Biology subjects

Liu, X.

Publications and source records attributed to Liu, X..

At least 199 records · Page 11Linked to original sources

bioRxiv2026

MorphOTU: A universal image-based framework for delineating biodiversity discovery

Biodiversity can be quantified only after organisms are assigned to reproducible units, yet most individuals encountered in nature lack reliable species-level identifications. Molecular operational taxonomic units can organize unnamed diversity, whereas image-based approaches generally depend on predefined species levels. Here, we show that operational biodiversity units can be derived directly from phenotypes. We developed morphOTU, a framework combining self-supervised representation learning, operational metric supervision, and adaptive hierarchical clustering to organize specimen images in continuous phenotypic space. Across five benchmark datasets comprising flowers, wood anatomy, and beetle habitus, morphOTUs recovered coherent species-level structure and produced -diversity estimates close to those obtained from expert identifications. This structure remained informative when species were excluded from representation learning, when labeled data were sparse, and per-species sampling was limited. In a heterogeneous field-survey dataset of 4,717 insects representing 269 species across 12 orders, fine-tuning on only 28 common species produced diversity estimates close to expert labels (Shannon index, 3.75 versus 3.53). Visual explanations localized variation to biologically meaningful structures, including body outliers, surface sculpture, floral symmetry, and wood vessels. Morphological units therefore provide an operational layer for organizing and quantifying biodiversity before, alongside, or in the absence of formal species names.

bioinformatics↗

bioRxiv2026

2-arm-PEG-oligocations transiently shield the liver sinusoids to mitigate off-target hepatic expression of mRNA lipid nanoparticles

Ionizable lipid nanoparticles (iLNPs) are powerful platforms for mRNA-based vaccines and immunotherapies; however, their intrinsic liver tropism compromises both safety and efficacy. Off-target hepatic protein expression from delivered mRNA raises safety concerns, and hepatic clearance limits efficient iLNP delivery to target organs. In this study, we address these challenges in mouse models by stealth-coating the liver sinusoidal endothelial (LSE) wall, the primary gateway for nanoparticle entry into the liver. Specifically, oligocations conjugated with two-armed PEG (2-arm-PEG-oligocations), a clinically relevant material used in oligonucleotide delivery trials, were employed to transiently anchor PEG to the LSE wall with balanced affinity, ensuring robust coating followed by gradual biliary clearance. This approach reduced hepatic protein expression from iLNPs, subsequently administered either systemically or locally, by more than tenfold. Importantly, the strategy preserved iLNP accumulation in the spleen, a key target organ for vaccines, effectively redirecting iLNPs from the liver to the spleen. Consequently, in vaccine applications, pre-injection of the 2-arm-PEG-oligocation preserved or even enhanced vaccination efficacy while minimizing concerns associated with antigen expression in the liver. In applications involving cytokine mRNA therapy, specifically intratumoral interleukin-12 (IL-12) mRNA administration, systemic pre-injection of the 2-arm-PEG-oligocation successfully reduced off-target hepatic IL-12 expression and subsequent systemic IL-12 exposure, while maintaining antitumor efficacy. Collectively, these results demonstrate that LSE-wall stealth coating is a generalizable strategy to improve both the safety and efficacy of iLNP-based mRNA vaccines and immunotherapies.

bioengineering↗

bioRxiv2026

NEURA: An agentic system for autonomous neuroimaging workflows

Neuroimaging research depends on heterogeneous software, multimodal data and multistage statistical workflows. Large language model (LLM)-based agents offer a route to automate these workflows, but their susceptibility to hallucination limits their credibility in scientific use. Here we introduce NEURA, a proof-carrying framework for hallucination-resistant neuroimaging automation. NEURA converts free-text research questions and neuroimaging datasets into executable analysis plans, validated outputs and structured reports. The system combines disease- and tool-aware planning with a deterministic verification layer inspired by formal proof: before any claim is retained for reporting, it must be checked against tool-derived evidence and domain axioms. On NeuroEval, an expert-curated benchmark of 110 neuroimaging tasks, NEURA achieved 89.5% planning accuracy, a 30.5% improvement over direct LLM queries. In a controlled hallucination-injection experiment, the verification layer detected all the injected error classes under the specified axiom bank and trust assumptions, with no false positives. In case studies of spinocerebellar ataxia type 3, NEURA reproduced cerebellar atrophy and abnormal diffusion patterns consistent with established pathology and independent expert analyses. Together, these findings show that coupling domain-grounded agency with proof-carrying verification can turn LLM-driven workflow automation from probabilistic self-checking into auditable scientific computation.

neuroscience↗

bioRxiv2026

YIPPEE/YPEL5 regulates circadian period by modulating PER ubiquitination and stability

Circadian clocks rely on evolutionarily conserved transcriptional-translational feedback loops to align physiology and behavior with the 24-hour day-night cycle. Post-translational modifications, especially ubiquitination, critically control core clock protein stability and circadian pace. In both fruit flies and mammals, the abundance of key pacemaker protein PERIOD (PER/PER2) must be tightly regulated to sustain circadian rhythms. However, the canonical phosphorylation-dependent pathway cannot fully account for turnover of PER in Drosophila. Here we identify Yippee as a critical regulator of PER level through a genetic screen of downregulating ubiquitination pathways. Knockdown of Yippee in circadian pacemaker neurons significantly lengthens the circadian period by [~]2 hours and elevates PER levels. Indeed, YIPPEE physically interacts with PER in fly head. Mechanistically, its mammalian homolog YPEL5 binds PER2, promotes K63-linked polyubiquitination and proteasomal degradation of PER2 independently of FASP-region phosphorylation. Lastly, downregulating YPEL5/Ypel5 prolongs circadian periods in human U2OS cells and mice. Our findings reveal an evolutionarily conserved mechanism in which Yippee/YPEL5 regulates circadian rhythms by mediating ubiquitin-dependent degradation of PER/PER2.

molecular biology↗

bioRxiv2026

Harnessing Human Immune Organoid model for Systemic and Comparative Analysis of Clinically Vaccine Adjuvants

Effective vaccine adjuvants are crucial for eliciting strong, specific, and long-lasting adaptive immune responses. However, traditional vaccine and adjuvant development is often hindered by prolonged animal testing, reliance on singular evaluation metrics, and batch variability, limiting the comparative analysis of immune responses induced by different adjuvants. To our knowledge, systematic adjuvant-focused analysis in human immune organoids remains limited, particularly across distinct antigen-conditioned immune baselines. Here, we established a matrix supported human tonsil immune organoid (M-hTIO) platform, derived from tonsil tissue for systemic adjuvant evaluation. Within 2 days, the platform replicated key germinal center (GC) features, with the potential for antigen-specific antibody production and plasmablast differentiation. Dynamic GC formation and T-B cell interactions were tracked using light-field imaging. Notably, IL-21 significantly accelerates GC-like remodeling and enhanced immune responses. Employing bulk cell transcriptomics we systematically analyzed early immune signatures and prolonged adaptive responses induced by five clinical-grade adjuvants (Alum, MnJ{beta}, AddaSO3, R848, ODN 1018) paired with influenza HA and SARS-CoV-2 RBD antigens across only adjuvant, weak and strong memory backgrounds. By integrating early and prolonged response phases, M-hTIO captured complementary dimensions of adjuvant activity, distinguishing rapid immune activation from sustained adaptive remodeling. Importantly, early activation signatures and prolonged adaptive remodeling provided complementary information, highlighting the value of temporally resolved adjuvant evaluation. AddaSO3 showed a consistent activity pattern across early and prolonged response phases, supporting both rapid immune activation and sustained adaptive remodeling. Moreover, our findings reveal shared/exclusive signaling pathways and gene networks. These results represent systematic and comparative analysis of clinically tested vaccine adjuvants in a human model, paving the way for high-throughput systems vaccinology research.

immunology↗

bioRxiv2026

SpaCir-VDJ: a broadly compatible circularization strategy for spatial immune repertoire profiling

Deciphering the spatial organization of immune clonal lineages is critical for understanding the evolution of adaptive immune responses. However, mainstream spatial transcriptomics platforms struggle to reconcile cost-effective short-read sequencing with the simultaneous and efficient capture of V(D)J sequences. To address this gap, we developed SpaCir-VDJ, an approach that utilizes a "multiplex PCR enrichment plus library circularization" strategy to integrate spatial barcodes and V(D)J CDR3 regions into a compact circular template optimized for standard short-read sequencing. This workflow is accompanied by an automated computational pipeline designed for circular-library data parsing, sequence reconstruction, spatial barcode deconvolution, and high-fidelity clonotype calling. In human lymphoid tissues, SpaCir-VDJ delineated germinal center (GC)-associated clonal organization and isotype-linked somatic hypermutation (SHM) patterns. Notably, lineage-based spatial analyses inferred inter-GC clonal redistribution events, suggesting that GCs in the analyzed samples may not be fully isolated reaction units. Functional perturbation provided orthogonal support for candidate SHM regulators identified by spatial analysis, with knockdown of MDM4, LYN, EAF2, and H2AFY each leading to reduced SHM. Finally, in gastric cancer, SpaCir-VDJ localized dominant BCR lineages within tertiary lymphoid structures and tumor-margin plasma cell-rich regions, with lineage patterns consistent with extension toward the tumor interior. We further observed a significant positive correlation between cumulative tumor mutations and spatial TCR diversity, consistent with a spatial association between local mutational burden and T-cell remodeling within the tumor microenvironment. Collectively, SpaCir-VDJ provides a scalable and cost-effective framework for interrogating the spatiotemporal dynamics of immune microenvironments.

immunology↗

bioRxiv2026

A Deep Learning-Based Scoring Framework for Large-Scale Multi-Donor Cardiotoxicity Screening

Cardiotoxicity remains a major cause of drug attrition and post-market withdrawal, yet the vast majority of environmental chemicals to which humans may be exposed remain uncharacterized for cardiotoxicity risk. Human induced pluripotent stem cell (hiPSC)-based testing has been proposed to address this gap. Here we present an unsupervised deep learning framework for multi-donor cardiotoxicity screening using high-throughput calcium transient recordings from hiPSC-derived cardiomyocytes (hiPSC-CMs). We used data from a library of 1,029 compounds that were tested in hiPSC-CM from five donors in concentration-response. An autoencoder trained exclusively on baseline signals quantified chemical-induced functional perturbations through reconstruction error, bypassing the need for labeled training data while capturing the full spectrum of calcium handling disruptions. Using this framework, we generated effect levels. Aggregation of donor-specific scores revealed substantial inter-individual variability in potential cardiotoxicity, underscoring the value of this approach for population-level risk prediction. We found that microbiocides, dyes, and pesticides to have potential concern, characterized by high toxicity scores and low inter-donor variability. This framework establishes a scalable, human-relevant, and genetically diverse platform for cardiotoxicity surveillance across both pharmacological and environmental chemical spaces, with direct implications for drug and chemical safety evaluation and prioritization for additional studies.

bioengineering↗

bioRxiv2026

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry

Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While genomic profiling has identified key oncogenic drivers and immune signatures, its use is limited by cost, technical demands and tissue availability. In addition, spatial transcriptomics provides spatially resolved molecular insights but remains challenging and time-consuming. To address this gap, we developed XpressO-Lung, an explanatory deep learning model that predicts gene expression heterogeneity spatially in tumor and its microenvironment on hematoxylin and eosin based diagnostic (Dx) whole-slide images (WSIs) by learning associations between tissue morphology and the corresponding bulk-transcriptomic data. Utilizing 200 LUAD cases from The Cancer Genome Atlas, XpressO-Lung predicted spatial expression patterns of NAPSA, TP53I3, CD8A, TTF1, KRT7, CDKN2A, FOXO1, KEAP1, RB1 and TP53 on Dx-WSIs with AUCs ranging from 0.64 to 0.92. The predicted spatial gene expression patterns aligned with the known morphologic interactions of the tumor and its microenvironment, capturing biological events directly on Dx-WSIs. These spatio-morpho-molecular associations were further validated using immunohistochemistry on an external set of clinical samples at Dartmouth Health, demonstrating concordance between model-predicted spatial patterns and observed histomorphologic features. By coupling predictive performance with spatial interpretability of gene expression on Dx-WSIs, the XpressO-Lung model bridges histopathology and bulk-transcriptomics, enabling explainable spatio-morpho-genomic analyses to advance biomarker discovery, therapeutic stratification and precision oncology in LUAD.

pathology↗

bioRxiv2026

Spatiotemporal Propagation of Sensorimotor Beta Bursts Across Adulthood

Beta activity (13-30 Hz) is a prominent feature of motor control, widely distributed across the cerebral cortex. However, how beta bursts (transient high-amplitude events) propagate along cortical organizational axes, such as the posterior-anterior gradient that coordinates cortical structure and function from sensory to association regions, remains poorly understood. Using magnetoencephalography (MEG) data from the Cambridge Centre for Ageing and Neuroscience (CamCAN) dataset, beta burst propagation was characterized using burst onset timing and optical flow analysis during motor tasks and rest in 573 participants (ages 18-88). Beta bursts propagated systematically along the posterior-anterior cortical axis during motor tasks, with propagation direction reversing between movement phases and exhibiting hemispheric asymmetry. In contrast, the resting state exhibited no consistent spatial organization of beta burst propagation. Propagation patterns in motor tasks significantly correlated with cortical distributions of GABAA, cholinergic, and mu-opioid receptors in a hemisphere-specific and phase-dependent manner. Propagation energy was highest in sensorimotor regions and decreased towards more peripheral areas of the cortex. Older adults exhibited significant temporal expansion of beta activity (earlier pre-movement, later post-movement), suggesting a mediation effect of age on reaction time. These results suggest that the propagation of beta bursts is influenced by cortical architecture and may provide a mechanistic explanation for the age-related slowing of motor function.

neuroscience↗

bioRxiv2026

Acquired resistance to the PRMT5 inhibitor confers collateral sensitivity to MEK inhibition in MTAP-null non-small cell lung cancer

Protein arginine methyltransferase 5 (PRMT5) is a synthetic lethal target in methylthioadenosine phosphorylase-deleted (MTAP-null) cancers. Second-generation MTA-cooperative PRMT5 inhibitors preferentially target MTAP-null cells while largely sparing MTAP-wildtype (MTAP-WT) cells, thereby improving tumor selectivity over first-generation PRMT5 inhibitors. Despite encouraging efficacy and safety signals in early clinical studies, the modest objective response rates (ORRs) observed with these inhibitors suggest that intrinsic or acquired resistance may limit their clinical benefit. Here, we investigated mechanisms of acquired resistance to the MTA-cooperative PRMT5 inhibitor BMS-986504/MRTX1719 in MTAP-null non-small cell lung cancer (NSCLC) cells and sought to identify therapeutic vulnerabilities that emerge upon resistance. Using multiple in vitro-derived resistant models, we found that acquired resistance was not fully explained by alterations in PRMT5 activity or reduced MTA levels. Instead, resistance was associated with collateral sensitivity to MEK inhibition and enrichment of MAPK-related transcriptional programs. Together, these findings identify MEK inhibition as an actionable collateral vulnerability in MTAP-null NSCLC cells that acquire resistance to PRMT5 inhibition.

cancer biology↗

bioRxiv2026

Structural Mechanism of Electron Shuttling in Inducible Nitric Oxide Synthase

Nitric oxide (NO) signaling is pivotal in numerous physiological processes and is implicated in a spectrum of human diseases. Nitric oxide synthases (NOS) initiate NO signaling and govern its magnitude and duration, making them key drug targets. Despite decades of investigation, the structural mechanism by which NOS enzymes transfer electrons from NADPH to haem remains incompletely understood. Here, we report cryo-electron microscopy (cryo-EM) studies of the inducible NOS (iNOS) homodimer in complex with calmodulin (CaM) captured under catalytic turnover conditions, resolving two important functional states: the electron input state and output state. In the input state, the FMN-binding subdomain (FMND) docks onto the FAD/NADPH-binding subdomain (FNR), positioning the FMN cofactor to accept electrons from FAD. The FMND then undergoes a large rotational movement to engage the oxygenase domain (OXY) of the other protomer, adopting the output state, which enables electron transfer from FMN to the haem center via W366. This dynamic movement of the FMND shuttles electrons from the reductase domain (RED) to the OXY active site in iNOS. A point mutation (S594E) that disrupts the FMND-OXY interface markedly reduces the catalytic activity of iNOS and traps a fraction of enzymes in a non-productive intermediate conformation. Together, these findings elucidate the structural mechanism of FMND-mediated electron transfer in the iNOS catalytic cycle.

biochemistry↗

bioRxiv2026

Interpretable Biological Sequence Clustering with iClust

Biological sequence clustering is a fundamental problem in bioinformatics, yet most existing methods mainly optimize clustering quality or efficiency while offering limited insight into why sequences are grouped together. This restricts their usefulness in downstream analysis, where representative sequences and clear cluster boundaries are often needed. To address this issue, we propose iClust, an interpretable clustering method that characterizes each cluster by a representative prototype and an adaptive radius. By adapting to local sequence structure rather than relying on a single global threshold, iClust produces clusters that are both meaningful and explainable. A final consolidation step further reduces tiny fragments and improves structural stability. Experiments on simulated and real biological sequence datasets show that iClust achieves competitive clustering performance while providing clearer cluster-level explanations than conventional threshold-based methods. In addition to its empirical impact as a practical clustering method for biological sequences, this article opens up new avenues for developing biological sequence clustering approaches from the viewpoint of interpretable machine learning.

bioinformatics↗

bioRxiv2026

Three-dimensional Virtual Adult Cardiomyocyte Transcriptomics

Obtaining transcriptomes of adult cardiomyocytes at single-cell resolution remains challenging due to their large size, elongated morphology, and frequent multinucleation. Although spatial transcriptomics preserves tissue architecture and captures gene expression in situ, current analytical frameworks largely rely on nuclear-based segmentation and are therefore poorly suited to adult cardiomyocytes. Furthermore, individual tissue sections capture only a fraction of a cardiomyocyte, preventing reconstruction of complete cell-level transcriptomes. Here we present three-dimensional virtual cardiomyocyte (3D-VirtualCM), a membrane-guided framework that integrates cell-contour similarity and optimal transport to reconstruct volumetric cardiomyocyte transcriptomes from consecutive spatial transcriptomic sections. Applying 3D-VirtualCM to infarcted adult mouse hearts, we generated a panoramic transcriptomic atlas spanning 100 m thickness at single-cell resolution. 3D-VirtualCM identified spatially and transcriptionally distinct cardiomyocyte populations within the infarct border zone, enabled high-throughput quantification of cardiomyocytes re-entering the cell cycle together with their associated molecular signatures, and revealed heterogeneous RNA distribution along the longitudinal axis of individual cardiomyocytes. By integrating three-dimensional cellular morphology with in situ transcriptomic data, 3D-VirtualCM provides a scalable approach for resolving cardiomyocyte states and spatial organization in physiological and pathological cardiac remodeling.

bioinformatics↗

bioRxiv2026

Interpretable Antibody-Antigen Structural Interface Prediction via Adaptive Graph Learning and Cyclic Transfer

Experimental structural methods can identify antibody-antigen interfaces with high precision, but they remain time-consuming and resource-intensive, limiting their application across the rapidly expanding space of antibody and antigen sequences. Computational models capable of predicting these interfaces could therefore accelerate antibody discovery and provide insight into the principles governing immune recognition. However, this problem remains challenging due to limited structural datasets, severe class imbalance, and the complex, non-local nature of biomolecular interactions. Here we present VASCIF (Variable-domain Antibody-antigen Structural Complex Interface Finder), a structure-aware framework built on a Masked Graph Attention (MGA) architecture that represents protein complexes as residue graphs and captures long-range structural dependencies through attention-based message passing. The framework is straightforward to implement and enables efficient inference, allowing substantially faster predictions than other existing structure-based approaches. Evaluated on curated structural complexes across multiple benchmark datasets using rigorous cross-validation, VASCIF achieves state-of-the-art performance for residue-level interface prediction. Interpretability analyses reveal that the model recovers biophysically meaningful interaction patterns consistent with known principles of antibody recognition, and redefining interfaces using larger residue distance thresholds ([~]10 [A]) significantly improves predictive performance. Together, VASCIF provides a practical predictive framework and new insights into antibody-antigen molecular recognition.

bioinformatics↗

bioRxiv2026

Engineering age-adaptive mRNA lipid nanoparticle cancer vaccines via reprogramming systemic gene expression

Lipid nanoparticle (LNP)-based mRNA vaccines have transformed cancer immunotherapy, yet their efficacy in older individuals, who represent the majority of cancer patients, remains poorly understood. Here, we uncover a critical and previously underappreciated barrier to mRNA LNP cancer vaccine performance in aged hosts: impaired systemic transgene expression. Using the SM-102 mRNA LNPs as a benchmark formulation, we show that while local immune activation and antigen presentation at the injection site and draining lymph nodes remain largely intact with age, transgene expression in peripheral organs, including the liver, lungs, and spleen, is markedly reduced. This deficit limits the magnitude and durability of CD8 and CD4 T cell responses and substantially compromises tumour control. Transcriptomic profiling further reveals that attenuated transgene expression parallels broad attenuation of antigen processing, presentation, and activation pathways in immune cells from aged animals, implicating impaired systemic mRNA translation as a central driver of the downstream immune defects and antitumour efficacy loss. Building on these insights, we identify a rationally selected LNP formulation that reestablishes distal antigen expression across age groups as an engineering strategy to revive T cell immunity, achieving full rescue of therapeutic efficacy in aged mice without additional intervention. Together, these findings establish systemic mRNA translation as a tuneable lever of vaccine performance and highlight that optimizing LNP formulations to sustain systemic transgene expression across age groups may enable next-generation, age-adaptive mRNA vaccines for cancer and other diseases of aging.

bioengineering↗

bioRxiv2026

Fruit trichome density outweighs cuticle thickness as the dominant barrier to postharvest water loss in tomato

Fruit cuticle thickness and biochemical composition have traditionally been regarded as the primary determinants of postharvest water loss in fleshy fruits. However, several reports indicate that some tomato mutants with thinner fruit cuticles or less cutin and waxes do not always show increased transpiration, suggesting that additional surface features influence postharvest water loss. Here, we s how that fruit trichome density is a previously underappreciated determinant of postharvest water loss in tomatoes. Using two independent mutants, cr-slhdziv7 and cr-slhdziv9, which exhibit reduced fruit trichome density, we found that both mutants displayed reduced water loss rates and extended shelf life during postharvest storage despite having thinner cuticles and reduced levels of key cutin monomers. Further molecular analyses, including RNA-seq, yeast one-hybrid (Y1H), and dual-luciferase reporter assays, revealed that these two HD-ZIP IV proteins not only regulate fruit trichome formation but also directly or indirectly modulate the expression of multiple cutin biosynthesis genes. Collectively, our results demonstrate that the benefit of reducing trichome-associated microchannels can outweigh the negative effects of cuticle thinning on postharvest water loss. This study establishes fruit trichome density as an important and previously underestimated target for improving postharvest fruit quality and shelf life. HighlightO_LIFruit trichome density controls tomato postharvest water loss and dominates over cuticle thickness barrier. C_LIO_LILess trichomes reduce microchannels, lowering transpiration despite thin cuticles and extending tomato fruit shelf life. C_LI

plant biology↗

bioRxiv2026

Geometry-enhanced protein language modeling enables discovery of novel antibiotic resistance genes

The global antibiotic resistome remains largely unexplored, not because antibiotic resistance genes (ARGs) are rare in the environment, but because many are evolutionarily distant from known ARGs. Current computational approaches primarily rely on sequence homology, and thus miss distant homologues. We develop GeoARG, a geometry-enhanced framework that integrates structural features with protein language models through knowledge distillation, enabling efficient large-scale screening using sequence input alone. Across multiple benchmarks, GeoARG substantially improves the detection of remotely homologous ARGs, particularly under low sequence identity and fragmented conditions. Large-scale metagenomic analysis uncovers 1,485 high-confidence ARG candidates that are highly divergent from known ARGs, expanding the phylogenetic and functional landscape of the resistome. Structural analyses further show that these candidates preserve active-site geometry and maintain stable ligand-binding configurations consistent with known resistance mechanisms. These results demonstrate that geometric constraints enable systematic expansion of the resistome and facilitate the discovery of evolutionarily distant yet functionally conserved genes. A public web server is available at https://ycclab.cuhk.edu.cn/GeoARG/.

bioinformatics↗

bioRxiv2026

Celastrol alleviates SGLT2 inhibitor-induced diabetic hyperketonemia by inhibiting hepatic ketogenesis

SGLT2 inhibitor (SGLT2i)-induced diabetic hyperketonemia is a life-threatening acute complication of diabetes. While Celastrol has been reported to exert beneficial effects on obesity; its potential role in ketogenesis remains unclear. In this study, Celastrol administration significantly attenuates the fasting-induced elevation of blood {beta}-hydroxybutyrate. Moreover, a 7-day course of Celastrol (1 mg/kg/day) leads to reductions in body weight and fat mass. Mechanistically, Celastrol specifically downregulates HMGCS2 expression and suppressess hepatic ketogenesis through inhibiting PPAR expression in the short term ([≤] 2 days). However, after prolonged treatment for 7 days, Celastrol modulates both PPARand serum free fatty acids (FFAs) levels. Furthermore, anti-ketogenic effect of Celastrol is abolished in Ppar{square} /{square} mice. Importantly, Celastrol effectively ameliorates SGLT2i-induced hyperketonemia. In summary, Celastrol curbs hepatic ketone overproduction in a PPAR-dependent manner, indicating its protective potential against SGLT2i-induced hyperketonemia.

molecular biology↗