Biology subjects

Barua, A.

Publications and source records attributed to Barua, A..

24 records · Page 2Linked to original sources

bioRxiv2020

An ancient, conserved gene regulatory network led to the rise of oral venom systems

Oral venom systems evolved multiple times in numerous vertebrates enabling exploitation of unique predatory niches. Yet how and when they evolved remains poorly understood. Up to now, most research on venom evolution has focussed strictly on the toxins. However, using toxins present in modern day animals to trace the origin of the venom system is difficult, since they tend to evolve rapidly, show complex patterns of expression, and were incorporated into the venom arsenal relatively recently. Here we focus on gene regulatory networks associated with the production of toxins in snakes, rather than the toxins themselves. We found that overall venom gland gene expression was surprisingly well conserved when compared to salivary glands of other amniotes. We characterised the meta-venom, a network of approximately 3000 non-secreted housekeeping genes that are strongly co-expressed with the toxins, and are primarily involved in protein folding and modification. Conserved across amniotes, this network was co-opted for venom evolution by exaptation of existing members and the recruitment of new toxin genes. For instance, starting from this common molecular foundation, Heloderma lizards, shrews, and solenodon, evolved venoms in parallel by overexpression of kallikreins, which were common in ancestral saliva and induce vasodilation when injected, causing circulatory shock. Derived venoms, such as those of snakes, incorporated novel toxins, though still rely on hypotension for prey immobilization. These similarities suggest repeated co-option of shared molecular machinery for the evolution of oral venom in mammals and reptiles, blurring the line between truly venomous animals and their ancestors.

evolutionary biology↗

bioRxiv2019

Draft genome sequence of a popular Indian tea genotype TV-1

Camellia assamica L. (O). Kuntze. var. TV-1 from Assam, India is one of the most important tea cultivar of Indian Tea Industry which botanically belongs to Assam type and has distinct morphological as well as chemical constituent in leaf than China type of tea. We here present the first draft genome sequence of TV-1, assembled in 2.93 Gb size comprising of 14,824 scaffolds and covering 97.66% of the predicted genome size (3.0 Gb) as determined by flow cytometry. The N50 value, longest and average contig size for this draft genome was found to be as 538.96 Kb, 3.64 Mb and 197.99 Kb, respectively. There were 495,747 SSRs (Di- to Hexa-mers) in the genome and the repeat contents accounted for 71.87% of the genome. About 94.40% genome completeness was predicted with BUSCO, maximum among the published and reported tea genomes. The clustering of the assembled genome with Hi-C data resulted in 15 clusters ranging from the size of 631.46 Mb to 14.39 Mb and accounting 99.32% of assembled genome. This data will act as the starting point for unravelling the important genes and differentiating Indian tea from other commercially available tea varieties.

plant biology↗

bioRxiv2019

High accuracy DNA sequencing on a small, scalable platform via electrical detection of single base incorporations

High throughput DNA sequencing technologies have undergone tremendous development over the past decade. Although optical detection-based sequencing has constituted the majority of data output, it requires a large capital investment and aggregation of samples to achieve optimal cost per sample. We have developed a novel electronic detection-based platform capable of accurately detecting single base incorporations. The GenapSys technology with its electronic detection modality allows the system to be compact, accessible, and affordable. We demonstrate the performance of the system by sequencing several different microbial genomes with varying GC content. The platform is capable of generating up to 2 Gb of high-quality nucleic acid sequence in a single run. We routinely generate sequence data that exceeds 99% raw accuracy with read lengths of up to 175 bp. Average quality scores remain above Q30 (99.9% raw sequencing accuracy) beyond 150 bp, with more than 85% of total bases at or above Q30. The utility of the platform is highlighted by targeted sequencing of the human genome. We show high concordance of SNP detection on the human NA12878 HapMap cell line with data generated on the Illumina sequencing platform. In addition, we sequenced a targeted panel of cancer-associated genes in a well characterized reference standard. With multiple library preparation approaches on this sample, we were able to identify low frequency mutations at expected allele frequencies.

genomics↗

bioRxiv2018

Mining Significant Features of Diabetes Mellitus Applying Decision Trees: A Case Study In Bangladesh

Diabetes is a chronic condition which is associated with an abnormally high level of sugar in the blood. It is a lifelong disease that causes harmful effects in human life. The goal of this research is to predict the severity of diabetes and find out significant features of it. In this work, we gathered diabetes patients records from Noakhali Diabetes Association, Noakhali, Bangladesh. Thus, We preprocessed our raw dataset by replacing and removing missing and wrong records respectively. Thus, CDT, J48, NBTree and REPtree decision tree based classification techniques were used to analyze this dataset. After this analysis, we evaluated classification outcomes of these decision tree classifiers and found the best decision tree model from them. In this work, CDT unpruned tree shows highest accuracy, precision, recall, f-measure, second highest AUROC and lowest RMSE than other models. Then, we extracted possible rules and significant features from this model and plasma glucose, plasma glucose 2hr after glucose and HDL-cholesterol have been found as the most significant features to predict the severity of Diabetes Mellitus. We hope this work will be beneficial to build a predictive system and complementary tool for diabetes treatment in future.

bioengineering↗

bioRxiv2018

Many options, few solutions: over 60 million years snakes converged on a few optimal venom formulations

Gene expression changes contribute to complex trait variations in both individuals and populations. However, how gene expression influences changes of complex traits over macroevolutionary timescales remains poorly understood. Being comprised of proteinaceous cocktails, snake venoms are unique in that the expression of each toxin can be quantified and mapped to a distinct genomic locus and traced for millions of years. Using a phylogenetic generalized linear mixed model, we analysed expression data of toxin genes from 52 snake species spanning the three venomous snake families, and estimated phylogenetic covariance, which acts as a measure of evolutionary constraint. We find that evolution of toxin combinations is not constrained. However, while all combinations are in principle possible, the actual dimensionality of phylomorphic space is low, with envenomation strategies focused around only four major toxins: metalloproteases, three-finger toxins, serine proteases, and phospholipases A2. While most extant snakes prioritize either a single or a combination of major toxins, they are repeatedly recruited and lost. We find that over macroevolutionary timescales the venom phenotypes were not shaped by phylogenetic constraints, which include important microevolutionary constraints such as epistasis and pleiotropy, but more likely by ecological filtering that permits a few optimal solutions. As a result, phenotypic optima were repeatedly attained by distantly related species. These results indicate that venoms evolve by selection on biochemistry of prey envenomation, which permit diversity though parallelism and impose strong limits, since only a few of the theoretically possible strategies seem to work well and are observed in extant snakes.

evolutionary biology↗

bioRxiv2018

A least microenvironmental uncertainty principle (LEUP) as a generative model of collective cell migration mechanisms.

Collective migration is commonly observed in groups of migrating cells, in the form of swarms or aggregates. Mechanistic models have proven very useful in understanding collective cell migration. Such models, either explicitly consider the forces involved in the interaction and movement of individuals or phenomenologically define rules which mimic the observed behavior of cells. However, mechanisms leading to collective migration are varied and specific to the type of cells involved. Additionally, the precise and complete dynamics of many important chemomechanical factors influencing cell movement, from signalling pathways to substrate sensing, are typically either too complex or largely unknown. The question is how to make quantitative/qualitative predictions of collective behavior without exact mechanistic knowledge. Here we propose the least microenvironmental uncertainty principle (LEUP) that serves as a generative model of collective migration without incorporation of full mechanistic details. Interestingly we show that the famous Vicsek model is a special case of LEUP. Finally, as a proof of concept, we apply the LEUP to quantitatively study ofthe collective behavior of spherical Serratia marcescens bacteria, where the underlying migration mechanisms remain elusive.

biophysics↗