Immunogenicity and protection of recombinant self-assembling ferritin-hemagglutinin nanoparticle influenza vaccine in mice

Purpose

Influenza virus remains a serious burden to global public health. Current influenza vaccine fails to provide impeccable protection efficacy to the annual seasonal influenza and cannot offer a timely response to potential pandemic influenza. It is necessary to develop next generation influenza vaccines to solve the current dilemma.

Materials and Methods

We developed a recombinant, self-assembling ferritin nanoparticle that presents the extracellular domain of the influenza hemagglutinin antigen on its surface, designated as ferritin-HA. After characterizing its structure and properties, we evaluated its capacity to trigger an immune response and offer protection against influenza virus challenge in a mouse model.

Results

The recombinant ferritin-HA protein expressed in Chinese hamster ovary cells assembles into nanoparticles of a defined size. This nanoparticle vaccine enhances the uptake efficiency of Dendritic cells and promotes their maturation. Immunization with ferritin-HA nanoparticle in mice induced high levels of immunoglobulin G, hemagglutination inhibition antibodies, and microneutralization antibodies, demonstrating their stronger immunogenicity compared to current split virion vaccines. Additionally, ferritin-HA nanoparticle conferred well protection against a lethal challenge with a heterologous H3N2 influenza virus in mice.

Conclusion

This study indicates that a self-assembling ferritin-HA nanoparticle has great potential for enhancing immune response and protective efficacy in mice, presenting a promising strategy for developing next generation influenza vaccine candidate.

PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network

Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.

Disinfection of influenza a viruses by Hypocrellin a-mediated photodynamic inactivation

BACKGROUND

Influenza A viruses can be transmitted indirectly by surviving on the surface of an object. Photodynamic inactivation (PDI) is a promising approach for disinfection of pathogens.

METHODS

PDI was generated using Hypocrellin A (HA) and red light emitting diode (625-635 nm, 280 W/m2). Effects of the HA-mediated PDI on influenza viruses H1N1 and H3N2 were evaluated by the reduction of viral titers compared to virus control. After selection of the HA concentrations and illumination times, the applicability of PDI was assessed on surgical masks. Reactive oxygen species (ROS) were determined using a 2'-7'-dichlorodihydrofluorescein diacetate fluorescence probe.

RESULTS

In solution, 10 μM HA inactivated up to 5.11 ± 0.19 log10 TCID50 of H1N1 and 4.89 ± 0.38 log10 TCID50 of H3N2 by illumination for 5 and 30 min, respectively. When surgical masks were contaminated by virus before HA addition, PDI inactivated 99.99% (4.33 ± 0.34 log reduction) of H1N1 and 99.40% (2.22 ± 0.39 log reduction) of H3N2 under the selected condition. When the masks were pretreated with HA before virus addition, PDI decontaminated 99.92% (3.11 ± 0.19 log reduction) of H1N1 and 98.71% (1.89 ± 0.20 log reduction) of H3N2 virus. The fluorescence intensity of 2',7'-dichlorofluorescein in photoactivated HA was significantly higher than the cell control (P > 0.05), indicating that HA efficiently generated ROS.

CONCLUSIONS

HA-mediated PDI is effective for the disinfection of influenza viruses H1N1 and H3N2. The approach could be an alternative to decontaminating influenza A viruses on the surfaces of objects.

Phylogenetic analysis of HA and NA genes of influenza A viruses in immunosuppressed inpatients in Beijing during the 2018-2020 influenza seasons

BACKGROUND

Influenza A viruses have undergone rapid evolution with virulent; however, complete and comprehensive data on gene evolution and amino acid variation of HA and NA in immunosuppressed patients was few. In this study, we analysed molecular epidemiology and evolution of influenza A viruses in immunosuppressed population, and immunocompetent population were used as controls.

METHODS

Full sequences of HA and NA of A(H1N1)pdm09 and A(H3N2) were acquired through reverse transcription-polymerase chain reaction (RT-PCR). HA and NA genes were sequenced using the Sanger method and phylogenetically analysed using ClustalW 2.10 and MEGA software version 11.0.

RESULTS

During the 2018-2020 influenza seasons, 54 immunosuppressed and 46 immunocompetent inpatients screened positive for influenza A viruses by using the quantitative real-time PCR (qRT-PCR) were enrolled. 27 immunosuppressed and 23 immunocompetent nasal swab or bronchoalveolar lavage fluid samples were randomly selected and sequenced using the Sanger method. A(H1N1)pdm09 were detected in 15 samples and the remaining 35 samples were A(H3N2) positive. By analyzing the HA and NA gene sequences of these virus strains, we found that all A(H1N1)pdm09 viruses shared high similarities to each other and the HA and NA genes of these viruses exclusively belonged to subclade 6B.1A.1. Some NA genes of A(H3N2) viruses were not in the same clade as those of A/Singapore/INFIMH-16-0019/2016 and A/Kansas/14/2017, which may have led to A(H3N2) being the dominant strain in the 2019-2020 influenza season. Both A(H1N1)pdm09 and A(H3N2) viruses showed similar evolutionary lineages patterns of HA and NA between immunosuppressed and immunocompetent patients. Compared with the vaccine strains, there were no statistically significant of HA and NA genes and amino acid sequences of influenza A viruses in immunosuppressed and immunocompetent patients. However, the oseltamivir resistance substitution of NA-H275Y and R292K have been observed in immunosuppressed patients.

CONCLUSIONS

A(H1N1)pdm09 and A(H3N2) viruses showed similar evolutionary lineages patterns of HA and NA between immunosuppressed and immunocompetent patients. Both immunocompetent and immunosuppressed patients have some key substitutions, which should be of note monitored, especially those with potential to affect the viral antigen.

Nonpharmaceutical interventions for COVID-19 disrupt the dynamic balance between influenza A virus and human immunity

During the COVID-19 epidemic, nonpharmaceutical interventions (NPIs) blocked the transmission route of respiratory diseases. This study aimed to investigate the impact of NPIs on the influenza A virus (IAV) outbreak. The present study enrolled all children with respiratory tract infections who came to the Children's Hospital of Zhejiang University between January 2019 and July 2022. A direct immunofluorescence assay kit detected IAV. Virus isolation and Sanger sequencing were performed. From June to July 2022, in Hangzhou, China, the positive rate of IAV infection in children has increased rapidly, reaching 30.41%, and children over 3 years old are the main infected population, accounting for 75% of the total number of infected children. Influenza A (H3N2) viruses are representative strains during this period. In this outbreak, H3N2 was isolated from a cluster of its own and is highly homologous with A/South_Dakota/22/2022 (2021-2022 Northern Hemisphere). Between isolated influenza A (H3N2) viruses and A/South_Dakota/22/2022, the nucleotide homology of the HA gene ranged from 97.3% to 97.5%; the amino acid homology was 97%-97.2%, and the genetic distance of nucleotides ranged from 0.05 to 0.052. Compared with A/South_Dakota/22/2022, the isolated H3N2 showed S156H, N159Y, I160T, D186S, S198P, I48T, S53D, and K171N mutations. There was no variation in 13 key amino acid sites associated with neuraminidase inhibitor resistance in NA protein. Long-term NPIs have significantly affected the evolution and transmission of the influenza virus and human immunity, breaking the dynamic balance between the IAV and human immunity.

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP²ISP to improve the prediction of protein-protein interaction sites. ctP²ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP²ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP²ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

Antigenic characterization of influenza and SARS-CoV-2 viruses

Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.

Bioinspired membrane-based nanomodulators for immunotherapy of autoimmune and infectious diseases

Autoimmune or infectious diseases often instigate the undesirable damages to tissues or organs to trigger immune-related diseases, which involve plenty of immune cells, pathogens and autoantibodies. Nanomedicine has a great potential in modulating immune system. Particularly, biomimetic nanomodulators can be designed for prevention, diagnosis and therapy to achieve a better targeted immunotherapy. With the development of materials science and bioengineering, a wide range of membrane-coated nanomodulators are available. Herein, we summarize recent advancements of bioinspired membrane-coated nanoplatform for systemic protection against immune-related diseases including autoimmune and infectious diseases. We also rethink the challenges or limitations in the progress of the therapeutic nanoplatform, and discuss the further application of the nanomodulators in the view of translational medicine for combating immune-related diseases.

Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction

The rapid mutations on hemagglutinin (HA) of influenza A virus (IAV) can lead to significant antigenic variance and consequent immune mismatch of vaccine strains. Thus, rapid antigenicity evaluation is highly desired. The subtype-specific antigenicity models have been widely used for common subtypes such as H1 and H3. However, the continuous emerging of new IAV subtypes requires the construction of universal antigenic prediction model which could be applied on multiple IAV subtypes, including the emerging or re-emerging ones. In this study, we presented Univ-Flu, series structure-based universal models for HA antigenicity prediction. Initially, the universal antigenic regions were derived on multiple subtypes. Then, a radial shell structure combined with amino acid indexes were introduced to generate the new three-dimensional structure based descriptors, which could characterize the comprehensive physical–chemical property changes between two HA variants within or across different subtypes. Further, by combining with Random Forest classifier and different training datasets, Univ-Flu could achieve high prediction performances on intra-subtype (average AUC of 0.939), inter-subtype (average AUC of 0.771), and universal-subtype (AUC of 0.978) prediction, through independent test. Results illustrated that the designed descriptor could provide accurate universal antigenic description. Finally, the application on high-throughput antigenic coverage prediction for circulating strains showed that the Univ-Flu could screen out virus strains with high cross-protective spectrum, which could provide in-silico reference for vaccine recommendation.

Epidemiology and evolutionary analysis of Torque teno sus virus

Single stranded (ss) DNA viruses are increasingly being discovered due to the ongoing development of modern technologies in exploring the virosphere. Characterized by high rates of recombination and nucleotide substitutions, it could be comparable to RNA virus ones. Torque teno sus virus (TTSuV) is a standard ssDNA virus with a high population diversity, whose evolution is still obscure, further, it is frequently found in co-infections with other viruses threatening the porcine industry and therefore share the same host and epidemiological context. Here, we implement and describe approach to integrate viral nucleotide sequence analysis, surveillance data, and a structural approach to examine the evolution of TTSuVs, we collected samples from pigs displaying respiratory signs in China and revealed a high prevalence of TTSuV1 and TTSuVk2, frequently as part of co-infections with porcine circoviruses (PCVs), especially in spleen and lung. In addition, thirty six strains sequenced were obtained to investigate their genetic diversity in China. The evolutionary history of TTSuVs were unveiled as following: At the nucleotide sequence level, TTSuVs ORF1 was confirmed to be a robust phylogenetic maker to study evolution comparably to full genomes. Additionally, extensive recombination discovered within TTSuVk2a (also 5 out of the 36 sequenced strains in this study revealed to be recombination). Then, pairwise distance, phylogenetic trees, and amino acid analysis confirmed TTSuVs species, and allowed to define circulating genotypes (TTSuV1a-1, 1a-2, 1b-1, 1b-2, 1b-3, and k2a-1, k2a-2, k2b). Selection analysis uncovered seven and six positive selected sites in TTSuV1 and TTSuVk2, respectively. At the protein structure level, mapping of sites onto the three-dimensional structure revealed that several positive selected sites locate into potential epitopes, which might related to the potential escaping from host immune response. Our result could assist future studies on swine ssDNA virus classification, surveillance and control.

Targeting Hemagglutinin: Approaches for Broad Protection against the Influenza A Virus

Influenza A viruses are dynamically epidemic and genetically diverse. Due to the antigenic drift and shift of the virus, seasonal vaccines are required to be reformulated annually to match with current circulating strains. However, the mismatch between vaccinal strains and circulating strains occurs frequently, resulting in the low efficacy of seasonal vaccines. Therefore, several “universal” vaccine candidates based on the structure and function of the hemagglutinin (HA) protein have been developed to meet the requirement of a broad protection against homo-/heterosubtypic challenges. Here, we review recent novel constructs and discuss several important findings regarding the broad protective efficacy of HA-based universal vaccines.