N-linked glycosylation enhances hemagglutinin stability in avian H5N6 influenza virus to promote adaptation in mammals

Clade 2.3.4.4 avian H5Ny viruses, namely H5N2, H5N6, and H5N8, have exhibited unprecedented intercontinental spread in poultry. Among them, only H5N6 viruses are frequently reported to infect mammals and cause serious human infections. In this study, the genetic and biological characteristics of surface hemagglutinin (HA) from clade 2.3.4.4 H5Ny avian influenza viruses (AIVs) were examined for adaptation in mammalian infection. Phylogenetic analysis identified an amino acid (AA) deletion at position 131 of HA as a distinctive feature of H5N6 virus isolated from human patients. This single AA deletion was found to enhance H5N6 virus replication and pathogenicity in vitro and in mammalian hosts (mice and ferrets) through HA protein acid and thermal stabilization that resulted in reduced pH threshold from pH 5.7 to 5.5 for viral-endosomal membrane fusion. Mass spectrometry and crystal structure revealed that the AA deletion in HA at position 131 introduced an N-linked glycosylation site at 129 which increases compactness between HA monomers thus stabilizes the trimeric structure. Our findings provide a molecular understanding of how HA protein stabilization promotes cross-species avian H5N6 virus infection to mammalian hosts

Leveraging Machine Learning for the Analysis and Prediction of Influenza A Virus

Influenza, commonly known as flu, is a respiratory disease that poses a significant challenge to global public health due to its high prevalence and potential for serious health complications. The disease is caused by influenza viruses, among which influenza A viruses are of particular concern. These viruses are known for their rapid transmission, potential to cause severe health issues, and frequent mutations, which underscore the need for ongoing research and surveillance. A key aspect of managing influenza outbreaks includes understanding host origins, antigenic properties, and the ability of influenza A viruses to transmit between species, as this knowledge is critical in forecasting outbreaks and developing effective vaccines. Traditional approaches, such as hemagglutination inhibition assays for antigenicity assessment and phylogenetic analysis to determine genetic relationships, host origins and subtypes, have been fundamental in understanding influenza viruses. These methods, while informative, often face limitations in terms of time, resources, and the ability to keep pace with the rapid evolutionary changes of viruses. To mitigate these limitations, this thesis uses advanced machine learning techniques to analyse critical protein sequence data from influenza A viruses, offering an alternative perspective for unravelling the complexities of influenza, and potentially opening new avenues for analysis without strict reliance on prior biological knowledge. The core of the thesis is the application and refinement of predictive models to determine host origins, subtypes, and antigenic relationships of influenza A viruses. These models are evaluated comprehensively, considering factors such as the impact of incomplete sequences, performance across various host taxonomies and individual hosts, as well as the influence of reference databases on model performance. This evaluation illuminates the potential of machine learning to enhance our understanding of influenza A viruses in real-world scenarios, pointing out the ongoing importance of this research in public health

Interpretable detection of novel human viruses from genome sequencing data

Viruses evolve extremely quickly, so reliable meth- ods for viral host prediction are necessary to safe- guard biosecurity and biosafety alike. Novel human- infecting viruses are difficult to detect with stan- dard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next- generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology- based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host pre- diction task. We propose a new approach for con- volutional filter visualization to disentangle the in- formation content of each nucleotide from its contri- bution to the final classification decision. Nucleotide- resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy- to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.Peer Reviewe

Émergence des virus influenza aviaires hautement pathogènes : la triade hôte, virus et microbiote

Les virus influenza aviaires faiblement pathogènes (VIAFP) appartenant aux sous-types H5 et H7 sont capables d'évoluer en virus influenza aviaires hautement pathogènes (VIAHP), suite à l'obtention d'un site de clivage polybasique (SCPB) au niveau de leur hémagglutinine (HA). HA peut alors être clivée par des protéases intracellulaires ubiquitaires et le virus est ainsi capable de se répliquer systémiquement. L'acquisition d'un SCPB est un évènement virus-dépendant, ayant lieu au sein d'un animal infecté par un VIAFP parental. Pour que l'émergence soit réussie, le VIAHP nouvellement formé doit probablement devenir un variant majoritaire, afin d'être transmis à d'autres individus. Nous avons étudié les paramètres menant à l'émergence de VIAHP et cette thèse a été divisée en deux parties : premièrement, nous avons évalué les capacités régulatrices du microbiote sur l'infection par un VIAHP. En administrant à des canards un mélange d'antibiotiques à large spectre, nous avons fortement déstabilisé leur microbiote. Animaux sains et traités aux antibiotiques ont ensuite été infectés avec un VIAHP. Nous avons montré que l'excrétion virale était significativement augmentée chez les animaux traités aux antibiotiques, ceci étant corrélé à une altération de la réponse immunitaire antivirale innée. Deuxièmement, afin de modéliser la compétition intra-hôte entre un VIAHP nouvellement apparu et son précurseur VIAFP, nous avons réalisé des coinfections in vivo et in ovo chez le poulet et le canard, en utilisant un VIAHP comme variant minoritaire et un VIAFP, identique en tout point au VIAHP, à l'exception du site de clivage, comme variant majoritaire. Nous avons montré que les interactions entre ces deux virus étaient radicalement différentes chez le poulet et chez le canard, et que le VIAHP avait un avantage sélectif moindre chez ce dernier. Ces travaux apportent de nouvelles connaissances sur les conditions menant à l'émergence de VIAHP, dont les épizooties ont un impact majeur sur la santé animale, l'économie et la santé publique.Low pathogenic avian influenza viruses (LPAIV) of the H5 and H7 subtype have the capacity to evolve to highly pathogenic avian influenza viruses (HPAIV), following the acquisition of a multi-basic cleavage site (MBCS) at the hemagglutinin (HA) cleavage site. This evolution allows the HA to be cleaved by intracellular ubiquitous proteases and the virus to spread systemically. The acquisition of MBCS is a virus-dependent event that occurs in a bird infected with a parental LPAIV. To emerge successfully, the newly formed HPAIV probably must become a predominant variant in order to overcome the transmission bottleneck between individuals. We investigated the factors leading to HPAIV emergence and the PhD was divided in two parts: firstly, we investigated to what extent the microbiota could contribute to the control of HPAIV infection in ducks. By treating ducks with a broad-spectrum antibiotic cocktail, we achieved a significant depletion of their microbiota. Groups of non-treated and antibiotic-treated ducks were infected with an HPAIV. We revealed that antibiotic-treated ducks had significantly higher viral excretion in the intestine, which correlated with an impaired intestinal antiviral immune response. Secondly, to model the intra-host competition between a newly formed HPAIV and its parental LPAIV, we co-infected embryonated eggs, chickens and ducks with a H5N8 HPAIV as a minority variant and a LPAIV that differed from the HPAIV only at the level of the HA cleavage site, as a majority variant. Our results demonstrate that chickens and ducks have opposite effects on the interaction between the H5N8 HPAIV and LPAIV and that HPAIV has a stronger selective advantage in chickens than in ducks. This work provides new insights on HPAIV emergence, which has a major impact on animal health, economy, as well as on public health

Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches

Since the early 1990s, porcine reproductive and respiratory syndrome (PRRS) virus outbreaks have been reported across various parts of North America, Europe, and Asia. The incursion of PRRS virus (PRRSV) in swine herds could result in various clinical manifestations, resulting in a substantial impact on the incidence of respiratory morbidity, reproductive loss, and mortality. Veterinary experts, among others, regularly analyze the PRRSV open reading frame-5 (ORF-5) for prognostic purposes to assess the risk of severe clinical outcomes. In this study, we explored if predictive modeling techniques could be used to identify the severity of typical clinical signs observed during PRRS outbreaks in sow herds. Our study aimed to evaluate four baseline machine learning (ML) algorithms: logistic regression (LR) with ridge and lasso regularization techniques, random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM), for the clinical impact classification of ORF-5 sequences and demographic data into high impact and low impact categories. First, baseline classifiers were evaluated using different input representations of ORF-5 nucleotides, amino acid sequences, and demographic data using a 10-fold cross-validation technique. Then, we designed a consensus voting ensemble approach to aggregate the different types of input representations for genetic and demographic data for classifying clinical impact. In this study, we observed that: (a) for abortion and pre-weaning mortality (PWM), different classifiers gained improvement over baseline accuracy, which showed the plausible presence of both genotypic-phenotypic and demographic-phenotypic relationships, (b) for sow mortality (SM), no baseline classifier successfully established such linkages using either genetic or demographic input data, (c) baseline classifiers showed good performance with a moderate variance of the performance metrics, due to high-class overlap and the small dataset size used for training, and (d) the use of consensus voting ensemble techniques helped to make the predictions more robust and stabilized the performance evaluation metrics, but overall accuracy did not substantially improve the diagnostic metrics over baseline classifiers

Fleas of fleas: The potential role of bacteriophages in Salmonella diversity and pathogenicity.

Non-typhoidal salmonellosis is an important foodborne and zoonotic infection, that causes significant global public health concern. Diverse serovars are multidrug-resistant and encode several virulence indicators, however, little is known on the role prophages play in driving these characteristics. Here, we extracted prophages from 75 Salmonella genomes, which represent the 15 most important serovars in the United Kingdom. We analysed the genomes of the intact prophages for the presence of virulence factors which were associated with; diversity, evolution and pathogenicity of Salmonella and to establish their genomic relationships. We identified 615 prophage elements from the Salmonella genomes, from which 195 prophages are intact, 332 being incomplete while 88 are questionable. The average prophage carriage was found to be more prevalent in S. Heidelberg, S. Inverness and S. Newport (10.2-11.6 prophages/strain), compared to S. Infantis, S. Stanley, S. Typhimurium and S. Virchow (8.2-9 prophages/strain) and S. Agona, S. Braenderup, S. Bovismorbificans, S. Choleraesuis, S. Dublin, and S. Java (6-7.8 prophages/strain), and S. Javiana and S. Enteritidis (5.8 prophages/strain). Cumulatively, 2760 virulence factors were detected from the intact prophages and associated with cellular functionality being linked to effector delivery/secretion system (73%), adherence (22%), magnesium uptake (2.7%), resistance to antimicrobial peptides (0.94%), stress/survival (0.4%), exotoxins (0.32%) and antivirulence (0.18%). Close and distant clusters were formed among the prophage genomes suggesting different lineages and associations with bacteriophages of other Enterobacteriaceae. We show that diverse repertoire of Salmonella prophages are associated with numerous virulence factors, and may contribute to diversity, pathogenicity and success of specific serovars

Fundamentals of SARS-CoV-2 Biosensors

COVID-19 diagnostic strategies based on advanced techniques are currently essential topics of interest, with crucial roles in scientific research. This book integrates fundamental concepts and critical analyses that explore the progress of modern methods for the detection of SARS-CoV-2