Biology subjects

Wang, G.

Publications and source records attributed to Wang, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Nuclear envelope-dependent heterochromatin positioning gates the timing of nucleolar assembly in the early embryo

The nucleolus is the largest nuclear condensate, yet how its assembly is developmentally timed remains poorly understood. In the Caenorhabditis elegans embryo, nucleoli normally appear at the 6- to 8-cell stage. Here we show that the LINC complex and nuclear lamina restrain premature nucleolar assembly through heterochromatin organization. Depletion of the embryonic LINC complex proteins SUN-1 or ZYG-12 induces precocious FIB-1-positive nucleoli at the 4-cell stage, particularly in the EMS and P2 blastomeres. LMN-1 depletion produces a more severe phenotype combining premature assembly with impaired disassembly. Loss of the heterochromatin anchor CEC-4 alone has modest effects but strongly suppresses precocious nucleolar assembly caused by LINC complex depletion. LINC complex and lamin perturbations alter the heterogeneity of HPL-2-marked chromatin, and FIB-1 condensates occupy locally HPL-2-depleted regions. These findings identify nuclear envelope-dependent heterochromatin organization as a developmental gate for nucleolar condensate assembly.

cell biology↗

bioRxiv2026

HSeeker: an algorithm for systematic H-DNA sequence identification

H-DNA is a naturally occurring intramolecular DNA triplex structure formed by Hoogsteen hydrogen bonds at homopurine-homopyrimidine mirror repeats and has functional roles in gene regulation, genome instability, and human disease. The existing H-DNA detection tools capture only a subset of H-DNA sequences, often missing relevant sequence features or failing to assess key aspects of structural stability. To address this gap, we present "HSeeker", a state-of-the-art computational tool that compiles a three-part algorithm to identify and score potential H-DNA-forming sequences. Using a center-outward search algorithm approach, HSeeker evaluates candidate hinge positions and spacer lengths while allowing configurable mirror mismatches and purine-pyrimidine composition thresholds. The greedy overlap removal phase resolves overlapping candidates by retaining the longest and most compact motif within each overlapping region. Finally, the thermodynamic stability scoring algorithm evaluates the candidate motifs using an experimentally informed scoring model that incorporates Hoogsteen G-G and A-A bonds, consecutive-pair stacking, and imposes penalties for mismatches and disrupted stacks. The scoring procedure also optimizes motif boundaries by trimming weak terminal positions and reassigning unstable arm positions to the spacer. HSeeker reports genomic coordinates, sequence information, pairing and stacking components, and an overall stability score. HSeeker is also user-friendly, available as a Python package and as a web application, supporting configurable, high-throughput analysis and exportability of predicted H-DNA motifs. HSeeker provides an accessible and reproducible framework for investigating the distribution and potential stability of H-DNA-forming sequences across genomic datasets.

genomics↗

bioRxiv2026

OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning

Computational modeling of cellular behavior--the virtual cell--has emerged as a stated grand challenge at the intersection of artificial intelligence and biology, yet existing foundation models remain specialized: single-cell models process dissociated transcriptomes only, spatial models require dedicated spatial-aware architectures, and perturbation predictors depend on manually curated knowledge bases that cap generalization. Here we introduce OCellus, a single nine-billion-parameter language model (Qwen3.5-9B) fine-tuned on twenty-two biological tasks that simultaneously addresses all three limitations through three coordinated technical contributions on a shared backbone. First, EvenClock encodes two-dimensional spatial coordinates as eighteen clockface sectors of text, enabling spatial reasoning on a vanilla language model without architectural modification; on ten spatial transcriptomics tasks OCellus attains 77 percent spatial-neighborhood accuracy, 96 percent spatial-cellchat accuracy, and 0.70 proportion-cosine similarity on spatial deconvolution, all without any spatial-aware architectural components. Second, per-gene language-model embeddings replace the Gene Ontology annotations that GEARS depends on, achieving Pearson correlation 0.945 on the Replogle 2022 perturbation benchmark versus 0.84 for GEARS across 457 completely unseen knockout genes. Third, OCellus-Agent provides a Planner-Router-Verifier natural-language interface that achieves 75 percent pipeline accuracy on eighty multi-task queries. Removing language-model embeddings collapses perturbation Pearson to 0.06, confirming that learned functional representations--not graph topology--drive the gain. As a cell-type encoder, OCellus ranks first among fourteen foundation models in linear-probe accuracy at 95.1 percent across four benchmark datasets, and reaches 72.6 percent average across twenty-two evaluated biological tasks--a 57-percentage-point absolute gain over the strongest baseline configuration. As a language model, OCellus uniquely generates natural-language explanations of its predictions, a capability absent from all competing methods. Code, pre-trained model weights, the graph-neural-network module, and the agent system will be made available upon publication.

bioinformatics↗

bioRxiv2026

Extracellular injection system combined with peptides for intracellular Staphylococcus aureus treatment

The inaccessibility of intracellular bacteria has long rendered the treatment of Staphylococcus aureus infections an challenge. Studies have demonstrated that the extracellular injection system PVC can accurately deliver proteins into cells, which would not need small molecules, and enables effective intracellular delivery of antimicrobial peptides for treatment. Accordingly, we selected antimicrobial peptides including Cecropin, LL37 and Indolicidin that possess potent bactericidal activity, and established the Directed Antimicrobial Assault platform (DAAT) by leveraging the intracellular delivery capacity of PVC. DAAT Cecropin, DAAT LL37 and DAAT Indolicidin inhibited intracellular bacteria in a dose-dependent manner, with DAAT LL37 reaching 86.76% inhibition; after 72 h of treatment, viable-cell numbers reduse to 66--82-fold those of the control. Tail-fibre retargeting enabled direct extracellular S. aureus killing, while combined DAAT therapy promoted wound healing in mice. These findings expand the utility of PVC-derived nanosyringes and establish DAAT as a modular platform for intracellular antimicrobial peptide therapy.

synthetic biology↗

bioRxiv2026

Machine Learning Gap-Fills Missing Transporter Kinetics in Biosystems Across Scales

Understanding transporter kinetics is essential for deciphering metabolite exchanges in biosystems, particularly for cells subject to substrate gradients. Nevertheless, the prediction of transporter kinetic parameters, maximum rate per gram protein (Vmax) and Michaelis-Menten constant (Km), has not yet been tackled. Here, we developed the first compound-protein interaction machine learning model of transporter Vmax and Km, MMTKPred, which achieved R2=0.553, RMSE=1.155 mmol/hr/g Protein and R2=0.330, RMSE=0.935 mM for log10-scaled Vmax and Km prediction, respectively. Moreover, we demonstrated MMTKPreds predictive power across biosystem scales, from capturing transporter kinetics modulated by point mutations and substrate changes at the molecular level, to enabling substrate-sensitive metabolic modelling of non-model yeasts at the cellular level, and rationalizing inter-species substrate competition in co-cultures. Collectively, MMTKPred effectively models metabolite transport spanning from molecular to multi- species scales, thereby offering a computational tool for rational microbial cell factory optimization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/735998v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@18e7bb6org.highwire.dtl.DTLVardef@15bec93org.highwire.dtl.DTLVardef@8ce92org.highwire.dtl.DTLVardef@31fb44_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMMTKPred, first transporter kinetics CPI model, reaches [~]1 log10 RMSE for Vmax and Km. C_LIO_LIMMTKPred captures the effects of point mutations and substrate changes on transporters. C_LIO_LIPredicted kinetics enables substrate sensitivity in metabolic flux modelling. C_LIO_LIPredicted kinetics explains inter-species substrate competition outcomes. C_LI

systems biology↗

bioRxiv2026

Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE

Cell-cell communication is fundamental to tissue organization, homeostasis, and disease progression. Recent advances in spatial transcriptomics provide unprecedented opportunities to systematically characterize ligand-receptor interactions directly within intact tissues. However, robust inference of spatial ligand-receptor interactions remains challenging because intrinsic features of spatial transcriptomics data, including spatial autocorrelation, variation in total molecular counts, and measurement errors, can induce spurious spatial co-expression and lead to inflated false-positive results. Most existing methods do not adequately account for these confounding factors, limiting the reliability of inferred cellular communication. Here, we present CONCISE, a statistical method for spatially constrained co-expression and ligand-receptor interaction inference that jointly models spatial autocorrelation, variation in total molecular counts, measurement errors, and spatial proximity constraints. CONCISE combines efficient moment-based parameter estimation with analytical hypothesis testing, enabling fast and statistically rigorous inference without restrictive distributional assumptions. Through extensive simulations, real-data permutation experiments, and biologically motivated negative-control analyses across different spatial transcriptomics platforms, we show that most existing methods presented inflated false-positive rates, whereas CONCISE achieved well-calibrated inference, robust false-positive control, and improved detection power. Application of CONCISE to high-resolution MERFISH and CosMx datasets from intestinal inflammation and non-small cell lung cancer further highlights its biological utility in disease contexts. CONCISE uncovered inflammation-associated fibroblast-specific interactions during intestinal inflammation and delineated complex tumor-immune and tumor-stromal signaling networks within the tumor microenvironment.

bioinformatics↗

bioRxiv2026

Aberrant chromatin looping by NUP98-HOXA9 is constrained by CTCF and facilitated by cohesin

The acute myeloid leukemia (AML) fusion protein NUP98-HOXA9 (NHA9) drives leukemogenesis by promoting aberrant chromatin loop formation through phase separation, yet the mechanisms underlying these interactions remain unclear. To address this, we dissect the interplay between NHA9 and individual loop extrusion factors using in situ Hi-C, CUT&RUN, RNA-seq, and Auxin-inducible degradation of CTCF or RAD21. CTCF was found to be dispensable for NHA9 loop formation, although CTCF binding constrained a subset of loops that emerged only upon CTCF depletion. In contrast, cohesin played a distance-dependent role where short-range NHA9 loops formed independently of RAD21, while long-range loops were strongly cohesin-dependent. Despite this requirement, RAD21 showed minimal enrichment at NHA9 loop anchors, indicating that NHA9 does not function as a canonical cohesin barrier. Instead, these findings support a non-canonical model in which cohesin transiently facilitates interactions between distal NHA9-bound loci, which are subsequently stabilized through NHA9 phase separation. Together, this work reveals a distinct mechanism of oncogenic chromatin looping in which NUP98-HOXA9 cooperates with canonical loop extrusion machinery to reprogram genome architecture in AML.

Molecular Biology↗

bioRxiv2026

COATS Identifies Copy-Number-Dependent Drivers and Enablers of Aneuploidy in Cancer

Aneuploidy is a hallmark of most cancers but at the same time has been shown to decrease cellular fitness. Thus, to solve this conundrum, a current hypothesis in the field is that specific SCNAs may promote tolerance to the aneuploid state and/or promote additional chromosomal instability (CIN) and more aneuploidy. In other words, gains or losses of oncogenes (OGs) and tumor suppressor genes (TSGs) can, in turn, drive further CIN, promoting additional somatic alterations, or enhance aneuploid cell survival. Despite their importance, CN-dependent OGs and TSGs associated with aneuploidy remain largely unidentified. Here, we present a new method, Copy-number-dependent Oncogenes And Tumor Suppressors (COATS), to identify pan-cancer and cancer-specific CN-dependent OGs and TSGs associated with aneuploidy (Aneu-OGs and Aneu-TSGs). COATS integrates information theory and statistical tests to analyze gene expression, copy number, and aneuploidy, and incorporates timing analysis to distinguish early drivers of CIN from late tolerance enablers. Interestingly, using the CINner simulation framework, we show that aneuploidy drivers tend to occur earlier than aneuploidy enablers. Applying COATS to 33 TCGA cancer types, we identified 479 pan-cancer amplification-dependent Aneu-OGs and 141 deletion-dependent Aneu-TSGs. For validation, we used shRNA to knock down CCT5, a COATS-identified pan-cancer Aneu-OG predicted to promote aneuploidy tolerance, in isogenic aneuploid and near-diploid cells. Strikingly, CCT5 depletion was selectively toxic in aneuploid cells, supporting its classification as an aneuploidy tolerance enabler gene. Overall, our study defines a set of CN-dependent genes associated with aneuploidy and points to candidate therapeutic targets for chromosomally unstable cancers.

Cancer Biology↗

bioRxiv2026

Multicellular Spatial Programs Define the Histopathological Architecture of Meningioma

Tumors are often described by cell types or gradients, but the organizing units of tumor tissue remain unclear. Using meningiomas, which show marked morphologic diversity despite constrained and recurrent genetics, we identify reproducible multicellular spatial molecular programs (SMPs) characterized by recurring cell-type mixtures that combine in different proportions across tumors. We built a multi-omic atlas of 147 human meningiomas (1.6 million cells/spots), integrating scRNA-seq, Visium, CosMx-RNA, and CosMx-Protein. Across platforms, SMPs mapped onto canonical whorl-lobule architecture and defined a structured ecological landscape linking hypoxic, immune-evasive states to vascularized, matrix-remodeling, and mineralization-rich states. For translation, we developed MeningNet, a hybrid ConvNeXt-Vision Transformer that infers SMPs directly from hematoxylin-and-eosin sections. MeningNet generalized across an internal replication cohort and 465 external whole-slide images, recovering cross-platform inference and showing significant association with CNS-WHO grade. These findings establish meningioma architecture as a reproducible histopathological framework for inferring spatial molecular state from routine pathology.

Cancer Biology↗

bioRxiv2026

A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes

Type 2 diabetes (T2D) arises from genetic susceptibility and chronic metabolic stress, but whether these converge on shared molecular programs in human populations remains unclear. Here, we develop a dish-to-biobank framework linking controlled {beta}-cell perturbation to population-scale disease genetics through the circulating plasma proteome, and apply it to T2D. scRNA-seq of human stem cell-derived islets under factorial glucose and palmitate exposure identifies their combination (glucolipotoxicity) as the condition eliciting the strongest SC-{beta} cell transcriptional response, with glucolipotoxicity-upregulated genes uniquely enriched for T2D heritability, monogenic diabetes genes, and rare-variant burden signals. CRISPR knockout of {beta}-cell identity regulators PAX6 and PDX1 aligns with this program, establishing convergence of environmental and genetic perturbations on a shared disease-relevant state. We then used the plasma proteome as an accessible population-scale readout of these experimentally defined {beta}-cell stress programs, scoring 45,956 UK Biobank White British participants. We define heritable stress signatures that associate with refined carbohydrate and saturated fat intake, and undergo trans-tissue genetic regulation, with a subset of variants showing diet-dependent effects. Together, these findings establish glucolipotoxicity as a genetically anchored model of {beta}-cell dysfunction and provide a generalizable framework for linking controlled cellular perturbations to human disease genetics at population scale.

genetics↗

bioRxiv2026

Same-section spatial metabolo-transcriptomics using Stereo-meta-seq reveals DHA-driven kidney maturation

A central unresolved question in developmental biology is whether local metabolites merely accompany, or actively instruct, tissue maturation. Addressing this question requires direct spatial coupling of metabolic states with genome-wide transcriptional programs in situ at high spatial resolution, which existing approaches do not readily achieve. Here, we introduce Stereo-meta-seq, a workflow that integrates quantitative MALDI-MSI with Stereo-seq spatial transcriptomics within a single tissue section. A conductive adapter was designed to overcome the electrical incompatibility of non-conductive Stereo-seq chips with vacuum MALDI platforms, improving efficiency of MSI detection that preserves RNA integrity. MALDI laser-ablation marks are retained in downstream Stereo-seq data and serve as intrinsic fiducials for direct co-registration at 10 m or 20 m resolution, enabling fine grained spatial metabolite-transcript integration. Applying Stereo-meta-seq to human kidney development, we uncover selective enrichment of docosahexaenoic acid (DHA) in maturing proximal tubules. Functional studies in human kidney organoids demonstrate that DHA activates PPAR-and HNF4-driven transcriptional programs and promotes proximal tubule maturation in vitro and after transplantation in vivo. These findings identify lipid metabolism as an instructive regulator of human nephrogenesis and establish Stereo-meta-seq as a practical platform for dissecting metabolite-gene coupling and tissue heterogeneity in situ.

Cell Biology↗

bioRxiv2026

Comparative genomics reveals potential mechanisms of invasion in Phragmites australis (common reed)

Biological invasions are transforming ecosystems worldwide, yet the genomic bases enabling certain species to dominate new environments remain poorly understood. Phragmites australis, a widespread wetland grass with invasive and native subspecies co-occurring in North America, provides a powerful system to investigate genomic mechanisms of invasiveness. We generated independent chromosome-scale genome assemblies for invasive P. australis ssp. australis and co-occurring native ssp. americanus and used comparative genomic and transcriptomic analyses to identify lineage-specific innovations associated with invasive success. The invasive subspecies exhibits genomic novelties through functionally-biased single-copy orthologs, intronless genes, and subgenome expression asymmetry, along with a stress-ready basal transcriptome relative to the native subspecies. Following the removal of aboveground shoots ("cutback"), which measures the ability to recover from damage, the invasive subspecies undergoes stronger transcriptional reprogramming, increased shoot production, and higher biomass accumulation compared to the native. It also displays expansion of gene families and coordinately expressed gene modules that support resource mobilization, growth responses to light, and stress tolerance. Beyond Phragmites, comparative analyses across multiple grass genomes, including eight invasive species with related non-invasive species, revealed repeated expansion of gene families associated with abiotic stress tolerance and developmental regulation, suggesting convergent adaptive strategies in the grass family for invasive success. Together, these results demonstrate genomic architecture linked to invasion success and highlight potential targets for managing invasive grasses.

Plant Biology↗

bioRxiv2026

DyMoTree decodes early cell state transitions and drivers from single-cell transcriptomes using a tree-structured neural network

Inferring early cell fate from single-cell RNA-sequencing data is essential for identifying cellular origins and fate plasticity in development and disease. However, existing methods often fail to exploit tree-structured lineage trajectories, limiting the accuracy and interpretability of fate mapping. Here we present DyMoTree, a computational framework that models cell fate decisions as nonlinear mappings between progenitor and terminal cell states under explicit lineage constraints. By integrating lineage graphs with a tree-structured neural architecture, DyMoTree learns lineage-resolved cell-state transition maps from single-cell transcriptomes, enabling robust inference of early fate bias and identification of fate-specific progenitor substates and driver genes. Across simulations, lineage-tracing experiments, and in vivo systems, DyMoTree outperformed existing methods in resolving early fate biases. Applications to mouse embryogenesis, lung adenocarcinoma progression, and CAR-T immunotherapy revealed regulatory programs underlying developmental and disease-associated transitions. DyMoTree provides a general framework for modeling lineage-resolved cell-state dynamics underlying development and disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/731114v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@11e715dorg.highwire.dtl.DTLVardef@1a4c695org.highwire.dtl.DTLVardef@e98805org.highwire.dtl.DTLVardef@1e14054_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Investigation of In Vivo Silk Scaffold Degradation by Decoupling Tissue Ingrowth Using a GPR-Driven Digital Twin Framework

Pelvic organ prolapse (POP) reconstruction is increasingly performed utilizing knitted silk meshes (KSM), yet tracking in vivo degradation kinetics remains challenging due to complex host tissue integration. This study developed an AI-driven semi-empirical framework utilizing Gaussian Process Regression (GPR) to bridge the kinetic mismatch between in vitro and in vivo environments. KSM scaffolds underwent 32 weeks of accelerated in vitro enzymatic degradation, with morphology (SEM), molecular conformation (FTIR), and mass loss being coupled with mechanical decay to train the GPR model. In vitro results revealed a multi-stage physical disintegration via a topochemical erosion pathway that preserved crystalline {beta}-sheet structures despite macro-scale mass and mechanical loss. When validated in a rat abdominal wall defect model, traditional tracking metrics encountered severe bottlenecks. Heterogeneous dye labeling caused premature fluorescence quenching by Week 16, while extensive tissue ingrowth masked gravimetric and SEM signatures. Intriguingly, a bi-phasic in vivo mechanical trajectory was identified, where initial degradation-led failure was followed by a secondary mechanical recovery driven by biomechanical synergy with neo-muscular tissue. Importantly, despite premature quenching, this work presents the first optical imaging approach to visually mapping the complete chronological breakdown of the scaffolds peripheral boundary layer in vivo, proving that outer functionalized layers eroded prior to internal silk cores. Furthermore, our GPR framework elegantly resolved the perennial technical barrier of tissue-mesh overlapping. By mathematically decoupling intrinsic polymer degradation from confounding tissue ingrowth, the model successfully achieved a first-of-its-kind prediction of the bare scaffolds long-term structural fate in a non-adhered state, providing a robust digital twin methodology for lifetime predictions of degradable biomaterials.

bioengineering↗

bioRxiv2026

Methylmalonic acid: a new target for Hadamard-edited MRS

BackgroundMethylmalonic acidemia (MMAemia) is a genetic metabolic disorder characterized by an accumulation of methylmalonic acid (MMA) and impaired energy metabolism leading to increased lactate (Lac). The signals of MMA (1.23 ppm) and Lac (1.33 ppm) overlap, making their separation using conventional MRS challenging. An MRS method to differentiate the two metabolites could enhance pathophysiological understanding and improve treatment monitoring - Hadamard-edited MRS has the potential to achieve this. PurposeTo develop a Hadamard-encoded J-difference editing approach for independent detection of MMA and Lac at 3T. MethodsA novel Hadamard-encoded editing scheme was implemented and evaluated with density-matrix simulations, phantom and in vivo experiments. The new four-step scheme uses frequency-selective editing pulses, applied at 3.2 ppm and 4.1 ppm to modulate the J-coupled methyl resonances of MMA and Lac, respectively. Hadamard combinations of the four sub-experiments yield the separate difference-edited spectra for each target metabolite. ResultsSimulations and phantom experiments clearly illustrate the separated signals of MMA and Lac. In vivo validation experiments show a Lac signal (but no MMA) in a healthy infant, and both Lac and MMA (separated into their respective Hadamard-combination spectra) in a patient with MMAemia. ConclusionHadamard-encoded editing at 3T can separate MMA and Lac signals and shows promise for studying altered metabolism in patients with MMAemia.

biochemistry↗

bioRxiv2026

Colchicine Directly Targets Aldehyde Dehydrogenase 2 (ALDH2) to Suppress Radiation-Induced Senescence and Atherosclerosis

BackgroundIonizing radiation (IR) accelerates atherosclerosis through induction of cellular senescence, DNA damage, defective efferocytosis, and dysregulation of clonal hematopoiesis (CH) drivers. Although low-dose colchicine reduces ischemic cardiovascular events in coronary artery disease, the precise molecular mechanisms underlying its vasculoprotective effects remain incompletely defined, and whether it mitigates radiation-associated vascular injury is unknown. MethodsBone marrow-derived macrophages (BMDMs) were pretreated with low-dose colchicine and exposed to 2 Gy IR. Molecular effects were assessed by RNA-seq, immunoblotting, and molecular docking. In vivo effects were tested in a partial carotid ligation (PLCL) model using spatial proteomics. Human monocyte-derived macrophages (HMDMs) from thoracic malignancy patients were analyzed before and after radiation therapy (RT). ResultsLow-dose colchicine suppressed IR-induced macrophage senescence signaling while preserving NRF2 activity. In a cell-free assay, colchicine directly activated aldehyde dehydrogenase 2 (ALDH2) in a dose-dependent manner (EC50 1-5 nM), identifying ALDH2 as a direct molecular target of colchicine. Following irradiation, colchicine restored ALDH2, reduced mitochondrial (mt)ROS-dependent p90 ribosomal S6 kinase (p90RSK) activation and lipid peroxidation, preserved TET2 and DNMT3A expression, and rescued impaired efferocytosis while preventing nicotinamide adenine dinucleotide (NAD) and adenosine triphosphate (ATP) depletion. These protective effects were ALDH2-dependent, as they were lost with ALDH2 inhibition or depletion and were mimicked by pharmacologic ALDH2 activation. In vivo, colchicine attenuated radiation-induced atherosclerosis and macrophage senescence-associated stemness (SAS). Consistently, macrophages from patients after RT showed reduced ALDH2 with increased mtROS, lipid peroxidation, and senescence. ConclusionThese findings identify ALDH2 as a previously unrecognized molecular target of colchicine that links mitochondrial redox control to suppression of radiation-induced macrophage senescence and atherosclerosis and may contribute to the efficacy of low-dose colchicine in cardiovascular disease.

cell biology↗

bioRxiv2026

A Sandwich-Structured Silk Fibroin Mesh with ROS-Responsive and Immunoregulatory Functions for Pelvic Floor Repair

Current biodegradable meshes for pelvic floor repair are constrained by a limited ability to actively modulate the hostile immune microenvironment following implantation. To address these challenges, we developed a functionalized degradable silk fibroin mesh (SFM) integrated with a reactive oxygen species (ROS)-responsive nanocomposite hydrogel. The resulting composite mesh, SFM@Gel-NP, features a "hydrogel-mesh-hydrogel" sandwich structure, wherein the hydrogel layers are loaded with self-assembled nanoparticles (NP-H-CRFP) for the co-delivery of the STING inhibitor H-151 and the ROS-scavenging agent catechin. This design provides immediate mechanical reinforcement while enabling microenvironment-triggered drug release. In vitro, NP-H-CRFP demonstrated efficient cellular uptake, significant ROS clearance, and effective attenuation of macrophage inflammation and apoptosis. In vivo, SFM@Gel-NP remodeled the local immune milieu by inhibiting the ROS/cGAS-STING/NF-{kappa}B axis, thereby promoting a shift from pro-inflammatory M1 toward pro-regenerative M2 macrophage polarization. This immunomodulatory effect, coupled with enhanced and well-organized collagen deposition--particularly of early type III collagen--resulted in improved tissue integration and repair. This work presents a novel strategy that combines structural reinforcement with active immune regulation, offering a promising next-generation solution for durable and functional pelvic floor reconstruction.

bioengineering↗

bioRxiv2026

Leveraging cis- and trans-variants to improve protein expression level prediction for proteome-wide association studies

Since genetic effects are often mediated through proteins, the analysis of proteomic data can provide insights into disease etiology. However, most studies lack proteomic data. To address this problem, we developed TransCisPredict to perform proteome-wide association studies (PWAS) at a biobank scale. TransCisPredict reduces computational burden through linkage-disequilibrium block selection which facilitates incorporating cis- and trans-variants to predict protein expression and performs protein-phenotype association analyses. To account for differences in protein regulatory architecture, four prediction methods are used for weight estimation, i.e., BayesR, Elastic Net, LASSO, and SuSiE. Five-fold cross-validation (CV) is used to select the optimal method for each protein. Weight estimation was performed using White British UK Biobank study subjects (N=42,644) with proteomic and genotype array data. Of the 2,920 available protein expression levels, 2,339 could be predicted with a CV-R2>0.05 when cis- and trans-variants were used. Since most methods are limited to cis-variation, for comparison only cis-variants were used for prediction yielding 466 proteins with a CV-R2>0.05. A PWAS was performed for 2,339 predicted protein expression levels and type 2 diabetes (T2D) using White British UK Biobank study subjects without proteomic data (N=364,132) followed by two-sample Mendelian randomization using a method that controls for horizontal pleiotropy for validation. Forty proteins were associated with T2D and validated. For the 466 cis-only predicted protein expression levels, three proteins were associated with T2D and validated. Incorporating both cis- and trans-variation using TransCisPredict facilitates the prediction of many more proteins compared to using cis-only variants thereby increasing the power of PWAS.

genetics↗