MicroRNAs (miRNAs) are small noncoding RNA molecules that play important roles in various biological processes. Much evidence shows that miRNAs are closely associated with numerous virus infections; however, involvement of cellular miRNAs in influenza A virus (IAV) infection is unclear. Here, we found that expression of miR-203 was up-regulated markedly via two different mechanisms during IAV infection. First, we examined the effects of type I interferon induced by IAV on direct activation of miR-203 expression. Next, we showed that DNA demethylation within the miR-203 promoter region in A549 cells induced its up-regulation, and that expression of DNA methyltransferase 1 was down-regulated following H5N1 virus infection. Ectopic expression of miR-203 in turn inhibited H5N1 virus replication by targeting down-regulator of transcription 1 (DR1), which was identified as a novel target of miR-203. Silencing DR1 in miR-203 knockout cells using a specific siRNA inhibited replication of the H5N1 virus, an effect similar to that of miR-203. In summary, the data show that host cell expression of miR-203 is up-regulated upon IAV infection, which increases antiviral responses by suppressing a novel target gene, DR1. Thus, we have identified a novel mechanism underlying the relationship between miR-203 and IAV infection.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype–genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes—receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp)—of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS-CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.

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.

Influenza A virus (IAV) is a leading cause of human respiratory infections and poses a major public health concern. IAV replication can affect the expression of DNA methyltransferases (DNMTs), and the subsequent changes in DNA methylation regulate gene expression and may lead to abnormal gene transcription and translation, yet the underlying mechanisms of virus-induced epigenetic changes from DNA methylation and its role in virus–host interactions remain elusive. Here in this paper, we showed that DNMT1 expression could be suppressed following the inhibition of miR-142-5p or the PI3K/AKT signaling pathway during IAV infection, resulting in demethylation of the promotor region of the 2′-5′-oligoadenylate synthetase-like (OASL) protein and promotion of its expression in A549 cells. OASL expression enhanced RIG-I-mediated interferon induction and then suppressed replication of IAV. Our study elucidated an innate immunity mechanism by which up-regulation of OASL contributes to host antiviral responses via epigenetic modifications in IAV infection, which could provide important insights into the understanding of viral pathogenesis and host antiviral defense.

The COVID-19 pandemic has frequently produced more highly transmissible SARS-CoV-2 variants, such as Omicron, which has produced sublineages. It is a challenge to tell apart high-risk Omicron sublineages and other lineages of SARS-CoV-2 variants. We aimed to build a fine-grained deep learning (DL) model to assess SARS-CoV-2 transmissibility, updating our former coarse-grained model, with the training/validating data of early-stage SARS-CoV-2 variants and based on sequential Spike samples. Sequential amino acid (AA) frequency was decomposed into serially and slidingly windowed fragments in Spike. Unsupervised machine learning approaches were performed to observe the distribution in sequential AA frequency and then a supervised Convolutional Neural Network (CNN) was built with three adaptation labels to predict the human adaptation of Omicron variants in sublineages. Results indicated clear inter-lineage separation and intra-lineage clustering for SARS-CoV-2 variants in the decomposed sequential AAs. Accurate classification by the predictor was validated for the variants with different adaptations. Higher adaptation for the BA.2 sublineage and middle-level adaptation for the BA.1/BA.1.1 sublineages were predicted for Omicron variants. Summarily, the Omicron BA.2 sublineage is more adaptive than BA.1/BA.1.1 and has spread more rapidly, particularly in Europe. The fine-grained adaptation DL model works well for the timely assessment of the transmissibility of SARS-CoV-2 variants, facilitating the control of emerging SARS-CoV-2 variants.

Influenza A viruses (IAV) modulate host antiviral responses to promote growth and pathogenicity. Here, we examined the multifunctional IAV nonstructural protein 1 (NS1) of influenza A virus to better understand factors that contribute to viral replication efficiency or pathogenicity. In 2009, a pandemic H1N1 IAV (A/California/07/2009 pH1N1) emerged in the human population from swine. Seasonal variants of this virus are still circulating in humans. Here, we compared the sequence of a seasonal variant of this H1N1 influenza virus (A/Urumqi/XJ49/2018(H1N1), first isolated in 2018) with the pandemic strain A/California/07/2009. The 2018 virus harbored amino acid mutations (I123V and N205S) in important functional sites; however, 108R and 189G were highly conserved between A/California/07/2009 and the 2018 variant. To better understand interactions between influenza viruses and the human innate immune system, we generated and rescued seasonal 2009 H1N1 IAV mutants expressing an NS1 protein harboring a dual mutation (R108K/G189D) at these conserved residues and then analyzed its biological characteristics. We found that the mutated NS1 protein exhibited systematic and selective inhibition of cytokine responses via a mechanism that may not involve binding to cleavage and polyadenylation specificity factor 30 (CPSF30). These results highlight the complexity underlying host–influenza NS1 protein interactions.