Biology subjects

Kundu, D.

Publications and source records attributed to Kundu, D..

9 recordsLinked to original sources

bioRxiv2026

O-GlcNAcylation effect on Tau in modulating its seeding and cellular transmission in Alzheimer's Disease

Post-translational modifications critically regulate neurodegenerative disease progression. In Alzheimers disease (AD) and tauopathies, Tau hyperphosphorylation promotes aggregation and pathological spreading, whereas O-GlcNAcylation has emerged as a protective modification alongside its interplay with phosphorylation. However, the role of in vitro site-specific and global O-GlcNAcylation in Tau proteinopathy remains elusive. Here, using full-length Tau-441 (2N4R), we showed that O-GlcNAcylated Tau aggregated slowly, formed distinct aggregate morphology and exhibited reduced seeding capacity compared to wild-type (WT) Tau. Under phase-separated conditions, O-GlcNAcylated Tau formed oligomer like condensates. Mutation of the key O-GlcNAc sites reduced O-GlcNAc-transferase (OGT) mediated O-GlcNAcylation, cellular transmission and affected cross-talk with phosphorylation relative to WT. OGT overexpression alleviated WT-Tau toxicity in cells, and O-Tau fibrils were less toxic to primary cortical neurons compared to WT Tau fibrils. Finally, an in-house novel site-specific S422 O-GlcNAc-Tau antibody revealed reduced S422 O-GlcNAcylation in AD brain tissues, highlighting its protective role in AD pathogenesis.

neuroscience↗

bioRxiv2025

RIPK1 regulates beta-cell fate via actions on gene expression and kinase signaling in a mouse model of beta-cell self-reactivity

Type 1 diabetes (T1D) is characterized by autoimmune destruction of pancreatic {beta}-cells, insulin insufficiency, and hyperglycemia. Receptor interacting protein kinase 1 (RIPK1) is a multifunctional regulator of cell fate with kinase and scaffolding functions, and we previously identified RIPKs as regulators of {beta}-cell cytotoxicity in vitro. Here we report that Ripk1 expression is increased in islets from aged non-obese diabetic (NOD) mice and {beta}-cells from T1D donors, suggesting that RIPK1 may drive cytokine- and autoimmune-mediated {beta}-cell demise in T1D. Using NIT-1 {beta}-cells derived from NOD mice, we observed that TNF+IFN{gamma} increase RIPK1 phosphorylation, caspase 3/7 activity, and cell death. In contrast, this cytotoxicity was blocked with small molecule RIPK1 inhibition or in Ripk1 gene-edited (Ripk1{Delta}) {beta}-cells. Co-labeling of caspase 3/7 activation and cell death in single cells revealed protection from caspase-dependent and -independent forms of death in Ripk1{Delta} cells. RNAseq uncovered differential cell death-, immune-, and identity-related gene expression, and kinome profiling identified changes in MAPK, Eph, JAK, and other kinase activity associated with protection from cell death in RIPK1 deficient {beta}-cells. Furthermore, in vitro co-culture assays and in vivo adoptive transfer experiments revealed that NIT-1 Ripk1{Delta} cells were protected from autoimmune destruction by splenocytes isolated from diabetic NOD mice. Collectively, our findings indicate that RIPK1 promotes {beta}-cell demise in response to cytokine and autoimmune stress via actions on gene expression and kinase signaling. Therapeutics targeting RIPK1 may provide novel opportunities for prevention or treatment of autoimmune diabetes.

cell biology↗

bioRxiv2024

Application of Quantum Tensor Networks for Protein Classification

Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various proteinrelated machine-learning problems. We classify proteins based on their sub-cellular locations--a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. Its noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.

bioinformatics↗

bioRxiv2023

A meta-analysis of rice phosphoproteomics data to understand variation in cell signalling across the rice pan-genome

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have re-analysed publicly available mass spectrometry proteomics datasets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,522 phosphosites on serine, threonine and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety. The data was clustered to identify groups of sites with similar patterns across rice family groups, for example those highly conserved in Japonica, but mostly absent in Aus type rice varieties - known to have different responses to drought. These resources can assist rice researchers to discover alleles with significantly different functional effects across rice varieties. The data has been loaded into UniProt Knowledge-Base - enabling researchers to visualise sites alongside other data on rice proteins e.g. structural models from AlphaFold2, PeptideAtlas and the PRIDE database - enabling visualisation of source evidence, including scores and supporting mass spectra.

bioinformatics↗

bioRxiv2023

Drug repurposing approach for potential Pfmrk inhibitors as antimalarial agents: an In-silico analysis

Malaria is a major global health issue due to the emergence of resistance to most of the available antimalarial drugs. There is an urgent need to discover new antimalarials to tackle the resistance issue. A CDK-like protein, Pfmrk from Plasmodium falciparum, plays a crucial role in regulating cell proliferation and shares 36.28% homology with humans CDK (hCDK7). Pfmrk complex with Pfcyc-1 and stimulates kinase activity. Also, Pfcyc-1 from P. falciparum, which has the highest sequence homology with human cyclin (Cyclin H), binds to and activates Pfmrk in a cyclin-dependent way. This is the first indication that cyclin subunits regulate human and plasmodial CDKs in a similar manner. In this study, molecular docking analysis of Pfmrk against the selected FDA-approved drugs acquired from the ZINC15 database. The top five drugs, Lurasidone, Vorapaxar, Donovex, Alvesco, and Orap, were screened based on binding energies of best-docked scores ranging between -8 kcal/mol and -12 kcal/mol. Based on Molecular dynamics simulations for 100ns, Lurasidone showed the highest binding affinity (-105.90 {+/-} 57.72 kJ/mol), followed by Donovex (-92.877 {+/-} 17.872 kJ/mol) and exhibited stable interactions with the amino acid residues present in the active site of Pfmrk. The outcomes of in silico investigation putatively suggested that Lurasidone and Donovex exhibit antimalarial potency and could be translated as potential Pfmrk inhibitors and developing new drugs based on further in-vitro studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/547694v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1e14204org.highwire.dtl.DTLVardef@1b522e8org.highwire.dtl.DTLVardef@106df75org.highwire.dtl.DTLVardef@389899_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsWe investigated the potential FDA-approved drugs for repurposing against the modelled protein Pfmrk. Alvesco, Donovex, Lurasidone, Orap, and Vorapaxar are potential FDA-approved drugs firmly binding with Pfmrk. Molecular docking and simulation studies show that Donovex and Lurasidone are potential inhibitors of the modelled Pfmrk protein. Donovex and Lurasidone are potential drugs that act as kinase inhibitors by binding to the ATP -binding site of the enzyme Pfmrk.

biochemistry↗

bioRxiv2023

Bayesian Inference of Chemical Mixtures in Risk Assessment Incorporating the Hierarchical Principle

Analyzing health effects associated with exposure to environmental chemical mixtures is a challenging problem in epidemiology, toxicology, and exposure science. In particular, when there are a large number of chemicals under consideration it is difficult to estimate the interactive effects without incorporating reasonable prior information. Based on substantive considerations, researchers believe that true interactions between chemicals need to incorporate their corresponding main effects. In this paper, we use this prior knowledge through a shrinkage prior that a priori assumes an interaction term can only occur when the corresponding main effects exist. Our initial development is for logistic regression with linear chemical effects. We extend this formulation to include non-linear exposure effects and to account for exposure subject to detection limit. We develop an MCMC algorithm using a shrinkage prior that shrinks the interaction terms closer to zero as the main effects get closer to zero. We examine the performance of our methodology through simulation studies and illustrate an analysis of chemical interactions in a case-control study in cancer.

cancer biology↗

bioRxiv2023

Characterization of the First Turtle Organoids: A Model for Investigating Unique Adaptations with Biomedical Potential

Painted turtles are remarkable for their well-developed freeze tolerance and associated resilience to hypoxia/anoxia, oxidative stress, and ability to supercool. They are, therefore, an ideal model for biomedical research on hypoxia-induced injuries (including strokes), tissue cooling during extensive surgeries, and organ cryopreservation. Yet, the seasonal reproduction and slow maturation of turtles hinder basic and applied biomedical research. To overcome these limitations, we developed the first adult stem cell-derived turtle hepatic organoids, which provide 3D self-assembled structures that mimic their original tissue and allow for in vitro testing and experimentation without constantly harvesting donor tissue and screening offspring. Our pioneering work with turtles represents the first for this vertebrate Order and complements the only other organoid lines from non-avian reptiles, derived from snake venom glands. Here we report the isolation and characterization of hepatic organoids derived from painted, snapping, and spiny softshell turtles spanning [~]175 million years of evolution, with a subset being preserved in a biobank. Morphological and transcriptomics revealed organoid cells resembling cholangiocytes, which was then compared to the tissue of origin. Deriving turtle organoids from multiple species and life stages demonstrates that our techniques are broadly applicable to chelonians, permitting the development of functional genomic tools currently missing in most herpetological research. When combined with genetic editing, this platform will further support studies of genome-to-phenome mapping, gene function, genome architecture, and adaptive responses to climate change, among others. We discuss the unique abilities of turtles, including their overwintering potential, which has implications for ecological, evolutionary, and biomedical research. SIGNIFICANCEHere we developed the first turtle-derived organoid biobank from the liver of multiple chelonians with a subset characterized via histology, RNA sequencing transcriptomics, single-nuclei RNA sequencing, and transmission electron microscopy. Furthermore, we discuss the potential of the 3D organoid model to investigate unique physiological adaptations of turtles which could unravel the molecular mechanisms underlying their overwintering capacity, opening the door for in vitro biomedical studies relevant to hepatic ischemia-reperfusion injury to organ cryopreservation, beyond fundamental ecology and evolution. This organoid biobank represents a novel resource for the scientific community to support research regarding the unique adaptations of this understudied Order of vertebrates.

genetics↗

bioRxiv2021

ZeBox: A novel non-intrusivecontinuous-use technology to trap and killairborne microbes

Preventing nosocomial infection is a major unmet need of our times. Existing air decontamination technologies suffer from demerits such as toxicity of exposure, species specificity, noxious gas emission, environment-dependent performance and high power consumption. Here, we present a novel technology called "ZeBox" that transcends the conventional limitations and achieves high microbicidal efficiency. In ZeBox, a non-ionizing electric field extracts naturally charged microbes from flowing air and deposits them on engineered microbicidal surfaces. The surfaces three dimensional topography traps the microbes long enough for them to be inactivated. The electric field and chemical surfaces synergistically achieve rapid inactivation of a broad spectrum of microbes. ZeBox achieved near complete kill of airborne microbes in challenge tests (5-9 log reduction) and >90% efficiency in a fully functional stem cell research facility in the presence of humans. Thus, ZeBox fulfills the dire need for a real-time, continuous, safe, trap-and-kill air decontamination technology.

microbiology↗

bioRxiv2019

An integrated landscape of protein expression in human cancer

Using public proteomics datasets, mostly available through the PRIDE database, we assembled a proteomics resource for 191 cancer cell lines and 246 clinical tumour samples, across 13 cancer lineages. We found that baseline protein abundance in cell lines was generally representative of tumours. However, when considering differences in protein expression between tumour subtypes, as exemplified in the breast lineage, many of these changes were no longer recapitulated in the cell line models. Integration of proteomics and transcriptomics data suggested that the level of transcriptional control in cell lines changed significantly depending on their lineage. Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis study including cancer-related proteomics datasets. We anticipate this aggregated dataset will be of significant aid to future studies requiring a reference to baseline protein expression in cancer.

bioinformatics↗