The emergence of new antigen branches of H9N2 avian influenza virus in China due to antigenic drift on hemagglutinin through antibody escape at immunodominant sites.

Vaccination is a crucial prevention and control measure against H9N2 avian influenza viruses (AIVs) that threaten poultry production and public health. However, H9N2 AIVs in China undergo continuous antigenic drift of hemagglutinin (HA) under antibody pressure, leading to the emergence of immune escape variants. In this study, we investigated the molecular basis of the current widespread antigenic drift of H9N2 AIVs. Specifically, the most prevalent h9.4.2.5-lineage in China was divided into two antigenic branches based on monoclonal antibody (mAb) hemagglutination inhibition (HI) profiling analysis, and 12 antibody escape residues were identified as molecular markers of these two branches. The 12 escape residues were mapped to antigenic sites A, B, and E (H3 was used as the reference). Among these, eight residues primarily increased 3`SLN preference and contributed to antigenicity drift, and four of the eight residues at sites A and B were positively selected. Moreover, the analysis of H9N2 strains over time and space has revealed the emergence of a new antigenic branch in China since 2015, which has replaced the previous branch. However, the old antigenic branch recirculated to several regions after 2018. Collectively, this study provides a theoretical basis for understanding the molecular mechanisms of antigenic drift and for developing vaccine candidates that contest with the current antigenicity of H9N2 AIVs.

Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.

Swine coronaviruses (CoVs) have been found to cause infection in humans, suggesting that Suiformes might be potential intermediate hosts in CoV transmission from their natural hosts to humans. The present study aims to establish convolutional neural network (CNN) models to predict host adaptation of swine CoVs. Decomposing of each ORF1ab and Spike sequence was performed with dinucleotide composition representation (DCR) and other traits. The relationship between CoVs from different adaptive hosts was analyzed by unsupervised learning, and CNN models based on DCR of ORF1ab and Spike were built to predict the host adaptation of swine CoVs. The rationality of the models was verified with phylogenetic analysis. Unsupervised learning showed that there is a multiple host adaptation of different swine CoVs. According to the adaptation prediction of CNN models, swine acute diarrhea syndrome CoV (SADS-CoV) and porcine epidemic diarrhea virus (PEDV) are adapted to Chiroptera, swine transmissible gastroenteritis virus (TGEV) is adapted to Carnivora, porcine hemagglutinating encephalomyelitis (PHEV) might be adapted to Primate, Rodent, and Lagomorpha, and porcine deltacoronavirus (PDCoV) might be adapted to Chiroptera, Artiodactyla, and Carnivora. In summary, the DCR trait has been confirmed to be representative for the CoV genome, and the DCR-based deep learning model works well to assess the adaptation of swine CoVs to other mammals. Suiformes might be intermediate hosts for human CoVs and other mammalian CoVs. The present study provides a novel approach to assess the risk of adaptation and transmission to humans and other mammals of swine CoVs.