Biology subjects

Dubey, T.

Publications and source records attributed to Dubey, T..

2 recordsLinked to original sources

bioRxiv2020

Photo-excited Toluidine Blue disaggregates the Repeat Tau and modulates Cytoskeletal structure in neuronal cells

Alzheimers disease is a progressive neurological disorder characterized by the intracellular accumulation of Tau protein aggregates. Inhibition of protein aggregation by photo-excited dyes is emerging as novel strategy for the treatment of certain diseases. Toluidine Blue is a basic phenothiazine dye having potency of photo-excitation by irradiation with red light at 630{+/-}20 nm. In present work, we studied the effect of Toluidine Blue and photo-excited TB on aggregation of repeat Tau in-vitro using Thioflavin S fluorescence assay, SDS-PAGE and electron microscopy. Results show that TB efficiently inhabited the formation of higher order aggregates. Moreover, the photo-excited TB led to disaggregation of the mature repeat Tau fibrils. Further, studies on the effect of Toluidine blue on cell viability and cytoskeleton network of Neuro2acells show that TB was not toxic to neuronal cells at lower concentrations but at high concentrations (> 5 M) both TB and photo-excited TB induced significant toxicity. Immunofluorescence studies on the cytoskeleton of Neuro2a cells show that Toluidine Blue and photo-excited Toluidine Blue treatment at non-toxic concentration of 0.5 M stimulated formation of actin rich lamellipodia and filopodia structures. Tubulin networks were also differentially modulated after the treatment of Toluidine Blue and photo-excited Toluidine Blue. End Binding protein 1 (EB1) levels were observed to increase after Toluidine Blue and photo-excited Toluidine Blue treatment indicating the accelerated microtubule polymerization. The overall study suggested that Toluidine Blue inhibited the aggregation of soluble Tau and photo-excited Toluidine Blue disaggregated the pre-formed Tau filaments.

cell biology↗

bioRxiv2017

SpliceVec: distributed feature representations for splice junction prediction

Identification of intron boundaries, called splice junctions, is an important part of delineating gene structure and functions. This also provides valuable in-sights into the role of alternative splicing in increasing functional diversity of genes. Identification of splice junctions through RNA-seq is by mapping short reads to the reference genome which is prone to errors due to random sequence matches. This encourages identification of splicing junctions through computa-tional methods based on machine learning. Existing models are dependent on feature extraction and selection for capturing splicing signals lying in vicinity of splice junctions. But such manually extracted features are not exhaustive. We introduce distributed feature representation, SpliceVec, to avoid explicit and biased feature extraction generally adopted for such tasks. SpliceVec is based on two widely used distributed representation models in natural language processing. Learned feature representation in form of SpliceVec is fed to multi-layer perceptron for splice junction classification task. An intrinsic evaluation of SpliceVec indicates that it is able to group true and false sites distinctly. Our study on optimal context to be considered for feature extraction indicates inclusion of entire intronic sequence to be better than flanking upstream and downstream region around splice junctions. Further, SpliceVec is invariant to canonical and non-canonical splice junction detection. The proposed model is consistent in its performance even with reduced dataset and class-imbalanced dataset. SpliceVec is computationally efficient and can be trained with user defined data as well.

bioinformatics↗