A New Kayfunavirus-like Escherichia Phage vB_EcoP-Ro45lw with Antimicrobial Potential of Shiga Toxin-Producing Escherichia coli O45 Strain

DeepPL: A deep-learning-based tool for the prediction of bacteriophage lifecycle

Bacteriophages (phages) are viruses that infect bacteria and can be classified into two different lifecycles. Virulent phages (or lytic phages) have a lytic cycle that can lyse the bacteria host after their infection. Temperate phages (or lysogenic phages) can integrate their phage genomes into bacterial chromosomes and replicate with bacterial hosts via the lysogenic cycle. Identifying phage lifecycles is a crucial step in developing suitable applications for phages. Compared to the complicated traditional biological experiments, several tools have been designed for predicting phage lifecycle using different algorithms, such as random forest (RF), linear support-vector classifier (SVC), and convolutional neural network (CNN). In this study, we developed a natural language processing (NLP)-based tool-DeepPL-for predicting phage lifecycles via nucleotide sequences. The test results showed that our DeepPL had an accuracy of 94.65% with a sensitivity of 92.24% and a specificity of 95.91%. Moreover, DeepPL had 100% accuracy in lifecycle prediction on the phages we isolated and biologically verified previously in the lab. Additionally, a mock phage community metagenomic dataset was used to test the potential usage of DeepPL in viral metagenomic research. DeepPL displayed a 100% accuracy for individual phage complete genomes and high accuracies ranging from 71.14% to 100% on phage contigs produced by various next-generation sequencing technologies. Overall, our study indicates that DeepPL has a reliable performance on phage lifecycle prediction using the most fundamental nucleotide sequences and can be applied to future phage and metagenomic research.

Phage Tail Fiber Protein as a Specific Probe for Recognition of Shiga Toxin-Producing Escherichia coli O91, O103, and O111

The ability to quickly identify specific serotypes of Shiga toxin-producing Escherichia coli (STEC) could facilitate the monitoring and control of STEC pathogens. In this study, we identified the receptors and receptor-binding proteins (RBPs) of three novel phages (pO91, pO103, and pO111) isolated from hospital wastewater. Recombinant versions of these RBPs (pO91-ORF43, pO103-ORF42, and pO111-ORF8) fused to a fluorescent reporter protein were then constructed. Both fluorescence microscopy and transmission electron microscopy showed that all three recombinant RBPs were bound to the bacterial surface. Indirect enzyme-linked immunosorbent assay was used to verify that each recombinant RBP bound specifically to E. coli O91, O103, or O111, but not to any of the 83 strains of E. coli with different O-antigens, nor to 10 other bacterial species that were tested. The recombinant RBPs adsorbed to their respective host bacteria within 10 min of incubation. The minimum concentration of bacteria required for detection by the recombinant RBPs was 33 colony-forming units (CFU)/mL (range: 3.3 × 10 to 3.3 × 108 CFU/mL). Furthermore, each recombinant RBP was also able to detect bacteria in lettuce, chicken breast meat, and infected mice, indicating that their usage will facilitate the detection of STEC and may help to reduce the spread of STEC-related infections and diseases.