Biology subjects

Bhattacharjee, S.

Publications and source records attributed to Bhattacharjee, S..

4 recordsLinked to original sources

bioRxiv2026

Understanding the physiological alterations of Vibrio cholerae upon exposure to L-ascorbic acid

The scourge of cholera remains a major global public health threat. It affects up to 4 million people worldwide and causes tens of thousands of deaths each year. The disease is experiencing a concerning resurgence in many parts of Africa, the Middle East, and Asia. To effectively tackle cholera and circumvent rising antimicrobial resistance, targeted biological and preventive approaches, complementing traditional rehydration, are urgently needed. In this regard, our group has demonstrated the efficacy of L-ascorbic acid in controlling the growth and pathogenesis of Vibrio cholerae in vitro. The present work further provides a mechanistic elucidation of the L-ascorbic acid-mediated physiological changes in V. cholerae and also bolsters such a non-antibiotic approach to control cholera.

microbiology↗

bioRxiv2018

A Visualization Tool to Evaluate Pairwise Protein Structure Alignment Algorithms

The alignment of two protein structures is a fundamental problem in structural bioinformatics. In this paper, we propose a novel approach to measure the effectiveness of a sample of three such algorithms, DALI, TM-align and EDAlignsse. The underlying premise of our approach is that structural proximity should translate into spatial proximity.

bioinformatics↗

bioRxiv2018

Vitexin alters Staphylococcus aureus surface hydrophobicity to interfere with biofilm formation.

Bacterial surface hydrophobicity is one of the determinant biophysical parameters of bacterial aggregation for being networked to form biofilm. Phytoconstituents like vitexin have long been in use for their antibacterial effect. The present work is aimed to characterise the effect of vitexin on S. aureus surface hydrophobicity and corresponding aggregation to form biofilm. We have found that vitexin shows minimum inhibitory concentration at 252 g/ml against S. aureus. Vitexin reduces cell surface hydrophobicity and membrane permeability at sub-MIC dose of 126 g/ml. The in silico binding analysis showed higher binding affinity of vitexin with surface proteins of S. aureus. Down regulation of dltA, icaAB and reduction in membrane potential under sub-MIC dose of vitexin, explains reduced S. aureus surface hydrophobicity. Vitexin has substantially reduced the intracellular adhesion of planktonic cells to form biofilm through interference of EPS formation, motility and subsequent execution of virulence. This was supported by the observation that vitexin down regulates the expression of icaAB and agrAC genes of S. aureus. In addition, vitexin also found to potentiate antibiofilm activity of sub-MIC dose of gentamicin and azithromycin. Furthermore, CFU count, histological examination of mouse tissue and immunomodulatory study justifies the in vivo protective effect of vitexin from S. aureus biofilm associated infection. Finally it can be inferred that, vitexin has the ability to modulate S. aureus cell surface hydrophobicity which can further interfere biofilm formation of the bacteria.\n\nImportanceThere has been substantial information known about role of bacterial surface hydrophobicity during attachment of single planktonic bacterial cells to any surface and the subsequent development of mature biofilm. This study presents the effect of flavone phytoconstituent vitexin on modulation of cell surface hydrophobicity in reducing formation of biofilm. Our findings also highlight the ability of vitexin in reducing in vivo S. aureus biofilm which will eventually outcompete the corresponding in vitro antibiofilm effect. Synergistic effect of vitexin on azithromycin and gentamicin point to a regime where development of drug tolerance may be addressed. Our findings explore one probable way of overcoming drug tolerance through application of vitexin in addressing the issue of S. aureus biofilm through modulation of cell surface hydrophobicity.

microbiology↗

bioRxiv2017

A Regression-based Framework for Scalable Pathway-guided Search in Genome-wide Association Studies.

Traditional unbiased genome-wide association studies (GWAS) have successfully identified thousands of loci associated with various complex diseases but there is evidence to suggest that many variants were missed at stringent genome-wide thresholds. Fortunately, there is a rapidly increasing amount of prior knowledge in publicly available genomic datasets and biological databases that can be harnessed to enhance the power of discovering SNPs/Genes from existing or new GWAS datasets. For most diseases, many of the identified loci tend to cluster into a few specific biological pathways/networks. From the point of view of disease etiology, such clustering is generally to be expected. This phenomenon can be exploited to conduct a more powerful genome-wide scan that is tailored to identify loci that are interconnected in pathways. We propose a scalable regression-based analytical framework to enable such a pathway-guided GWAS and demonstrate that it provides significant gains in power to detect disease associated SNPs. Our method requires two inputs, namely a) genome-wide summary level data (e.g., SNP p-values) and b) a grouping of genes into biologically meaningful categories (e.g., a database of pathways). It automatically adjusts the input p-values by incorporating the knowledge derived adaptively from the data and the pathways specified. The method involves a regularized logistic regression analysis to derive priors of each SNP and then re-weights the p-values of SNPs so as to maximize overall power of making discoveries. It increases the power to discover SNPs co-clustering into some of these pathways, while maintaining the global type-1 error (FWER) at the desired level. We used whole-genome simulations and summary data from real GWA studies of psoriasis, SLE, coronary artery disease and type-2 diabetes to illustrate the power improvement achieved by pathway-guided search. Our pipeline implemented as an R package can flexibly handle large number of prior annotations possibly derived from multiple databases.

genetics↗