Biology subjects

Guo, H.

Publications and source records attributed to Guo, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Neuronal Hyperactivity Induces Tau Phosphorylation Selectively in Excitatory Pyramidal Neurons Which Contributes to GluA2 Regulation

While tau plays a role in regulating neuronal network excitability, a process thought to be dysregulated in Alzheimers disease (AD), the molecular mechanisms linking tau and hyperexcitability remain unclear. To investigate interactions between endogenous tau and neuronal activity, we increased neuronal activity using bicuculline or suppressed activity using tetrodotoxin for 24 hours in rat hippocampal neurons. Chronic neuronal hyperactivity increased dendritic tau phosphorylation at S202/T205. This response was observed only in excitatory CA1 and CA3 pyramidal neurons, but not in inhibitory or dentate granule neurons. Dendritic phospho-tau positively correlated with neuronal activation after bicuculline treatment, as measured by cfos expression, and was prevented with inhibition of protein kinase A (PKA). To examine the functional consequences of tau S202/T205 phosphorylation, hippocampal neurons were transfected with wild-type tau or tau containing mutations preventing phosphorylation at these sites. Wildtype tau overexpression increased tau phosphorylation and reduced neuronal surface GluA2, which was prevented by phospho-site mutation. Extending these findings to mice, chronic low-dose picrotoxin treatment increased endogenous S202/T205 tau phosphorylation in CA3 neurons. In picrotoxin-treated mice, cfos expression was associated with increased tau phosphorylation specifically in CA3 and CA1 neurons. Together, these findings identify activity-dependent, endogenous tau phosphorylation as a selective response to hyperexcitation in excitatory hippocampal neurons downstream of PKA activation. The data further link tau phosphorylation to altered GluA2 regulation, suggesting a potential molecular connection between neuronal hyperactivity, tau phosphorylation, and synaptic dysfunction relevant to AD.

neuroscience↗

bioRxiv2026

Shared and distinct immunometabolic states link to divergent pathology in cutaneous mycobacterial infections

Cutaneous mycobacterial infections provide a common tissue setting in which related intracellular pathogens elicit distinct pathological responses, but the innate immune basis of this divergence remains incompletely understood. Here, we combined single-cell profiling from 33 individuals with histological and functional analyses, examining healthy skin and lesions from multibacillary leprosy (MB), paucibacillary leprosy (PB), cutaneous tuberculosis (CTB) and Mycobacterium marinum infection (Mari). All infections showed lower mononuclear phagocyte and higher natural killer (NK) cell proportions within the CD45 compartment, alongside shared and infection-associated dendritic cell, macrophage and NK cell programs. CTB and Mari displayed broader macrophage antimicrobial signatures, whereas MB showed lipid-associated programs and attenuated interferon-associated effector expression across multiple macrophage subsets. Reduced expression of fructose-1,6-bisphosphatase 1 (FBP1) was associated with the absence of Langhans giant cells in MB, and pharmacological FBP1 inhibition nearly abolished fusion of macrophages. Compared with NK cells from Mari, MB NK cells showed reduced central carbon and oxidative metabolism signatures alongside increased lipid-associated programs. Functional experiments showed that inhibition of glucose utilization, disruption of mitochondrial function, or co-culture with foamy macrophages abundant in MB lesions reduced NK cell IFN-{gamma} production. These findings identify shared and distinct innate immune programs and suggest that their interplay with local metabolic environments shapes divergent pathology across cutaneous mycobacterial infections.

immunology↗

bioRxiv2026

MOD-scTWAS: Leveraging gene co-expression for single-cell transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) provide an effective framework for identifying genes associated with complex traits. Population-scale single-cell transcriptomic data enable genetically regulated expression (GReX) prediction and TWAS analyses at cell-type resolution, but the predictive performance of existing single-cell TWAS methods remains limited. Here, we develop MOD-scTWAS, a module-based method that jointly models GReX for genes within co-expression modules to borrow information across genes. Starting from a generative model for single-cell gene expression, MOD-scTWAS accounts for the heteroscedasticity and cross-gene correlation of individual-level pseudobulk expression in joint GReX prediction. In cross-validation analyses of the OneK1K dataset, MOD-scTWAS achieved higher mean GReX prediction accuracy than scTWAS across all 14 cell types and increased the number of imputable genes. When applied to TWAS analyses of UK Biobank quantitative hematological traits, MOD-scTWAS identified more significant cell type-gene-trait associations than scTWAS. These results demonstrate the potential of leveraging gene co-expression through joint modeling to improve cell-type-specific GReX prediction and TWAS discovery.

genetics↗

bioRxiv2026

Interferon gamma-induced MX1 and IFITM1 inhibit Varicella-Zoster Virus Replication

Interferon gamma (IFN-{gamma}) is a potent cytokine secreted following primary varicella-zoster virus (VZV) infection. Our previous studies demonstrated that pretreatment with IFN-{gamma} completely inhibited VZV replication in lung fibroblast MRC-5 and retinal epithelial ARPE-19 but not in melanoma MeWo cells, suggesting that IFN-{gamma}-stimulated protein(s) inhibit viral replication. Our microarray analysis revealed that 34 interferon-stimulated genes (ISGs) were upregulated by greater than 2.5-fold at 8 h post-treatment in both ARPE-19 and MRC-5 cells compared to those of MeWo cells. The depletion of CTSS, GBP4, MX1, IFIT1, IFITM1 and IRF1 by siRNA in IFN-{gamma}-treated cells significantly increased VZV yields. Ectopic overexpression of interferon-induced myxovirus resistance 1 (MX1) or transmembrane protein 1 (IFITM1) significantly reduced VZV yield both in ARPE-19 and MeWo cells. IFITM1 abrogated viral major immediate-early IE62 protein-mediated trans-activation. IFITM1 reduced the level of VZV immediate-early IE62 protein, but not the IE62 mRNA, suggesting that IFITM1 expression reduces the expression level of IE62 by post-transcriptional regulation. Cell-free VZV failed to infect human primary epidermal keratinocytes, in which the basal level of endogenous MX1 gene expression is very high. Pharmacologic inhibition of JAK signaling reduced MX1 expression and enhanced VZV replication in calcium chloride-induced differentiated keratinocytes. Collectively, these findings identify MX1 and IFITM1 as key IFN-{gamma}-induced restriction factors that inhibit VZV replication and suggest that suppression of JAK-STAT signaling can promote VZV replication in differentiated keratinocytes.

microbiology↗

bioRxiv2026

Algebraic Morphogenesis Through Cochain Operators

AO_SCPLOWBSTRACTC_SCPLOWCellular automata and graph reaction-diffusion systems encode local spatial interactions in different mathematical forms. We develop a cochain-operator calculus for these two settings. Over a finite field Fq, every local rule on a finite neighborhood has a unique reduced polynomial representative. On an oriented line, the coboundary and endpoint maps recover the left and right shifts. Our main theorem shows that these operators, together with linear operations, constant cochains, and the degree-zero cup product, generate every finite-radius polynomial cellular automaton. Explicit formulas for Rules 30, 110, and 22 show how reflection-invariant linear coupling, directed transport, and nonlinear neighbor interactions enter the calculus. On a general graph, d*d is the unweighted combinatorial Laplacian and enters a graph reaction- diffusion recurrence. Over [R], the term - Dd*d with D [≥] 0 admits the usual diffusion interpretation; over Fq, the corresponding expression defines modular coupling without an intrinsic order. In the morphogenetic examples, we therefore distinguish pattern-generating dynamics from finite-state observation and use the Betti numbers of active induced subcomplexes to summarize observed patterns. This yields a common algebraic representation without identifying real-valued diffusion with finite-field dynamics.

biophysics↗

bioRxiv2026

Single-cell profiling reveals MMP7-associated epithelial transition and immune-stromal remodeling in esophageal carcinogenesis

The progression from reflux esophagitis (RE) to Barretts esophagus (BE) and esophageal adenocarcinoma (EAC) is a major route of inflammation-associated esophageal tumorigenesis, but the cellular transitions involved remain incompletely understood. We performed single-cell transcriptomic profiling of RE, BE, and EAC tissues and integrated public EAC datasets, yielding 51,283 cells across the RE-BE-EAC continuum. The analysis identified stage-associated epithelial and microenvironmental differences. Pseudotime analysis placed Basal epithelial cells along two differentiation branches, one toward mature epithelial states and the other through proliferative and gastric-type metaplastic states toward copy number alteration-bearing malignant epithelial cells. MMP7 increased along this malignant branch, was enriched in cancer-associated epithelial cells. Its knockdown reduced the proliferation and migration of OE19 and OE33 cells, supporting an association with proliferative and migratory phenotypes. In parallel, the microenvironment shifted toward immune suppression and stromal remodeling, with increased T-cell exhaustion, macrophage polarization toward tumor-associated states, and enrichment of CAF-like myofibroblasts. Ligand-receptor analysis further suggested an epithelial-centered communication network involving MDK-SDC2/LRP1/NCL interactions between malignant epithelial cells and fibroblast, T-cell, and myeloid compartments. These results identify MMP7-associated epithelial states and candidate MDK-related communication pathways for further longitudinal and functional validation.

cancer biology↗

bioRxiv2026

GalaxyVS: Exploring 100-Billion Compounds in Seconds

We present GalaxyVS, a hardware-software co-designed virtual screening framework built to explore the 100-billion commercially accessible chemical space in seconds, deployed at the National Supercomputing Center in Tianjin. Built upon the dense vector retrieval paradigm of DrugCLIP, GalaxyVS bypasses the structural dependencies and computational overhead of classical docking to enable rapid screening against experimentally determined as well as geometrically feasible pockets on AlphaFold-predicted structures. To scale this paradigm to the 100-billion level, the system must overcome the significant computational burden of offline representation encoding, critical memory and I/O bottlenecks during online retrieval, and the risks of diversity collapse and precision loss within final screening results. Utilizing the heterogeneous supercomputing infrastructure, GalaxyVS accelerates the offline encoding through deep operator adaptations and resolves online retrieval bottlenecks via disk-native vector indexing coupled with in-memory staging to ensure both broad accessibility and high throughput. Concurrently, a two-stage refinement protocol effectively mitigates diversity collapse and ensures high-fidelity affinity ranking. Consequently, GalaxyVS achieves a daily scoring throughput of 1.5 x 1016 target-ligand pairs, representing a six-orders-of-magnitude leap over previous supercomputing records. Driven by this throughput, we screened nearly 100,000 protein structures across six species against the 100-billion compound library in just 16 hours. The resulting comprehensive cross-species interaction landscape, GalaxyDB, will be openly released at https://galaxyvs.drugclip.com.

bioinformatics↗

bioRxiv2026

Fetal MRI reveals altered prenatal cortical surface area in fetuses later diagnosed with autism spectrum disorder

Autism spectrum disorder (ASD) is increasingly conceptualized as a condition rooted in altered prenatal neurodevelopment, yet in vivo evidence from fetal brain imaging remains limited. Using retrospective fetal MRI and surface-based morphometry, we investigated cortical development in 15 fetuses later diagnosed with ASD (77%; mean gestational age [GA] = 26.7 weeks) without major structural brain abnormalities and compared them with 60 typically developing controls (57% male; mean GA = 28.4 weeks). Fetuses later diagnosed with ASD showed significantly reduced whole-brain inner cortical plate surface area compared with controls ({beta} = -0.08 {+/-} 0.02 SE, p = 0.002, partial {superscript 2} = 0.13), corresponding to an estimated [~]7.7% reduction (predicted at GA = 28.1 weeks). Lobar mixed-effects analyses demonstrated broadly distributed reductions across all cortical lobes (FDR-corrected p = <0.001-0.024; Cohens d = -0.06 to -0.10), with modest regional heterogeneity indicating relatively greater frontal and insular involvement (groupxlobe: F = 19.31, p = 0.002, {superscript 2} = 0.08). Surface area findings remained directionally stable across sensitivity analyses, including restriction to neurodevelopmentally confirmed controls and models accounting for image quality variability, although effect sizes were attenuated after quality adjustment. Normative modeling further demonstrated subtle negative deviations from typical prenatal cortical surface area trajectories in ASD (mean Z = -0.27, p = 0.018). These findings suggest that aspects of cortical morphogenesis may diverge prenatally in individuals later diagnosed with ASD and suggest the feasibility of fetal MRI-based surface morphometry for studying early neurodevelopmental variation associated with ASD risk.

neuroscience↗

bioRxiv2026

Microbiota-derived TMAO links PM2.5 exposure to cognitive impairment: A gut liver brain axis

Although fine particulate matter (PM2.5) is linked to cognitive impairment, the mechanistic links between pulmonary exposure and neurodegeneration remain poorly understood. This study investigates the role of the gut-liver-brain axis in mediating PM2.5-induced neurotoxicity. We demonstrate that three weeks of intratracheal PM2.5 instillation in mice causes significant memory deficits, impaired hippocampal adult neurogenesis, and reduced synaptic plasticity in both sexes. Metagenomic analysis revealed that PM2.5 alters gut microbiota composition, specifically by upregulating pathways involved in trimethylamine (TMA) synthesis. This microbial shift led to a systemic increase in the metabolite trimethylamine N-oxide (TMAO) and its accumulation in the hippocampus. Mechanistic experiments revealed that TMAO drives neurotoxicity by activating the hippocampal PERK-mediated endoplasmic reticulum (ER) stress pathway. Critically, these deficits were reversed by suppressing hepatic TMAO production via either pharmacological inhibitors or genetic knockdown. Additionally, hippocampal-specific PERK silencing or dietary resveratrol intervention attenuated hippocampal ER stress, thereby protecting against PM2.5-induced cognitive and hippocampal impairments. These results identify the microbial metabolite TMAO as a key mediator of air pollution-related neurotoxicity and highlight the gut-liver-brain axis as a promising therapeutic strategy to counteract the neurological impacts of air pollution.

animal behavior and cognition↗

bioRxiv2026

Chemoproteomic Characterization of GPX4 Covalent Ligands and Targeted Degradation

Despite intensive efforts, the ferroptosis gatekeeper glutathione peroxidase 4 (GPX4) remains difficult to selectively target due to stringent structural constraints surrounding its catalytic selenocysteine, which impose tight requirements on warhead reactivity and geometry. Here, leveraging a chemoproteomic approach, we characterize a potent and selective covalent GPX4 inhibitor featuring a pyrimidinylmethyl isourea warhead and define the chemical features underlying its proteome-wide selectivity. This chemotype enables tunable electrophile reactivity through steric and electronic modulation of leaving group ability, suggesting potential broader utility for targeting other recalcitrant proteins. Building on this scaffold, we further develop two selective GPX4 degraders - one CRBN-dependent and the other CRBN-independent - enabling complementary modulation of GPX4 through both inhibition and degradation. Together, these molecules expand the GPX4 chemical toolbox for more nuanced interrogation of GPX4 biology.

cancer biology↗

bioRxiv2026

Molecular mechanism of M1-mediated influenza A virus assembly

Influenza A virus assembly is orchestrated by matrix protein 1 (M1), which engages ribonucleoproteins, mediates glycoprotein incorporation, and scaffolds lipid envelopment, but how these activities are performed and coordinated is unclear. We determined high-resolution structures of M1 within virions, virus-like particles, and in vitro. We found that M1 binds phosphatidylinositol 4,5-bisphosphate at the membrane, oligomerizes through an electrostatic interface, and switches between three alternative conformational states in different regions of the virion. The M1 conformer at the front associates with ribonucleoproteins and initiates budding. In the body of the virion, two conformers alternate to create an extended filament with seam-like discontinuities. Enrichment of the seam-prone conformation at the virion rear induces envelope closure and exposes a binding site for the cytoplasmic tail of neuraminidase (NA) to promote virus release. Conformational switching within a polarized M1 lattice thereby couples organization of the viral components with shaping of the virion architecture.

microbiology↗

bioRxiv2026

Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement

BackgroundMechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically relevant targets in HF. MethodsWe collected and analyzed left ventricular apical myocardial tissue and clinical data from 208 patients undergoing LVAD implantation across five centers. Pre-implant transcriptomic profiles (22,373 mRNA transcripts) were integrated with 59 clinical variables using supervised machine learning with repeated cross-validation to identify and prioritize features associated with myocardial recovery, defined as a binary outcome based on improvement in left ventricular ejection fraction (LVEF [≥]40%) and left ventricular end-diastolic diameter (LVEDD [≤]5.9 cm). We also modeled functional (LVEF) and structural (LVEDD) improvement as a continuous outcome without any predefined LVEF and LVEDD pathological thresholds. Feature prioritization was followed by validation in human myocardial tissue and mechanistic interrogation in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). ResultsIntegrative models achieved modest discrimination for myocardial recovery as a binary categorical outcome (maximum mean cross-validated area under the curve 0.73{+/-}0.15), identifying clinical features such as HF duration, LVEDD, HF pharmacologic therapy, and device configuration. Leucine-rich repeat neuronal 4C-like (LRRN4CL), measured in human myocardium, consistently emerged as a top transcriptomic predictor across both binary and continuous metric models (functional and structural). Higher pre-LVAD LRRN4CL expression was associated with reduced likelihood of myocardial recovery and localized primarily to cardiomyocytes. In iPSC-CMs, LRRN4CL overexpression localized to the sarcoplasmic reticulum, induced transcriptional remodeling characterized by suppression of contractile pathways and activation of stress programs, impaired calcium handling, impaired contraction-relaxation kinetics, and diminished mitochondrial respiratory reserve capacity. ConclusionsIntegration of clinical and myocardial transcriptomic data identifies LRRN4CL as a novel marker associated with impaired myocardial recovery following LVAD-mediated ventricular unloading and systemic circulatory support. These findings move beyond predictive modeling, linking integrative computational discovery to cardiomyocyte dysfunction and providing a translational framework for biologically informed risk stratification and therapeutic targeting for myocardial recovery. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABSO_LIIntegrative clinical and myocardial transcriptomic modeling identifies LRRN4CL as a novel molecular determinant of structural and functional changes after LVAD-mediated ventricular unloading and enhanced systemic circulatory support. C_LIO_LIElevated LRRN4CL expression is associated with adverse remodeling signatures, impaired calcium handling, and stress responses in human iPSC-derived cardiomyocytes. C_LIO_LIExperimental overexpression of LRRN4CL directly disrupts calcium cycling, contractile performance, and mitochondrial respiration linking molecular signature to functional phenotype. C_LI What Are the Clinical Implications?O_LIIdentification of LRRN4CL as a marker associated with impaired myocardial recovery supports future efforts toward biologically informed risk stratification for patients undergoing LVAD therapy. C_LIO_LILRRN4CL as a marker of cardiac improvement potential may extend beyond advanced HF to earlier stage disease patients and inform prognosis, risk stratification, and response to medical therapies. C_LIO_LIThese findings highlight LRRN4CL-associated pathways as potential therapeutic targets and demonstrate how integrative clinical-transcriptomic approaches can move beyond clinical prediction toward identification of new biologically precise therapeutic targets in HF following a bedside to bench and back approach. C_LI

bioinformatics↗

bioRxiv2026

Large Language Model Agent Enables Autonomous Machine Learning Model Building for Biomedicine

Machine learning accelerates biomedical discovery, but creating effective predictive models requires specialized human expertise and demanding manual effort. Researchers must iteratively design pipelines, select architectures, and debug code. This challenge is particularly severe in biomedicine because of the heterogeneous datasets, sparse annotations, and complex evaluation protocols that are common in the domain. We present AIDO.Builder, an agentic artificial intelligence system that fully automates the entire life-cycle of biomedical model development. Provided only with a natural language task description and a target metric, AIDO.Builder autonomously constructs executable training and evaluation pipelines. The system selects suitable modeling strategies, executes experiments, and uses automated feedback-loop to iteratively revise its own code, configurations, and training procedures. It flexibly adapts to new tasks by training specialized models de novo or by using pretrained foundation models to build predictive models through task-appropriate adaptation. We show that across diverse biomedical benchmarks, AIDO. Builder produces highly competitive solutions against human alternatives, while eliminating the manual iteration previously required for robust model development. By automating the translation of raw data into reliable AI models, AIDO.Builder demonstrates how AI itself can be used to accelerate AI for biomedical research.

bioinformatics↗

bioRxiv2026

Inhibition of CKAMP44 attenuated seizure activity via protein phosphatase 3 regulatory subunit B-mediated GluA1 phosphorylation and synaptic transmission

Temporal lobe epilepsy (TLE) is a complex neurological disorder characterized by spontaneous recurrent seizures and its underlying mechanism remains elusive. This study aimed to investigate the role of cystine-knot AMPAR modulating protein 44 (CKAMP44) in the pathological process of TLE and its potential as a therapeutic target using kainic acid (KA)-induced epilepsy mouse model of TLE. Our results showed that CKAMP44 protein and mRNA expression was significantly increased and primarily localized to neurons during the chronic phase of TLE. Nkx2-1 regulated the transcription of CKAMP44 in the hippocampus brain tissues of KA-induced TLE mice. Inhibition of CKAMP44 suppressed seizure susceptibility and severity in the KA-induced epilepsy mice via behavioral and local field potential monitoring. Furthermore, inhibition of CKAMP44 decreased frequency and amplitudes of spontaneous excitatory postsynaptic currents indicating that the excitatory synaptic transmission was reduced in an in vitro epilepsy model. Mechanistically, inhibition of CKAMP44 specifically upregulated the membrane surface expression of GluA1 and the phosphorylation level of GluA1-ser831 by downregulating protein phosphatase 3 regulatory subunit B(PPP3r2) expression. Overexpression of PPP3r2 downregulated the phosphorylation level and surface expression of GluA1, which ultimately exacerbated the seizure activity suppressed by CKAMP44 knockdown. Collectively, our results indicate that CKAMP44 may be a potential therapeutic target for the treatment of TLE.

pathology↗

bioRxiv2026

Harnessing AI to Build Virtual Cells

A virtual cell is a world model of a cell: a computational system that predicts, simulates and programs cellular processes across modalities and scales. An important path toward this goal is to model how genetic and chemical perturbations give rise to transcriptional responses, a core capability for disease understanding and drug discovery. However, current approaches remain expert-intensive, relying on iterative manual model design, training and debugging over months. Here we present VCHarness, an autonomous AI system that constructs perturbation-response models by combining an AI coding agent with multimodal biological foundation models. The system explores large spaces of architectures and training pipelines with minimal human intervention, iteratively generating, evaluating and refining candidate models. Across multiple perturbation-response benchmarks, VCHarness identifies architectures that outperform expert-designed approaches while reducing development time from months to days. It further uncovers non-obvious architectural patterns associated with improved performance, indicating that automated search can extend beyond conventional design strategies. These results suggest a shift from manually engineered models toward autonomous systems for constructing components of virtual cell world models, enabling scalable and data-driven exploration of cellular systems.

bioinformatics↗

bioRxiv2026

The nuclear transport receptor Impβ is a regulator of actin polymerization

Nuclear transport receptors are best known for mediating nucleocytoplasmic transport (NCT) through nuclear pore complexes. Here, we uncover an unexpected function of the primary import receptor importin-{beta} (Imp{beta}) as a direct regulator of the actin cytoskeleton. Imp{beta} associates with stress fibers and the cell cortex and promotes actin polymerization through direct interactions. Disrupting Imp{beta}-actin binding impairs stress fiber formation and suppresses cell migration well before NCT is affected. In 3D spheroids, perturbing Imp{beta} further compromises tissue integrity, as reflected in changes to nuclear curvature and ellipticity. Together, these findings identify Imp{beta} as a regulator of actin organization in both 2D and 3D contexts, revealing a direct link between the nuclear transport machinery and the cytoskeleton.

cell biology↗

bioRxiv2026

Polyclonal-Monoclonal Transition in Lung Squamous Cell Carcinoma Evolution

The evolutionary trajectory of lung squamous cell carcinoma (LUSC) remains poorly defined, hindering the development of effective therapies. By integrating genomic and transcriptomic sequencing from human LUSC specimens, we delineated a polyclonal-to-monoclonal evolutionary trajectory during LUSC progression. This evolutionary pattern was corroborated by single-cell RNA sequencing, which revealed consistent tumor cell heterogeneity. Specifically, the SBS5 mutational signature was enriched and correlated with poor prognosis independent of tumor stage. By further utilizing the spontaneous LUSC mouse model to identify key genomic and genetic events in LUSC progression, we observed that the JNK pathway was inhibited and that cytoskeleton-related pathways were dysregulated during LUSC development, and identified the mutations in the JNK pathway (e.g., DACT1) and cytoskeletal regulators (e.g., KIF26A). Collectively, these findings established a polyclonal-monoclonal evolution paradigm for LUSC, potentially regulated by JNK pathways, which could benefit LUSC precision therapeutics.

genomics↗

bioRxiv2026

In vivo-compatible spatial multi-omics via hydrogen peroxide-independent APEX2 labeling

Proximity labeling (PL) technologies like APEX2 have transformed spatial multi-omics in live cells, but their long-standing dependence on hydrogen peroxide (H2O2) disrupts redox signaling and prevents use in live animals. Here we introduce H2O2-independent APEX2 (Hi-APEX), which uses a clickable tetrazine-phenol probe, requiring no enzyme engineering. We show that APEX2 directly catalyzes TP radical formation without H2O2 via a mechanism requiring the probes tetrazine group and a key histidine residue. We benchmarked Hi-APEX-based spatial multi-omics by mapping the mitochondrial matrix and dynamic secretomes. Hi-APEX significantly outperforms traditional APEX in capturing redox-sensitive processes such as stress response and ferroptosis, enabling discovering authentic stress granule components and protein interaction networks for mitochondria-localized GPx4. One mGPx4 interactor TRMT61B--known to regulate mitochondrial m{superscript 1}A modifications--promotes ferroptosis. Crucially, Hi-APEX achieves full in vivo compatibility, enabling direct PL in tumor xenografts and hippocampal neurons, thereby expanding PL-based spatial multi-omics from cellular systems to living organisms.

biochemistry↗