Molecular epidemiology and genetic diversity of canine coronavirus from domestic dogs in Chengdu, China from 2020 to 2021 using a multiplex RT-PCR.

Recent reports on identification of canine coronavirus (CCoV) in humans have emphasized the urgency to strengthen surveillance of animal CoVs. The fact that recombinations between CCoV with feline, porcine CoVs brought about new types of CoVs indicated that more attention should be paid to domestic animals like dogs, cats and pigs, and the CoVs they carried. However, there are about ten kinds of CoVs that infect above animals, and thus representative CoVs with zoonotic potentials were considered in this study. Multiplex RT-PCR against CCoV, Feline coronavirus (FCoV), porcine deltacoronavirus and porcine acute diarrhea syndrome coronavirus was developed to investigate the prevalence of CoVs from domestic dogs in Chengdu, Southwest China. Samples from a total of 117 dogs were collected from a veterinary hospital, and only CCoV (34.2%, 40/117) was detected. Therefore, this study focused on CCoV and its characteristics of S, E, M, N and ORF3abc genes. Compared with CoVs that are capable of infecting humans, CCoV strains showed highest nucleotide identity with the novel canine-feline recombinant detected from humans (CCoV-Hupn-2018). Phylogenetic analysis based on S gene, CCoV strains were not only clustered with CCoV-II strains, but also closely related to FCoV-II strains ZJU1617 and SMU-CD59/2018. As for assembled ORF3abc, E, M, N sequences, CCoV strains had the closest relationship with CCoV-II (B203_GZ_2019, B135_JS_2018 and JS2103). What's more, specific amino acid variations were found, especially in S and N proteins, and some mutations were consistent with FCoV, TGEV strains. Altogether, this study provided a novel insight into the identification, diversification and evolution of CoVs from domestic dogs. It is of top priority to recognize zoonotic potential of CoVs, and continued comprehensive surveillance will help better understand the emergence, spreading, and ecology of animal CoVs.

Determinative Role of Non-structural Protein Genes in Genus Identification of Coronaviruses

Interrelationship of coronavirus genus with key fragments of viral genome was investigated. Genes of structural proteins (S-gene of spike protein and N-gene of nucleocapsid protein) and ORF1ab of polyprotein pp1ab, that in infected cell is split into 16 non-structural proteins, were considered as such fragments. Statistical method based on averaged codon distribution in the genes of genus prototype variants was applied in the work to recognize genus of coronavirus. High reliability of this method has been demonstrated earlier in recognizing the 15 species and serotypes of the flaviviruses, such as viruses of yellow fever, dengue fever, various encephalitides, etc. For each key fragment of the coronavirus genome the numerical experiments on identification of genus for the 3242 viral genomes from the GenBank have been done. The highest reliability (98%) was achieved, when ORF1ab frequency codon characteristics were used. It appeared to be that in recognizing genus of Gammacoronavirus, basing on spike protein gene, about half of the 345 genomes of this genus were identified as Betacoronavirus (84.6%) and Alphacoronavirus (15.4%). Analogous phenomenon of significant error appeared in determinating Alphacoronavirus genus, basing on nucleocapsid protein gene, also. However, these significant errors may be a consequence of the coronavirus genome plasticity in the result of homologous recombinations between the viral genomes. © 2022 IEEE.

A high-throughput, online tool for investigating host-pathogen macromolecular interactions buried in scientific literature

This work introduces a low-latency, searchable web tool for biologist and healthcare researchers to quickly explore a large number of host-pathogen interactions (HPI) reported in scientific publication. Our database contains 23,581 generic HPI and 257 COVID-19 related HPI extracted from 32 million PubMed s. The data was automatically collected by running our high-precision biomedical text mining system, which consumes much less effort than manual curation while still provides reliable output. Web URL: philm2web.live © 2021 IEEE.

A Constraint Propagation Approach for Identifying Biological Pathways in COVID-19 Knowledge Graphs

Many mechanisms within biological systems can be modeled as pathways, chains of interactions between proteins, genes, chemicals, and other biological entities. These interactions can be represented using a graph structure, more specifically a knowledge graph representing known or inferred information about the entities in question. In this context, we propose a constraint propagation approach for identifying paths in a graph structure which represent potential biological pathways. We apply this approach to a knowledge graph dataset which was semantically extracted from literature on COVID-19. © 2021 IEEE.