Biology subjects

Ghosal, S.

Publications and source records attributed to Ghosal, S..

12 recordsLinked to original sources

bioRxiv2026

Selective Stabilization of HRAS2 i-Motif DNA by TMPyP4: A Multimodal Biophysical and Thermodynamic Investigation

I-motif (iM) DNA structures, formed by cytosine-rich sequences, are increasingly acknowledged for their involvement in gene regulation, maintenance of genomic stability, and their emerging potential as therapeutic targets, particularly in cancer. Despite their biological relevance, the discovery of selective small-molecule probes that can specifically recognize and interact with iM DNA remains an ongoing challenge. In this study, we have used TMPyP4 and screened for its ability to bind various iM DNA constructs, including HRAS1, HRAS2, VEGF, CMYC, CKIT and H-Telo. Structure-activity relationship analyses revealed that specific substitution patterns conferred selectivity towards HRAS2 iM target. Comprehensive spectroscopic investigations, including UV-Vis absorption, steady-state and time-resolved fluorescence, and fluorescence anisotropy, uncovered key photophysical signatures of binding, including significant hypochromic and bathochromic shifts, enhanced fluorescence emission, and prolonged fluorescence lifetimes. Circular dichroism (CD),thermal denaturation (UV-melting) and thermodynamic investigations confirmed that TMPyP4 effectively stabilized the HRAS2 iM structures without disrupting their native topologies. Meanwhile, FT-IR spectroscopy revealed local structural rearrangements upon TMPyP4 binding, offering further evidence of molecular interaction. Collectively, these findings provide valuable insights into the molecular recognition of iM DNA by TMPyP4 and highlight its promise as both selective HRAS2 iM-binding agent and responsive fluorescent probe. This work lays a strong foundation for the development of novel tools for studying iM structures in biological systems and for designing future therapeutics targeting iM DNA in cancer and related diseases.

biophysics↗

bioRxiv2025

VariantFormer: A hierarchical transformer integrating DNA sequences with genetic variations and regulatory landscapes for personalized gene expression prediction

1Accurately predicting gene expression from DNA sequence remains a central challenge in human genetics. Current sequence-based models overlook natural genetic variation across individuals, while population-based models are restricted to variants observed within specific cohorts. Here, we present VariantFormer, a 1.2-billion-parameter transformer that predicts gene-level RNA abundance directly from personalized diploid genomes. Trained on 21,004 genome-transcriptome pairs from 2,330 donors, VariantFormer achieves state-of-the-art performance across both sequence- and population-based prediction tasks, while generalizing better to out-of-distribution contexts--including somatic mutation settings in cancer cell lines--and main-taining robustness across ancestries. Beyond expression prediction, VariantFormer improves eQTL effect size estimation compared to prior methods, with notable gains for lower-frequency and ancestry-specific variants. In applications to Alzheimers disease, VariantFormer gene embeddings prioritize likely causal genes and relevant tissue contexts, and in silico mutagenesis of known APOE alleles faithfully recovers known risk modifying effects. Together, these results establish VariantFormer as a scalable, diploid-aware framework for variant interpretation and personalized gene expression modeling across tissues and populations.

genomics↗

bioRxiv2025

Intracellular delivery of full-length antibodies via organ targeted lipid nanoparticles

Antibodies are proteins prized for their ability to bind to extracellular antigens with exceptionally high affinities and specificities. These features have motivated researchers to utilize antibody-antigen binding to inhibit intracellular disease targets in the proteome, yet delivery of antibodies into the cytosol of cells has long been a considerable challenge. Here, we outline the development of a novel lipid nanoparticle (LNP) platform for delivering antibodies into cells to selectively inhibit disease-relevant cytosolic targets. This approach efficiently delivers various therapeutic antibodies into multiple cancer cell lines, inhibiting key transcription factors in inflammatory and cancer signaling pathways. We further demonstrate systemic delivery of therapeutic antibodies in disease models, including alpha-synuclein-specific antibodies for Parkinsons disease and RelA-specific IgGs for acute lung injury using targeted LNP formulations. This work establishes a promising method for using LNPs for the delivery of antibody and antibody-derived therapeutics intracellularly to treat numerous proteome targets.

bioengineering↗

bioRxiv2025

Cholesteryl esters and high protein-to-lipid ratios distinguish Non-Vesicular Extracellular Particles from Extracellular Vesicles

Extracellular vesicles (EVs) are central to intercellular communication, yet the mechanisms underlying their biogenesis and diversity remain incompletely understood. Here, we integrate meta-analysis, advanced lipidomic, protein-to-lipid profiling, and super-resolution imaging to define the fundamental principles governing EV heterogeneity. Our meta-analysis of published transmission electron microcrographs across kingdoms reveals a highly conserved 110 nm average diameter and 200 nm upper size limit for intraluminal vesicles (ILVs), which are secreted as exosomes. Besides classical EV populations, we also characterize a distinct nanoparticle class: 167000 xg pellet of non-vesicular extracellular particles (167k-NVEPs), which exhibit a significantly higher protein-to-lipid ratio than 14000 xg pellet of large EVs (14k-lEVs) and 100000 xg pellet of small EVs (100k-sEVs), as measured by both biochemical assays and Raman spectroscopy. Lipid profiling demonstrates that 167k-NVEPs exhibit significant enrichment in cholesteryl esters and triacylglycerols, lipids typically associated with lipid droplets and the endosome/lysosome system. Analysis of lipid carbon-chain lengths reveals distinct signatures: 167k-NVEPs show pronounced enrichment at 16 and 18 carbons, while 100k-sEVs display enrichment at 32 and 34 carbons. This divergence indicates a potential connection to flexible biogenesis pathways. Marker heterogeneity across EV populations, confirmed by confocal and super-resolution microscopy, further underscores the limitations of relying on canonical tetraspanins for EV classification. Notably, 167k-NVEPs (likely exomeres) exhibit enrichment of Arf6 and CD63. Together, our findings provide compositional, biophysical, and molecular evidence supporting the formal recognition of 167k-NVEPs as a distinct class of extracellular particles and enabling exploring in disease biology and therapeutic delivery. Significance StatementExtracellular vesicles (EVs) are critical mediators of intercellular communication, yet their classification remains clouded by ambiguity in terms of their composition and biogenesis. This study resolves critical uncertainties through a cross-kingdom meta-analysis, establishing a conserved [~]110nm diameter and [~]200 nm upper size limit for intraluminal vesicles (ILVs), the precursors to exosomes. More significantly, we identify non-vesicular extracellular particles (167k-NVEPs) as a distinct class based on their unique sterol-rich lipidome, enrichment in lipids of 16 and 18 carbon chain length, elevated protein-to-lipid ratio, and functional cargo delivery. These features, alongside evidence of non-canonical origin and functional cargo delivery, establish NVEPs as a discrete class of extracellular particles.

cell biology↗

bioRxiv2025

Future flooding tolerant rice germplasm: resilience afforded beyond Sub1A gene

Developing high-yielding, flood-tolerant rice varieties is essential for enhancing productivity and livelihoods in flood-prone ecologies. We explored genetic avenues beyond the well-known SUB1A gene to improve flood resilience in rice. We screened a collection of 6,274 elite genotypes from IRRIs germplasm repository for submergence and stagnant flooding tolerance over multiple seasons and years. This rigorous screening identified 89 outstanding elite genotypes, among which thirty-seven exhibited high submergence tolerance, surpassing the survival rate of SUB1A introgression genotypes by 40-50%. Thirty-five genotypes showed significant tolerance to stagnant flooding, and 17 demonstrated dual tolerance capabilities, highlighting their adaptability to varying flood conditions. The genotypes identified have a broader genetic diversity and harbor 86 key QTLs and genes related to traits such as grain quality, grain yield, herbicide resistance, and various biotic and abiotic traits, highlighting the richness of the identified elite collection. Besides germplasm, we introduce an innovative breeding approach called Transition from Trait to Environment (TTE). TTE leverages a parental pool of high-performing genotypes with complete submergence tolerance to drive population improvement and enable genomic selection in the flood breeding program. Our approach of TTE achieved a remarkable 65% increase in genetic gain for submergence tolerance, with the resulting fixed breeding genotypes demonstrating exceptional performance in flood-prone environments of India and Bangladesh. The elite genotypes identified herein represent invaluable genetic resources for the global rice research community. By adopting the TTE approach, which is trait agonistic, we establish a robust framework for developing more resilient genotypes using advanced breeding tools. Plain Language SummaryTo address climate challenges, an urgent focus is necessary to identify and develop flood-tolerant rice varieties, particularly for flood-prone ecosystems across Asia and Africa. We screened 6,274 elite genotypes from IRRIs germplasm and identified 89 promising lines with improved tolerance to submergence and stagnant flooding. Among these, 37 demonstrated 40-50% greater submergence tolerance than SUB1A introgression lines, 35 exhibited stagnant flooding tolerance, and 17 showed dual tolerance. These genotypes contain 86 key QTLs and genes associated with yield, grain quality, and biotic and abiotic tolerance traits. A new breeding strategy, the Transition from Trait to Environment (TTE) approach, was developed. We achieved a genetic gain of 65% for submergence tolerance in rice using this method. The newly identified germplasm provides invaluable genetic resources for the global rice research community to develop flood-tolerant rice genotypes. Core ideas The SUB1A gene, enabling rice to survive underwater for 14 days, marked a significant breakthrough. We have identified elite genotypes with submergence tolerance significantly surpassing the SUB1A gene-mediated tolerance. The diverse elite genotypes identified harbor 86 key genes and QTLs that affect various traits positively. Developed a unique breeding strategy for implementing population improvement in challenging environments. The new breeding strategy demonstrated a genetic gain of 65% for submergence tolerance.

plant biology↗

bioRxiv2024

Discovery of a single-subunit oligosaccharyltransferase that enables glycosylation of full-length IgG antibodies in Escherichia coli

Human immunoglobulin G (IgG) antibodies are one of the most important classes of biotherapeutic agents and undergo glycosylation at the conserved N297 site in the CH2 domain, which is critical for IgG Fc effector functions and anti-inflammatory activity. Hence, technologies for producing authentically glycosylated IgGs are in high demand. While attempts to engineer Escherichia coli for this purpose have been described, they have met limited success due in part to the lack of available oligosaccharyltransferase (OST) enzymes that can install N-linked glycans within the QYNST sequon of the IgG CH2 domain. Here, we identified a previously uncharacterized single-subunit OST (ssOST) from the bacterium Desulfovibrio marinus that exhibited greatly relaxed substrate specificity and, as a result, was able to catalyze glycosylation of native CH2 domains in the context of both a hinge-Fc fragment and a full-length IgG. Although the attached glycans were bacterial in origin, conversion to a homogeneous, asialo complex-type G2 N-glycan at the QYNST sequon of the E. coli-derived hinge-Fc was achieved via chemoenzymatic glycan remodeling. Importantly, the resulting G2-hinge-Fc exhibited strong binding to human Fc{gamma}RIIIa (CD16a), one of the most potent receptors for eliciting antibody-dependent cellular cytotoxicity (ADCC). Taken together, the discovery of a unique ssOST from D. marinus provides previously unavailable biocatalytic capabilities to the bacterial glycoprotein engineering toolbox and opens the door to using E. coli for the production and glycoengineering of human IgGs and fragments derived thereof.

synthetic biology↗

bioRxiv2024

Nanoparticle-mediated delivery of peptide-based degraders enables targeted protein degradation

The development of small molecule-based degraders against intracellular protein targets is a rapidly growing field that is hindered by the limited availability of high-quality small molecule ligands that bind to the target of interest. Despite the feasibility of designing peptide ligands against any protein target, peptide-based degraders still face significant obstacles such as, limited serum stability and poor cellular internalization. To overcome these obstacles, we repurposed lipid nanoparticle (LNP) formulations to facilitate the delivery of Peptide-based proteolysis TArgeting Chimeras (PepTACs). Our investigations reveal robust intracellular transport of PepTAC-LNPs across various clinically relevant human cell lines. Our studies also underscore the critical nature of the linker and hydrophobic E3 binding ligand for efficient LNP packaging and transport. We demonstrate the clinical utility of this strategy by engineering PepTACs targeting two critical transcription factors, {beta}-catenin and CREPT (cell-cycle-related and expression-elevated protein in tumor), involved in the Wnt-signalling pathway. The PepTACs induced target-specific protein degradation and led to a significant reduction in Wnt-driven gene expression and cancer cell proliferation. Mouse biodistribution studies revealed robust accumulation of PepTAC-LNPs in the spleen and liver, among other organs, and PepTACs designed against {beta}-catenin and formulated in LNPs showed a reduction in {beta}-catenin levels in the liver. Our findings demonstrate that LNPs can be formulated to encapsulate PepTACs, thus enabling robust delivery and potent intracellular protein degradation.

bioengineering↗

bioRxiv2024

Bioreversible anionic cloaking enables intracellular protein delivery with ionizable lipid nanoparticles

Protein-based therapeutics comprise a rapidly growing subset of pharmaceuticals, but enabling their delivery into cells for intracellular applications has been a longstanding challenge. To overcome the delivery barrier, we explored a reversible, bioconjugation-based approach to modify the surface charge of protein cargos with an anionic "cloak" to facilitate electrostatic complexation and delivery with lipid nanoparticle (LNP) formulations. We demonstrate that the conjugation of lysine-reactive sulfonated compounds can allow for the delivery of various protein cargos using FDA-approved LNP formulations of the ionizable cationic lipid DLin-MC3-DMA (MC3). We apply this strategy to functionally deliver RNase A for cancer cell killing as well as a full-length antibody to inhibit oncogenic {beta}-catenin signaling. Further, we show that LNPs encapsulating cloaked fluorescent proteins distribute to major organs in mice following systemic administration. Overall, our results point towards a generalizable platform that can be employed for intracellular delivery of a wide range of protein cargos.

bioengineering↗

bioRxiv2023

BEATRICE: Bayesian Fine-mapping from Summary Datausing Deep Variational Inference

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimers disease. In comparison to the baselines, only BEATRICE can successfully find the APOE{epsilon} 2 allele, a commonly associated variant of Alzheimers. Thus, we show that BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

genetics↗

bioRxiv2022

Mitofusin-2 in nucleus accumbens D2-MSNs regulates social dominance and neuronal function

The nucleus accumbens (NAc) is a brain hub regulating motivated behaviors, including social competitiveness. Mitochondrial function in the NAc is critically implicated in the association between anxiety and social competitiveness, and the mitochondrial fusion protein mitofusin 2 (Mfn2) in NAc neurons has been shown to regulate anxiety-related behaviors. However, it remains unexplored whether accumbal Mfn2 levels also affect social behavior and whether Mfn2 actions in the emotional and social domain are driven by distinct cell types. Here, we found that subordinate-prone highly anxious rats show reduced accumbal Mfn2 levels and that Mfn2 overexpression promotes dominant behavior. In mice, selective Mfn2 downregulation in NAc dopamine D2 receptor-expressing medium spiny neurons (D2-MSNs) induced social subordination, accompanied by reduced mitochondrial function and decreased neuronal excitability. Instead, D1-MSN-targeted Mfn2 downregulation affected competitive ability only transiently mainly by increases in anxiety-like behaviors. Our results assign dissociable cell-type specific roles to Mfn2 in the NAc in modulating social dominance and anxiety.

neuroscience↗

bioRxiv2022

Combining phenotypic and genomic data to improve prediction of binary traits

Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here main traits) of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs often have information on secondary traits (or phenotypes) that are easy to measure. Our goal is to improve prediction of main traits with interpretable relations by combining the two data types using variable selection techniques. However, the genomic characteristics can overwhelm the set of secondary traits, so a standard technique may fail to select any phenotypic variables. We develop a new statistical technique that ensures appropriate representation from both the secondary traits and the phenotypic variables for optimal prediction. When two data types (markers and secondary traits) are available, we achieve improved prediction of a binary trait by two steps that are designed to ensure that a significant intrinsic effect of a phenotype is incorporated in the relation before accounting for extra effects of genotypes. First, we sparsely regress the secondary traits on the markers and replace the secondary traits by their residuals to obtain the effects of phenotypic variables as adjusted by the genotypic variables. Then, we develop a sparse logistic classifier using the markers and residuals so that the adjusted phenotypes may be selected first to avoid being overwhelmed by the genotypes due to their numerical advantage. This classifier uses forward selection aided by a penalty term and can be computed effectively by a technique called the one-pass method. It compares favorably with other classifiers on simulated and real data.

genomics↗

bioRxiv2021

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Phenotypes of Disease

AO_SCPLOWBSTRACTC_SCPLOWWe propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.

bioinformatics↗