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Zhang, L.

Publications and source records attributed to Zhang, L..

At least 127 records · Page 7Linked to original sources

Engineering Carbon Nanotube Quantum Well Defects with Recognition Tripeptides for Optical Detection of Extracellular Vesicles in Plasma

Extracellular vesicles (EVs) carry molecular signatures of their originating cells and have thus emerged as promising biomarkers. However, their clinical utility remains limited due to their low abundance and the modest sensitivity of current EV detection methods in complex biological environments. Here, we present a quantum well defect functionalized carbon nanotube sensor coupled with integrin-recognition RGD tripeptide for EV detection in human plasma. Leveraging the abundance of integrins on EV surfaces, we targeted 5{beta}1, V{beta}1, and V{beta}3 subtypes. The nanosensor exhibited robust hypsochromic shifts in defect emission upon integrin binding, achieving sub-picomolar detection limits for integrin subunits and quantifying EVs at concentrations as low as 104 EVs{middle dot}mL-1 for glioblastoma, ovarian cancer, and fibroblast cell-derived EV types. Molecular dynamics simulation indicated that integrin docking at the RGD-coupled quantum defect can substantially reshape the interfacial environments of the quantum defects, explaining the high sensitivity in EV detection in complex biological media. Finally, transmembrane protein analysis validated the expression of surface integrins across the tested EV types. The modular nanosensor construct can be targeted to detect disease-associated EV subpopulations, advancing EV-based diagnostics.

biochemistry↗

Epigenome Alterations and 3D Chromatin Architecture Remodeling in Inflammatory Macrophage Activation under Diabetic Conditions

Aberrant monocyte/macrophage activation in diabetes drives chronic inflammation and complications. While epigenetic mechanisms are implicated, the role of 3D-chromatin reorganization remains unclear. Using integrated multi-Omics, we profiled gene expression and 3D-chromatin architecture in human CD14+ monocyte-differentiated macrophages treated with high glucose + TNF- (HT) mimicking the diabetic milieu. HT induced inflammatory programs resembling those in diabetes, and dynamically altered chromatin accessibility, enhancer-promoter loops, transcriptionally active/inactive (A/B) compartments, and topologically associated domains. Inflammatory genes exhibited increased, whereas cell-cycle and metabolic genes showed reduced chromatin accessibility and enhancer-promoter interactions. These architectural changes facilitated the convergence of lineage-specific and signal-dependent transcription factors at enhancer-promoter loops, forming network hubs orchestrating pro-inflammatory responses. Similar enhancer-promoter interactions were observed in monocytes from individuals with diabetes. Perturbing chromatin interactions via CRISPR-interference targeting HT-induced enhancers suppressed inflammatory gene expression. Results from this first comprehensive enhancer connectome maps in macrophages under diabetic conditions reveal disease-associated rewiring of the 3D epigenome, highlighting epigenetic mechanisms as potential therapeutic targets. TeaserDiabetic conditions rewire 3D chromatin organization and enhancer-promoter interactions in human macrophages, promoting chronic inflammation.

genomics↗

Shared binding sites for the chromosomal architectural protein Su(Hw) mediate physical interactions between Drosophila TAD boundaries

The head-to-tail pairing of the nhomie and homie boundaries is responsible for the formation of a stem-loop TAD (topologically associated domain) that encompasses the even-skipped (eve) gene and its regulatory elements. TAD boundaries like homie and nhomie have partner preferences and one mechanism thought to be important in linking compatible boundaries is shared binding sites for the same architectural protein. Both eve boundaries have a single binding site for the polydactyl zinc finger protein Su(Hw). Here we have investigated the role of the shared Su(Hw) sites in the long-distance regulation of a dual reporter by the eve enhancers. Consistent with this mechanism, we find that when the transgene nhomie or homie boundary has a mutation in the Su(Hw) binding site, reporter activation by the eve enhancers is severely compromised. Using MicroC we show the mutating the single Su(Hw) substantially weakens the ability of the nhomie and homie boundaries in the transgene to physically pair with the two endogenous eve boundaries. Su(Hw) binding sites are not, however, sufficient as the gypsy su(Hw) insulator is unable to support long-distance regulation. In a less demanding transvection assay mutations in the nhomie and homie Su(hw) site have only a modest effect on regulatory interactions. The eve boundaries can mediate transvection with the gypsy insulator; however. they are not compatible with the dCTCF Fab-8 boundary even in the transvection assay. Finally, we show that the regulatory and physical interactions of the nhomie boundary in the transgene and the two endogenous eve boundaries are incompatible with the popular cohesin-loop extrusion model.

genetics↗

COLOR-3D: a versatile tool for revealing novel 3D histological features

Hematoxylin and eosin (H&E) has been the fundamental method for visualising tissue morphology. Recent advances in tissue clearing and microscopy have enabled the observation of tissue morphology in 3D, but incomplete penetration of nucleic acid dyes has remained the bottleneck. To address this, we develop a new staining chemistry called Cyclodextrin and Organic solvent-assisted deep Labelling of ORgans in 3D (COLOR-3D), which attains the best penetration depth and homogeneity among state-of-the-art methods. We also demonstrate the scalability of COLOR-3D and its compatibility with other staining modalities. To bridge the gap between 3D histology and its wider application in biomedical research and histopathology, we develop a computational pipeline to convert 3D fluorescence images into bright-field H&E images, enabling the creation of a 3D atlas of both normal tissues and pathological specimens. Apart from qualitative observation of tissue morphology, COLOR-3D also enables quantitative analysis for studying biological phenomena. In the mouse liver, we discover rare populations of tetranuclear hepatocytes as well as m16n, t4n and t8n hepatocytes. We also propose the first structural model of the liver lobule based on 3D histology. With a more complete penetration, we reveal the following aging-related changes in tissue microarchitecture, including an increase in extreme nuclear polyploidy, and the disruption of vasculature and portal triad.

pathology↗

Annotation-free sequence-level multimodal graph learning reveals gut microbiome signatures of atherosclerotic cardiovascular disease

BackgroundAtherosclerotic cardiovascular disease (ACVD) remains one of the leading causes of mortality, and the gut microbiome has been implicated in ACVD-related metabolic and inflammatory processes. However, current microbiome-based disease prediction frameworks predominantly rely on annotation-dependent taxonomic or pathway-level abundance profiles. While effective for community-level association analysis, these approaches compress metagenomic information into predefined biological categories and may consequently obscure fine-grained sequence variation, uncharacterized microbial fragments, regulatory sequence signals and sequence-based structure information embedded within metagenomic DNA. Whether disease-relevant microbiome information can be directly learned from raw metagenomic sequences without prior annotation remains incompletely explored. ResultsWe developed MLMGCN-CVD, an annotation-free sequence-level multimodal graph-learning framework for ACVD prediction directly from gut metagenomic DNA fragments. Rather than relying on taxonomic aggregation, MLMGCN-CVD integrates pretrained genomic language-model embeddings, sequence-derived structural priors, and read-mapping quantitative evidence to learn disease-associated representations at the DNA fragment level. In a primary cohort comprising 218 ACVD patients and 187 healthy controls, MLMGCN-CVD achieved an area under the receiver operating characteristic curve (AUC) of 0.978 under grouped 10-fold cross-validation. External validation on an independent cohort (PRJNA615842) yielded an AUC of 0.940, outperforming relative- abundance-, pathway-, and sequence-only reference frameworks. Importantly, model-prioritized fragments independently recovered multiple microbiome-associated functions previously implicated in ACVD, including trimethylamine (TMA) metabolism, O-antigen of lipopolysaccharide (LPS) biosynthesis, phosphotransferase systems (PTS), and Enterobacteriaceae-associated modules. Beyond these established signatures, Rfam-supported analyses identified candidate regulatory RNA-associated signals, including riboswitches and Bacterial small RNAs, suggesting that regulatory sequence information may contribute previously underexplored discriminatory signals within the ACVD gut microbiome. Several representative sequence elements further showed significant correlations with host cardiometabolic indicators. ConclusionsOur findings suggest that microbiome-associated disease signals extend beyond conventional taxonomic or pathway abundance summaries and can be directly inferred from raw metagenomic sequences. By shifting microbiome modelling from annotation-dependent community aggregation toward annotation-free sequence-level representation learning, MLMGCN-CVD provides a framework for uncovering biologically informative regulatory and functional signals embedded within metagenomic data.

bioinformatics↗

Novel Muscle-Tropic AAV Capsids with Dramatically Enhanced Transduction and Safety Profiles in Non-Human Primates

Adeno-associated virus (AAV) gene therapy holds immense promise for treating muscular dystrophies, yet its efficacy and safety are constrained by the suboptimal tissue tropism of natural serotypes. Here, we employed the REACH platform, which combines rational design and directed evolution, to engineer muscle targeting vectors. Systemic administration in non-human primates (NHPs) revealed that lead candidate M1 mediates a >10-fold increase in skeletal muscle transduction compared to the AAV9 and 2-3 fold higher than MyoAAV, while concurrently achieving a remarkable 183-fold reduction in liver distribution. Furthermore, M1 exhibited significant de-targeting from key off-target tissues, including dorsal root ganglia (11 fold), lung (27 fold), spleen (2 fold), and kidney (2 fold). These findings demonstrate that the REACH platform can generate AAV capsids with simultaneously enhanced muscle tropism and favorable safety profiles, addressing a critical bottleneck in muscle-directed gene therapy.

bioengineering↗

Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia

Temporal dynamics of the peripheral blood transcriptome are crucial for understanding leukemia evolution and response to therapy because they can reveal how gene expression programs drive abnormal cell states, disease heterogeneity, and treatment resistance. Using a mathematical model of state-transitions, we studied the temporal dynamics of peripheral blood messenger RNA (mRNA) and microRNA (miRNA) transcriptomes in a mouse model of acute myeloid leukemia (AML). In our state-transition model, mRNA and miRNA transcriptomes are represented as a particle undergoing Brownian motion in a two-dimensional multiomic potential landscape. Following chemotherapy, we observed a temporal desynchronization between mRNA and miRNA transcriptomic responses corresponding to an asymmetric shift in the landscape. Specifically, mRNA trajectories responded almost immediately post-treatment, whereas miRNA responses were delayed by approximately two weeks. Clustering analysis identified that the temporal delay is driven by a prominent cluster of miRNAs from the imprinted Dlk1-Dio3 region. Although previously implicated in acute promyelocytic leukemia, lymphomas, and metabolic dysregulation, this provides the first evidence linking the Dlk1-Dio3 locus to AML chemotherapy response and treatment-induced transcriptomic desynchronization. This framework offers an innovative dynamics-based strategy to identify biological drivers of therapeutic response and novel therapeutic targets across hematological malignancies. Key PointsO_LITreatment can induce desynchronization between messenger RNA and microRNA transcriptome dynamics in a murine model of AML C_LIO_LIMicroRNAs from the imprinted Dlk1-Dio3 locus are identified as key contributors to desynchronization and may serve as therapeutic targets C_LI

cancer biology↗

Wheat MYB transcription factor TaMYB83-7B regulates seed dormancy by influencing the balance between abscisic acid and gibberellin

In wheat, weak seed dormancy (SD) is related to an increased tendency for pre-harvest sprouting (PHS), which reduces yield and quality. However, the molecular mechanism underlying SD remains elusive. Here, we identified a wheat R2R3-MYB transcription factor (TaMYB83-7B) related to SD. Expression analysis showed that TaMYB83-7B was highly expressed in wheat seeds, and was more highly expressed in strong-dormancy varieties than in weak-dormancy varieties. Sequence and association analysis indicated that T/C mutations at -907 bp and -1133 bp in the TaMYB83-7B promoter were significantly associated with wheat SD, with C at both sites related to strong dormancy. Dual-luciferase reporter assays demonstrated that the transcriptional activity of the TaMYB83-7B promoter was significantly higher in strong-dormancy varieties than in weak-dormancy varieties. Further analyses indicated that TaMYB83-7B functions as a transcriptional inhibitor. Germination experiments revealed that overexpression of TaMYB83-7B significantly enhanced SD, while its loss-of-function reduced SD. Finally, TaMYB83-7B was found to regulate SD by influencing the balance between abscisic acid (ABA) and gibberellin (GA) in wheat seeds. Overall, the results of this study enhance our understanding of the complex regulatory mechanism underlying SD, and provide gene targets and molecular markers for the genetic improvement of PHS resistance in wheat.

molecular biology↗

Physiological trade-offs drive the archaeal dominance and carbon turnover in deep subsurface

Marine sediments host a vast deep biosphere, yet how microorganisms persist under severe energy limitation and govern long-term organic carbon (OC) preservation remains poorly understood. Here we show that archaea, primarily Bathyarchaeia, systematically displace bacteria with depth and form net growth zones across East China Sea shelf deep sediments. Multi-omics analyses and bioenergetic modelling reveal that this transition is driven by sustained archaeal metabolism of diverse recalcitrant OC compounds, and a physiological trade-off that prioritizes cellular maintenance over growth, minimizing mortality in deep sediments. This strategy triggers a fundamental shift in sedimentary carbon turnover: from rapid bacterial degradation of labile OC near the surface to persistent archaea-driven turnover of recalcitrant OC at depth. We estimate that Bathyarchaeia mediate [~]77% of total OC degradation after 1,000 years of burial, corresponding to [~]18% ([~]11.4 Pg C) of millennial OC degradation in global shelf sediments. These findings identify subsurface archaea as key microbial regulators of long-term OC preservation and reveal how physiological trade-offs sustain life and carbon turnover in the energy-limited deep biosphere.

microbiology↗

A General Statistical Framework for Hardy-Weinberg Equilibrium Inference on the X Chromosome

Testing for Hardy-Weinberg equilibrium (HWE) is a fundamental component of genetic data analysis, widely used for quality control and model validation. Although HWE testing is well established for autosomal loci, inference on the X chromosome is more complex due to sex-specific genotype structures and potential sex differences in minor allele frequency (sdMAF). Existing tests differ in their assumptions about sdMAF and male sample inclusion, often leading to distinct but poorly characterized null hypotheses. We develop a general statistical framework for HWE inference using the robust allele-based regression model. By formulating HWE testing as an assessment of allele-level dependence, the framework directly parameterizes Hardy-Weinberg disequilibrium, unifies existing Pearson{chi} 2-based tests under explicit modeling assumptions, and clarifies their null hypotheses, degrees of freedom, and sensitivity to sdMAF. The framework also accommodates covariate and population-structure adjustment within a unified regression-based formulation. The proposed framework provides robust, interpretable, and flexible inference, establishing a unified statistical foundation for HWE testing across autosomal and X-chromosomal regions. Simulation studies and analysis of high-coverage 1000 Genomes Project data demonstrate that commonly used X-chromosome tests can exhibit inflated type I error or misleading inference when sdMAF is present.

genetics↗

Deciphering interaction syntax via decoupling intrinsic lineages and niche pressure

Spatial transcriptomics enables the mapping of gene expression within intact tissues, yet a fundamental gap remains between knowing where cells are and understanding how they interact. A cells measured transcriptome reflects both its intrinsic lineage identity and niche pressure. Here we introduce TRINUS, a self-supervised model that deciphers interaction syntax by generative decoupling of a cells intrinsic lineage identity from the extrinsic niche pressure. TRINUS maintains a library of context-free cell prototypes to isolate lineage identity while modeling cooperative interaction dependencies among neighbors. We validated TRINUS on synthetic datasets with known interaction logic and benchmarked it against existing methods with superior performance in cell clustering and spatial domain detection. Applied across diverse platforms and biological systems, TRINUS resolves multi-level interaction syntax and maps tissue-wide interaction patterns in colorectal cancer, and identifies stage-specific signaling dependencies and time-dependent receptor windows during mouse organogenesis. We also show TRINUSs bidirectional in silico engineering capability in the ovarian tumor microenvironment, where forward perturbation revealed subtype-specific macrophage immunosuppressive programs via virtual transplantation and inverse design identified molecular modifications in macrophages predicted to rescue adjacent T-cell function. Collectively, TRINUS provides a practical tool for interaction syntax discovery and predictive tissue engineering on spatial transcriptomics data.

bioinformatics↗

Decoding heterogeneous aging clocks and disease risk stratification using a metabolomic foundation model

Metabolomic aging clocks estimate biological age by modeling metabolite concentrations, thereby capturing aging signals from healthspan and adverse outcomes. However, existing clocks generally assume homogeneous aging trajectories and yield only a single age acceleration metric, limiting their capacity to capture inter-individual metabolic heterogeneity and characterize nuanced individual-level representations. To address these limitations, we proposed MetAgeFormer, a transformer-based metabolomic model pre-trained on nuclear magnetic resonance (NMR) metabolomic profiles from over 430,000 participants in UK Biobank via self-supervised learning. This large-scale pre-training enables MetAgeFormer to learn a metabolomic representation space that captures the complex, nonlinear structure of systemic metabolism as reflected in NMR data. Building on MetAgeFormer, we developed a mortality-informed metabolomic aging clock by fine-tuning an attached survival module, deriving age acceleration that demonstrates significant associations with multiple age-related diseases and factors. We further validated zero-shot transfer in the independent Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. More importantly, we utilized embeddings generated by MetAgeFormer to identify 13 distinct metabolic subtypes and consolidated them into four meta-subtypes with markedly divergent susceptibility profiles for major age-related diseases, particularly type 2 diabetes and neurodegenerative disorders. This finding empirically demonstrated substantial metabolic heterogeneity across populations, persisting even at comparable levels of age acceleration. To enhance clinical applicability, we further employed contrastive learning to distill a lightweight model that approximates the learned metabolomic representation space using only 14 routine clinical blood test measurements as inputs. Both hold-out testing within UK Biobank and external validation in the China Health and Retirement Longitudinal Study replicated similar disease onset patterns across the identified subtypes, underscoring the robust generalizability of MetAgeFormer and supporting its translational potential as a scalable framework for metabolomic aging assessment and early disease risk stratification.

bioinformatics↗

TBI-166 inhibits DlaT and LpdC, exhibiting synergistic effects with Bedaquiline and Pyrazinamide against Mycobacterium tuberculosis

Tuberculosis (TB), particularly drug-resistant tuberculosis (DR-TB), remains a critical global challenge and underscores the urgent need for novel drugs and innovative combination regimens with distinct mechanisms of action. Here, we characterize an all-oral three-drug regimen comprising TBI-166, bedaquiline(BDQ), and pyrazinamide(PZA), which displays strong synergistic antimicrobial activity in vitro against both replicating and non-replicating Mycobacterium tuberculosis (MTB) and has previously shown superior bactericidal and sterilizing efficacy to standard HRZ and BPaL regimens in murine TB models(1). Time-kill studies demonstrate that the triple regimen outperforms dual-drug combinations, accelerating bacterial clearance across multiple physiological states. Mechanistic investigations revealed that the TBI-166-BDQ-PZA combination induces a comprehensive collapse of energy and redox homeostasis, marked by profound ATP depletion, robust accumulation of reactive oxygen species (ROS), and marked disruption of the intracellular NAD(H) pool. TBI-166, a novel riminophenazine analogue of clofazimine (CFZ) currently in phase II clinical trials, emerged as a key contributor to this metabolic stress. Metabolomic profiling and {superscript 1}3C-based flux analysis show that TBI-166 slows glycolysis and the tricarboxylic acid (TCA) cycle while enhancing flux through the pentose phosphate and nicotinate pathways, thereby lowering the NADH/NAD ratio and diminishing MTB metabolic flexibility under environmental stress. In parallel, TBI-166 downregulates the dormancy regulator DosR and its regulon, further compromising adaptation to non-replicating states. Multi-omics analyses, together with biochemical and biophysical assays, identify the pyruvate dehydrogenase complex (PDHc) components DlaT and LpdC as direct molecular targets of TBI-166, with drug binding leading to potent inhibition of their enzymatic activities. Collectively, these findings define the mechanism of action of TBI-166 and provide a molecular rationale for its inclusion in potent, all-oral, short-course regimens. More broadly, they highlight the therapeutic potential of metabolically targeted combinations that destabilize energy metabolism, redox balance, and metabolic adaptability to improve DR-TB treatment outcomes.

microbiology↗

Engineering Endogenous T Cell Receptors to Recognize Cancer Neoantigens Using a Hybrid Physics-AI Approach

T cell receptors (TCRs) are critical for immune surveillance and successful adaptive immune response against foreign antigens. TCRs drive this key arm of the immune system through recognition of peptide epitopes presented on MHC complexes. However, they are limited due to their stochastic nature and generation via genetic recombination. In silico design of functional TCRs that target defined peptide epitopes would be of considerable utility but has up until now been unsuccessful. Here, we develop an artificial intelligence (AI)-powered approach using a hybrid physics-based simulation and generative AI that successfully engineers TCRs against defined epitopes presented by MHC-I. We use this approach to design TCRs against two cancer antigens, a HERC1 neoantigen and an immunogenic neoepitope in mutant EGFR. We engineer multiple TCRs against the HERC1 neoantigen which activate T cells in response to exposure to peptide-MHC I and kill cancer cells more effectively than a patient-derived TCR. In addition, we used generative AI to design functional TCRs that target the EGFR T790M neoantigen, engineering greater specificity against the mutant sequence. We present an AI-based approach to TCR design with broad utility for efforts to engineer TCRs and for the development of new cell therapies. One sentence summaryArtificial intelligence-based approach enables the directed engineering of functional TCRs with enhanced features that target cancer neoantigens.

immunology↗

An Engineered Halotolerant Chimeric T7 RNA Polymerase for High-Yield, Low-Immunogenicity Synthesis of RNA via Simple Batch Transcription

The rapid advancement of mRNA therapeutics has imposed stringent requirements on both the quality and scalability of in vitro transcription (IVT) products. However, the accumulation of double-stranded RNA (dsRNA) byproducts and 3-terminal heterogeneity during T7 RNA polymerase (T7 RNAP)-mediated transcription can robustly trigger deleterious innate immune responses and compromise translation efficiency. Existing enzyme engineering strategies frequently struggle to reconcile the trade-offs between salt tolerance, volumetric productivity, and product purity. Here, we report a novel engineering strategy for halotolerant T7 RNAP by fusing optimized mutant polymerases with diverse DNA-binding domains (e.g., Sso7d, MC1). This approach orchestrated the development of a series of chimeric T7 RNAP mutants designed to bolster catalytic activity and template selectivity under high-salt conditions while concurrently suppressing RNA-dependent RNA polymerase (RdRP) activity. Our lead chimeric mutants exhibited exceptional salt tolerance and processivity in the presence of up to 270 mM NaCl. Notably, these mutants significantly diminished dsRNA formation to less than 0.001%, while markedly improving transcript integrity and 3 homogeneity, thereby facilitating superior translation efficiency for both linear mRNA and circular RNA (circRNA). Crucially, this heightened salt tolerance does not necessitate a trade-off in RNA yield, affording broader flexibility for downstream process optimization. In an enzymatic circRNA synthesis system, these mutants enabled a non-fed-batch configuration with high initial rNTP concentrations (15 mM each), resulting in a 50% increase in yield and achieving an unprecedented titer of 15 mg/mL. This research provides a robust enzymological solution that harmonizes quality and productivity for the industrial-scale manufacturing of high-concentration, low-immunogenicity RNA.

genomics↗

Modeling Meibomian Gland Development and Dysfunction: A Mouse-Derived Organoid System Reveals Hippo-YAP as a Critical Regulator

The developmental program governing meibomian gland (MG) morphogenesis and proliferation remains poorly understood, largely due to the lack of physiologically relevant model systems. Here, we established a novel high-fidelity, three-dimensional organoids model derived from mouse meibomian gland (mMGO) epithelium. Transcriptomic and phenotypic analyses demonstrated that mMGOs faithfully recapitulate postnatal gland development in vivo, including dynamic transcription program, branching morphogenesis, lineage differentiation, and functional lipid accumulation. Leveraging this model, we identified the Hippo-YAP pathway as a pivotal regulator of MG epithelial proliferation and homeostasis for the first time. YAP inhibition severely impaired organoids growth, while pharmacological inhibition of Hippo pathway with XMU-MP-1 enhanced proliferation and progenitor clonogenicity. Crucially, in inflammation-induced atrophic organoids, XMU-MP-1 treatment rescued YAP nuclear localization and stimulated regrowth and functional restoration. Our study provided new mechanistic insights and a robust organoids platform for MG development research, and nominated targeted Hippo pathway inhibition as a promising strategy to reverse glandular atrophy in meibomian gland dysfunction.

developmental biology↗

The gut microbiota metabolite Urolithin A mitigates JAK signaling to suppress cytokine-mediated autoimmune diseases

Aberrant activation of type I interferon (IFN-I) is closely related to the development of autoimmune diseases. The metabolic regulation of cytokine signaling is essential for immune homeostasis. In this study, we characterized Urolithin A(UA), a natural gut-derived metabolite, as an inhibitor of Janus kinase (JAK) signaling. UA was found to broadly dampen JAK phosphorylation and the downstream signaling induced by cytokines such as type I interferons (IFN-I), type II interferons (IFN-II), and interleukin-6 (IL-6). UA can directly bind to JAK1 JH1 domain and treatment with UA attenuated autoimmune pathogenesis in Trex1-KO mice, IMQ-induced SLE and psoriasis models. Our findings unveil that UA is an anti-inflammatory metabolite that promotes immune homeostasis and could be used to treat inflammatory and autoimmune diseases.

immunology↗

H2AX C-Terminal Dipeptide Truncation: A Master Switch of the DNA Damage Response.

Phosphorylation of histone H2AX at serine 139 ({gamma}H2AX) by ATM/ATR kinases is a central marker of the DNA damage response (DDR), widely used to detect DNA double-strand breaks. However, the molecular basis for tissue- and context-specific variation in {gamma}H2AX signaling remains poorly defined. Here we discover a post-translational truncation of H2AX, catalyzed by lysine demethylase 4A (KDM4A), which removes two C-terminal amino acids critical for ATM/ATR-dependent phosphorylation. This truncation renders H2AX refractory to {gamma}H2AX formation, effectively bypassing canonical DDR signaling. Truncated H2AX accumulates in select cell lines, primary cells, solid tumors, and normal tissues. Genetic knockdown or pharmacologic inhibition of KDM4A reduces H2AX truncation, restores {gamma}H2AX induction, and enhances DNA repair capacity. Conversely, KDM4A overexpression promotes H2AX truncation, impairs {gamma}H2AX signaling, and exacerbates DNA damage accumulation. This previously unrecognized regulatory axis implicates KDM4A catalyzed H2AX truncation as a superseding mechanism that represses the canonical DDR and disrupts the correlation between {gamma}H2AX and DNA damage. This dioxygenase-based protease mechanism represents a new class of proteases and is the first example of c-terminal dipeptide protein truncation. This discovery has broad implications in the basic science of genome maintenance, wound healing, cancer, combinatorial therapy, precision medicine, and technologies such as gene editing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/723272v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1b4050forg.highwire.dtl.DTLVardef@1b8333aorg.highwire.dtl.DTLVardef@2b27c5org.highwire.dtl.DTLVardef@13ff699_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗