Biology subjects

Singha, M.

Publications and source records attributed to Singha, M..

3 recordsLinked to original sources

bioRxiv2023

Aberrant splicing in Huntington's disease via disrupted TDP-43 activity accompanied by altered m6A RNA modification.

Huntingtons disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the first exon of the HTT gene encoding huntingtin. Prior reports have established a correlation between CAG expanded HTT and altered gene expression. However, the mechanisms leading to disruption of RNA processing in HD remain unclear. Here, our analysis of the reported HTT protein interactome identifies interactions with known RNA-binding proteins (RBPs). Total, long-read sequencing and targeted RASL-seq of RNAs from cortex and striatum of the HD mouse model R6/2 reveals increased exon skipping which is confirmed in Q150 and Q175 knock-in mice and in HD human brain. We identify the RBP TDP-43 and the N6-methyladenosine (m6A) writer protein methyltransferase 3 (METTL3) to be upstream regulators of exon skipping in HD. Along with this novel mechanistic insight, we observe decreased nuclear localization of TDP-43 and cytoplasmic accumulation of phosphorylated TDP-43 in HD mice and human brain. In addition, TDP-43 co-localizes with HTT in human HD brain forming novel nuclear aggregate-like bodies distinct from mutant HTT inclusions or previously observed TDP-43 pathologies. Binding of TDP-43 onto RNAs encoding HD-associated differentially expressed and aberrantly spliced genes is decreased. Finally, m6A RNA modification is reduced on RNAs abnormally expressed in striatum from HD R6/2 mouse brain, including at clustered sites adjacent to TDP-43 binding sites. Our evidence supports TDP-43 loss of function coupled with altered m6A modification as a novel mechanism underlying alternative splicing/unannotated exon usage in HD and highlights the critical nature of TDP-43 function across multiple neurodegenerative diseases.

neuroscience↗

bioRxiv2021

Graphsite: ligand-binding site classification using deep graph neural network

Binding sites are concave surfaces on proteins that bind to small molecules called ligands. Types of molecules that bind to the protein determine its biological function. Meanwhile, the binding process between small molecules and the protein is also crucial to various biological functionalities. Therefore, identifying and classifying such binding sites would enormously contribute to biomedical applications such as drug repurposing. Deep learning is a modern artificial intelligence technology. It utilizes deep neural networks to handle complex tasks such as image classification and language translation. Previous work has proven the capability of deep learning models handle binding sites wherein the binding sites are represented as pixels or voxels. Graph neural networks (GNNs) are deep learning models that operate on graphs. GNNs are promising for handling binding sites related tasks - provided there is an adequate graph representation to model the binding sties. In this communication, we describe a GNN-based computational method, GraphSite, that utilizes a novel graph representation of ligand-binding sites. A state-of-the-art GNN model is trained to capture the intrinsic characteristics of these binding sites and classify them. Our model generalizes well to unseen data and achieves test accuracy of 81.28% on classifying 14 binding site classes.

bioinformatics↗

bioRxiv2020

GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.

bioinformatics↗