Biology subjects

Yuan, Q.

Publications and source records attributed to Yuan, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

Fine-tuning an intracellular allosteric pocket programs GPCR signaling bias

Biased signaling of G protein-coupled receptors (GPCRs) offers a route for precise therapeutics with fewer adverse effects. Intracellular allosteric ligands can impose such bias, yet how subtle chemical changes within these pockets reshape receptor-transducer coupling and downstream signaling remains unclear. Here, we systematically fine-tuned the intracellular allosteric pocket of neurotensin receptor 1 (NTSR1) through chemical modification of the {beta}-arrestin-biased ligand SBI-553. Minor changes to the ligand scaffold altered transducer-coupling preferences, generating distinct profiles of G-protein and {beta}-arrestin signaling. Cryo-electron microscopy structures of NTSR1-{beta}-arrestin2 complexes bound to designed allosteric probes revealed ligand-dependent remodeling of the intracellular cavity that reshapes the receptor-effector interface. Alanine scanning identified transducer-sensitive residues within and beyond the immediate ligand-contact region. In mice, SBI-553 and N065 produced distinct physiological profiles, with N065 improving survival in a lethal endotoxemia model. Finally, phosphoproteomic profiling revealed partially overlapping but distinct downstream signaling profiles among the three modulators. Together, these findings establish the intracellular allosteric pocket as a tunable determinant of transducer coupling and show that closely related modulators can generate distinct signaling and physiological profiles.

pharmacology and toxicology↗

bioRxiv2026

β-arrestin1 directly engages Gαs to sustain endosomal GPCR signaling

{beta}-arrestins ({beta}arrs) are canonical terminators of G protein-coupled receptor (GPCR) signaling, yet internalized receptors paradoxically sustain {beta}arr-dependent Gs/cAMP signaling from endosomes. How a desensitizing scaffold can instead promote G protein activation has remained unresolved, because the receptor-{beta}arr-G protein assemblies described so far position the two transducers on opposite faces of the receptor, with no contact between them. Using the parathyroid hormone type 1 receptor (PTH1R), we show that {beta}arr1 binds Gs directly, engages its nucleotide-free state, and does so far more strongly than it engages G{beta}{gamma}. A cryo-EM structure of the agonist-bound PTH1R-{beta}arr1-Gs complex at ~3.1 &Aring resolution reveals how: {beta}arr1 anchors to the phosphorylated receptor C-tail, leaving the transmembrane core free for Gs, and reorients to build a direct, three-node interface in which its finger, C- and back loops clamp the Switch I, Switch II and 3/{beta}5 regions of Gs. Disrupting either side of this interface abolishes complex assembly and selectively collapses the internalization-dependent phase of cAMP production, and the same interface governs {beta}arr1-Gs coupling at the glucagon-like peptide-1 receptor. {beta}-arrestin1 therefore acts as a positive regulator that holds Gs in a signaling-competent state, providing a unifying structural basis for sustained endosomal GPCR signaling.

biochemistry↗

bioRxiv2026

Ourotide: decoding the hierarchical peptide recognition for generative design

The historical dichotomy between small-molecule pocket and extended protein interfaces misrepresents the physical reality of peptide recognition.1,2 Here we show that peptide binding is not a simple structural intermediate but a distinctly multimodal landscape comprising small-molecule-like pockets, protein-like interfaces, and a previously unrecognized third mode. This third regime is governed by a hierarchical subpocket architecture where a flattened surface achieves near-complete peptide engagement through spatially partitioned hydrophobic components. To maintain stability, an incompletely enclosed dominant anchor cooperates with highly hydrated auxiliary subpockets and an asymmetric receptor coupling mechanism that concentrates energy in an adjacent continuous water network. Because this unique binding mode suffers from extreme data scarcity, standard deep learning models fail to capture its physics.3,4 To resolve this, we mapped these specific geometric signatures to mine structurally faithful training distributions from global protein interactomes. Based on these data, we trained Ourotide, a deep learning framework coupling conditional geometric flow matching with interface-aware affinity learning. Evaluated across peptides up to 65 residues, Ourotide outperforms generalist models in backbone accuracy, interface recovery, and affinity prediction. This approach suggests that overcoming data scarcity in the physical sciences requires physics-guided data augmentation rather than naive statistical scaling.

bioinformatics↗

bioRxiv2026

Single-atom inhibition of oncogenic drivers through cysteine coordination

Small-molecule inhibitors rely on molecular recognition within suitable binding pockets, leaving many disease-associated proteins difficult to target. Here, we introduce the concept of a single-atom inhibitor in which gold (Au) engages critical cysteine residues of oncogenic drivers to suppress their activity. We used an AI-assisted few-shot learning approach to identify EGFR-targeting peptides for in vivo Au delivery and showed that the lead candidate, 10714, promoted Au accumulation in EGFR-expressing cells and tumors. In vivo, Au exploited its intrinsic affinity for cysteine to inhibit two structurally distinct oncogenic proteins, engaging Cys797 in EGFR T790M and the mutation-derived Cys12 in KRAS G12C adjacent to their respective nucleotide-binding pockets. Structural and computational analyses supported stabilization of inactive nucleotide-bound states, while mutation of these cysteine residues abrogated Au-mediated inhibition. 10714-Au consequently suppressed oncogenic signaling, reduced non-small-cell lung cancer cell viability, and inhibited tumor growth in EGFR- and KRAS-mutant xenograft models and patient-derived organoids. These findings establish proof of principle for single-atom inhibition across structurally distinct oncogenic drivers and suggest that localized atomic coordination could provide an alternative mode of target engagement to conventional pocket-dependent inhibition.

molecular biology↗

bioRxiv2026

Mechanochemical cues control the coupling of metabolic and migratory patterns in cancer

Confined migration is essential for metastasis, yet how cells adapt their migratory and metabolic programs across stiffness-varying microenvironments remains unclear. We uncover a stiffness-dependent mechano-metabolic switch governing migration. In stiff microchannels, cells utilize the osmotic engine model (OEM), relying on NHE1 activity, front-polarization, and glycolysis. In soft microchannels, migration is OEM-independent and requires pyruvate-fueled oxidative phosphorylation (OxPHOS). This OxPHOS-driven motility depends on Arp3, {beta}1-integrin and integrin-linked kinase, which increase membrane tension in confinement that in turn triggers TRPM7-mediated calcium influx and RhoA-/myosin-II contractility. Activating and polarizing NHE1, via overexpression, hypoxia or elevated viscosity, restore OEM- and glycolysis-dependent migration in soft microchannels, bypassing the need for actin polymerization in vitro and in chick embryos. Mitochondria addition reinstates Arp3 polarization and enhances migration in NHE1-overexpressing cells, enabling engagement of both mechanisms in vitro and in zebrafish. These findings uncover a previously unrecognized mechano-metabolic link, revealing that intracellular rewiring overrides stiffness-dependent metabolic demands.

cell biology↗

bioRxiv2026

Detection of Spatially Aberrant Cells in Spatial Transcriptomics Data by Conformal Prediction

The hexagonal organization of epithelial cells represents a fundamental feature of normal tissue architecture, reflecting the precise spatial coordination that underlies healthy biological structure. Disruptions to this organization--manifesting as spatially aberrant spots with abnormal gene expression and misplaced positioning--are closely associated with disease initiation and progression. Here, we introduce SPADE, a computational framework that integrates single-cell RNA sequencing and spatial transcriptomics data to quantitatively characterize and detect spatial aberrancy. SPADE leverages a variational autoencoder coupled with Gaussian mixture modeling for cell-type embedding and spatial deconvolution, and incorporates conformal prediction to enable uncertainty-calibrated identification of aberrant spots. Through extensive validation, SPADE demonstrates superior performance in identifying biologically meaningful aberrant spots.

bioinformatics↗

bioRxiv2026

Targeting cellular senescence alleviates bone marrow aging

Aging of the hematopoietic system impairs hematopoietic stem cell (HSC) function and alters bone marrow niche behavior, increasing susceptibility to anemia, infections, and hematologic malignancies. Here, pharmacologic clearance of senescent cells with the PROTAC compound 753b simultaneously targeting BCL-xL and BCL-2 reverses key secretory, transcriptional, and functional hallmarks of hematopoietic aging with low toxicity, restoring balanced lineage output. Single-cell RNA sequencing further demonstrates that 753b treatment attenuates aging-associated transcriptional signatures in HSCs, while selectively eliminating senescent, pro-survival niche cells without grossly perturbing niche composition. Functionally, 753b suppresses pro-inflammatory cues from both niche and hematopoietic cells including those emanating from neutrophil progenitors, rebalancing global bone marrow secretory ecosystem across stromal and hematopoietic compartments. Collectively, we identify 753b-induced senescent cell clearance as a powerful strategy to rejuvenate aged hematopoiesis and re-establish homeostatic communication between HSCs and their microenvironment, with implications for mitigating age-related hematologic dysfunction and improving hematologic health in older individuals.

cell biology↗

bioRxiv2026

Near-Infrared-II Chemo-Optogenetics for Deep-Brain Stimulation

Precise activation of ion channels enables fine-tuned control of neuronal excitability, providing a powerful strategy for dissecting and modulating neural circuits. Although optogenetics offers high spatiotemporal neuronal manipulation with cell-type specificity, its reliance on visible light (400-650 nm) limits tissue penetration to millimeter depths, restricting applications in deep-brain stimulation. Here we report a near-infrared-II (NIR-II)-sensitive calcium channel, HaloNeu, created by genetically fusing a circularly permuted HaloTag (cpHaloTag) to the thermo-sensitive transient receptor potential vanilloid 1 (TRPV1) and covalently conjugating NIR-II photothermal nanotransducers (HPN). The HaloNeu enables non-invasive neuromodulation at depths up to 1.0 cm at ultralow laser power ([~]60 mW/cm2) and up to 5.0 cm under the safe exposure limit ([~]1 W/cm2) with 1064 nm laser illumination. Remarkably, HaloNeu maintains stable, on-demand neuron-specific modulation for over two months in vivo, providing sustained activation of ventral tegmental area (VTA) circuits and effective alleviation of Parkinsonian symptoms in mouse models. These results establish HaloNeu as a robust and versatile platform for cell-type-specific, deep-tissue, and chronic neuromodulation, with broad implications for neuroscience and neurotherapeutics.

neuroscience↗

bioRxiv2026

Towards autonomous biology: Compiler-Verified Protocols as a Foundation for Real World AI Execution

Artificial intelligence has advanced from analyzing experimental data to autonomously generating hypotheses, designing experiments, and coordinating closed loop discovery. Yet the translation from computational reasoning to physical execution remains bottlenecked by the experimental protocol, which in biology still relies on ambiguous natural-language descriptions: a medium other engineering disciplines abandoned decades ago in favor of compiler verified specification languages. This deficit fragments reproducibility along three axes: protocol accuracy, pre execution verification, and cross platform portability. Existing formalisms address only subsets of these challenges, trading expressiveness for rigor, portability for standardization, or usability for provenance. Here we introduce the Biology Protocol Language (BPL), a domain specific language with a biology-native type system in which every quantity carries physical units, every reagent declares its physical form, and every container maintains compiler-tracked state, so that implicit assumptions must be stated explicitly and physically impossible operations are rejected at compile time. We further develop BPL-COGEN, a pipeline that couples a fine tuned 30 billion parameter language model with the deterministic compiler in a closed generate validate repair loop, iteratively correcting the translation from natural language SOPs to BPL through compiler diagnostics until all physical, dimensional, and state constraints are satisfied. On a benchmark of 300 published Nature Protocols papers, BPL COGEN achieved an overall fidelity score of 95.1 against the source protocols as ground truth. Wet-lab experiment and cross-platform validation in GFP expression library construction and HPLC to UHPLC method translation confirmed that a single BPL source yielded reproducible execution across manual and liquid handler assisted contexts. The results established a novel pipeline that generates compiler-verified protocols, which is an essential prerequisite for physically embodied AI in biology.

synthetic biology↗

bioRxiv2026

AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing

Monoclonal antibodies (mAbs) are critical in disease diagnostics and therapeutics, yet the performance of mass spectrometry (MS)-based de novo sequencing remains incompletely characterized due to limited antibody-specific datasets and the absence of a standardized benchmark framework. Here we present AbNovoBench, a comprehensive framework for evaluating data analysis strategies for mAb de novo sequencing. It features the largest high-quality dataset to date, generated in-house, comprising 1,638,248 peptide-spectrum matches from 131 mAbs across six species and 11 proteases, supplemented by eight mAbs with known full-length sequence for end-to-end reconstruction assessment. Employing a unified training dataset, we systematically benchmarked 13 deep learning-based de novo peptide sequencing algorithms and three assembly strategies across peptide sequencing metrics (accuracy, robustness, efficiency, error types) and assembly metrics (coverage depth, assembly score). AbNovoBench (https://abnovobench.com) provides an online platform enriched with curated antibody MS resources and pre-trained models, enabling customizable antibody sequencing workflows, accelerating antibody-specific algorithms development, and improving reproducibility in proteomics.

systems biology↗

bioRxiv2026

Optimizing broadly neutralizing antibodies via all-atom interaction modeling and pre-trained language models

Antibody optimization is a fundamental challenge, and the identification of antibody-antigen interactions is crucial in the optimization process. However, current methods cannot accurately predict antibody-antigen interactions, providing limited functional guidance to improve the time-consuming and costly traditional optimization techniques. Here, we present InterAb and InterAb-Opt, a unified computational framework that integrates all-atom modeling with antibody language models to predict antibody-antigen interactions and enable antibody optimization. Leveraging the proposed all-atom modeling approach, AtomInter, and pre-trained antibody language models, InterAb outperforms existing methods in predicting antibody specificity and antibody-antigen binding affinity. InterAb successfully identified influenza A virus-binding antibodies from an antibody library and accurately detected high-affinity antibodies in the AIntibody competition. Empowered by the robust functional insights from InterAb, InterAb-Opt was developed to optimize broadly neutralizing antibodies. For R1-32 antibody, biolayer interferometry results reveal that 85%, 80%, 90%, and 67.5% of the 40 InterAb-Opt-optimized antibodies exhibit enhanced binding affinities to wild-type SARS-CoV-2, Lambda, BQ.1.1, and EG.5.1, respectively, with a maximum improvement of up to 96-fold. For the newly emerging BA.2.86 and KP.3, 55% and 52.5% of the optimized antibodies notably transition from non-binding to binding. Neutralization assays demonstrated that the optimized antibodies exhibited enhanced neutralization activity across multiple targets, highlighting the capability of InterAb-Opt in engineering broadly neutralizing antibodies. This technology enables precise analysis of antibody-antigen interactions and optimization of broadly neutralizing antibodies, holding promise for addressing challenges in immune evasion and vaccine design.

bioinformatics↗

bioRxiv2026

Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization

Protein design is revolutionizing biotechnology, yet existing approaches struggle to balance structural foldability with functional performance. Structure-based models excel at generating stable protein backbones but often overlook critical functional properties, while protein language models capture evolutionary and functional signals but frequently predict sequences lacking structural stability. Integrating these complementary approaches remains challenging due to their inherently conflicting objectives. We present MAProt, a multiagent framework that synergistically combines structure-based and protein language model-based methods for protein design. Each agent specializes in a distinct aspect of the design objective: the structure-based agent (e.g., ProteinMPNN) ensures compatibility with the target backbone, while protein language model-based agents (e.g., ESM, SaProt) capture evolutionary plausibility and functional potential. To reconcile conflicts and achieve optimal trade-offs, we introduce a Pareto-based negotiation module that enables effective multi-objective coordination and consensus among agents. Extensive experiments on benchmark datasets demonstrate that MAProt achieves a remarkable improvement over state-of-the-art baselines, and generalizes robustly across a range of tasks, including thermodynamic folding stability design, functional protein design, and high-affinity antibody design. These results highlight the power of collaborative optimization for advancing rational protein engineering. Codehttps://github.com/biomed-AI/MAProt

bioinformatics↗

bioRxiv2025

GPR15 and CD38 define a subset of peripheral blood pathogenic effector Th2 cells associated with active eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a chronic allergic disease driven by exposure to culprit antigens. Due to the local nature of the inflammation, diagnosis and assessment are limited to invasive procedures. Based on prior single-cell RNA sequencing (scRNA-seq) data linking peripheral GPR15+ pathogenic effector Th2 (peTh2) cells to esophageal tissue peTh2s, we hypothesized the direct involvement of GPR15+ peTh2 cells in EoE pathogenesis and aimed to further evaluate their association with EoE disease status. We subjected samples from subjects with or without EoE to flow cytometry (n = 74 peripheral blood, 17 biopsy) and scRNA-seq (n = 27 peripheral blood, 10 biopsy). Expression of GPR15 by peripheral peTh2 cells was increased in EoE, and these cells expressed increased CD38 in active EoE--findings recapitulated in esophageal biopsies. We also identified a peTh2-associated, CD38-containing gene expression program that peripheral GPR15+ peTh2 cells upregulated in active EoE. The level of upregulation was distinct from other circulating peTh2 cells and was more similar to that seen in esophageal peTh2 cells. An association between expression of GPR15 by peripheral peTh2 cells, the aryl hydrocarbon receptor was strongest in subjects with EoE, suggesting an environmental exposure or susceptibility. The magnitude of GPR15 expression by peripheral peTh2 cells could effectively in discriminate active EoE from no EoE in our study population (AUC 0.93). Our data suggest that EoE-related peTh2 cells are identifiable and accessible in the peripheral blood, and could be exploited in both clinical practice as a non-invasive biomarker and continued investigation into mechanisms driving EoE. One sentence summaryGPR15 marks a subset of peripheral blood pathogenic effector Th2 cells associated with eosinophilic esophagitis (EoE) that upregulate CD38 during active disease - an observation that has potential to be used for non-invasive diagnosis and monitoring of EoE and that has suggests new mechanisms driving this increasingly prevalent allergic disease.

immunology↗

bioRxiv2025

Vascular and Lymphatic Dysregulation via Non-EndoMT Col2a1 Signaling in Bisphosphonate-Related Osteonecrosis of the Jaw

Bisphosphonate-related osteonecrosis of the jaw (BRONJ) is one of the most severe complications of antiresorptive therapy. Although impaired angiogenesis is commonly observed in BRONJ, the underlying vascular mechanisms remain poorly understood, impeding the development of effective treatments. In this study, we integrated whole-organ tissue-clearing imaging, single-cell transcriptomics, and proteomics to construct a multimodal atlas of the mandibular microenvironment in both mouse and human BRONJ. Our findings revealed gross vascular and lymphatic rarefaction in BRONJ, along with 4 endothelial subtypes uniformly exhibiting aberrant upregulation of Col2a1. Moreover, fibroblasts and macrophages emerged as key endothelial interactors, collectively underscoring dysregulation of the endothelium-matrix-immune axis. Unlike homeostatic Dmp1/Tfap2a fibroblasts and Stab1 macrophages, BRONJ lesions featured pathological Lrrc15/Chad fibroblasts and Il6 macrophages, which promote ectopic chondrogenesis and inflammation. Mechanistically, BRONJ activated the COL2A1-CD44 axis (EC-to-fibroblast/macrophage signaling) and COL2A1-SDC4 axis (EC-to-fibroblast signaling), whereas lineage tracing excluded endothelial-to-mesenchymal transition (EndoMT), implicating extracellular matrix remodeling as the driver of dysfunction. Cross-species validation in human mandibles further confirmed vascular-lymphatic dysregulation, inflammatory activation, and aberrant chondrogenesis. Overall, this study establishes vascular and lymphatic dysfunction as a central pathological hallmark of BRONJ, driving matrix remodeling dysregulation and immune-inflammatory imbalance, and identifies the COL2A1-CD44/SDC4 axis as a potential therapeutic target for BRONJ.

cell biology↗

bioRxiv2025

Molecular architecture of OXGR1 reveals evolutionary conserved mechanisms for metabolite surveillance

The ability of cells to sense and respond to metabolic signals is fundamental to life, yet the molecular mechanisms underlying metabolite surveillance remain incompletely understood. Here, we elucidate the structural basis of metabolite recognition by OXGR1, a GPCR that monitors key intermediates in the tricarboxylic acid cycle (TCA). Using cryo-electron microscopy, we determined four cryo-EM structures of OXGR1 bound to -ketoglutarate (AKG), itaconate (ITA), and structural related succinate (SUC) and maleate (MA). These structures reveal a positively charged binding pocket and an extensive hydrogen-bond network critical for OXGR1 recognizing dicarboxylic acids. In addition, we identify a distinct arrangement of hydrophobic residues that modulates ligand potency and selectivity. Mutational analysis and molecular dynamics simulations further demonstrate that non-canonical micro-switch motifs, including FRY and NLxxY, are essential for ligand recognition and receptor activation. Comparative structural and evolutionary analyses indicate that these mechanisms are conserved across species, underscoring the critical role of OXGR1 in maintaining metabolic homeostasis. Together, our findings provide a mechanistic framework for metabolite sensing via OXGR1 and suggest potential strategies for therapeutic modulation of metabolic and inflammatory diseases.

molecular biology↗

bioRxiv2025

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

AlphaFold has set a new standard for predicting protein structures from primary sequences; however, it faces challenges with protein complexes across species, engineered proteins, and antigen-antibody, where co-evolutionary signals may be sparse or missing. Herein, we present ProTact, a SE(3)-invariant geometric graph neural network that integrates physics-informed geometric complementarity and trigonometric constraints as robust inductive biases to enhance protein-protein contact predictions. ProTact is applicable to both experimental and predicted monomer structures and utilizes a modulated key point matching algorithm to approximate accurate docking poses. Experimental evaluations demonstrate that ProTact consistently outperforms state-of-the-art sequence-based and structure-based methods on benchmark datasets, achieving notable relative improvements of 31.63% in Precision@10 for CASP 13 and 14 targets, and 31.94% for DIPS-Plus datasets. Moreover, when combined with AlphaFold3 as re-scoring functions, ProTact surpasses its default confidence scores, offering over 30.48% improvements in the low-MSA contexts. We anticipate that the proposed framework will advance our understanding of protein interactions, functions, and design.

bioinformatics↗

bioRxiv2025

Probing the CaV1.2 interactome in heart failure identifies a positive modulator of inotropy and lusitropy

BackgroundMaladaptive changes in the function, expression and localization of proteins involved in calcium-handling worsen the impaired contractility of systolic heart failure (HF). Standard proteomics techniques require cell lysis and so are unable to characterize changes specific to the critical sub-cellular domain bounded by the T-tubule and the sarcoplasmic reticulum, known as the cardiac dyad. Traditional approaches are also less likely to capture low-affinity protein-protein interactions on lipid membranes. To improve our understanding of heart failure pathophysiology, we applied proximity proteomics to the cardiac dyad of mice with ischemic cardiomyopathy. MethodsUsing two lines of transgenic mice expressing fusion proteins of the engineered ascorbate peroxidase APEX2 with subunits of the cardiac voltage gated calcium channel, CaV1.2, we labeled the dyad proteome of live, intact myocytes from healthy mice (N=6) and mice with coronary artery ligation HF (N=5) by peroxidase-catalyzed biotinylation. Quantitative mass spectrometry with isobaric tandem mass tags (TMT) was used to assess alterations in the local dyad proteome in myocytes from mice with chronic, remodeled HF. We subsequently generated a mouse with inducible cardiac overexpression of Galectin-1 to examine the effects of this protein on cardiac function and more specifically on the calcium-handling properties of mature myocytes, using cellular electrophysiology, calcium imaging, and echocardiography. ResultsFrom mice with HF, we found significant enrichment of 43 proteins defined by their abundance and proximity to transgenic CaV1.2 1C channels, and a significant reduction in 22 proteins, out a of a total of 2326 proteins quantified. We also significantly enriched 286 proteins, and saw a reduction in 13 proteins, defined by proximity and abundance to CaV1.2 {beta}2B-subunits, out of a total 2236 proteins identified. Pathway analysis revealed HF is associated with increased abundance of components of the 26s proteasome and microtubules in the dyad, as well as the dimerizing, carbohydrate binding protein Galectin-1. Cardiac specific overexpression of Galectin-1 in healthy mice increases activation of CaV1.2 and the Ryanodine receptor and accelerates myocyte relaxation through phosphorylation of Phospholamban. ConclusionsUsing proximity proteomics to examine the effects of HF in vivo, we find increased localization of Galectin-1 to the cardiac dyad, and that overexpression of Galectin-1 accelerates calcium kinetics in the heart.

cell biology↗

bioRxiv2025

Clonal hematopoiesis driven by Dnmt3a mutations promotes metabolic disease development

Clonal hematopoiesis (CH) is associated with an increased risk of non-hematologic chronic diseases including metabolic disorders, yet the causality remains poorly defined. DNMT3A is the most frequently altered gene in CH, commonly through monoallelic loss-of-function (LOF) and Arg882His (RH) hotspot mutations. Here we demonstrate in a mouse model that CH driven by Dnmt3a RH and especially LOF promotes obesity, diabetes, and chronic liver disease, further exacerbated by high-fat diet (HFD).

pathology↗