Biology subjects

De, R. K.

Publications and source records attributed to De, R. K..

4 recordsLinked to original sources

bioRxiv2024

NeuroMDAVIS: Visualization of single-cell multi-omics data under deep learning framework

Single-cell technologies have favoured extensive advancements in cell-type discovery, cell state identi-fication, development of lineage tracing and disease understanding among others. Further, single-cell multi-omics data generated using modern technologies provide several views of omics contribution for the same set of cells. However, dimension reduction and visualization of biological datasets (single or multi-omics) remain a challenging task since obtaining a low-dimensional embedding that preserves information about local and global structures in data, is difficult. Further, combining different views obtained from each omics layer to interpret the underlying biology is even more challenging. Earlier, we have developed NeuroDAVIS which can perform the task of visualization of high-dimensional datasets of a single modality while preserving cluster-structures within the data. Nevertheless, there is no model so far that supports joint visualization of multi-omics datasets. Joint visualization refers to transforming the feature space of each individual modality and combining them to produce a latent embedding that supports visualization of the multi-modal dataset in the newly transformed feature space. In this work, we introduce NeuroMDAVIS which is a generalized version of NeuroDAVIS for visualization of biological datasets having multiple modalities. To the best of our knowledge, NeuroMDAVIS is the first of its kind multi-modal data visualization model. It is able to learn both local and global relationships in the data while generating a low-dimensional embedding useful for downstream tasks. NeuroMDAVIS competes against state-of-the-art visualization models like t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Fast interpolation-based t-SNE (Fit-SNE), and the Siamese network-based visualization method (IVIS).

bioinformatics↗

bioRxiv2023

In Silico Therapeutic Intervention on Cytokine Storm in COVID-19

The recent global COVID-19 outbreak, attributed by the World Health Organization to the rapid spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), underscores the need for an extensive exploration of virological intricacies, fundamental pathophysiology, and immune responses. This investigation is vital to unearth potential therapeutic avenues and preventive strategies. Our study delves into the intricate interaction between SARS-CoV-2 and the immune system, coupled with exploring therapeutic interventions to counteract dysfunctional immune responses like the cytokine storm (CS), a driver of disease progression. Understanding these immunological dimensions informs the design of precise multiepitopetargeted peptide vaccines using advanced immunoinformatics and equips us with tools to confront the cytokine storm. Employing a control theory-based approach, we scrutinize the perturbed behavior of key proteins associated with cytokine storm during COVID-19 infection. Our findings support ACE2 activation as a potential drug target for CS control and confirm AT1R inhibition as an alternative strategy. Leveraging deep learning, we identify potential drugs to individually target ACE2 and AT1R, with Lomefloxacin and Fostamatinib emerging as standout options due to their close interaction with ACE2. Their stability within the protein-drug complex suggests superior efficacy among many drugs from our deep-learning analysis. Moreover, there is a significant scope for optimization in fine-tuning protein-drug interactions. Strong binding alone may not be the sole determining factor for potential drugs; precise adjustments are essential. The application of advanced computational power offers novel solutions, circumventing time-consuming lab work. In scenarios necessitating both ACE2 and AT1R targeting, optimal drug combinations can be derived from our analysis of drug-drug interactions, as detailed in the manuscript.

bioinformatics↗

bioRxiv2022

UMINT: Unsupervised Neural Network For Single Cell Multi-Omics Integration

AO_SCPLOWBSTRACTC_SCPLOWMulti-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to the high dimensionality of data and noise associated with each platform. Non-overlapping features and technical batch effects in the data make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards. In addition, existing methods for single cell multi-omics integration are computationally expensive. This has encouraged the development of a novel architecture that produces a robust model for integration of high-dimensional multi-omics data, which would be capable of learning meaningful features for further downstream analysis. In this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions, and provides substantial reduction in the number of parameters. It is capable of learning a latent low-dimensional embedding that can capture useful data characteristics. The effectiveness of UMINT has been evaluated on benchmark CITE-seq (paired RNA and surface proteins) datasets. It has outperformed existing state-of-the-art methods for multi-omics integration.

bioinformatics↗

bioRxiv2019

Catestatin improves insulin sensitivity in diet-induced obese mice: in vivo and in silico validation

Obesity is characterized by a state of chronic, unresolved inflammation in insulin-targeted tissues. Obesity-induced inflammation accumulates proinflammatory macrophages in adipose tissue and liver. Proinflammatory cytokines released from tissue macrophages inhibits insulin sensitivity. Chromogranin A (CgA) peptide catestatin (CST: hCgA352-372) improves obesity-induced hepatic insulin resistance by reducing inflammation and inhibiting proinflammatory macrophage infiltration. Obesity leads to inflammation-induced endoplasmic reticulum (ER) stress and insulin resistance. We reasoned that the anti-inflammatory effects of CST would alleviate ER stress. CST decreased obesity-induced ER dilation in hepatocytes and macrophages. CST reduced phosphorylation of UPR signaling molecules and increased phosphorylation of insulin signaling molecules. We developed an in silico state space model mimicking dynamics of integrated ER stress and insulin pathways. Proportional-Integral-Derivative (PID) controllers helped in checking whether the reduction of phosphorylated PERK resulting in attenuation of ER stress, resembling CST effect, could enhance insulin sensitivity. The simulation results showed CST not only decreased ER stress but also enhanced insulin sensitivity. Simulation results also revealed that enhancement of AKT phosphorylation overcame effects of high ER stress to achieve insulin sensitivity.

systems biology↗