Metavirome Analysis Reveals a High Prevalence of Porcine Hemagglutination Encephalomyelitis Virus in Clinically Healthy Pigs in China.

Six swine coronaviruses (SCoVs), which include porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), porcine hemagglutination encephalomyelitis virus (PHEV), porcine respiratory coronavirus (PRCV), swine acute diarrhea syndrome coronavirus (SADS-CoV), and porcine delta coronavirus (PDCoV), have been reported as infecting and causing serious diseases in pigs. To investigate the genetic diversity and spatial distribution of SCoVs in clinically healthy pigs in China, we collected 6400 nasal swabs and 1245 serum samples from clinically healthy pigs at slaughterhouses in 13 provinces in 2017 and pooled them into 17 libraries by type and region for next-generation sequencing (NGS) and metavirome analyses. In total, we identified five species of SCoVs, including PEDV, PDCoV, PHEV, PRCV, and TGEV. Strikingly, PHEV was detected from all the samples in high abundance and its genome sequences accounted for 75.28% of all coronaviruses, while those belonging to TGEV (including PRCV), PEDV, and PDCoV were 20.4%, 2.66%, and 2.37%, respectively. The phylogenetic analysis showed that two lineages of PHEV have been circulating in pig populations in China. We also recognized two PRCVs which lack 672 nucleotides at the N-terminus of the S gene compared with that of TGEV. Together, we disclose preliminarily the genetic diversities of SCoVs in clinically healthy pigs in China and provide new insights into two SCoVs, PHEV and PRCV, that have been somewhat overlooked in previous studies in China.

Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning.

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV, and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome, including previously unappreciated features in spike, RdRp, and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural, and immunologic features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.