The antigenic landscape of human influenza N2 neuraminidases from 2009 until 2017.

Human H3N2 influenza viruses are subject to rapid antigenic evolution which translates into frequent updates of the composition of seasonal influenza vaccines. Despite these updates, the effectiveness of influenza vaccines against H3N2-associated disease is suboptimal. Seasonal influenza vaccines primarily induce hemagglutinin-specific antibody responses. However, antibodies directed against influenza neuraminidase (NA) also contribute to protection. Here, we analysed the antigenic diversity of a panel of N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. The antigenic breadth of these NAs was determined based on the NA inhibition (NAI) of a broad panel of ferret and mouse immune sera that were raised by infection and recombinant N2 NA immunisation. This assessment allowed us to distinguish at least four antigenic groups in the N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. Computational analysis further revealed that the amino acid residues in N2 NA that have a major impact on susceptibility to NAI by immune sera are in proximity of the catalytic site. Finally, a machine learning method was developed that allowed to accurately predict the impact of mutations that are present in our N2 NA panel on NAI. These findings have important implications for the renewed interest to develop improved influenza vaccines based on the inclusion of a protective NA antigen formulation.

Two proteins, the hemagglutinin and the neuraminidase, protrude from the surface of the influenza virus. Their detection by the immune system allows the host organism to mount defences against the viral threat. The virus evolves in response to this pressure, which manifests as changes in the appearance of its hemagglutinin and neuraminidase. This process, known as antigenic drift, leads to the proteins evading detection. It is also why flu vaccines require frequent updates, as they rely on 'training' the immune system to recognise the most important strains in circulation ­ primarily by exposing it to appropriate versions of hemagglutinin. While the antigenic drift of hemagglutinin has been extensively studied, much less is known about how the neuraminidase accumulates mutations, and how these affect the immune response. To investigate this question, Catani et al. selected 43 genetically distant neuraminidases from human viral samples isolated between 2009 and 2017. Statistical analyses were applied to define their relatedness, revealing that a group of closely related neuraminidases predominated from 2009 to 2015, before they were being taken over by a second group. A third group, which was identified in viruses isolated in 2013, was remarkably close to the neuraminidase of strains that circulated in the late 1990s. The fourth and final group of neuraminidases was derived from influenza viruses that normally circulate in pigs but can also occasionally infect humans. Next, Catani et al. examined the immune response that these 43 neuraminidases could elicit in mice, as well as in ferrets ­ the animal most traditionally used in influenza research. This allowed them to pinpoint which changes in the neuraminidase sequences were important to escape recognition by the host. Data obtained from the two model species were comparable, suggesting that these experiments could be conducted on mice going forward, which are easier to work with than ferrets. Finally, Catani et al. used machine learning to build a computational model that could predict how strongly the immune system would respond to a specific neuraminidase variant. These findings could help guide the development of new vaccines that include neuraminidases tailored to best prime and train the immune system against a larger variety of strains. This may aid the development of 'supra-seasonal' vaccines that protect against a broad range of influenza viruses, reducing the need for yearly updates.

Systematic review and meta-analysis of genome-wide pooled CRISPR screens to identify host factors involved in influenza A virus infection.

The host-virus interactome is increasingly recognized as an important research field to discover new therapeutic targets to treat influenza. Multiple pooled genome-wide CRISPR-Cas screens have been reported to identify new pro- and antiviral host factors of the influenza A virus. However, at present, a comprehensive summary of the results is lacking. We performed a systematic review of all reported CRISPR studies in this field in combination with a meta-analysis using the algorithm of meta-analysis by information content (MAIC). Two ranked gene lists were generated based on evidence in 15 proviral and 4 antiviral screens. Enriched pathways in the proviral MAIC results were compared to those of a prior array-based RNA interference (RNAi) meta-analysis. The top 50 proviral MAIC list contained genes whose role requires further elucidation, such as the endosomal ion channel TPCN1 and the kinase WEE1. Moreover, MAIC indicated that ALYREF, a component of the transcription export complex, has antiviral properties, whereas former knockdown experiments attributed a proviral role to this host factor. CRISPR-Cas-pooled screens displayed a bias toward early-replication events, whereas the prior RNAi meta-analysis covered early and late-stage events. RNAi screens led to the identification of a larger fraction of essential genes than CRISPR screens. In summary, the MAIC algorithm points toward the importance of several less well-known pathways in host-influenza virus interactions that merit further investigation. The results from this meta-analysis of CRISPR screens in influenza A virus infection may help guide future research efforts to develop host-directed anti-influenza drugs. IMPORTANCE: Viruses rely on host factors for their replication, whereas the host cell has evolved virus restriction factors. These factors represent potential targets for host-oriented antiviral therapies. Multiple pooled genome-wide CRISPR-Cas screens have been reported to identify pro- and antiviral host factors in the context of influenza virus infection. We performed a comprehensive analysis of the outcome of these screens based on the publicly available gene lists, using the recently developed algorithm meta-analysis by information content (MAIC). MAIC allows the systematic integration of ranked and unranked gene lists into a final ranked gene list. This approach highlighted poorly characterized host factors and pathways with evidence from multiple screens, such as the vesicle docking and lipid metabolism pathways, which merit further exploration.

Staying on track - Keeping things running in a high-end scientific imaging core facility.

Modern life science research is a collaborative effort. Few research groups can single-handedly support the necessary equipment, expertise and personnel needed for the ever-expanding portfolio of technologies that are required across multiple disciplines in today's life science endeavours. Thus, research institutes are increasingly setting up scientific core facilities to provide access and specialised support for cutting-edge technologies. Maintaining the momentum needed to carry out leading research while ensuring high-quality daily operations is an ongoing challenge, regardless of the resources allocated to establish such facilities. Here, we outline and discuss the range of activities required to keep things running once a scientific imaging core facility has been established. These include managing a wide range of equipment and users, handling repairs and service contracts, planning for equipment upgrades, renewals, or decommissioning, and continuously upskilling while balancing innovation and consolidation.

Predicting and improving complex beer flavor through machine learning.

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

SNPeffect 5.0: large-scale structural phenotyping of protein coding variants extracted from next-generation sequencing data using AlphaFold models.

BACKGROUND: Next-generation sequencing technologies yield large numbers of genetic alterations, of which a subset are missense variants that alter an amino acid in the protein product. These variants can have a potentially destabilizing effect leading to an increased risk of misfolding and aggregation. Multiple software tools exist to predict the effect of single-nucleotide variants on proteins, however, a pipeline integrating these tools while starting from an NGS data output list of variants is lacking. RESULTS: The previous version SNPeffect 4.0 (De Baets in Nucleic Acids Res 40(D1):D935-D939, 2011) provided an online database containing pre-calculated variant effects and low-throughput custom variant analysis. Here, we built an automated and parallelized pipeline that analyzes the impact of missense variants on the aggregation propensity and structural stability of proteins starting from the Variant Call Format as input. The pipeline incorporates the AlphaFold Protein Structure Database to achieve high coverage for structural stability analyses using the FoldX force field. The effect on aggregation-propensity is analyzed using the established predictors TANGO and WALTZ. The pipeline focuses solely on the human proteome and can be used to analyze proteome stability/damage in a given sample based on sequencing results. CONCLUSION: We provide a bioinformatics pipeline that allows structural phenotyping from sequencing data using established stability and aggregation predictors including FoldX, TANGO, and WALTZ; and structural proteome coverage provided by the AlphaFold database. The pipeline and installation guide are freely available for academic users on https://github.com/vibbits/snpeffect and requires a computer cluster.

Making Bioinformatics Training Events and Material More Discoverable Using TeSS, the ELIXIR Training Portal.

Many trainers and organizations are passionate about sharing their training material. Sharing training material has several benefits, such as providing a record of recognition as an author, offering inspiration to other trainers, enabling researchers to discover training resources for their personal learning path, and improving the training resource landscape using data-driven gap analysis from the bioinformatics community. In this article, we present a series of protocols for using the ELIXIR online training registry Training eSupport System (TeSS). TeSS provides a one-stop shop for trainers and trainees to discover online information and content, including training materials, events, and interactive tutorials. For trainees, we provide protocols for registering and logging in and for searching and filtering content. For trainers and organizations, we also show how to manually or automatically register training events and materials. Following these protocols will contribute to promoting training events and add to a growing catalog of materials. This will concomitantly increase the FAIRness of training materials and events. Training registries like TeSS use a scraping mechanism to aggregate training resources from many providers when they have been annotated using Bioschemas specifications. Finally, we describe how to enrich training resources to allow for more efficient sharing of the structured metadata, such as prerequisites, target audience, and learning outcomes using Bioschemas specification. As increasing training events and material are aggregated in TeSS, searching the registry for specific events and materials becomes crucial. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching for training events and materials in TeSS Support Protocol: Integrating TeSS widgets on your website Basic Protocol 2: Logging in to TeSS using an institutional account Alternate Protocol: Creating and logging in to a TeSS account Basic Protocol 3: Manual registration of training events in TeSS Basic Protocol 4: Manual registration of training materials in TeSS Basic Protocol 5: Registration of a content provider in TeSS Basic Protocol 6: Automated harvesting of training events and materials in TeSS.

Acinetobase: the comprehensive database and repository of Acinetobacter strains.

Acinetobacter baumannii is one of the most problematic nosocomial pathogens that can efficiently thrive within hospital settings, mainly due to resistances toward antibiotics, desiccation, disinfectants, human serum and oxidative stress. Recently, increased resistance against last-resort antibiotics earns this bacterium the highest priority concern classified by the Centers for Disease Control and Prevention and the World Health Organization. An obvious hallmark of this bacterium is the high heterogeneity observed among A. baumannii isolates, with a limited core genome. This feature complexifies the study of A. baumannii bacteria as an entity, subsequently reflected in a diversity of phenotypes of not only antimicrobial and environmental resistance but also virulence. A high degree of genome plasticity, along with the use of a limited subset of established strains, can lead to strain-specific observations, decreasing the global understanding of this pathogenic agent. Phenotypic variability of A. baumannii strains is easily observable such as with the macrocolony morphologies, in vitro and in vivo virulence, natural competence level, production of different capsular polysaccharide structures and cellular densities. Some strains encode an extensive amount of virulence factors, while others, including the established strains, lack several key ones. The lack/excess of genes or specific physiological processes might interfere with in vivo and in vitro experiments, thus providing a limited impact on the global understanding of Acinetobacter bacteria. As an answer to the high heterogeneity among A. baumannii strains, we propose a first comprehensive database that includes the bacterial strains and the associated phenotypic and genetic data. This new repository, freely accessible to the entire scientific community, allows selecting the best bacterial isolate(s) related to any biological question, using an efficient and fast exchange platform. Database URL: https://acinetobase.vib.be/.

ADAR1 prevents autoinflammation by suppressing spontaneous ZBP1 activation.

The RNA-editing enzyme adenosine deaminase acting on RNA 1 (ADAR1) limits the accumulation of endogenous immunostimulatory double-stranded RNA (dsRNA)1. In humans, reduced ADAR1 activity causes the severe inflammatory disease Aicardi-Goutières syndrome (AGS)2. In mice, complete loss of ADAR1 activity is embryonically lethal3-6, and mutations similar to those found in patients with AGS cause autoinflammation7-12. Mechanistically, adenosine-to-inosine (A-to-I) base modification of endogenous dsRNA by ADAR1 prevents chronic overactivation of the dsRNA sensors MDA5 and PKR3,7-10,13,14. Here we show that ADAR1 also inhibits the spontaneous activation of the left-handed Z-nucleic acid sensor ZBP1. Activation of ZBP1 elicits caspase-8-dependent apoptosis and MLKL-mediated necroptosis of ADAR1-deficient cells. ZBP1 contributes to the embryonic lethality of Adar-knockout mice, and it drives early mortality and intestinal cell death in mice deficient in the expression of both ADAR and MAVS. The Z-nucleic-acid-binding Zα domain of ADAR1 is necessary to prevent ZBP1-mediated intestinal cell death and skin inflammation. The Zα domain of ADAR1 promotes A-to-I editing of endogenous Alu elements to prevent dsRNA formation through the pairing of inverted Alu repeats, which can otherwise induce ZBP1 activation. This shows that recognition of Alu duplex RNA by ZBP1 may contribute to the pathological features of AGS that result from the loss of ADAR1 function.

How tech-savvy employees make the difference in core facilities: Recognizing core facility expertise with dedicated career tracks: Recognizing core facility expertise with dedicated career tracks.

Core facilities have a different mission than academic research labs. Accordingly, they require different career paths and structures.

PLAZA 5.0: extending the scope and power of comparative and functional genomics in plants.

PLAZA is a platform for comparative, evolutionary, and functional plant genomics. It makes a broad set of genomes, data types and analysis tools available to researchers through a user-friendly website, an API, and bulk downloads. In this latest release of the PLAZA platform, we are integrating a record number of 134 high-quality plant genomes, split up over two instances: PLAZA Dicots 5.0 and PLAZA Monocots 5.0. This number of genomes corresponds with a massive expansion in the number of available species when compared to PLAZA 4.0, which offered access to 71 species, a 89% overall increase. The PLAZA 5.0 release contains information for 5 882 730 genes, and offers pre-computed gene families and phylogenetic trees for 5 274 684 protein-coding genes. This latest release also comes with a set of new and updated features: a new BED import functionality for the workbench, improved interactive visualizations for functional enrichments and genome-wide mapping of gene sets, and a fully redesigned and extended API. Taken together, this new version offers extended support for plant biologists working on different families within the green plant lineage and provides an efficient and versatile toolbox for plant genomics. All PLAZA releases are accessible from the portal website: https://bioinformatics.psb.ugent.be/plaza/.

Unipept Visualizations: an interactive visualization library for biological data.

SUMMARY: The Unipept Visualizations library is a JavaScript package to generate interactive visualizations of both hierarchical and non-hierarchical quantitative data. It provides four different visualizations: a sunburst, a treemap, a treeview and a heatmap. Every visualization is fully configurable, supports TypeScript and uses the excellent D3.js library. AVAILABILITY AND IMPLEMENTATION: The Unipept Visualizations library is available for download on NPM: https://npmjs.com/unipept-visualizations. All source code is freely available from GitHub under the MIT license: https://github.com/unipept/unipept-visualizations.

ADAR1 interaction with Z-RNA promotes editing of endogenous double-stranded RNA and prevents MDA5-dependent immune activation.

Loss of function of adenosine deaminase acting on double-stranded RNA (dsRNA)-1 (ADAR1) causes the severe autoinflammatory disease Aicardi-Goutières syndrome (AGS). ADAR1 converts adenosines into inosines within dsRNA. This process called A-to-I editing masks self-dsRNA from detection by the antiviral dsRNA sensor MDA5. ADAR1 binds to dsRNA in both the canonical A-form and the poorly defined Z conformation (Z-RNA). Mutations in the Z-RNA-binding Zα domain of ADAR1 are common in patients with AGS. How loss of ADAR1/Z-RNA interaction contributes to disease development is unknown. We demonstrate that abrogated binding of ADAR1 to Z-RNA leads to reduced A-to-I editing of dsRNA structures formed by base pairing of inversely oriented short interspersed nuclear elements. Preventing ADAR1 binding to Z-RNA triggers an MDA5/MAVS-mediated type I interferon response and leads to the development of lethal autoinflammation in mice. This shows that the interaction between ADAR1 and Z-RNA restricts sensing of self-dsRNA and prevents AGS development.

TRAPID 2.0: a web application for taxonomic and functional analysis of de novo transcriptomes.

Advances in high-throughput sequencing have resulted in a massive increase of RNA-Seq transcriptome data. However, the promise of rapid gene expression profiling in a specific tissue, condition, unicellular organism or microbial community comes with new computational challenges. Owing to the limited availability of well-resolved reference genomes, de novo assembled (meta)transcriptomes have emerged as popular tools for investigating the gene repertoire of previously uncharacterized organisms. Yet, despite their potential, these datasets often contain fragmented or contaminant sequences, and their analysis remains difficult. To alleviate some of these challenges, we developed TRAPID 2.0, a web application for the fast and efficient processing of assembled transcriptome data. The initial processing phase performs a global characterization of the input data, providing each transcript with several layers of annotation, comprising structural, functional, and taxonomic information. The exploratory phase enables downstream analyses from the web application. Available analyses include the assessment of gene space completeness, the functional analysis and comparison of transcript subsets, and the study of transcripts in an evolutionary context. A comparison with similar tools highlights TRAPID's unique features. Finally, analyses performed within TRAPID 2.0 are complemented by interactive data visualizations, facilitating the extraction of new biological insights, as demonstrated with diatom community metatranscriptomes.

AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data.

Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.

Navigating the Global Protein-Protein Interaction Landscape Using iRefWeb.

iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .

A framework to assess the quality and impact of bioinformatics training across ELIXIR.

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.

Protein Homeostasis Database: protein quality control in E.coli.

MOTIVATION: In vivo protein folding is governed by molecular chaperones, that escort proteins from their translational birth to their proteolytic degradation. In E.coli the main classes of chaperones that interact with the nascent chain are trigger factor, DnaK/J and GroEL/ES and several authors have performed whole-genome experiments to construct exhaustive client lists for each of these. RESULTS: We constructed a database collecting all publicly available data of experimental chaperone-interaction and -dependency data for the E.coli proteome, and enriched it with an extensive set of protein-specific as well as cell context-dependent proteostatic parameters. We made this publicly accessible via a web interface that allows to search for proteins or chaperone client lists, but also to profile user-specified datasets against all the collected parameters. We hope this will accelerate research in this field by quickly identifying differentiating features in datasets. AVAILABILITY AND IMPLEMENTATION: The Protein Homeostasis Database is freely available without any registration requirement at http://PHDB.switchlab.org/.

The Plant PTM Viewer, a central resource for exploring plant protein modifications.

Post-translational modifications (PTMs) of proteins are central in any kind of cellular signaling. Modern mass spectrometry technologies enable comprehensive identification and quantification of various PTMs. Given the increased numbers and types of mapped protein modifications, a database is necessary that simultaneously integrates and compares site-specific information for different PTMs, especially in plants for which the available PTM data are poorly catalogued. Here, we present the Plant PTM Viewer (http://www.psb.ugent.be/PlantPTMViewer), an integrative PTM resource that comprises approximately 370 000 PTM sites for 19 types of protein modifications in plant proteins from five different species. The Plant PTM Viewer provides the user with a protein sequence overview in which the experimentally evidenced PTMs are highlighted together with an estimate of the confidence by which the modified peptides and, if possible, the actual modification sites were identified and with functional protein domains or active site residues. The PTM sequence search tool can query PTM combinations in specific protein sequences, whereas the PTM BLAST tool searches for modified protein sequences to detect conserved PTMs in homologous sequences. Taken together, these tools help to assume the role and potential interplay of PTMs in specific proteins or within a broader systems biology context. The Plant PTM Viewer is an open repository that allows the submission of mass spectrometry-based PTM data to remain at pace with future PTM plant studies.

Scop3D: Online Visualization of Mutation Rates on Protein Structure.

Scop3D is a tool that automatically annotates protein structure with sequence conservation starting from a set of protein sequence variants. We present a complete upgrade and rewrite of Scop3D. We have included a DNA module that allows the analysis of single nucleotide polymorphisms in relation to the structural context of the protein. Scop3D therefore forms a bridge between genomics and protein structure. Moreover, Scop3D is now also available through an intuitive web-interface that makes the tool highly user-friendly.