A Mutation Network Method for Transmission Analysis of Human Influenza H3N2

Global pattern and determinant for interaction of seasonal influenza viruses

BACKGROUND

The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored.

METHODS

Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM).

RESULTS

A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1.

CONCLUSION

The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

Variation in synonymous evolutionary rates in the SARS-CoV-2 genome

Introduction

Coronavirus disease 2019 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Influential variants and mutants of this virus continue to emerge, and more effective virus-related information is urgently required for identifying and predicting new mutants. According to earlier reports, synonymous substitutions were considered phenotypically silent; thus, such mutations were frequently ignored in studies of viral mutations because they did not directly cause amino acid changes. However, recent studies have shown that synonymous substitutions are not completely silent, and their patterns and potential functional correlations should thus be delineated for better control of the pandemic.

Methods

In this study, we estimated the synonymous evolutionary rate (SER) across the SARS-CoV-2 genome and used it to infer the relationship between the viral RNA and host protein. We also assessed the patterns of characteristic mutations found in different viral lineages.

Results

We found that the SER varies across the genome and that the variation is primarily influenced by codon-related factors. Moreover, the conserved motifs identified based on the SER were found to be related to host RNA transport and regulation. Importantly, the majority of the existing fixed-characteristic mutations for five important virus lineages (Alpha, Beta, Gamma, Delta, and Omicron) were significantly enriched in partially constrained regions.

Discussion

Taken together, our results provide unique information on the evolutionary and functional dynamics of SARS-CoV-2 based on synonymous mutations and offer potentially useful information for better control of the SARS-CoV-2 pandemic.

Development of the H3N2 influenza microneedle vaccine for cross-protection against antigenic variants

Due to the continuously mutating nature of the H3N2 virus, two aspects were considered when preparing the H3N2 microneedle vaccines: (1) rapid preparation and (2) cross-protection against multiple antigenic variants. Previous methods of measuring hemagglutinin (HA) content required the standard antibody, thus rapid preparation of H3N2 microneedle vaccines targeting the mutant H3N2 was delayed as a result of lacking a standard antibody. In this study, H3N2 microneedle vaccines were prepared by high performance liquid chromatography (HPLC) without the use of an antibody, and the cross-protection of the vaccines against several antigenic variants was observed. The HA content measured by HPLC was compared with that measured by ELISA to observe the accuracy of the HPLC analysis of HA content. The cross-protection afforded by the H3N2 microneedle vaccines was evaluated against several antigenic variants in mice. Microneedle vaccines for the 2019–20 seasonal H3N2 influenza virus (19–20 A/KS/17) were prepared using a dip-coating process. The cross-protection of 19–20 A/KS/17 H3N2 microneedle vaccines against the 2015–16 seasonal H3N2 influenza virus in mice was investigated by monitoring body weight changes and survival rate. The neutralizing antibody against several H3N2 antigenic variants was evaluated using the plaque reduction neutralization test (PRNT). HA content in the solid microneedle vaccine formulation with trehalose post-exposure at 40℃ for 24 h was 48% and 43% from the initial HA content by HPLC and ELISA, respectively. The vaccine was administered to two groups of mice, one by microneedles and the other by intramuscular injection (IM). In vivo efficacies in the two groups were found to be similar, and cross-protection efficacy was also similar in both groups. HPLC exhibited good diagnostic performance with H3N2 microneedle vaccines and good agreement with ELISA. The H3N2 microneedle vaccines elicited a cross-protective immune response against the H3N2 antigenic variants. Here, we propose the use of HPLC for a more rapid approach in preparing H3N2 microneedle vaccines targeting H3N2 virus variants.

New framework for recombination and adaptive evolution analysis with application to the novel coronavirus SARS-CoV-2

The 2019 novel coronavirus (SARS-CoV-2) has spread rapidly worldwide and was declared a pandemic by the WHO in March 2020. The evolution of SARS-CoV-2, either in its natural reservoir or in the human population, is still unclear, but this knowledge is essential for effective prevention and control. We propose a new framework to systematically identify recombination events, excluding those due to noise and convergent evolution. We found that several recombination events occurred for SARS-CoV-2 before its transfer to humans, including a more recent recombination event in the receptor-binding domain. We also constructed a probabilistic mutation network to explore the diversity and evolution of SARS-CoV-2 after human infection. Clustering results show that the novel coronavirus has diverged into several clusters that cocirculate over time in various regions and that several mutations across the genome are fixed during transmission throughout the human population, including D614G in the S gene and two accompanied mutations in ORF1ab. Together, these findings suggest that SARS-CoV-2 experienced a complicated evolution process in the natural environment and point to its continuous adaptation to humans. The new framework proposed in this study can help our understanding of and response to other emerging pathogens.