Fish gut microbiome confers various effects to the host fish; this includes overall size, metabolism, feeding behaviour and immune response in the fish. The emergence of antimicrobial-resistant (AMR) bacteria and hard to cure fish diseases warrant the possible utilization of gut microbes that exhibits a positive effect on the fish and thus lead to the usage of these microbes as probiotics. The widespread and systematic use of antibiotics has led to severe biological and ecological problems, especially the development of antibiotic resistance that affects the gut microbiota of aquatic organisms. Probiotics are proposed as an effective and environmentally friendly alternative to antibiotics, known as beneficial microbes. At the same time, prebiotics are considered beneficial to the host's health and growth by decreasing the prevalence of intestinal pathogens and/or changing the development of bacterial metabolites related to health. Uprise of sequencing technology and the development of intricate bioinformatics tools has provided a way to study these gut microbes through metagenomic analysis. From various metagenomic studies, ample of information was obtained; such information includes the effect of the gut microbiome on the physiology of fish, gut microbe composition of different fish, factors affecting the gut microbial composition of the fish and the immunological effect of gut microbes in fish; such this information related to the fish gut microbiome, their function and their importance in aquaculture is discussed in this review.

The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, antimicrobial resistance (AMR) genes, and virulence genes. However, currently, no single-step tools are available for this purpose. Hence, we have developed a tool capable of checking all three conditions required for the selection of suitable therapeutic phage candidates. This tool consists of an ensemble of machine-learning-based predictors for determining the presence of temperate markers (integrase, Cro/CI repressor, immunity repressor, DNA partitioning protein A, and antirepressor) along with the integration of the ABRicate tool to determine the presence of antibiotic resistance genes and virulence genes. Using the biological features of the temperate markers, we were able to predict the presence of the temperate markers with high MCC scores (>0.70), corresponding to the lifestyle of the phages with an accuracy of 96.5%. Additionally, the screening of 183 lytic phage genomes revealed that six phages were found to contain AMR or virulence genes, showing that not all lytic phages are suitable to be used for therapy. The suite of predictors, PhageLeads, along with the integrated ABRicate tool, can be accessed online for in silico selection of suitable therapeutic phage candidates from single genome or metagenomic contigs.

We characterized the complete genome of a lytic Dickeya chrysanthemi bacteriophage, DchS19, which was isolated from a soil sample in Sungai Petani, Kedah, Malaysia. The phage, from the Autographviridae family, has a 39,149-bp double-stranded DNA genome containing 49 protein-coding genes and shares 94.65% average nucleotide identity with Erwinia phage pEp_SNUABM_12.