Biology subjects

Pal, S.

Publications and source records attributed to Pal, S..

61 records · Page 4Linked to original sources

bioRxiv2020

A mathematical model of heat shock response to study the competition between protein folding and aggregation

Proteins, under conditions of cellular stress, typically tend to unfold and form lethal aggregates leading to neurological diseases like Parkinsons and Alzheimers. A clear understanding of the conditions that favor dis-aggregation and restore the cell to its healthy state after they have been stressed is therefore important in dealing with these diseases. The heat shock response (HSR) mechanism is a signaling network that deals with these undue protein aggregates and aids in the maintenance of homeostasis within a cell. This framework, on its own, is a mathematically well studied mechanism. However, not much is known about how the various intermediate mis-folded protein states of the aggregation process interact with some of the key components of the HSR pathway such as the Heat Shock Protein (HSP), the Heat Shock transcription Factor (HSF) and the HSP-HSF complex. In this article, using kinetic parameters from the literature, we propose and analyze two mathematical models for HSR that also include explicit reactions for the formation of protein aggregates. Deterministic analysis and stochastic simulations of these models show that the folded proteins and the misfolded aggregates exhibit bistability in a certain region of the parameter space. Further, the models also highlight the role of HSF and the HSF-HSP complex in reducing the time lag of response to stress and in re-folding all the mis-folded proteins back to their native state. These models therefore call attention to the significance of studying related pathways such as the HSR and the protein aggregation and re-folding process in conjunction with each other.

systems biology↗

bioRxiv2020

Modeling gene expression evolution with EvoGeneX uncovers differences in evolution of species, organs and sexes

While DNA sequence evolution has been well studied, the expression of genes is also subject to evolution, yet the evolution of gene expression is currently not well understood. Recently, new tissue/organ-specific gene expression datasets spanning several organisms across the tree of life, have become available, providing the opportunity to study gene expression evolution in more detail. While a theoretical model to study the evolution of continuous traits exists, in practice computational methods often cannot confidently distinguish between alternative evolutionary scenarios. This lack of power has been attributed to modest numbers of species considered in these studies. We hypothesised that biological replicates can be used to increase predictive power of these models. With this in mind, we introduce EvoGeneX, a computationally efficient method to uncover the mode of gene expression evolution based on the Ornstein-Uhlenbeck process. Importantly, in addition to modelling expression variations between species, EvoGeneX models within-species variation. Furthermore, to facilitate comparative analysis of gene expression evolution, we introduce a formal approach, based on Michaelis-Menten equation, to measure the dynamics of evolutionary divergence of a group of genes in terms of groups asymptotic divergence level and rate. Finally, we used these tools to preform the first analysis the evolution of gene expression across different body parts, species, and sexes of the Drosophila genus. Our analysis revealed that neutral expression evolution can be confidently rejected in favor of purifying selection in nearly half of the genes. In addition, we quantified differences in the evolutionary dynamics of male and female gonads and uncovered interesting examples of adaptive gene expression evolution.

evolutionary biology↗

bioRxiv2019

Physico-biochemical and molecular responses of Acacia auriculiformis to salinity stress

Salinity limits the growth and yield of many crops across the globe and is considered as major threat to agriculture. Acacia auriculiformis, an important salt tolerant crop, is growing abundantly in the salt-affected mangrove areas of Sunderban, West Bengal, India. In the present study, we have reported the physiological, molecular and antioxidant response of this crop to a gradient of salt treatments ranging between 0 and 800 mM NaCl. As a stress response, the antioxidant enzymes viz. superoxide dismutase (SOD), catalase (CAT) and guaiacol peroxidase (GPX) were highly activated at 200, 400 and 800 mM NaCl respectively. Antioxidant metabolites such as phenols and thiols elevated with increasing salt treatments thus augmenting antioxidant activity with significant positive relationship with phenol content. Similarly, phenylalanine ammonia lyase (PAL) activity was up-regulated in a dose dependent manner with significant relationship with phenol content. This study also reported the phenolic profile for the first time in A. auriculiformis with the abundance of flavonoids. In addition, transcriptional up-regulation of Na+/H+ antiporter gene (NHX1) and the development of robust vascular tissues was noticed at 400 mM NaCl stress compared to control, while further stress at 800 mM NaCl induced poor vascular tissue growth but with higher PAL activity and consequent higher phenol content. Based on this observation, a model for salt tolerance mechanism of A. auriculiformis has been proposed.

biochemistry↗

bioRxiv2019

Bioinformatics Pipeline using JUDI: Just Do It

Large-scale data analysis in Bioinformatics requires executing several software in a pipelined fashion. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g., Snakemake and Nextflow, have been developed to ensure in case of multiple invocation of the pipeline, only the bare minimum stages that are affected by the changes across invocations get executed. However, when the pipeline needs to be executed with different settings of parameters, e.g., thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution of the pipeline. We developed JUDI on top of a Python based WMS, DoIt, for a systematic handling of pipeline parameter settings based on the principles of DBMS that simplifies plug-and-play scripting. The effectiveness of JUDI is demonstrated in a pipeline for analyzing large scale HT-SELEX data for transcription factor and DNA binding where JUDI reduces scripting by a factor of five.\n\nAvailabilityhttps://github.com/ncbi/JUDI

bioinformatics↗

bioRxiv2018

Novel analysis of HT-SELEX data elucidates the role of DNA shape in transcription factor binding

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the question if in the absence of any sequence similarity to the binding motif, DNA shape can still increase probability of binding was yet to be addressed.\n\nTo address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allowed us to detect evidence for the role of DNA shape features in TF binding.\n\nOur approach revealed that, even in the absence of the sequence motif, TFs have propensity to weakly bind to DNA molecules enriched in specific shape features. Surprisingly, we also found that some properties of DNA shape contribute to promiscuous binding of all tested TF families. Strikingly, such promiscuously bound shapes correspond to the most frequent shape formed by the DNA. We propose that this promiscuous binding facilitates diffusing of TFs along the DNA molecule before it is locked in its binding site.

bioinformatics↗

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↗

bioRxiv2017

Stability of Commonly Used Haematological Parameters in Samples Stored at 33°C, 22°C and 4°C

AimThis study aimed to investigate the analytical bias and imprecision in haematological parameters induced by storage at 4{degrees}C, 22{degrees}C and 33 {degrees}C.\n\nMethodsThree K2EDTA anticoagulated vials of blood were collected from each of twenty blood donors and stored at 4{degrees}C, 22{degrees}C and 33{degrees}C respectively. Readings from each vial were taken at 0, 4, 6, 12, 24, 48 and 72 hours after collection on the Sysmex XP-100 analyser. The mean and median shift of the parameters relative to the baseline and the coefficient of variation for each time-temperature combination were calculated. The shift was compared to the maximum acceptable bias.\n\nResultsHaemoglobin, Red Blood Cell Count, White Blood Cell Count, Mean Corpuscular Haemoglobin were stable for at least twenty four hours at 33{degrees}C. Haematocrit, Mean Corpuscular Volume and Platelet Counts were stable for less than four hours at 33{degrees}C. All the above parameters were stable for longer at 22{degrees}C and 4{degrees}C. The three-part differential count showed instability within four hours at 33 {degrees}C.\n\nConclusionsStrict pre-analytical control is needed at 33{degrees}C or above due to the marked instability of most parameters. However, Haemoglobin, Red Blood Cell Count, White Blood Cell Count and Mean Corpuscular Haemoglobin remain relatively stable even at 33{degrees}C.\n\nKey MessageHaematology samples exposed to temperatures of 33{degrees}C or above show rapid change in MCV, HCT,MCHC, RDW, Platelet Counts and three-part differential counts. Settings where prolonged exposure to these temperatures cannot be avoided should rely on the more stable parameters of Haemoglobin, RBC Counts, MCH and WBC Counts.

pathology↗