Enriching public descriptions of marine phages using the Genomic Standards Consortium MIGS standard

Characterization of a novel phage SPX1 and biological control for biofilm of Shewanella in shrimp and food contact surfaces

Shewanella putrefaciens, commonly found in seafood, forms tenacious biofilms on various surfaces, contributing to spoilage and cross-contamination. Bacteriophages, owing to their potent lytic capabilities, have emerged as novel and safe options for preventing and eliminating contaminants across various foods and food processing environments. In this study, a novel phage SPX1 was isolated, characterized by a high burst size (43.81 ± 3.01 PFU/CFU) and a short latent period (10 min). SPX1 belongs to the Caudoviricetes class, exhibits resistance to chloroform, and sensitivity to ultraviolet. It shows stability over a wide range of temperatures (30-50 °C) and pH levels (3-11). The genome of phage SPX1 consists of 53,428 bp with 49.72 % G + C composition, and lacks tRNAs or virulence factors. Genome analysis revealed the presence of two endolysins, confirming its biofilm-removal capacity. Following the treatment of shrimp surface biofilm with the optimal MOI of 0.001 of phage SPX1 for 5 h, the bacterial count decreased by 1.84 ± 0.1 log10 CFU/cm2 (> 98.5 %). Biofilms on the surfaces of the three common materials used in shrimp processing and transportation also showed varying degrees of reduction: glass (1.98 ± 0.01 log10 CFU/cm2), stainless steel (1.93 ± 0.05 log10 CFU/cm2), and polyethylene (1.38 ± 0.1 log10 CFU/cm2). The study will contribute to phage as a novel and potent biocontrol agent for effectively managing S. putrefaciens and its biofilm, ensuring a reduction in spoilage bacteria contamination during the aquaculture, processing, and transportation of seafood products.

Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks

Viruses of microbes are ubiquitous biological entities that reprogram their hosts' metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only "who is there?" we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding "who do they infect?" Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, 'Virus Host Range network' (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which meaningful signals of viral adaptation to host were computed from genomic sequences. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is able to infer the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets.