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Chowdhury, R.

Publications and source records attributed to Chowdhury, R..

At least 19 recordsLinked to original sources

FluxSprout: Single-cell metabolic modeling of vascular sprouting in zebrafish

Genome-scale metabolic modeling offers a powerful framework for identifying cell-state-specific metabolic vulnerabilities, yet its application to heterogeneous biological systems remains limited by uncertainty in network annotations, thermodynamic feasibility, transcriptome-to-flux inference, and target specificity. Here, we introduce FluxSprout, a single-cell transcriptomics-informed genome-scale metabolic modeling framework to resolve metabolic heterogeneity during zebrafish sprouting angiogenesis. By integrating single-cell RNA sequencing of 10,090 quality-controlled cells with Zebrafish-GEM, we reconstructed network ensembles for 14 endothelial and hemogenic states. We established four safeguards: stoichiometric auditing, loop auditing, cell-cycle-controlled testing, and ensemble specificity screening. First, we identified four inorganic pyrophosphatase reactions misannotated as oxidative phosphorylation, showing that apparent pathway essentiality arose only with unbounded exchange fluxes. Second, 19.5% of reactions could carry thermodynamically infeasible loop flux, including every candidate of a conventional screen. Third, tip-like and stalk-like cells differed mainly in cell-cycle phase, and elevated glycolysis separated sprouting from established vessels only in gene expression. In contrast, sprouting vessels had higher predicted growth capacity at both constraint strengths, with serine and nucleotide synthesis following in fixed proportion. Fourth, screening 825 genes across four network-extraction settings and 13 non-endothelial lineages left no candidate called by more than one setting. Notably, SLC25A20, that screen's lead candidate, had no predicted effect in any FASTCORE network. Although the data derive from a single pooled clutch and require experimental validation, FluxSprout provides a transferable, uncertainty-aware framework for distinguishing genuine metabolic dependencies from artifacts of network reconstruction, model assumptions, and cellular heterogeneity, and identifies biosynthetic capacity as a testable feature of sprouting vessels.

systems biology↗

Synapsin-1 and actin form ordered nanoscale assemblies

Liquid-liquid phase separation is a vital and ubiquitous principle of subcellular organization. Several actin-binding proteins have recently been shown to undergo liquid-liquid phase separation, forming micron-sized droplets that assemble actin into distinct network shapes. However, our knowledge of how these phase-separated proteins organize actin filaments at the nanoscale is rather limited. Here, we seek to address this knowledge gap by investigating synapsin-1 condensates through a combination of computational simulations and nanoscale imaging experiments. Our results show that while synapsin-1 condensates by themselves lack any special structural organization, the addition of actin filaments results in the generation of a regularly organized actin scaffold within the condensates. The organized scaffold forms both when actin and synapsin-1 are added simultaneously and when actin is added after the formation of synapsin condensates. Dissolving the condensates, by synapsin-1 removal, leaves the actin scaffold largely unaffected in both simulations and experiments, implying that this scaffold should guide droplet reformation when synapsin-1 is added back. Taken together, our findings suggest that the actin-rich component of the condensate retains a structural memory that promotes and guides the reassembly of synapsin-actin condensates. This structural memory may be important in the long-term maintenance of synaptic function in vivo.

biophysics↗

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

KDM6A Loss Confers an Invasive Phenotype in Osteosarcoma by Activating Cytoplasmic YAP-Dependent β-catenin Stabilisation

Osteosarcoma (OS) is the most common primary malignant bone tumour and is characterised by aggressive growth, early metastasis, and a very stagnant clinical outcome. Although epigenetic dysregulation has been implicated in OS progression, the mechanisms linking epigenetic alterations to metastatic signalling remain unclear. Here, we identified the lysine-protein demethylase 6A (KDM6A/UTX) as a critical suppressor of OS metastasis and uncovered a novel regulatory axis involving the Hippo/YAP and Wnt/{beta}-catenin signalling. Initial bioinformatics analyses revealed frequent KDM6A alterations and significantly reduced expression in OS patient datasets, which correlated with metastatic disease and poor prognosis. Functional inhibition of KDM6A by pharmacological inhibitors and siRNA induced a hyper-invasive phenotype, marked by elevated mesenchymal markers, enhanced cytoskeletal remodelling, increased transendothelial adhesion and decreased chemotherapeutic drug sensitivity. Importantly, restoration of KDM6A expression effectively counteracted these effects. Mechanistically, KDM6A loss activated Wnt/{beta}-catenin signalling, resulting in nuclear translocation of {beta}-catenin and transcriptional activation of genes associated with stemness and invasion. Therefore, inhibition of {beta}-catenin reversed the invasive phenotype. Further analysis revealed that KDM6A regulated Hippo signalling through epigenetic control of the negative regulator of Yes-Associated Protein (YAP)-LATS1. KDM6A inhibition led to enrichment of H3K27me3, a repressive mark, at the LATS1 promoter. Accumulated YAP was predominantly localised in the cytoplasm, where it interacted with GSK3{beta} and contributed to the stabilisation of {beta}-catenin by preventing its proteasomal degradation. Collectively, our findings identify a novel KDM6A-LATS1-YAP-{beta}-catenin signalling axis that drives metastatic progression in OS.

cancer biology↗

A sex-specific regulator expands the embryonic ocular field to generate a novel visual system

Morphological novelties often emerge through the redeployment of conserved developmental programs, but how such programs become activated in new developmental contexts remains poorly understood. We investigated the evolutionary origin of the turbanate eyes (TurbEyes) of mayflies, a male-specific additional visual system. Using single-cell transcriptomics, chromatin accessibility profiling and functional genetics, we show that this novelty develops through deployment of the ancestral retinal determination gene network (RDGN). We identify a novel, male-specific paralog of the sex determination transcription factor Doublesex, DsxM, that regulates the eye-specification gene eyeless/Pax6 and promotes the expansion of the embryonic ocular field. Functional assays impairing dsxM activity reduce this male-specific expansion and causes defects in TurbEye development. Our findings reveal a developmental mechanism by which a sex-specific regulator generates a new developmental territory, enabling the spatial redeployment of a conserved developmental program and to generate a novel male-specific visual system.

evolutionary biology↗

Spatial Metabolic Modeling Reveals Zinc–Citrate Rewiring and Therapeutic Vulnerabilities in Prostate Cancer

Prostate cancer exhibits a metabolic phenotype distinct from the Warburg paradigm, characterized by reprogramming of the zinc-citrate secretory axis that normally drives citrate accumulation and secretion in prostatic epithelium. How this metabolic rewiring is organized across the spatial architecture of tumors and linked to androgen receptor (AR) signaling remains poorly understood. We integrated spatial transcriptomics data from twelve regions spanning normal, inflamed, and Gleason-graded prostate tissues with genome-scale metabolic modeling to quantify metabolic activity across tissue states. The analysis identified the zinc-citrate-aconitase axis as a central feature of prostate cancer metabolic reprogramming, linking increased aconitase activity to enhanced citrate export, acetyl-CoA generation, and de novo lipogenesis. Metabolic similarity was more strongly associated with spatial proximity than with histological classification, suggesting field-level metabolic reprogramming beyond visible pathological boundaries. AR activity displayed grade-dependent associations with lipid metabolic pathways, indicating heterogeneous coupling between androgen signaling and metabolism across tumor regions. Consistent with the established biology of prostate cancer, predicted flux distributions did not support a classical Warburg phenotype. A flux-based target prioritization framework identified HMGCR, FASN, and SLC25A1 as candidate therapeutic targets, with independent support from TCGA-PRAD expression profiles and DepMap CRISPR essentiality data. Together, these findings provide a spatially resolved view of prostate cancer metabolism, establish the zinc-citrate axis as a dominant feature of metabolic organization, and identify candidate metabolic vulnerabilities for therapeutic intervention.

Cancer Biology↗

Multimodal Hypersampling of the adult human heart resolves tissue-specific cell states and disease-associated variants

Cardiovascular diseases often arise in anatomically specialised regions of the heart that remain poorly represented in existing single-cell and spatial atlas references. Here we profiled 28 regions of the adult human heart from 36 donors, combining paired single-nucleus RNA and chromatin accessibility assays with spatial transcriptomics. The resulting atlas integrates 1,059,175 expression profiles and 452,614 chromatin accessibility profiles, and defines spatial niches across the heart. In cardiac valves, we identify a valve fibroblast-macrophage niche that is polarised to the non-fibrosa surface and expands with age. Aortic stenosis genetic risk was enriched in inflammatory regulatory programmes of the Valve Fibroblast Immune cell state. In pulmonary veins, we define PITX2+ Myocardial Sleeve Cells, a region-restricted cardiomyocyte population in which a fine-mapped 4q25 atrial fibrillation variant intersects a cell type-specific open chromatin region. In coronary arteries, we resolve a RUNX1-marked synthetic smooth muscle layer in the subintima that provides a molecular identity for diffuse intimal thickening, a constitutive feature of the healthy vessel wall and a substrate for atherosclerosis, and show by perturbation in human smooth muscle cells that RUNX1 promotes cell-cycle entry while restraining inflammatory cytokine gene expression. Finally, allele-specific accessibility together with deep learning models fine-tuned to predict regulatory activity from DNA sequence identify cell type-specific regulatory effects at GWAS loci for atrial fibrillation, coronary artery disease and calcific aortic valve stenosis, including a variant whose Valve Fibroblast-specific effect on the polyamine transporter ATP13A3 is opposite in direction to the effect seen in bulk-tissue eQTL references that lack valve cells. Together with these cardiac sequence models and CardioSleuth, an AI-powered exploration engine for cardiac regulatory effects, this atlas links cardiac anatomy, gene regulation and disease genetics at single-cell resolution. By placing genetic risk in its precise cellular and anatomical context, it provides a foundation for mechanistic disease understanding, cell-state-specific therapeutic target nomination, and improved interpretation of inherited cardiovascular risk to inform future therapy and management.

Cell Biology↗

Cyclome: Large-scale replica-exchange dynamics of 930 cyclic peptide reveal thermal stability and critical metal-binding behavior

Cyclic peptides are recognized as versatile scaffolds for therapeutic and functional applications due to their structural stability and resistance to degradation. Despite this promise, systematic analysis and prediction of their thermal stability remain limited by fragmented data resources, inadequate sequence comparison methods, and the lack of cyclicity-aware computational models. We provide a comprehensive, multi-scale computational framework to characterize cyclic peptides. First, we unified four fragmented public repositories of cyclic peptides into a single largest curated resource of 930 cyclic peptides, Cyclome930. This integrates cyclic topology, sequence, experimental structural coordinates, and source organism annotations into a consistently featurized dataset. Cyclome930 thus expands the dataset of annotated cyclic peptides by [~]3.4 fold (from 276 to 930). Second, we developed a novel cyclic sequence alignment algorithm that explicitly accounts for rotational symmetry and knot topology, enabling more accurate scoring of sequence similarity than conventional linear alignments. Third, we investigate the thermal stability of cyclic peptides using extensive all-atom replica-exchange molecular dynamics (100ns; REMD) simulations, allowing conformational sampling across 298 K - 400 K and track its stress tensors with increasing temperature. Finally, these simulation-derived thermo-stability metrics were used to train a machine learning model to predict cyclic peptide melting points from sequence and topology (STop2Melt). Crucially, the model introduces cyclicity-aware embeddings derived from ESMc representations coupled with cyclic offset vector, capturing the peptides knot topology. STop2Melt achieved strong predictive performance on held-out peptides and outperforms baseline methods that neglect cyclic structure. Finally, we scored Cyclome930 (cyclic ligands) for critical mineral metal binding using a multi-classifier model (CritiCL). To our knowledge, Cyclome930 represents the first effort in peptide literature to integrate physics-based temperature ramped simulations, cyclic sequence similarity scoring, machine learning for thermal stability prediction and scoring them for critical metal binding. Cyclicity-aware computational toolchains (cyclome930.studio/) provide a foundational resource for computational design of stable cyclic peptide prototype libraries thereby annotating and expanding genomic islands linked to critical mineral recovery.

bioinformatics↗

Polypharmacology of an Optimal Kinase Library

Despite decades of research, current understanding of the spectrum of targets bound by kinase inhibitors remains incomplete. This complicates mechanism of action studies, drug repurposing, and development of new therapies. Here, we describe kinome-wide profiling of an optimal kinase library (OKL) comprising 192 small molecules selected based on stage of clinical development, chemical diversity, and target coverage. Our results show that polypharmacology is widespread and independent of regulatory approval. The generally understood ("assigned") targets of approved molecules are not necessarily the most potently inhibited and off-targets include multiple understudied kinases. Moreover, median selectivity has not increased over time We illustrate how an OKL in combination with detailed kinome profiling can be used to identify potential toxicity targets, repurpose anti-inflammatory drugs for neurodegenerative and infectious diseases, and perform chemical genetic studies. Our studies also highlight how much remains to be discovered about the chemistry and biology of one of the largest classes of human therapeutics.

pharmacology and toxicology↗

Structural and Metabolic Characterization of Ni(I)-inhibitors Provide a Robust Anti-Methanogenicity Scoring System

Atmospheric methane (CH4) acts as a key contributor to global warming and a short-lived climate forcer. CH4 mitigation represents the most promising means to address short-term climate change. Ruminant enteric CH4 produced by methanogenic archaea represents 27.2% of global CH4 emissions. Only a few of the direct methanogenesis inhibitors identified bear high mitigation potential hence it is important to investigate their underlying modes of action. Here, we elucidated biophysical and thermodynamic interplay between known inhibitors and cofactor F430, to determine their stoichiometric ratios and binding affinities. We leverage this prior in a robust contrastive learning approach to functionally cluster known sixteen inhibitors and 53,959 bovine-linked metabolites. We demonstrate a multi-factor optimization protocol to identify putative inhibitors with: (i) high bacterial membrane permeability, (ii) no adverse effect to ruminal fermentation, (iii) known degradation pathway, and (iv) direct commercial availability. Subsequent in vitro assays and community metabolic modeling with a first set of eight treatment molecules revealed structo-metabolic priors that tie thermodynamic signatures of inhibition to metabolic flux shifts. We established a multi-scale workflow that transforms ostensibly negative compounds into mechanistic insight, linking rumen metabolic flux shifts to MCR-F430-Ni(I) inhibition chemistry as a foundation for rational methane-mitigation design. COVER ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/708075v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1d38d21org.highwire.dtl.DTLVardef@1d681d9org.highwire.dtl.DTLVardef@1e70c0corg.highwire.dtl.DTLVardef@1c8223f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

DRP1-mediated mitochondrial dynamics orchestrate EMT in glioblastoma cells

BackgroundEpithelial to mesenchymal transition (EMT), a differentiation process, frequently imparts invasive properties in Glioblastoma Multiforme (GBM), which leads to a poor prognosis. Cells lose apical-basal polarity, cell-cell connections, and/or chemo-resistance during EMT, which can result in the spread of cancer and the acquisition of additional stem cell-like traits. It is unclear how organelle dynamics influence EMT in this respect. The interaction between cytoskeletal and mitochondrial regulators governing GBM cell EMT is explored in this article. Results and DiscussionIn GBM cells, we observed that TGF-{beta}-induced EMT led to a proliferative arrest, which was accompanied by a fragmented mitochondrial morphology, elevated expression of fission markers such as DRP1, MFF, and FIS1, and most importantly, localization of mitochondria near the cell boundaries. An increase in mitochondrial ROS accompanied this, but their functional status was indicated by a higher oxygen consumption rate (OCR). Additionally, cytoskeleton re-distribution and EMT reversal were the outcomes of si-RNA-mediated elimination of the fission-marker DRP-1 or pharmacological inhibition of fission by Mdivi-1. On the other hand, drugs that disrupt the cytoskeleton, shifted the spatial distribution of mitochondria to the perinuclear area, which had an adverse effect on EMT. Notably, it was shown that RhoA, a protein that helps organize the actin cytoskeleton, co-immunoprecipitates with DRP1 and governs both cytoskeletal dynamics and mitochondrial fission in its presence. ConclusionOur research sheds substantial insight on the current interactions between the cytoskeleton and mitochondrial spatial dynamics that control EMT in GBM cells, which may have significant therapeutic implications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/694080v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@81b796org.highwire.dtl.DTLVardef@3bec9aorg.highwire.dtl.DTLVardef@2a79c3org.highwire.dtl.DTLVardef@9ff14a_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

A switch from histone methyltransferase-EZH2 to demethylase KDM6A activity marks reinitiation of proliferation of drug treated colorectal cancer cells

Colorectal cancer (CRC) is one of the deadliest cancers, ranking third in cancer incidence worldwide. These tumor cells often adopt unique strategies under drug stress to attain a reversible drug-tolerant state and evade cell death. However, the molecular adaptations associated with this transitory emergence of the drug-tolerant state remain elusive. Herein, epigenetic alterations often dictate such reversible dynamic changes, and this study aims to characterize the role of specific epigenetic modifiers governing CRC cell survival under drug pressure and their subsequent relapse. We observed that under drug stress there is a drastic increase in the histone-repressive mark-H3K27me3, linked to an enhanced expression of EZH2 driving transcriptional inhibition of cell proliferation-associated genes and a proliferative arrest. Interestingly, drug-induced oxidative stress increased the expression of the P65 protein, which was found to interact and regulate EZH2 expression. Quenching of ROS, drug vacation, or P65 inactivation compromised EZH2 activity concurrent with a re-initiation of cell proliferation. Interestingly, this reversal to proliferative state was associated with an elevated activity of the histone lysine demethylase-KDM6A. The promoter elements of the proliferative genes were now occupied by KDM6A instead of EZH2. Accordingly, a genetic knockdown or pharmacological inhibition of KDM6A in vitro not only resulted in increased cell death but also prevented emergence of the re-proliferative CRC cells. Furthermore, KDM6A inhibition in combination with chemotherapy drug, resulted in an increased tumor regression in vivo. Our study thus highlights the importance of KDM6A as a therapeutic target in preventing CRC growth and relapse which can have future therapeutic implications.

cancer biology↗

LncRNA YIYA enhances pancreatic cancer proliferation under high-glucose conditions through RAS and PKM2 mediated metabolic reprogramming that reinforces the Warburg phenotype

Diabetes and the resulting hyperglycemia are metabolic complications frequently linked to pancreatic cancer (PC). In this study, we observe that the long non-coding RNA (lncRNA)-YIYA, which resides in a genomic region (1q32), frequently altered in various cancers, is overexpressed under conditions of chronic or intermittent high glucose (HG; 25 mM) compared to normal glucose (NG; 5.5 mM) in pancreatic ductal adenocarcinoma (PDAC) cells. It plays a pivotal role in regulating cellular metabolism-linked proliferation of PDAC cells, both under 2D and 3D conditions. Analysis of clinical datasets suggested that patients with elevated YIYA expression generally experience reduced survival in pancreatic cancer. Mechanistically, YIYA was found to regulate cellular energy metabolism and promote a Warburg phenotype, thereby facilitating the rapid proliferative status of PDAC cells. YIYA-mediated effects were found to be dependent on KRAS. Interestingly, the RIP assay revealed that YIYA interacts not only with KRAS but also with an important enzyme catalyzing the final step of the biochemical process, glycolysis, pyruvate kinase M2 (PKM2). YIYA was found to be predominantly localised in the cytoplasm, protecting the stability of the KRAS protein under a hyperglycaemic state. Importantly, YIYA knockdown enhanced autophagy-mediated degradation of KRAS. Our study is the first to identify YIYA as a glucose-responsive lncRNA in PDAC cells, and to reveal a novel YIYA-mediated connection between oncogenic KRAS signalling and metabolic reprogramming. Targeting YIYA represents a promising therapeutic strategy by disrupting the KRAS-PKM2-Warburg axis, thereby sensitizing PDAC cells. Graphical abstracts figure: Proposed model illustrating the effect of hyperglycemia on lncRNA YIYA-mediated regulation of KRAS signaling and metabolic reprogramming in pancreatic cancerUnder hyperglycemic conditions, elevated glucose uptake through GLUT (Glucose Transporter) enhances glycolytic flux, leading to increased levels of PEP (Phosphoenolpyruvate) and activation of PKM2 (Pyruvate Kinase M2), which results in increased pyruvate flux toward the LDH (Lactate Dehydrogenase) pathway and subsequent increased lactate production, promoting the Warburg effect. Downregulation of MPC (Mitochondrial Pyruvate Carrier) also supports reduced shuttling of pyruvate towards the TCA (Tricarboxylic Acid) cycle. On the other hand, constitutively activated master regulator KRAS activates the downstream proliferative signalling via AKT and other DTs (Downstream targets), which upregulates various genes, including lncRNA YIYA aka LINC00538. YIYA exists in a functional complex with KRAS and PKM2, further reinforcing glycolytic and proliferative signalling. Additionally, YIYA suppresses autophagy-mediated degradation of KRAS. Together, these events contribute to hyperglycemia-driven epigenetic changes, metabolic reprogramming, and tumor progression in pancreatic cancer cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/685546v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@13d271org.highwire.dtl.DTLVardef@15d01fcorg.highwire.dtl.DTLVardef@12d7225org.highwire.dtl.DTLVardef@a11c5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Structurally Informed Fitness Landscapes for Surveillance of Emerging PRRSV Variants

Antibodies play a central role in neutralizing pathogens through direct interference with viral entry and recruitment of effector immune cells. However, viruses employ sophisticated escape mechanisms to evade these defenses, primarily through mutations in surface glycoproteins that reduce antibody binding affinity or alter critical functional domains. Antibody escape remains a formidable challenge in drug design efforts for Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), a pathogen notorious for its rapid evolution and structural plasticity. Here we present EscaPRRS (Escape scoring for PRRS virus), a Bayesian variational autoencoder trained on ESM-2 protein language model embeddings to predict the fitness landscape and escape propensities of 52,622 mutants (recorded over 10 years) of the immunodominant GP5 glycoprotein encoded by PRRSV ORF5 gene. Unlike conventional models that estimate escape propensity only from sequence information, EscaPRRS circumvents the need for extensive alignments, integrating contributions from surface accessibility and biochemical dissimilarity at the binding interface. Our escape propensity scores demonstrate reliable structural and biological fidelity, with EscaPRRS scores correlating with binding affinities on seven different porcine receptor proteins (Pearson r = 0.74). Notably, EscaPRRS captures seasonal trends in immune evasion, highlighting its applicability in forecasting and surveillance of emerging/re-emerging PRRSV variants. IMPORTANCEPorcine Reproductive and Respiratory Syndrome Virus (PRRSV) remains the most economically detrimental illness for swine products, causing more than $1.2 billion in annual production loss in the United States, with indirect implications to food security and human health. PRRSV infection is known to severely affect porcine alveolar macrophages (PAMs), causing respiratory difficulties, following blockage of inflammatory signals that aids easy viral reproduction in the host cell. Identifying critical amino acid mutations at the highly variant GP5 glycoprotein attached to the viral cell membrane provides information on structural features linked to antigenic diversity and antibody neutralization, that potentially lead to clinical outbreaks in sow farms. Our effort employs machine learning approaches to learn patterns from a large number of sequences to map these critical domains to three-dimensional structures to score them for antibody escape tendencies. This greatly enhances our understanding of receptor and antibody binding mechanisms in PRRSV GP5.

biochemistry↗

Cisplatin-induced oxidative stress regulates YAP to modulate epigenome promoting survival of osteosarcoma cells

The widely used chemotherapeutic drug cisplatin (CDDP) is an integral part of the pre-operative chemotherapy protocol for high-grade osteosarcoma (OS). However, despite an aggressive treatment regimen, drug refractoriness is a major hindrance to successful therapy. We previously identified key transcriptomic alterations essential for the survival of OS cells following CDDP exposure. In the present study, we further demonstrate that CDDP treatment resulted in a ROS-dependent enrichment of the repressive histone mark H3K27me3 at the upstream promoter regions of growth-promoting genes such as CCNA2, and on the promoter of the negative regulator of Yes-Associated Protein (YAP)-LATS1, thereby contributing to their transcriptional repression. This was associated with a growth arrest, and quenching of ROS with N-acetyl cysteine (NAC) reversed it. Importantly, repression of LATS1 led to an increased nuclear localization of YAP, while pharmacological or genetic ablation of YAP reduced CDDP-mediated induction of repressive marks. YAP was further found to co-localize and co-immunoprecipitate with the Polycomb Repressive Complex 2 (PRC2) catalytic member-the histone methyl transferase-EZH2, indicating its putative role in mediating transcriptional repression. In lieu of the above, inhibition of YAP or reversal of the repressive chromatin state using a histone deacetylase (HDAC) inhibitor sensitized OS cells to a low-dose CDDP treatment as well. Overall, the present study demonstrates an interplay between oxidative stress, epigenetics, and YAP in modulating OS cell fate post CDDP exposure.

cancer biology↗

Clustering within a single-component biomolecular condensate

Biomolecular condensates (BMCs) are assemblies of hundreds to many thousands of macromolecules within cells that are organized without physical barriers. Condensate function is dictated not only by its molecular composition, but also by substructural organization and molecular mobility. One hypothesis for the onset of multiple protein aggregation diseases is that the increased densities of specific proteins within BMCs promotes the formation of solid inclusions. However, deciphering the internal structural and functional properties of BMCs at the nanoscale and identifying the initiating events of inclusion formation requires tools with high spatiotemporal precision. Here we show using single molecule and other microscopy approaches that single component Fused in Sarcoma (FUS) condensates exhibit confinement and contain clusters with higher FUS density even at early timepoints. Upon aging, condensates displayed altered physical properties and reduced monomer mobility, and yet most FUS monomers diffused throughout the condensate within seconds. While an increase in connectivity over time explains reduced mobility, the large fraction of molecules retaining high mobility suggests a sponge-like structure rather than a system-spanning network. These findings indicate that a pseudo-equilibrium between distinct structural connectivities can exist within simple condensates, suggesting the potential for substantial structural and functional complexity of BMCs at the nanoscale.

biophysics↗

Caterpillar counter-adaptations to hooked trichomes: does the coevolutionary arms race continue?

Plants protect themselves against herbivory with diverse chemical and physical defences, and many herbivores have evolved counter-adaptations against these defences. So far, little is known about herbivore counter-adaptations to physical plant defences and their underlying mechanisms. Here, we investigated how specialised Heliconiini caterpillars are able to cope with the hooked trichomes of Passiflora adenopoda vines. P. adenopoda trichomes have sharp tips reinforced with silica, in contrast to simpler trichomes of other Passiflora that do not deter Heliconiini larvae. While Heliconius melpomene and H. erato caterpillars placed on P. adenopoda leaves were arrested and their soft cuticle was pierced by the sharp hooked trichomes, H. charithonia and Dryas iulia caterpillars of all sizes could move easily over the leaves without any injury. The superior performance of these two species on trichome-bearing leaves can be explained by their thicker and more puncture-resistant cuticle. Morphological and penetrometry measurements showed that the relevant cuticle regions in H. charithonia and D. iulia caterpillars are significantly thicker and more puncture-resistant than in H. melpomene and H. erato. When we tested the ability of the caterpillars to feed on P. adenopoda leaves with hooked trichomes, only H. charithonia survived, indicating the existence of further, previously unknown post-ingestive adaptations. Understanding insect counter-adaptations to physical plant defences is essential for studying the evolutionary arms-race between plants and insect herbivores.

evolutionary biology↗

Robust Prediction of Enzyme Variant Kinetics with RealKcat

Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly in resolving the effects of mutations at catalytically essential residues. Existing models frequently overlook the functional consequences of such perturbations, often defaulting to wild-type predictions even in cases of substantial activity loss, thereby limiting their reliability for enzyme design and mechanistic inference. Here, we introduce RealKcat, a machine learning framework trained on KinHub-27k, a rigorously curated dataset of 27,176 experimentally reported enzyme-substrate entries consolidated from BRENDA, SABIO-RK, and UniProt and verified across 2,158 primary sources. To ensure biochemical realism, kinetic parameters were collapsed into order-of-magnitude bins, enabling predictions that are tolerant to experimental noise yet sensitive to functional shifts. RealKcat integrates ESM embeddings for enzyme sequences with ChemBERTa embeddings of affiliated substrate, producing a unified feature space of the chemical conversion that supports robust multi-class classification of both catalytic turnover (kCat) and substrate affinity (KM). Across cross-validation, hold-out, out-of-distribution, and few-shot evaluations--including a dense mutational landscape of alkaline phosphatase (PafA)--RealKcat consistently capturead the direction and magnitude of mutation-induced changes, while preserving discrimination in both wild-type and mutant contexts. Importantly, structural descriptors were deliberately excluded, as naive integration of structural features has been shown to impair model generalization, underscoring the primacy of rigorous dataset curation, biologically informed task formulation, and balanced evaluation metrics. RealKcat establishes a scalable and mutation-sensitive framework for enzyme kinetics prediction, offering a biologically grounded platform for enzyme engineering, metabolic modeling, and therapeutic design. Significance StatementEnzymes catalyze biochemical reactions that sustain life, and accurate measurement of their efficiency--expressed through turnover number (kCat) and substrate affinity (KM)--is fundamental to biotechnology, synthetic biology, and even pharmaceutical innovation. Yet experimental assays remain prohibitive, time-intensive, and sensitive to conditions such as pH, temperature, and ionic strength of the assay buffer, while existing computational approaches often lack sensitivity to catalytic-site mutations and are constrained by inconsistencies in public databases. RealKcat addresses these gaps by introducing a rigorously curated dataset (KinHub-27k) derived from manual review of 2,158 articles and augmented with 5,278 synthetic catalytic variants generated through alanine substitution at annotated catalytic residues. Leveraging protein and substrate embeddings and a classification scheme based on order-of-magnitude kinetic bins, RealKcat achieves state-of-the-art functional e-accuracy and, critically, demonstrates sensitivity to catalytic perturbations. By adopting e-accuracy--a performance metric that evaluates predictions within {+/-}1 order of magnitude, aligning with the practical utility of enzyme kinetics--RealKcat provides biologically meaningful assessments that conventional metrics often obscure. This work establishes a robust, mutation-aware predictive platform that advances computational enzyme design and extends applicability to biomanufacturing, metabolic engineering, and precision medicine.

systems biology↗