Biology subjects

Chaudhuri, S.

Publications and source records attributed to Chaudhuri, S..

11 recordsLinked to original sources

bioRxiv2026

Quantum transduction from electron spin state to a signaling state in a wild-type LOV photoreceptor

1Whether coherent spin dynamics can control biological regulation remains an open question. Here we show that the electron spin state in the wild-type Avena sativa phototropin 1 Light-Oxygen-Voltage domain 2 (AsLOV2) modulates FMN-cysteine photoadduct formation, the covalent bond that defines its signaling state. Although AsLOV2 was thought magnetically inactive, we observe a magnetic field effect (MFE) on its fluorescence, emerging above ~100 mT and strengthening to 1300 mT. The MFE establishes a spin-correlated radical pair on the photoadduct pathway, one rendered inaccessible to direct spectroscopy by the same strong coupling that sets the high-field onset. It arises from g-factor asymmetry within the closely spaced FMN-cysteine radical pair overcoming its dipolar coupling to drive coherent singlet-triplet interconversion. We furthermore observe an MFE in UV-Vis absorption that, unlike fluorescence, shows the field modulates productive bond formation itself. LOV domains are ubiquitous biological regulators, suggesting that quantum transduction relying on radical pairs as molecular qubits may be a general property among natural and engineered photoreceptors.

biophysics↗

bioRxiv2026

Enhanced photosynthetic efficiency and ROS modulation promote cold stress tolerance of indica rice

Indica rice, being a tropical crop, is highly sensitive to cold temperature. Cold stress affects vegetative growth, photosynthetic efficiency, along with reproductive features. Genetic resource screening in diverse landraces is an approach for identifying cold-tolerant traits. Here, we have characterised a boro germplasm, CB1, with an efficient germination rate and growth vigour when treated at chilling temperatures. CB1 seedlings show a higher survival rate compared to IR36 when subjected to prolonged chilling stress. Biochemical analyses indicated efficient ROS modulation, higher chlorophyll content, enhanced photosystem II efficiency and unique stomatal traits, leading to higher relative water content in CB1 plants during stress and recovery. Transcriptome analysis supported upregulation of chlorophyll biosynthesis, photosystem, & light harvesting complex and ROS scavenger genes in CB1 seedlings. Interestingly, high D1 protein turnover in CB1 promotes damage-repair of PSII for efficient photosynthesis. Furthermore, key transcription factors for stomatal development and expression of photosynthetic genes were upregulated in CB1 during stress recovery. Notably, higher expression of OsGLK1 and enrichment of GLK1 targets were observed in CB1 plants during chilling stress and recovery. Taken together, our results suggested that CB1 plants exhibit cold tolerance by modulating photosynthesis efficiency and stomatal behavior for better adaptability and survival against chilling temperature. HIGHLIGHTSThe efficient photosynthetic recovery, active ROS scavenging system and maintenance of water content through regulating stomatal traits, enhance the survival of indica germplasm CB1 against chilling stress.

plant biology↗

bioRxiv2026

Nanoparticle encapsulation enhances spatial distribution of Panobinostat to treat metastatic medulloblastoma via the intrathecal route

Medulloblastoma (MB) is an aggressive central nervous system (CNS) malignancy that primarily affects children and frequently exhibits metastasis to the leptomeninges of the brain and spinal cord. We developed a {beta}-Cyclodextrin-poly({beta}-Amino Ester) nanoparticle system to deliver the histone deactylase inhibitor (HDACi) Panobinostat to MB by the intrathecal route. Various imaging methods were utilized to study nanoparticle and payload fate following infusion into the cerebrospinal fluid (CSF) of mice via cisterna magna or lumbar access points. Nanoparticles dramatically improved penetration of hydrophobic small molecules into distal regions of the spinal cord. Panobinostat-loaded nanoparticles were effective at treating patient-derived MB, activating pharmacodynamic targets, slowing growth of the primary tumor, decreasing incidence of metastasis at the time of death, and ultimately prolonging survival. These studies provide insight into the mechanisms mediating transport of colloids and therapeutic molecules in the subarachnoid space and highlight new approaches for treating metastatic disease in the CNS.

bioengineering↗

bioRxiv2025

Oral dosing of the nucleoside analog obeldesivir is efficacious against RSV infection in African green monkeys

Respiratory syncytial virus (RSV) is a significant cause of morbidity and mortality in high-risk populations. Although prophylactic options are available, there are no effective oral therapeutics for RSV infection. Obeldesivir (ODV) is an orally bioavailable prodrug of the nucleoside analog GS-441524, which is converted intracellularly to its active nucleoside triphosphate and inhibits the RSV RNA polymerase. Here we report the potent antiviral activity of ODV against geographically and temporally diverse RSV A and B clinical isolates (EC50: 0.20-0.66 M). Resistance selection studies with ODV and GS-441524 against RSV identified a single amino acid substitution, I777L, in the L polymerase with reduced susceptibility (3.3-3.8-fold) to ODV and GS-441524, indicating a high barrier for resistance development. In an African green monkey RSV infection model, once-daily oral ODV doses of 30 or 90 mg/kg initiated [~]24 hours post-infection significantly reduced log10 viral RNA copies/mLxday area under the curve by 69-92% in the upper and lower respiratory tracts. Together, these preclinical data support the clinical evaluation of ODV for the treatment of RSV infection.

microbiology↗

bioRxiv2024

Cell Type Differentiation Using Network Clustering Algorithms

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

bioinformatics↗

bioRxiv2024

Medicinal chemistry of benzoisoxazole-4,7-dione analogues identifies the ribosomal small subunit as a target for specific mycobacterial translational fidelity

A central dogma of molecular biology is the "speed-accuracy trade-off," where ribosomes must slow down to ensure accurate protein synthesis. In mycobacteria, a high basal level of mistranslation at glutamine and asparagine codons, caused by an indirect tRNA aminoacylation pathway, promotes tolerance to the antibiotic rifampicin. While pharmacologically increasing translational fidelity is a promising strategy to combat antibiotic tolerance, the underlying mechanisms remain poorly understood. Here, we screened 9,000 synthetic compounds and identified benzo[d]isoxazole-4,7-diones as a novel chemical class that reduces mycobacterial mistranslation. Medicinal chemistry optimization yielded a lead compound, 9787, with superior potency in decreasing mistranslation and reversing rifampicin tolerance. Using competitive chemical proteomics, we identified the 30S ribosomal protein S5 (RpS5) as the specific cellular target. Remarkably, compound 9787 enhances translational fidelity at concentrations that do not measurably impact the overall rate of protein synthesis. Our findings challenge the universality of the speed-accuracy trade-off, demonstrating that fidelity can be improved independently of translation speed. This work reveals that the ribosomal small subunit is a druggable target for modulating translational quality control and introduces a new strategy for combating antibiotic-tolerant bacteria without the associated fitness cost of slowed translation. ImportanceA fundamental principle in molecular biology holds that ribosomes face a trade-off between translation speed and accuracy: going faster means making more errors, while maintaining high fidelity requires slowing down. This study challenges that paradigm by identifying a small molecule that increases translational accuracy in mycobacteria without affecting the rate of protein synthesis. The compound targets ribosomal protein S5 and specifically reduce errors arising from physiologically mischarged tRNAs - a quality control problem distinct from the well-studied codon*anticodon mismatches. This form of mistranslation contributes to antibiotic tolerance in tuberculosis, making it a potential therapeutic target. Our findings reveal unexpected flexibility in how ribosomes maintain translation quality and suggest that pharmacologically increasing fidelity without the fitness cost of slowed protein synthesis may be an attractive strategy for combating antibiotic-tolerant bacteria.

microbiology↗

bioRxiv2023

Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations

School closures were used as strategies to mitigate transmission in the COVID-19 pandemic. Understanding the nature of SARS-CoV-2 outbreaks and the distribution of infections in classrooms could help inform targeted or precision preventive measures and outbreak management in schools, in response to future pandemics. In this work, we derive an analytical model of Probability Density Function (PDF) of SARS-CoV-2 secondary infections and compare the model with infection data from all public schools in Ontario, Canada between September-December, 2021. The model accounts for major sources of variability in airborne transmission like viral load and dose-response (i.e., the human bodys response to pathogen exposure), air change rate, room dimension, and classroom occupancy. Comparisons between reported cases and the modeled PDF demonstrated the intrinsic overdispersed nature of the real-world and modeled distributions, but uncovered deviations stemming from an assumption of homogeneous spread within a classroom. The inclusion of near-field transmission effects resolved the discrepancy with improved quantitative agreement between the data and modeled distributions. This study provides a practical tool for predicting the size of outbreaks from one index infection, in closed spaces such as schools, and could be applied to inform more focused mitigation measures. Author summaryAt the start of the COVID-19 pandemic, there was huge uncertainty around the risks of SARS-CoV-2 spread in classrooms. In the absence of early predictions surrounding classroom risks, many jurisdictions across countries closed in-person education. There is great interest in adopting a more precision approach to better inform future interventions in the context of airborne virus risks. For this purpose, we need tools that can predict the probability of the size of outbreaks within classrooms along with the impact of interventions including masks, better ventilation, and physical distancing by limiting the number of students per classroom. To this end, we have developed a robust but practical model that yields the probability of secondary infections stemming from index cases occurring within schools on a given day. During model development, the major underlying physical and biological factors that dictate the disease transmission process, both at long-range and close-range, have been accounted for. This enables our model to modify its predictions for different scenarios - and possibly allows its use beyond schools. Finally, the models predictive capability has been verified by comparing its outputs with publicly available data on SARS-CoV-2 diagnoses in Ontario public schools. To our knowledge, this is the first time an analytical model derived from mostly first principles describes real-world infection distributions, satisfactorily. The quantitative match between the theoretical prediction and real-world data offers the proposed model as a possible powerful tool for better-informed precision pandemic mitigation strategies in indoor environments like schools.

biophysics↗

bioRxiv2022

The Arabidopsis ARID-HMG protein AtHMGB15 modulates JA signalling by regulating MYC2 during pollen development

In flowering plants, jasmonic acid (JA) signalling regulates the complex process of male gametophyte development. JA signalling initiates with the activation of MYC2 transcription factor, for the expression of several JA responsive genes throughout stamen development and pollen maturation. However, the regulation of JA signalling during different developmental stages of male gametophytes is still less understood. In this study we have characterized T-DNA insertion line of AtHMGB15. Phenotypic characterization of athmgb15-4 mutant plants showed delayed bolting, shorter siliques and reduced seed set compared to wildtype. Moreover, deletion of AtHMGB15 resulted in defective pollen morphology, delayed pollen germination, abberant pollen tube growth and a higher percentage of non-viable pollen population in athmgb15-4 compared to wildtype. Molecular analysis indicated down-regulation of JA-biosynthesis and JA-signalling genes viz MYC2, MYB21 and MYB24 in athmgb15-4 mutant. Furthermore, jasmonic acid and its derivatives were found almost ten-fold lower in athmgb15-4 flowers. However, exogenous application of jasmonate could restore pollen morphology and pollen germination, suggesting that impaired JA signalling is responsible for the pollen phenotype in athmgb15 mutant. AtHMGB15 physically interacts with MYC2 protein to form the transcription activation complex for promoting transcription of genes responsible for JA signalling during stamen and pollen development. Collectively, our findings indicate that AtHMGB15, a plant specific DNA binding protein of the ARID-HMG group, acts as a positive regulator of JA signalling to control the spatiotemporal expression of key regulators responsible for stamen and pollen development.

developmental biology↗

bioRxiv2022

Unique molecular signatures sustained in circulating monocytes and regulatory T cells in Convalescent COVID-19 patients

Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and COVID-19-associated long-term complications, remains largely unknown. Herein, we characterized multi-omic single-cell profiles of circulating immune cells in the peripheral blood of 100 patients, including covenlesent COVID-19 and sero-negative controls. The reduced frequencies of both short-lived monocytes and long-lived regulatory T (Treg) cells are significantly associated with the patients recovered from severe COVID-19. Consistently, sc-RNA seq analysis reveals seven heterogeneous clusters of monocytes (M0-M6) and ten Treg clusters (T0-T9) featuring distinct molecular signatures and associated with COVID-19 severity. Asymptomatic patients contain the most abundant clusters of monocyte and Treg expressing high CD74 or IFN-responsive genes. In contrast, the patients recovered from a severe disease have shown two dominant inflammatory monocyte clusters with S100 family genes: S100A8 & A9 with high HLA-I whereas S100A4 & A6 with high HLA-II genes, a specific non-classical monocyte cluster with distinct IFITM family genes, and a unique TGF-{beta} high Treg Cluster. The outpatients and seronegative controls share most of the monocyte and Treg clusters patterns with high expression of HLA genes. Surprisingly, while presumably short-ived monocytes appear to have sustained alterations over 4 months, the decreased frequencies of long-lived Tregs (high HLA-DRA and S100A6) in the outpatients restore over the tested convalescent time (>= 4 months). Collectively, our study identifies sustained and dynamically altered monocytes and Treg clusters with distinct molecular signatures after recovery, associated with COVID-19 severity.

immunology↗

bioRxiv2019

Massively parallel single-cell B-cell receptor sequencing enables rapid discovery of diverse antigen-reactive antibodies

Using high-throughput single-cell B-cell receptor sequencing (scBCR-seq) we obtained accurately paired full-length heavy- and light-chain variable domains from thousands of individual B cells in a massively parallel fashion. We sequenced more than 250,000 B cells from rat, mouse and human repertoires to characterize their lineages and expansion. In addition, we immunized rats with chicken ovalbumin and profiled antigen-reactive B cells from lymph nodes of immunized animals. The scBCR-seq data recovered 81% (n = 56/69) of B-cell lineages identified from hybridomas generated from the same set of B cells that were subjected to scBCR-seq. Importantly, scBCR-seq identified an additional 710 candidate lineages that were not recovered as hybridomas. We synthesized, expressed and tested 93 clones from the identified lineages and found that 99% (n = 92/93) of the clones were antigen-reactive. Our results establish scBCR-seq as a powerful tool for antibody discovery.

immunology↗

bioRxiv2018

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their trans criptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the predictive power of GERAS to identify genome-wide molecular factors that correlate with aging. We show that one of these factors, junb, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling to detect pro-aging factors and candidate genes associated with aging.

bioinformatics↗