Unveiling CRESS DNA Virus Diversity in Oysters by Virome

Abstract

Oysters that filter feed can accumulate numerous pathogens, including viruses, which can serve as a valuable viral repository. As oyster farming becomes more prevalent, concerns are mounting about diseases that can harm both cultivated and wild oysters. Unfortunately, there is a lack of research on the viruses and other factors that can cause illness in shellfish. This means that it is harder to find ways to prevent these diseases and protect the oysters. This is part of a previously started project, the Dataset of Oyster Virome, in which we further study 30 almost complete genomes of oyster-associated CRESS DNA viruses. The replication-associated proteins and capsid proteins found in CRESS DNA viruses display varying evolutionary rates and frequently undergo recombination. Additionally, some CRESS DNA viruses have the capability for cross-species transmission. A plethora of unclassified CRESS DNA viruses are detectable in transcriptome libraries, exhibiting higher levels of transcriptional activity than those found in metagenome libraries. The study significantly enhances our understanding of the diversity of oyster-associated CRESS DNA viruses, emphasizing the widespread presence of CRESS DNA viruses in the natural environment and the substantial portion of CRESS DNA viruses that remain unidentified. This study’s findings provide a basis for further research on the biological and ecological roles of viruses in oysters and their environment.

Identification and classification of the genomes of novel microviruses in poultry slaughterhouse

“…A total of 577 sequences were aligned with each other using DIAMOND ( Buchfink et al, 2015 ) (version 0.9.14.115, options: --evalue = 0.00001) to build a matrix of sequence similarities. Based on the Score value output by DIAMOND alignment, Gephi ( Bastian et al, 2009 ) (version 0.9.7, Layout selected as Fruchterman Reingold, Rescale weight = True, with other parameters set to default) was used to construct a clustering network graph [appropriate score values were selected based on clustering effect to form clear clusters as demonstrated in previous studies ( Jiang et al, 2023 ; Zhu et al, 2024 )]. The nodes were colored on different sequence sources, hosts, or virus classification results.…”