Biology subjects

Das, V.

Publications and source records attributed to Das, V..

13 recordsLinked to original sources

bioRxiv2026

Repurposed COMT Inhibitors Tolcapone and Entacapone Selectively Suppress Aggregation and Seeding of P301 Mutant TAU in Human Neuronal Models

Background and PurposePathogenic aggregation and propagation of seed-competent TAU assemblies drive tauopathies. MAPT P301 mutations accelerate aggregation and enhance seed competence, yet pharmacological strategies selectively targeting these pathogenic species remain limited. We investigated whether the clinically approved catechol-O-methyltransferase inhibitors tolcapone (TOL) and entacapone (ENT) preferentially modulate mutant TAU aggregation and seeding. Experimental ApproachTOL and ENT effects on TAU aggregation were evaluated via cell-free assays, surface plasmon resonance (SPR), and in silico docking. Functional consequences of compound-modified fibrils were assessed in mutant TAU-expressing SH-SY5Y cells. Translational relevance was examined in human induced pluripotent stem cell (hiPSC)-derived neurons exposed to pathogenic K18 fibrils, followed by post-seeding compound treatment. Key ResultsBoth compounds dose-dependently inhibited TAU aggregation, exhibiting greater potency, stronger SPR binding affinities, and more favorable computed interaction energies for P301S mutant versus wild-type TAU. Fibrils formed with TOL or ENT induced less downstream TAU oligomerization and phosphorylation in SH-SY5Y cells, with TOL showing superior protection. In hiPSC-derived neurons, post-seeding treatment with either compound decreased fibril-induced, sarkosyl-insoluble TAU aggregation and phosphorylation without overt cytotoxicity. Conclusion and ImplicationsTOL and ENT preferentially inhibit the aggregation and seeding of pathogenic P301 mutant TAU. This supports mutation-focused pharmacological strategies and highlights catechol scaffolds as viable starting points for the development of disease-modifying therapeutics. Future research must determine the precise interaction mechanisms with aggregation intermediates and evaluate in vivo efficacy in animal models.

neuroscience↗

bioRxiv2026

GxP Single-cell RNA-seq and Spatial Transcriptomics end-to-end pipeline for clinical research

Single-cell/nucleus RNA-sequencing and Spatial Transcriptomics are powerful tools for investigating cellular heterogeneity and tissue architecture that have deepened our disease understanding. Their broader adoption in clinical and regulated settings, however, is hindered by challenges related to data integrity, regulatory compliance, reproducibility, and scalability. To address this gap, we developed NNclinSSOAP (Novo Nordisk Clinical Single-cell Spatial Omics Analytical Pipeline) - a modular, GxP-ready end-to-end computational pipeline, that combines established single-cell workflows with a new Nextflow pipeline for Spatial Transcriptomics. NNclinSSOAP transforms RNA sequencing and Xenium spatial data into integrated, annotated single-cell objects and spatially resolved tissue maps. Designed to support mechanistic studies and clinical endpoint generation, it enables traceable and reproducible processing of large-scale datasets, scalable for both local and HPC environments. Here, we provide a step-by-step guide for using NNclinSSOAP. All code and data are publicly available. Using a standard laptop, the pipeline can be executed within 1.5 hours.

bioinformatics↗

bioRxiv2026

Semaglutide Attenuates Neuroinflammation in Mice

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have shown promise in preclinical models of neurodegeneration, with emerging evidence suggesting these effects may be driven by modulation of neuroinflammation. However, the cellular mechanisms underlying GLP-1 RA effects on neuroinflammation remain poorly understood. Here, using a mouse model of lipopolysaccharide-induced neuroinflammation, we investigated how semaglutide coordinates cellular responses to resolve neuroinflammation. We find that semaglutide prevents brain infiltration of neutrophils, excessive cytokine release, and suppresses neuroinflammation-associated transcriptional signatures specifically in microglia, endothelial cells, and a subset of pericytes. Mechanistically, we identify a subset of Glp1r-expressing neurons in the dorsal vagal complex that, upon semaglutide treatment, regulate genes involved in anti-inflammatory signaling. Semaglutide-modulated pathways overlap with inflammatory signatures found in human neurodegenerative diseases, including Alzheimers disease, suggesting broad relevance for conditions involving neuroinflammation. Together, these findings reveal how GLP-1R signaling orchestrates resolution of neuroinflammation through coordinated multi-cellular programs.

neuroscience↗

bioRxiv2025

Elevated glucose in kidney organoids induces tissue-intrinsic inflammation driving epithelial detachment

Diabetic kidney disease involves hyperglycemia, inflammation, and epithelial cell dysfunction, but the relationship between these factors is not well understood. We demonstrate that human kidney organoids treated with elevated glucose exhibit a phenotype of epithelial detachment driven by tissue-intrinsic upregulation of proinflammatory cytokines, which can be targeted therapeutically. High glucose induces morphological deterioration of organoids featuring podocyte and tubular cell detachment without cytotoxicity. Cytokine addition sensitizes organoids to intermediate concentrations of elevated glucose. Transcriptomic analysis reveals that high glucose levels affect cytokine, inflammation, signaling, and cell adhesion pathways, resembling changes in human diabetic kidneys. Inhibitors of cytokines and signaling pathways rescue the high glucose phenotype, which is independent of osmotic effects. Thus, elevated glucose triggers a tissue-intrinsic inflammatory cascade to produce an organ-specific phenotype in epithelial cells. This paradigm is relevant for understanding and potentially treating diabetic complications in the kidneys and possibly other organs.

cell biology↗

bioRxiv2024

PLK1-Mediated Phosphorylation Cascade Activates the Mis18 Complex to Ensure Centromere Inheritance

Accurate chromosome segregation requires the attachment of spindle microtubules to centromeres, which are epigenetically defined by the enrichment of CENP-A nucleosomes. During DNA replication, existing CENP-A nucleosomes undergo dilution as they get redistributed among the two DNA strands. To preserve centromere identity, CENP-A levels must be restored in a cell-cycle controlled manner orchestrated by the Mis18 complex. Here we provide a comprehensive mechanistic basis for PLK1-mediated licensing of CENP-A loading. We demonstrate that PLK1 interacts with Mis18 and Mis18BP1 subunits of the Mis18 complex by recognising self-primed phosphorylations of Mis18 (S54) and Mis18BP1 (T78 and S93) through its Polo-box binding domain. Disrupting these PLK1 phosphorylations perturbed the centromere recruitment of HJURP and new CENP-A loading. Biochemical and functional analyses show that phosphorylation of Mis18 and subsequent PLK1 binding is required to activate the Mis18/{beta} complex for robust Mis18/{beta}-HJURP interaction. Thus, our study reveals key molecular events underpinning the licensing role of PLK1 in ensuring accurate centromere inheritance. One-Sentence SummaryPLK1 phosphorylation cascade licenses CENP-A loading by facilitating HJURP centromere recruitment via Mis18/{beta} activation.

cell biology↗

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

Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis

Atherosclerosis is a pervasive contributor to cardiovascular diseases including ischemic heart disease and stroke. Despite the advance and success of effective lipid lowering-therapies and hypertensive agents, the residual risk of an atherosclerotic event remains high and improving disease understanding and development of novel therapeutic strategies has proven to be challenging. This is largely due to the complexity of atherosclerosis with a spatial interplay of multiple cell types within the vascular wall. Here, we generated an integrative high-resolution map of human atherosclerotic plaques by combining single-cell RNA-seq from multiple studies and novel spatial transcriptomics data from 12 human specimens to gain insights into disease mechanisms. Comparative analyses revealed cell-type and atherosclerosis-specific expression changes and associated alterations in cell-cell communication. We highlight the possible recruitment of lymphocytes via different endothelial cells of the vasa vasorum, the migration of vascular smooth muscle cells towards the lumen to become fibromyocytes, and cell-cell communication in the plaque, indicating an intricate cellular interplay within the adventitia and the subendothelial space in human atherosclerosis.

bioinformatics↗

bioRxiv2023

A Benchmark of state-of-the-art Deconvolution Methods in Spatial Transcriptomics: Insights from Cardiovascular Disease and Chronic Kidney Disease

A major challenge in sequencing based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds-to thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. While ST is becoming essential for drug discovery especially in Cardiometabolic diseases, to date no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods Cell2location, RCTD and spatialDWLS have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess best performance when using human data from Cardiovascular Disease (CVD) data and Chronic Kidney Disease (CKD) from patients at different pathological states, evaluated using expert annotation. In this benchmark, we found that all three methods performed comparably well in deconvoluting verifiable cell types including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples while Cell2location on average achieved the highest performance across test experiments. While all three methods had similar accuracies Cell2location need less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.

bioinformatics↗

bioRxiv2023

Differential seeding by exogenous R2 and R3 fibrils influences autophagic degradation of intracellular tau aggregates in Tau K18 P301S cells

Aggregation of misfolded tau protein is a common feature of tauopathies. Cells employ diverse mechanisms to eliminate misfolded tau with different conformations, contributing to the varied clinical and pathological manifestations of tauopathies. This study focuses on the clearance of seeded tau aggregates following the induction of biosensor cells with R2 and R3 fibrils that exhibit distinct aggregation kinetics and seeding potencies. Our hypothesis is that the dissimilarity in time-dependent intracellular seeding induced by R2 and R3 fibrils underlies the variation in autophagy failure. These discrepancies may account for the heterogeneity of pathology and disease progression between 3R and 4R tauopathies, given the absence of R2 in 3R tau isoforms. In R2-induced cells, alterations in p62 and LC3II/I levels, indicative of proteotoxic stress and autophagy failure, occur sooner than in R3-induced cells. Conversely, LAMP1 levels remained unaffected, suggesting a failure in the fusion of aggregate-containing autophagosomes with lysosomes. This autophagic failure may increase seed-dependent intracellular aggregation in induced cells. Consequently, we assessed the impact of autophagy inducers on the clearance of intracellular tau aggregates in induced cells. Epigallocatechin gallate (EGCG) demonstrated the highest efficacy in inducing autophagy and reducing p62 levels, decreasing seeding and clearing aggregates. Overall, this study elucidates the differential effects of prion-like R2 and R3 strains on autophagy and highlights how compounds like EGCG can selectively reduce tau aggregation to treat specific tauopathies. We provide insights into the distinct mechanisms of autophagy failure and autophagy clearance of intracellular aggregates in cells induced with R2 and R3 fibrils.

neuroscience↗

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↗

bioRxiv2022

SARS-CoV-2 and its variants, but not Omicron, induces thymic atrophy and impaired T cell development

Pathogenic infections cause thymic atrophy, perturb thymic-T cell development and alter immunological response. Previous studies reported dysregulated T cell function and lymphopenia in coronavirus disease-19 (COVID-19) patients. However, immune-pathological changes, in the thymus, post severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection have not been elucidated. Here, we report SARS-CoV-2 infects thymocytes, depletes CD4+CD8+ (double positive; DP) T cell population associated with an increased apoptosis of thymocytes, which leads to severe thymic atrophy in K18-hACE2-Tg mice. CD44+CD25-T cells were found to be enriched in infected thymus, indicating an early arrest in the T cell developmental pathway. Further, Interferon gamma (IFN-{gamma}) was crucial for thymic atrophy, as anti-IFN-{gamma} antibody neutralization rescued the loss of thymic involution. Therapeutic use of remdesivir (prototype anti-viral drug) was also able to rescue thymic atrophy. While Omicron variant of SARS-CoV2 caused marginal thymic atrophy, delta variant of SARS-CoV-2 exhibited most profound thymic atrophy characterized by severely depleted DP T cells. Recently characterized broadly SARS-CoV-2 neutralizing monoclonal antibody P4A2 was able to rescue thymic atrophy and restore thymic developmental pathway of T cells. Together, we provide the first report of SARS-CoV-2 associated thymic atrophy resulting from impaired T cell developmental pathway and also explains dysregulated T cell function in COVID-19.

immunology↗

bioRxiv2021

Integrated Omics Analysis Reveals Sirtuin Signaling is Central to Hepatic Response to a High Fructose Diet

BackgroundDietary high fructose (HFr) is a known metabolic disruptor contributing to development of obesity and diabetes in Western societies. Initial molecular changes from exposure to HFr on liver metabolism may be essential to understand the perturbations leading to insulin resistance and abnormalities in lipid and carbohydrate metabolism. We studied vervet monkeys (Clorocebus aethiops sabaeus) fed a HFr (n=5) or chow diet (n=5) for 6 weeks, and obtained clinical measures of liver function, blood insulin, cholesterol and triglycerides. In addition, we performed untargeted global transcriptomics, proteomics, and metabolomics analyses on liver biopsies to determine the molecular impact of a HFr diet on coordinated pathways and networks that differed by diet. ResultsWe show that integration of omics data sets improved statistical significance for some pathways and networks, and decreased significance for others, suggesting that multiple omics datasets enhance confidence in relevant pathway and network identification. Specifically, we found that sirtuin signaling and a peroxisome proliferator activated receptor alpha (PPARA) regulatory network were significantly altered in hepatic response to HFr. Integration of metabolomics and miRNAs data further strengthened our findings. ConclusionsOur integrated analysis of three types of omics data with pathway and regulatory network analysis demonstrates the usefulness of this approach for discovery of molecular networks central to a biological response. In addition, metabolites aspartic acid and docosahexaenoic acid (DHA), protein ATG3, and genes ATG7, HMGCS2 link sirtuin signaling and the PPARA network suggesting molecular mechanisms for altered hepatic gluconeogenesis from consumption of a HFr diet.

systems biology↗

bioRxiv2018

A cell-of-origin epigenetic tracer reveals clinically distinct subtypes of high grade serous ovarian cancer

High grade serous ovarian cancer (HGSOC) is a major unmet need in oncology. The persistent uncertainty on its originating tissue has contributed to hamper the discovery of oncogenic pathways and effective therapies. Here we define the DNA methylation print that distinguishes the human fimbrial (FI) and ovarian surface epithelia (OSE) and develop a robust epigenetic cell-of-origin tracer that stratifies HGSOC in FI-and OSE-originated tumors across all available cohorts. We translate this origin-based stratification into a clinically actionable transcriptomic signature, demonstrating its prognostic impact on patients survival and identifying novel network level dysregulations specific for the two disease subtypes.

cancer biology↗