Identification of Potential Probiotics in the Midgut of Mulberry Silkworm, Bombyx mori Through Metagenomic Approach.

Microorganisms play an important role in the growth and development of numerous insect species. The mulberry silkworm, Bombyx mori (Lepidoptera), harbors several bacteria in its midgut aiding the metabolic processes; however, the variability of bacterial spp. present in the midgut and their role(s) in the growth and development of the silkworm are poorly understood. The present work compares the diversity of midgut bacterial communities in silkworms of variable voltinism (Pure Mysore, PM: multivoltine; CSR2: bivoltine and PM × CSR2: crossbreed) through metagenomics. The predominance of Enterococcus (30.30%) followed by Bacillus (16.96%) was observed in PM, whereas Lactobacillus (56.56%) followed by Enterococcus (10.58%) was seen only in CSR2. Interestingly, crossbreed midgut harbored diverse bacterial communities (36.21% Lactobacillus, 25.94% Bacillus, 8.1% Enterococcus, and 18.37% uncultured bacteria). Metagenomic profiles indicate variability in the gut bacterial population in different kinds of silkworms influencing the physiological activities accordingly. The dominant bacteria, particularly lactobacilli, bacilli, and enterococci could be further explored for identifying the potential probiotic consortia based on a literature survey and potential involvement in nutrient absorption, disease/stress tolerance, and improved economic traits.

STRUCTURE PLOT: a program for drawing elegant STRUCTURE bar plots in user friendly interface.

BACKGROUND: Understanding structure of the population is one of the major objective of many genetic studies. The program STRUCTURE is commonly used to infer population structure using multi-locus genotype data. However, a tool with graphical-user interface is currently not available to visualize STRUCTURE bar plots. RESULTS: We introduce STRUCTURE PLOT, a program for drawing STRUCTURE bar plots. The program generates publication ready, aesthetic STRUCTURE bar plots by using individual Q matrix from STRUCTURE or CLUMPP output. The program is very simple to use and includes variety of options like sorting bar by original order or by K, and selection of colors from R colors or RColorBrewer palette. Individual or population labels can be printed below or above the plot in any angle. Size of the graph and label can be defined, and option is provided to save plot in variety of picture formats in user defined resolution. CONCLUSION: The program is implemented as a web application for online users and also as a standalone shiny application. Web application is compatible to majority of leading web browsers and standalone version can be launched using a simple R command. The program can be freely accessed at http://btismysore.in/strplot.

A simple approach for predicting protein-protein interactions.

The availability of an increased number of fully sequenced genomes demands functional interpretation of the genomic information. Despite high throughput experimental techniques and in silico methods of predicting protein-protein interaction (PPI); the interactome of most organisms is far from completion. Thus, predicting the interactome of an organism is one of the major challenges in the post-genomic era. This manuscript describes Support Vector Machine (SVM) based models that have been developed for discriminating interacting and non-interacting pairs of proteins from their amino acid sequence. We have developed SVM models using various types of sequence compositions e.g. amino acid, dipeptide, biochemical property, split amino acid and pseudo amino acid composition. We also developed SVM models using evolutionary information in the form of Position Specific Scoring Matrix (PSSM) composition. We achieved maximum Matthews's correlation coefficient (MCC) of 1.00, 0.52 and 0.74 for Escherichia coli, Saccharomyces cerevisiae, and Helicobacter pylori, using dipeptide based SVM model at default threshold. It was observed that the performance of a prediction model depends on the dataset used for training and testing. In case of E. coli MCC decreased from 1.0 to 0.67 when evaluated on a new dataset. In order to understand PPI in different cellular environment, we developed species-specific and general models. It was observed that species-specific models are more accurate than general models. We conclude that the primary amino acid sequence based descriptors could be used to differentiate interacting from non-interacting protein pairs. Some amino acids tend to be favored in interacting pairs than non-interacting ones. Finally, a web server has been developed for predicting protein-protein interactions.