Biology subjects

Ghosh, B.

Publications and source records attributed to Ghosh, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Astrocytic sEH-EET signaling regulates microglial lysosomal function and suppresses the STING-Type I interferon pathway in Alzheimer's disease

Background: The development of {beta}-amyloid (A{beta}) pathology in Alzheimer's disease (AD) is accompanied by profound changes in astrocytes and microglia and associated neuroinflammation. Soluble epoxide hydrolase (sEH), which degrades anti-inflammatory epoxyeicosatrienoic acids (EETs), is elevated in the AD brain, yet how EETs restrain inflammation remains poorly understood. Methods: We performed single-nucleus RNA sequencing (snRNA-seq) on brains from wild-type, sEH knockout, 5xFAD, and 5xFAD; sEH knockout mice, and used primary mouse microglia and human induced pluripotent stem cell (iPSC)-derived microglia to define the underlying mechanism. To test cell-type-specific contributions, we generated astrocyte- and endothelial-specific she knockout mice and crossed them with 5xFAD mice. Results: Genetic disruption of sEH in 5xFAD mice markedly reduced the abundance of a type I interferon (IFN)-responsive microglial subcluster and lowered phospho-STING (pSTING) levels. In primary microglia, supplementation with 11,12-EET attenuated STING activation and IFN target gene expression; blocking lysosomal acidification abolished this effect and caused pSTING to accumulate, identifying lysosomal degradation as a critical route for terminating STING signaling. 11,12-EET also enhanced microglial lysosomal acidification and A{beta} phagocytosis. Astrocytic, but not endothelial, disruption of sEH recapitulated the reductions in A{beta} pathology and microglial STING activation seen in global sEH-deficient mice, identifying astrocytes as the major cellular source of EETs regulating this pathway. Conclusion: We define an astrocyte-microglia axis in which astrocytic sEH limits EET availability and thereby sustains microglial STING signaling. By promoting lysosomal clearance of pSTING, EETs suppress type I IFN signaling and enhance microglial A{beta} clearance, highlighting sEH inhibition as a therapeutic strategy targeting both neuroinflammation and lysosomal dysfunction in AD.

neuroscience↗

bioRxiv2026

A Bayesian approach to volumetric analysis in rare neurodegenerative disorders

Identifying regional brain atrophy is critical for understanding neurodegenerative disorders. Traditionally, group comparisons rely on frequentist statistics, utilising $p$-values and point estimates that offer limited characterization of uncertainty. Bayesian methods provide an alternative by estimating effect sizes and credible intervals while incorporating prior knowledge and uncertainty. This study evaluated the feasibility of Bayesian hierarchical modelling to assess regional brain atrophy in Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS) compared to healthy controls. Participants meeting diagnostic criteria for CBS or PSP, together with healthy controls, were recruited from the Cambridge Centre for Parkinson-plus. Grey matter volume, cortical thickness, and subcortical volumes were derived using FreeSurfer for 210 cortical and 36 subcortical regions defined by the Human Brainnetome Atlas. Participants with PSP were classified into Richardson syndrome (PSP-RS), corticobasal syndrome variant (PSP-CBS), frontal variant (PSP-F), and other presentations (PSP-Other). Bayesian hierarchical ANOVA models with appropriate priors were used to estimate group differences. Regions of practical equivalence (ROPE) were calculated to assess evidence of meaningful group effects. A total of 428 people were included (113 PSP-RS, 30 PSP-CBS, 23 PSP-Other, 19 PSP-F, 81 CBS, and 162 healthy controls). For grey matter volume, there was evidence of whole brain volume loss compared to controls for all disease groups except PSP-Other, which showed evidence of no difference. For cortical thickness, there was evidence of global cortical thinning in CBS compared to controls, but for no other group. For subcortical volume all groups showed evidence of volume loss compared to controls. Patterns of regional atrophy were identified in keeping with previous literature, with the largest effect observed in the left pre-motor thalamus (30\% reduction in CBS). There was greater loss of subcortical volume in PSP-RS compared to other PSP subgroups. Bayesian hierarchical modelling provides a feasible framework for quantifying regional brain atrophy, with interpretable effect size estimates and uncertainty measures. This approach complements conventional statistical methods, particularly in the assessment of rare neurodegenerative and neuropsychiatric disorders.

neuroscience↗

bioRxiv2026

PWO1 and TRB proteins coordinate chromatin regulation to prevent premature differentiation and ectopic lignin deposition in Arabidopsis

The Arabidopsis PWWP-DOMAIN INTERACTOR OF POLYCOMBS1 (PWO1) and Telomere Repeat-Binding Proteins 1-3 (TRB1-3, TRBs) associate with distinct and shared protein complexes involved in epigenetic regulation, yet their cooperative roles in chromatin control and plant development remain largely unexplored. Here, we show that the interaction between PWO1 and TRBs is evolutionarily conserved. Both PWO1 and TRBs associate with plant telomeres, interact at these regions, and are co-enriched at subsets of interspersed telo-box motifs across regulatory regions genome-wide. TRBs facilitate PWO1 binding at shared genomic regions, including telo-box motifs. PWO1 and TRBs share a substantial number of genomic targets and preferentially bind chromatin regions associated with transcriptionally active states, whereas TRBs alone associate with repressive marks at thousands of loci. Genetic analyses show that the pwo1 trb1 trb3 triple mutant displays severe developmental defects, including main stem arrest and early maturation associated with aberrant lignin deposition in interfascicular tissues. In the triple mutant, key enzymes in the lignin biosynthesis pathway are upregulated, indicating that PWO1, TRB1, and TRB3 cooperatively regulate secondary cell wall formation. Together, our findings provide new insights into how PWO1 and TRBs cooperate to regulate chromatin states and orchestrate plant development, highlighting their central role in controlling gene expression programs. Significance statementThis study shows that PWO1 and TRB proteins co-occupy telomeres, including interspersed telo-box motifs, to regulate chromatin organization and plant development, particularly ectopic lignin deposition. Our findings reveal how these nuclear protein factors coordinate epigenetic states in Arabidopsis thaliana, providing a framework for understanding the control of developmental programs. O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/740627v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1a3d544org.highwire.dtl.DTLVardef@1067082org.highwire.dtl.DTLVardef@1c49163org.highwire.dtl.DTLVardef@252171_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract:C_FLOATNO Evolutionarily conserved PWO-TRB interactions and their shared roles in chromatin regulation and plant development. Created with BioRender.com. C_FIG

plant biology↗

bioRxiv2026

Cancer Phenotypic Plasticity Quantification using Morphology-Migration Coupled Metric in Live Label-Free Optical Microscopy

Cancer phenotypic plasticity drives invasion, treatment resistance, and relapse. Quantifying how cells dynamically couple morphology and migration in real time, without molecular labels, remains unsolved. Static molecular markers report on protein expression state rather than functional migratory behavior. Existing image-based metrics treat shape and migration as independent features, missing the coordinated coupling that defines plastic migratory states. We introduce Directional Shape Coupling (DSC), a quantitative metric purpose-built for live label-free imaging. DSC integrates movement direction consistency, shape deformation, and directional-shape alignment into a single interpretable score. Component weights are derived from PCA, adapting automatically to any dataset without manual tuning. Applied to differential interference contrast imaging of pancreatic cancer cells on a tissue-mimicking substrate recapitulating desmoplastic tumor stroma, DSC exhibited a large phenotype-associated effect size,{varepsilon} 2 = 0.65, across five distinct migratory phenotypes within a genetically homogeneous population, demonstrating that behavioral heterogeneity is structured and non-genetic. DSC encodes information orthogonal to classical shape and motion descriptors. Critically, DSC reveals that dynamic shape adaptation to mechanical cues rather than directional commitment drives phenotypic identity in this system. DSC provides the label-free imaging community a transparent, generalizable framework for quantifying dynamic non-genetic plasticity directly from live imaging data.

biophysics↗

bioRxiv2026

LsGCRPred: lncRNA-SNP regulated gene expression-based breast and ovarian cancer risk prediction model

Long non-coding RNAs (lncRNAs) and single nucleotide polymorphisms (SNPs) within them play crucial role in cancer susceptibility and disease outcomes. Breast and ovarian cancers, characterized by genetic heterogeneity, present significant challenges for precise diagnosis and treatment. Despite recent advancements in personalized medicine, inclusion of lncRNA-SNP (LSNP) markers into cancer risk detection panels remains limited. In this work, we put forward LSNP regulated gene expression based breast and ovarian cancer risk prediction model LsGCRPred (LSNP-Gene Interaction Based Cancer Risk Prediction Model). Notably, our approach accounts for the tissue-specificity of lncRNAs as well the benefit for individuals with predisposing conditions. Additionally, pathway analysis revealed the involvement of the LSNP interacting genes in key cancer regulating pathways. TaqMan genotyping and qPCR were performed to confirm the presence of selected LSNPs in ovarian and breast cancer cell lines along with the significant expression of the lncRNA and associated gene transcripts. These findings highlight previously overlooked genetic variants within lncRNA loci and their regulatory impact on disease outcomes, providing insights into personalized cancer diagnosis and treatment strategies. The tool LsGCRPred can be accessed as a standalone version on GitHub. Github Link: https://github.com/zglabDIB/LsGCRPred

cancer biology↗

bioRxiv2026

A Comprehensive Evaluation of Protein Structure Prediction Models for Short Peptides

Short peptides pose distinct challenges for computational structural biology due to their lack of stable tertiary structures, high conformational flexibility, and limited evolutionary signals. To address how modern deep-learning architectures navigate these challenges, we conducted a comprehensive benchmarking of five state-of-the-art protein structure prediction models: AlphaFold2, RoseTTAFold2, ESMFold, OmegaFold, and DMPfold2. Using a curated dataset of experimentally determined short peptide structures (10-49 amino acids) from the Protein Data Bank, we systematically evaluated predictive performance across varying sequence lengths and secondary structure classes. Our results demonstrate that prediction accuracy systematically improves with peptide length. Furthermore, all models perform significantly better on -helical and mixed-structure peptides compared to {beta}-sheet-rich and intrinsically disordered sequences. Among the evaluated methods, AlphaFold2 and the single-sequence language models, ESMFold and Omegafold proved to be the most consistent and accurate overall. We also observed that internal model confidence scores are imperfectly calibrated for short peptides, necessitating cautious interpretation. Finally, by extending our analysis to the dbAMP3 dataset of uncharacterized antimicrobial peptides, we demonstrate that a multi-model consensus approach provides a rational framework for identifying robust structural hypotheses in the absence of experimental reference structures.

biophysics↗

bioRxiv2026

E-InfertilityTest: An Explainable AI Framework for Male Infertility Assessment

Male infertility has emerged as a significant concern in modern society, with genetic defects as one of the major underlying cause behind it. This impairment negatively impacts sperm motility and morphology, leading to conditions such as Asthenozoospermia (reduced sperm motility), Teratozoospermia (abnormal sperm morphology) and sometimes Asthenoteratozoospermia (both motility and morphology defects). Assisted reproductive technologies (ART), such as in-vitro fertilization (IVF), offer a potential solution for such cases but with a low success rate. Classical semen analysis provides only a phenotypic snapshot without revealing the fertilizing potential of the sperms. Hence, in order to screen the functional sperm population as well as to get a deeper insight into the reasons underlying the aberrant sperm population, it is important to study their genetic profile. In this work, we have performed a meta analysis of the transcriptomic data of infertile sperms from Asthenozoospermia and Teratozoospermia patients with that from fertile sperms of normal individuals. Thereafter we have screened a signature gene set which has been used to develop a prediction model named Explainable Infertility Test (E-InfertilityTest) to classify between fertile versus infertile sperm at the preliminary level. For each prediction, it will also provide the set of genes which are playing a dominant role towards such prediction. Thus, it will provide patient specific dominant gene expression profile responsible for the aberration. This work warrants validation experiments in future to substantiate the models performance in a clinical setting. User can access the tool named E-InfertilityTest as a standalone version on GitHub. Github Linkhttps://github.com/zglabDIB/einfertility.git

bioinformatics↗

bioRxiv2025

Sodium Aurothiomalate Induces Ferroptosis by Targeting GPX4 via Gold-Dependent Thiomalate Covalent Modification

Ferroptosis, an iron-dependent form of oxidative cell death, is predominantly regulated by glutathione peroxidase 4 (GPX4), making it a promising target for cancer therapy. However, the majority of GPX4 inhibitors, most of which contain a chloroacetamide moiety such as RSL3, are limited by poor pharmacokinetic properties and off-target effects, hindering their preclinical translation. Utilizing a range of interdisciplinary methodologies, we show that sodium aurothiomalate (ATM), a drug approved by many agencies, induces ferroptosis by covalently targeting GPX4 via formation of a selenenylsulfide bond. In preclinical models of neuroblastoma and acute myeloid leukemia (AML), ATM combined with ferric ammonium citrate (FAC) yields a synergistic effect, resulting in a significant reduction in tumor growth. Mechanistically, ATM disrupts GPX4 activity by covalently binding thiomalate to the active site selenocysteine, while modification of specific cysteine residues leads to destabilization of the protein and impaired binding to phospholipids. We propose that these covalent modifications are achieved through a unique reaction mechanism, in which the gold component of ATM acts as a thiol-masking carrier and is only transiently present, being subsequently displaced and allowing the reaction of the thiomalate moiety with the target selenocysteine or cysteine. Our data lay the foundation for development of novel, drug-like thiol-based GPX4 inhibitors.

biochemistry↗

bioRxiv2025

Riboflavin metabolism shapes FSP1-driven ferroptosis resistance

Membrane protection against oxidative insults is achieved by the concerted action of glutathione peroxidase 4 (GPX4) and endogenous lipophilic antioxidants such as ubiquinone and vitamin E. Deficiencies in these protective systems lead to an increased propensity to phospholipid peroxidation and ferroptosis. More recently, ferroptosis suppressor protein 1 (FSP1) was identified as a critical ferroptosis inhibitor acting via regeneration of membrane-embedded antioxidants. Yet, regulators of FSP1 are largely uncharacterised, and their identification is essential for understanding the mechanisms buffering phospholipid peroxidation and ferroptosis. Here, we conducted a focused CRISPR-Cas9 screen to uncover factors influencing FSP1 function, identifying riboflavin (vitamin B2) as a new modulator of ferroptosis sensitivity. We demonstrate that riboflavin, unlike other vitamins that act as radical-trapping antioxidants, supports FSP1 stability and the recycling of lipid-soluble antioxidants, thereby mitigating phospholipid peroxidation. Furthermore, we show that the riboflavin antimetabolite roseoflavin markedly impairs FSP1 function and sensitises cancer cells to ferroptosis. Thus, we uncover a direct and actionable role for riboflavin in maintaining membrane integrity by promoting membrane tolerance to lipid peroxidation. Our findings provide a rational strategy to modulate the FSP1-antioxidant recycling pathway and underscore the therapeutic potential of targeting riboflavin metabolism, with implications for understanding the interaction of nutrients and their contributions to a cells antioxidant capacity.

cell biology↗

bioRxiv2025

Thermodynamic uncertainty relation constraints information transmission through cell signaling systems

Cell signaling systems are involved in sensing changes in the environment by activating a set of transcription factors (TF) that typically diffuse within the nucleus to trigger transcription of the required genes. The TFs can diffuse randomly in and out of the nucleus leading to fluctuations in different components which severely limits the accuracy of estimating environmental input. The diffusion usually happens through the nuclear pore complexes which enables the tf protein to enter the nucleus either passively or actively depending on its size. In this study, we explored the role of diffusion on the information transmission capacity of a set of tfs using a coupled mathematical and machine learning approaches to experimental data in yeast under several stress conditions. We found that the the activation followed by biased diffusion of transcription factors (TF) towards the nucleus triggers amplifying magnitude of the overall TF currents towards the nucleus as well as reduces the fluctuations. In fact, to our surprise the diffusion rate estimated from the data is found to be positively correlated with the protein mass, indicating the possibility of active diffusion since a negative correlation is expected in case of passive diffusion. The active diffusion in fact facilitates faster entry to the nucleus enabling faster information transmission with nuclear protein concentration as output. Additionally, higher copy number of the TF also improves information transmission by reducing overall noise in the output. However, improved information owing to faster active diffusion and higher copy number comes at an extra cost of increased entropy production due to the inherent thermodynamic uncertainty relation (TUR)for non-equilibrium systems. A linear optimization analysis demonstrates a corelation between the protein size and the optimized protein number which corroborates the actual observation. Thus, experimental measurements coupled with diffusion based theoretical models demonstrate the role of diffusion on optimizing cellular information processing.

systems biology↗

bioRxiv2024

Randomly oriented microgrooved hydrogel guides cellular motility, modulates speed, and governs directionality of cellular spread

Cell migration is a fundamental biological process, yet the mechanisms underlying how cells sense and navigate complex environments remain poorly understood. In this study, we developed a system of randomly oriented microgrooves, designed at cellular length scales, to explore motility intelligence in response to varied topographies. These microgrooves allowed cells to freely choose their migratory paths, revealing key insights into how cells sense and adapt to topological cues. Using fibroblast cells migrating over these grooved substrates, we examined cellular processes such as actin cytoskeleton remodeling, cell adhesion dynamics, and the impact of groove alignment on migration speed and directionality. Our results demonstrate that cells align their cytoskeletal structures to groove geometries, forming actin-rich anchors that enhance migration in groove-aligned environments. Cells migrating in grooves aligned with their intrinsic polarity exhibited faster, more directed migration compared to those in misaligned or control conditions. This work advances our understanding of cell-topology interaction and provides new perspectives for tissue engineering applications in cancer therapy and wound healing.

bioengineering↗

bioRxiv2024

Unraveling the Impact of Gene Length on Kinetic Parameters: Implications in Drug Target selection

Gene expression is a multifaceted process crucial to understanding molecular biology and pharmacology. Our research focuses on elucidating the intricate relationship between gene length and kinetic parameters, such as Si, Kon, Koff, and SKoff, which significantly influence the mean expression levels of genes.Using a two-state stochastic gene expression model implemented in Python, we analyzed single-cell transcriptomics data to predict kinetic parameters for each gene. We classified genes into short and long categories, revealing distinct patterns in the relationship between gene length and these parameters. Our results indicate that burst size plays a critical role in mean expression, highlighting its importance for identifying gene targets that require lower drug doses for therapeutic effects.

bioinformatics↗

bioRxiv2024

Label-free acoustic and optical microscopy of live tumor spheroids in hydrogel for high-throughput 3D In-vitro drug screening

3D cell cultures, including spheroids, have become essential tools in cancer research and drug discovery due to their ability to more accurately mimic in-vivo tissue environments compared to traditional 2D cultures. However, imaging these thick, complex structures remains a challenge, as conventional optical microscopy techniques are limited by shallow depth penetration. This study explores the complementary use of gradient light interference microscopy (GLIM) and scanning acoustic microscopy (SAM) for label-free imaging of 3D spheroid clusters embedded in hydrogels. GLIM offers high-resolution optical imaging but struggles with depth in dense samples, while SAM provides greater depth penetration and a larger field of view, albeit with lower resolution. By correlating SAM and GLIM imaging, this study demonstrates how the two techniques can be synergistically used to enhance the visualization of spheroids, capturing both large-scale structural features and fine cellular details. The benefits make such a platform suitable for screening high-number multi-well plates and evaluating necrotic and angiogenic features from the core of the thick sample. Such platforms have the potential of combining acoustic and optical imaging modalities for high-throughput screening and physical characterization in 3D cell culture research, advancing our understanding of drug efficacy in complex biological systems.

bioengineering↗

bioRxiv2024

A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA: a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders given target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. The ability of our model to generate peptides conditioned on the target receptors binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

bioinformatics↗

bioRxiv2024

Proteomic analysis of gut in Labeo rohita reveals ECM as Key Player in host's Response to Aeromonas hydrophila Infection

In the aquaculture sector, one of the challenges include disease outbreaks such as bacterial infections, particularly from Aeromonas hydrophila (Ah), impacting both wild and farmed fish. In this study, we conducted a proteomic analysis of the gut tissue in Labeo rohita following Ah infection to elucidate the protein alterations and its implications for immune response. Our findings reveal significant dysregulation in extracellular matrix (ECM) associated proteins during Ah infection, with increased abundance of elastin and Collagen alpha-3(VI) contributing to matrix rigidity. Pathway and enrichment analysis of differentially expressed proteins (DEPs) highlights the involvement of ECM-related pathways, including Focal adhesions, Integrin cell surface interactions, and actin cytoskeleton organization. Focal adhesions, crucial for connecting intracellular actin bundles to the ECM, play a pivotal role in immune response during infections. Increased abundance of integrin alpha 1, integrin beta 1, and Tetraspanin suggests their involvement in the hosts response to Ah infection. Proteins associated with actin cytoskeleton reorganization, such as myosin, tropomyosin, and phosphoglucomutase, exhibit increased abundance, influencing changes in cell behavior. Additionally, upregulated proteins like LTBP1 and Fibrillin-2 contribute to TGF-{beta} signaling and focal adhesion, indicating their role in immune regulation. The study also identifies elevated levels of laminin, galectin 3, and tenascin-C, which interact with integrins and other ECM components, influencing immune cell migration and function. These proteins, along with decorin and lumican, act as immunomodulators, coordinating pro- and anti-inflammatory responses. ECM fragments released during pathogen invasion serve as "danger signals," initiating pathogen clearance and tissue repair through Toll-like receptor signaling. IMPORTANCEThe study underscores the critical role of the extracellular matrix (ECM) and its associated proteins in the immune response of aquatic organisms during bacterial infections like Aeromonas hydrophila (Ah). Understanding the intricate interplay between ECM alterations and immune response pathways provides crucial insights for developing effective disease control strategies in aquaculture. By identifying key proteins and pathways involved in host defense mechanisms, this research lays the groundwork for targeted interventions to mitigate the impact of bacterial infections on fish health and aquaculture production.

microbiology↗

bioRxiv2024

Computational approach for decoding Malaria Drug Targets from Single-Cell Transcriptomics and finding potential drug molecule

Malaria is a deadly disease caused by Plasmodium parasites. While potent drugs are available in the market for malaria treatment, over the years, Plasmodium parasites have successfully developed resistance against many, if not all, front-line drugs. This poses a serious threat to global malaria eradication efforts, and the continued discovery of new drugs is necessary to tackle this debilitating disease. With the advent of recent unprecedented progress in machine learning techniques, single-cell transcriptomic in Plasmodium offers a powerful tool for identifying crucial proteins as a drug target and subsequent computational prediction of potential drugs. In this study, We have implemented a mutual-information-based feature reduction algorithm with a classification algorithm to select important proteins from transcriptomic datasets (sexual and asexual stages) for Plasmodium falciparum and then constructed the protein-protein interaction (PPI) networks of the proteins. The analysis of this PPI network revealed key proteins vital for the survival of Plasmodium falciparum. Based on the function and identification of a few strong binding sites on a couple of these key proteins, we computationally predicted a set of potential drug molecules using a deep learning-based technique. Lead drug molecules that satisfy ADMET and drug-likeliness properties are finally reported out of the generated drugs. The study offers a general computational pipeline to identify crucial proteins using scRNA-seq data sets and further development of potential new drugs.

bioinformatics↗

bioRxiv2023

Mechano-chemical Insights in Diabetic Kidney Disease through 3D Pathotypic Model of Mesangium

Diabetic kidney disease (DKD) is often diagnosed only after irreversible damage, limiting early treatment. The kidney mesangium, a structure highly sensitive to mechanical forces, plays a key role in early fibrosis development, yet its response to early disease cues like stiffness and biochemical changes is poorly understood. This is due to limitations in current in vitro models, poor in vivo accessibility, and static biopsy samples. To address this, we developed a 3D in vitro model of the mesangial microenvironment using stiffness-tunable gelatin methacrylate (GelMA) hydrogels that mimic healthy and fibrotic kidney tissue. Exposing mesangial cells to glucose and TGF-{beta}1 led to altered cell shape, increased dry mass, and elevated expression of fibrotic markers (-SMA and collagen IV), especially under stiffer conditions, indicating a synergistic effect of biochemical and mechanical stress. These responses were integrated using Gaussian process regression to create a 3D "severity cube" that maps DKD progression across mechanical and chemical inputs. This system quantifies early mesangial transitions and reveals key fibrosis-related mechanisms. By combining organotypic modeling with interpretable inference, our platform offers a predictive tool for early disease stratification and a basis for studying subclinical fibrosis progression in DKD.

biophysics↗

bioRxiv2023

Decreased fucosylation impacts epithelial integrity and increases risk for COPD.

COPD causes significant morbidity and mortality worldwide. Epithelial damage is fundamental to disease pathogenesis, although the mechanisms driving disease remain undefined. Published evidence from a COPD cohort (SPIROMICS) and confirmed in a second cohort (COPDgene) demonstrate a polymorphism in Fucosyltransferese-2 (FUT2) is a trans-pQTL for E-cadherin, which is critical in COPD pathogenesis. We found by MALDI-TOF analysis that FUT2 increased terminal fucosylation of E-cadherin. Using atomic force microscopy, we found that FUT2-dependent fucosylation enhanced E-cadherin-E-cadherin bond strength, mediating the improvement in monolayer integrity. Tracheal epithelial cells from Fut2-/- mice have reduced epithelial integrity, which is recovered with reconstitution of Fut2. Overexpression of FUT2 in COPD derived epithelia rescues barrier function. Fut2-/- mice show increased susceptibility in an elastase model of disease developing both emphysema and fibrosis. We propose this is due to the role of FUT2 in proliferation and cell differentiation. Overexpression of FUT2 significantly increased proliferation. Loss of Fut2 results in accumulation of Spc+ cells suggesting a failure of alveolar type 2 cells to undergo transdifferentiation to alveolar type 1. Using a combination of population data, genetically manipulated mouse models, and patient-derived cells, we present a novel mechanism by which post-translational modifications modulate tissue pathology and serve as a proof of concept for the development of a disease-modifying target in COPD.

cell biology↗