Dynamics of Influenza A (H5N1) virus protein sequence diversity

DiMA: sequence diversity dynamics analyser for viruses

Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic, and therapeutic interventions against viruses. DiMA is a novel tool that is big data-ready and designed to facilitate the dissection of sequence diversity dynamics for viruses. DiMA stands out from other diversity analysis tools by offering various unique features. DiMA provides a quantitative overview of sequence (DNA/RNA/protein) diversity by use of Shannon's entropy corrected for size bias, applied via a user-defined k-mer sliding window to an input alignment file, and each k-mer position is dissected to various diversity motifs. The motifs are defined based on the probability of distinct sequences at a given k-mer alignment position, whereby an index is the predominant sequence, while all the others are (total) variants to the index. The total variants are sub-classified into the major (most common) variant, minor variants (occurring more than once and of incidence lower than the major), and the unique (singleton) variants. DiMA allows user-defined, sequence metadata enrichment for analyses of the motifs. The application of DiMA was demonstrated for the alignment data of the relatively conserved Spike protein (2,106,985 sequences) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the relatively highly diverse pol gene (2637) of the human immunodeficiency virus-1 (HIV-1). The tool is publicly available as a web server (https://dima.bezmialem.edu.tr), as a Python library (via PyPi) and as a command line client (via GitHub).

A Systematic Bioinformatics Approach for Mapping the Minimal Set of a Viral Peptidome

Sequence changes in viral genomes generate protein sequence diversity that enables viruses to evade the host immune system, hindering the development of effective preventive and therapeutic interventions. The massive proliferation of sequence data provides unprecedented opportunities to study viral adaptation and evolution. An alignment-free approach removes various restrictions posed by an alignment-dependent approach for studying sequence diversity. The publicly available tool, UNIQmin, offers an alignment-free approach for studying viral sequence diversity at any given rank of taxonomy lineage and is big data ready. The tool performs an exhaustive search to determine the minimal set of sequences required to capture the peptidome diversity within a given dataset. This compression is possible through the removal of identical sequences and unique sequences that do not contribute effectively to the peptidome diversity pool. Herein, we describe a detailed four-part protocol utilizing UNIQmin to generate the minimal set for the purpose of viral diversity analyses, alignment-free at any rank of the taxonomy lineage, using the recent global public health threat Monkeypox virus (MPX) sequence data as a case study. The protocol enables a systematic bioinformatics approach to study sequence diversity across taxonomic lineages, which is crucial for our future preparedness against viral epidemics. This is particularly important when data are abundant, freely available, and alignment is not an option. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Tool installation and input file preparation Basic Protocol 2: Generation of a minimal set of sequences for a given dataset Basic Protocol 3: Comparative minimal set analysis across taxonomic lineage ranks Basic Protocol 4: Factors affecting the minimal set of sequences.

A novel data augmentation approach for influenza A subtype prediction based on HA proteins

Influenza, a pervasive viral respiratory illness, remains a significant global health concern. The influenza A virus, capable of causing pandemics, necessitates timely identification of specific subtypes for effective prevention and control, as highlighted by the World Health Organization. The genetic diversity of influenza A virus, especially in the hemagglutinin protein, presents challenges for accurate subtype prediction. This study introduces PreIS as a novel pipeline utilizing advanced protein language models and supervised data augmentation to discern subtle differences in hemagglutinin protein sequences. PreIS demonstrates two key contributions: leveraging pre-trained protein language models for influenza subtype classification and utilizing supervised data augmentation to generate additional training data without extensive annotations. The effectiveness of the pipeline has been rigorously assessed through extensive experiments, demonstrating a superior performance with an impressive accuracy of 94.54% compared to the current state-of-the-art model, the MC-NN model, which achieves an accuracy of 89.6%. PreIS also exhibits proficiency in handling unknown subtypes, emphasizing the importance of early detection. Pioneering the classification of HxNy subtypes solely based on the hemagglutinin protein chain, this research sets a benchmark for future studies. These findings promise more precise and timely influenza subtype prediction, enhancing public health preparedness against influenza outbreaks and pandemics. The data and code underlying this article are available in https://github.com/CBRC-lab/PreIS.

System Biology Approach to Identify the Hub Genes and Pathways Associated with Human H5N1 Infection

INTRODUCTION

H5N1 is a highly pathogenic avian influenza virus that can infect humans and has an estimated fatality rate of 53%. As shown by the current situation of the COVID-19 pandemic, emerging and re-emerging viruses such as H5N1 have the potential to cause another pandemic. Thus, this study outlined the hub genes and pathways associated with H5N1 infection in humans.

METHODS

The genes associated with H5N1 infection in humans were retrieved from the NCBI Gene database using "H5N1 virus infection" as the keyword. The genes obtained were investigated for protein-protein interaction (PPI) using STRING version 11.5 and studied for functional enrichment analysis using DAVID 2021. Further, the PPI network was visualised and analysed using Cytoscape 3.7.2, and the hub genes were obtained using the local topological analysis method of the cytoHubba plugin.

RESULTS

A total of 39 genes associated with H5N1 infection in humans significantly interacted with each other, forming a PPI network with 38 nodes and 149 edges modulating 74 KEGG pathways, 76 biological processes, 13 cellular components, and 22 molecular functions. Further, the PPI network analysis revealed that 33 nodes interacted, forming 1056 shortest paths at 0.282 network density, along with a 1.947 characteristic path length. The local topological analysis predicted IFNA1, IRF3, CXCL8, CXCL10, IFNB1, and CHUK as the critical hub genes in human H5N1 infection.

CONCLUSION

The hub genes associated with the H5N1 infection and their pathways could serve as diagnostic, prognostic, and therapeutic targets for H5N1 infection among humans.

Characterization of an intracellular humanized single-chain antibody to matrix protein (M1) of H5N1 virus

We developed a human intracellular antibody based on the M1 protein from avian influenza virus H5N1 (A/meerkat/Shanghai/SH-1/2012) and then characterized the properties of this antibody. The M1 protein sequence was amplified by RT-PCR using the cDNA of the H5N1 virus as a template, expressed in bacterial expression system BL21 (DE3) and purified. A human strain, high affinity, and single chain antibody (HuScFv) against M1 protein was obtained by phage antibody library screening using M1 as an antigen. A recombinant TAT-HuScFv protein was expressed by fusion with the TAT protein transduction domain (PTD) gene of HIV to prepare a human intracellular antibody against avian influenza virus. Further analysis demonstrated that TAT-HuScFv could inhibit the hemagglutination activity of the 300 TCID50 H1N1 virus, thus providing preliminary validation of the universality of the antibody. After two rounds of M1 protein decomposition, the TAT-HuScFv antigen binding site was identified as Alanine (A) at position 239. Collectively, our data describe a recombinant antibody with high binding activity against the conserved sequences of avian influenza viruses. This intracellular recombinant antibody blocked the M1 protein that infected intracellular viruses, thus inhibiting the replication and reproduction of H5N1 viruses.

An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria.

Applied Proteomics in 'One Health'

‘One Health’ summarises the idea that human health and animal health are interdependent and bound to the health of ecosystems. The purpose of proteomics methodologies and studies is to determine proteins present in samples of interest and to quantify changes in protein expression during pathological conditions. The objectives of this paper are to review the application of proteomics technologies within the One Health concept and to appraise their role in the elucidation of diseases and situations relevant to One Health. The paper develops in three sections. Proteomics Applications in Zoonotic Infections part discusses proteomics applications in zoonotic infections and explores the use of proteomics for studying pathogenetic pathways, transmission dynamics, diagnostic biomarkers and novel vaccines in prion, viral, bacterial, protozoan and metazoan zoonotic infections. Proteomics Applications in Antibiotic Resistance part discusses proteomics applications in mechanisms of resistance development and discovery of novel treatments for antibiotic resistance. Proteomics Applications in Food Safety part discusses the detection of allergens, exposure of adulteration, identification of pathogens and toxins, study of product traits and characterisation of proteins in food safety. Sensitive analysis of proteins, including low-abundant ones in complex biological samples, will be achieved in the future, thus enabling implementation of targeted proteomics in clinical settings, shedding light on biomarker research and promoting the One Health concept.

A paucigranulocytic asthma host environment promotes the emergence of virulent influenza viral variants

Influenza virus has a high mutation rate, such that within one host different viral variants can emerge. Evidence suggests that influenza virus variants are more prevalent in pregnant and/or obese individuals due to their impaired interferon response. We have recently shown that the non-allergic, paucigranulocytic subtype of asthma is associated with impaired type I interferon production. Here, we seek to address if this is associated with an increased emergence of influenza virus variants. Compared to controls, mice with paucigranulocytic asthma had increased disease severity and an increased emergence of influenza virus variants. Specifically, PB1 mutations exclusively detected in asthmatic mice were associated with increased polymerase activity. Furthermore, asthmatic host-derived virus led to increased disease severity in wild-type mice. Taken together, these data suggest that at least a subset of patients with asthma may be more susceptible to severe influenza and may be a possible source of new influenza virus variants.