Combine thermal processing with polyvalent phage LPEK22 to prevent the Escherichia coli and Salmonella enterica contamination in food

Characterization, antibacterial, and cytotoxic activities of silver nanoparticles using the whole biofilm layer as a macromolecule in biosynthesis

Recently, multi-drug resistant (MDR) bacteria are responsible for a large number of infectious diseases that can be life-threatening. Globally, new approaches are targeted to solve this essential issue. This study aims to discover novel antibiotic alternatives by using the whole components of the biofilm layer as a macromolecule to synthesize silver nanoparticles (AgNPs) as a promising agent against MDR. In particular, the biosynthesized biofilm-AgNPs were characterized using UV-Vis spectroscopy, electron microscopes, Energy Dispersive X-ray (EDX), zeta sizer and potential while their effect on bacterial strains and normal cell lines was identified. Accordingly, biofilm-AgNPs have a lavender-colored solution, spherical shape, with a size range of 20-60 nm. Notably, they have inhibitory effects when used on various bacterial strains with concentrations ranging between 12.5 and 25 µg/mL. In addition, they have an effective synergistic effect when combined with phage ZCSE9 to inhibit and kill Salmonella enterica with a concentration of 3.1 µg/mL. In conclusion, this work presents a novel biosynthesis preparation of AgNPs using biofilm for antibacterial purposes to reduce the possible toxicity by reducing the MICs using phage ZCSE9.

Prokaryotic virus host prediction with graph contrastive augmentaion

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

Isolation, characterization of Enterococcus phages and their application in control of E. faecalis in milk

AIMS

Isolation and characterization of Enterococcus phages and application of phage cocktail to control E. faecalis in milk.

METHODS AND RESULTS

For phage isolations, double layer agar method was used. Host range of the phages were determined by the spot test. Twelve phages with varying host ranges were isolated. Phages PEF1, PEF7b, and PEF9 with different host ranges and lytic activities were selected for phage cocktails. Compared to two-phages cocktails tested, the cocktail containing all the three phages displayed stronger antibacterial and biofilm removal activities. The cocktail treatment reduced viable E. faecalis in biofilm by 6 log within 6 h at both 30°C and 4°C. In milk, the cocktail gradually reduced the viable count of E. faecalis and the count reached below the lower limit of detection at 48 h at 4°C.

CONCLUSION

The strong bactericidal and biofilm removal activities of the phage cocktail suggest the potential of this cocktail as a natural biocontrol agent for combating E. faecalis in milk.