Biology subjects

Bhattacharjee, S.

Publications and source records attributed to Bhattacharjee, S..

58 records · Page 4Linked to original sources

bioRxiv2019

Individual-Specific fMRI-Subspaces Improve Functional Connectivity Prediction of Behavior

There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.\n\nHighlightsO_LIWe decomposed rs-fMRI signals into common subspace & individual-specific subspace\nC_LIO_LICommon subspace is shared across all Human Connectome Project (HCP) participants\nC_LIO_LICommon subspaces are different across runs, suggesting state-specific effects\nC_LIO_LIIndividual-specific subspaces are unique to individuals\nC_LIO_LIRemoval of common subspace signals improve behavioral prediction by 11.7%\nC_LI

neuroscience↗

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↗