integrated information infrastructures supporting regional and local pandemic response programs at the stage(s) when the outbreak no longer can be contained at its source.

METHODS

Nominal group methods for reaching consensus on complex problems were used to transform requirements data obtained from international experts into an implementation protocol. The analysis was performed in a cyclical process in which the experts first individually provided input to working documents and then discussed them in conferences calls. Argument-based representation in design patterns was used to define the protocol at technical, system, and pandemic evidence levels.

RESULTS

The Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER) outlines the implementation of information infrastructure aligned with pandemic response programs. The protocol covers analyses of the community at risk, the response processes, and response impacts. For each of these, the protocol outlines the implementation of a supporting information infrastructure in hierarchical patterns ranging from technical components and system functions to pandemic evidence production.

CONCLUSIONS

The PROSPER protocol provides guidelines for implementation of an information infrastructure for pandemic response programs both in settings where sophisticated health information systems already are used and in developing communities where there is limited access to financial and technical resources. The protocol is based on a generic health service model and its functions are adjusted for community-level analyses of outbreak detection and progress, and response program effectiveness. Scientifically grounded reporting principles need to be established for interpretation of information derived from outbreak detection algorithms and predictive modeling.

Identifying genetic markers of adaptation for surveillance of viral host jumps

Adaptation is often thought to affect the likelihood that a virus will be able to successfully emerge in a new host species. If so, surveillance for genetic markers of adaptation could help to predict the risk of disease emergence. However, adaptation is difficult to distinguish conclusively from the other processes that generate genetic change. In this Review we survey the research on the host jumps of influenza A, severe acute respiratory syndrome-coronavirus, canine parvovirus and Venezuelan equine encephalitis virus to illustrate the insights that can arise from combining genetic surveillance with microbiological experimentation in the context of epidemiological data. We argue that using a multidisciplinary approach for surveillance will provide a better understanding of when adaptations are required for host jumps and thus when predictive genetic markers may be present.

ViralZone: a knowledge resource to understand virus diversity

The molecular diversity of viruses complicates the interpretation of viral genomic and proteomic data. To make sense of viral gene functions, investigators must be familiar with the virus host range, replication cycle and virion structure. Our aim is to provide a comprehensive resource bridging together textbook knowledge with genomic and proteomic sequences. ViralZone web resource (www.expasy.org/viralzone/) provides fact sheets on all known virus families/genera with easy access to sequence data. A selection of reference strains (RefStrain) provides annotated standards to circumvent the exponential increase of virus sequences. Moreover ViralZone offers a complete set of detailed and accurate virion pictures.