The array of microorganisms residing at various body-sites of human, known as the human microbiome, is one of the key determinants of human health and disease. A proper understanding of the deviation from healthy microbiome composition and functionality in various disease states is an essential first step to explore the role and mechanism of microbiome in human health and diseases. The overall goal of our microbiome and metagenomics research is systems-level characterization of metabolic interactions within microbial communities in both healthy and different disease states by high-throughput sequencing techniques and state-of-art software development. This understanding could play a pivotal role in the development of beneficial microbiome services through putative identification of set of microbes and their products and is of utmost importance in generating dietary-based intervention efforts, safe drug development, probiotics, prebiotics and postbiotics in disease prevention. Some of the planned projects in this area are as follows:
Most bacterial species, pathogens or commensals, are clonal in nature, represented by the strains with distinct phenotypes circulating as a limited number of genetically related (i.e. clonal) lineages. The stability of such (adapted) clonal lineages has been demonstrated to be strong enough, both temporally and spatially, to decipher consistent clonal association with important traits like specific virulence potentials or antibiotic resistance profiles.
To pursue this goal, we will have a unique ‘variome’ approach for each pathogenic bacterial species to detect potential candidate genes across the entire genome and mutations therein as high-resolution clonal markers to associate specific virulence and/or (multi-)drug resistance properties of interest. Variome as a term refers to the sum of genetic variations in a species population. Our population genomics approach will map all the variations at pan-genomic population level and perform genome-wide analysis to study (patho)adaptive co-evolutionary network of genes/proteins and their association to specific host-compartments, geographical locations, epidemic/endemic outbreaks, disease and/or antibiotic resistant phenotypes in hosts. The long-term goal is to develop –
The present era is experiencing exponential growth of bacterial genome sequences in public databases. However, even with the mammoth data available, to identify the functional changes in a protein, e.g. those that are physiologically important or virulence-associated, biomedical researchers still primarily focus on laboratory-induced mutations, mostly limited to knockouts. Three major factors seriously limit the usefulness and applicability of bacterial genomic ‘bigdata’ to the experimental research community: (i) high level of inaccuracy, non-uniformity and redundancy in genome annotations; (ii) lack of high-performance and high-throughput predictive tools to associate naturally occurring variations with virulence or (multi-)drug resistance functions; (iii) absence of an automated systematic pipeline for experimental researchers to derive potential (patho)adaptive role of a variation in the sequenced draft genome with reference to a comprehensive database.
We will target the group of enterobacterial species incorporating a vast array of important pathogens, e.g. Escherichia coli, Shigella spp., Klebsiella pneumoniae, Salmonella enterica, Serratia marcescens, to name a few. Based on the metadata repository of species-specific and enterobacteria-specific identifiers for each and every locus, the approach will be in a composite context of sequence, diversity and functional variations in other closely related species across the enterobacterial group. The discovery is aimed at detecting, storing and visualizing clinically important bacterial strategies of changing a protein toward better fitness via natural evolution as a guide for experimental analysis.