Biology subjects

Jiang, Q.

Publications and source records attributed to Jiang, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

ProtCell: a foundation model for single-cell proteomics

Single-cell proteomics provides a direct functional readout of cellular states, but targeted assays measure limited and non-identical protein panels, fragmenting information across experiments. Here, we present ProtCell, a foundation model developed using approximately 380 million cells from 98 datasets for pretraining and evaluation. ProtCell combines protein-identity and continuous-abundance embeddings in a transformer encoder and learns cell- and protein-level representations through masked abundance modeling. ProtCell first connects fragmented profiles through transferable annotation and batch correction while preserving biological organization. It then extends limited profiles through abundance reconstruction and inference of an unmeasured cytotoxic protein program enriched in natural killer cells and memory CD8$^+$ T cells. The learned protein dependencies further guide the selection of compact panels that retain information for profile reconstruction and biological classification. ProtCell thus provides a framework for connecting fragmented proteomic profiles, extending information beyond measured panels and guiding more informative measurements.

bioinformatics↗

bioRxiv2026

A Cis-Element in magu-2 Mediates Apical mRNA Localization via Active Transport in C. elegans

mRNA subcellular localization is a key mechanism of post-transcriptional gene regulation and is widely conserved across cell types and species. Although increasing evidence suggests that transcripts associated with epithelial polarity also undergo asymmetric subcellular distribution, the trafficking patterns and regulatory mechanisms governing this process remain elusive. MAGU-2, the Caenorhabditis elegans ortholog of the Drosophila polarity gene stardust (sdt), has previously been reported to demonstrate apical mRNA enrichment. In this study, a truncation screen of magu-2 exon 3 was performed, leading to the identification of a 270-nt SH3 domain-containing fragment that drives apical localization of both its encoded mRNA and the corresponding truncated MAGU-2 protein, indicating that this 270-nt fragment within magu-2 is sufficient to direct the apical localization of both the transcript and its protein product. Live imaging, RNAi-mediated knockdown of DHC-1, BicD, or EGAL-1, together with nocodazole treatment, revealed that this mRNA undergoes active transport and that its apical targeting is disrupted upon impairment of dynein/BicD/Egalitarian function or microtubule integrity is compromised, implicating the conserved dynein/BicD/Egalitarian axis in magu-2 mRNA trafficking. While deletion of this 270-nt element exerted no overt effect on epithelial polarity or development, these findings provide insight into the active transport of epithelial polarity- related transcripts and position magu-2 as a useful model for dissecting cis-elements and trans-acting factors modulating subcellular RNA localization.

cell biology↗

bioRxiv2026

EpiZoo: a DNA sequence-aware foundation model for cross-species single-cell epigenomics

Large-scale single-cell epigenomic atlases characterize chromatin regulatory landscapes across diverse biological contexts, spanning cell types, tissues, individuals and species. Foundation models provide an opportunity to capture the full spectrum of cellular diversity in these atlases, yet current models remain largely confined to individual species by genomic coordinate dependence and overlook regulatory information encoded in DNA sequences. Here we introduce EpiZoo, a DNA sequence-aware foundation model for cross-species single-cell epigenomics. EpiZoo converts million-dimensional single-cell epigenomic profiles from diverse species into compact cell sentences that integrate DNA-encoded regulatory information, sequence-independent epigenomic context and accessibility-based importance. Built around a mixture-of-experts transformer and containing 2.6 billion parameters in total, EpiZoo is pretrained on our manually curated multi-species Omni-scATAC corpus of approximately 20.9 million cells to learn regulatory programs across species. On external datasets excluded from pretraining, EpiZoo achieves state-of-the-art performance in fundamental single-cell analysis tasks, including feature extraction, cell type annotation and data imputation. Its sequence-aware architecture enables extension to evolutionarily diverse species, and supports comparative analysis of regulatory conservation and divergence during primate evolution. Benefiting from this modeling design, EpiZoo enables context-aware prioritization of somatic mutations in cancer and prediction of cell-type-specific chromatin accessibility from DNA sequences across genomic regions and species.

bioinformatics↗

bioRxiv2026

Psychedelic drug action at dendrites is gated by behavioral state and serotonin receptors

How psychedelics act on cortical dendrites to produce long-lasting structural plasticity remains poorly understood. Here, we characterize the effects of psilocybin on dendritic calcium dynamics in pyramidal tract neurons of the mouse medial frontal cortex. Psilocybin transiently increases calcium event rates in apical dendritic tufts over a time course that parallels the drugs pharmacokinetics in the brain. This acute effect is brain state-dependent, occurring selectively during quiet wakefulness, and was abolished by cell type-specific deletion of the 5-HT2A receptor. Under control conditions, dendritic calcium signaling predicts subsequent spine formation, but this relationship is not preserved following psilocybin administration. Together, these findings reveal that psilocybin engages brain state- and 5-HT2A receptor-dependent dendritic signaling, while altering the relationship between acute dendritic activity and long-term structural plasticity. The results suggest that the mechanisms linking acute dendritic signaling to structural remodeling differ between physiological and psychedelic-induced plasticity.

neuroscience↗

bioRxiv2026

A superfolding sfPepper enables superresolution and single-molecule RNA imaging in live cells

Fluorescent RNAs (FRs) have emerged as powerful tools for the visualization of RNA in live cells. However, the poor folding of FRs is a major limiting factor in many advanced RNA imaging applications. Here, we describe a novel strategy to significantly increase the folding efficiency of FRs. By introducing a stabilizing stem and performing targeted mutagenesis at key junction regions, we develop a superfolder variant of the Pepper aptamer, termed sfPepper, that exhibits a striking twofold increase in folding efficiency compared with Pepper under physiological conditions. sfPepper exhibits much improved thermostability and reduced ion dependence and, most importantly, is substantially brighter than Pepper in live cells. sfPepper enables robust imaging of diverse noncoding RNAs and messenger RNAs with increased signal-to-background ratios and stimulated emission depletion (STED) superresolution imaging of CUG trinucleotide repeat-containing toxic RNAs. Remarkably, only four sfPepper repeats facilitate sensitive single-molecule mRNA tracking, revealing dynamic heterogeneity among ER-associated mRNA molecules. Together, this study establishes an efficient strategy for improving FR folding and offers a powerful tool for fluorescently labelling RNAs in live cells.

molecular biology↗

bioRxiv2026

Barcode-integrated reverse transcription for accurate, complete, and low-input RNA sequencing

In conventional total-RNA sequencing, unwanted RNA is removed by ribosomal-RNA depletion or messenger-RNA enrichment, and contaminating genomic DNA is cleared by DNase, both before reverse transcription. A sample index is added only later, during amplification, so each sample is carried through reverse transcription and cleanup on its own. These steps lose material that low-input samples cannot spare, and the per-sample handling limits throughput. Here we describe BIRT (Barcode-Integrated Reverse Transcription), a hairpin primer that carries a sample barcode and begins reverse transcription preferentially at RNA 3' ends, folding both barcoding and 3'-end capture into first-strand synthesis. Barcoding at reverse transcription enables early pooling, so samples are combined before any cleanup. It also authenticates RNA against DNA contamination: the primer barcodes RNA 909-fold more often than genomic DNA, and non-barcoded reads, which report DNA, are discarded. The 3'-end preference recovers terminal sequence that random priming loses: for a typical small nucleolar RNA, 66% of reads begin within two nucleotides of the mature 3' end, against a few percent for random hexamers. We pair BIRT with PERD (Probes for Excess RNA Depletion), a modular set of blocking probes that removes unwanted RNA within the same reaction without distorting expression (Pearson r = 0.98). We have applied BIRT to 8,079 samples across seven species, from cultured cells and primary tissue to FFPE and extracellular-vesicle RNA.

molecular biology↗

bioRxiv2026

Pathogen context reshapes antimicrobial peptide generation

Antimicrobial peptides are a promising source of new anti-infective agents, but their discovery is limited by a practical bottleneck: selecting a small set of candidates to synthesize and test for a specific pathogen. Existing peptide generators can expand antimicrobial-like sequence space, but most leave pathogen specificity to predictors or filters after generation. Here, we use AMPHORA, a pathogen-conditioned sequence-structure generator, to build antimicrobial peptide libraries shaped by target context. AMPHORA couples a short-peptide representation model to latent-flow generation conditioned on target class, genome-derived features and strain-description text. In matched, partial and shuffled controls, pathogen context redirected generated peptide pools beyond broad activity labels, with the strongest effects when genome and text conditions were combined. Counterfactual generation from the same initial noise showed that strain descriptions primarily changed amino-acid choices, whereas genome-derived features contributed more strongly to predicted structural properties. Across species, AMPHORA produced target-dependent enrichment, and matched bacterial context shifted APEX-predicted potency-score distributions compared with class-only generation. The generated peptides remained diverse, novel and peptide-like by sequence and predicted-structure analyses. Collectively, our results establish pathogen context as an active design signal for antimicrobial peptide generation and provide a target-aware route to pre-synthesis library design.

bioinformatics↗

bioRxiv2026

Internalized Components of Membrane Attack Complexes Disrupt Proteostasis and Acquire Alarmin-Like Properties

Immune effects of membrane attack complexes (MAC) have been widely attributed to their abilities to cause cell death. Here, we show that the MAC component, C9, forms non-cytolytic aggregates with pro-inflammatory effects. Intracellular aggregates of C9 are detected within inflamed tissues of patients in association with endothelial cell (EC) activation but not increased cell death. We identify NUMBL as a Rab35 effector that directly binds surface-bound C9 to promote C9 internalization and entry into the endolysosomal pathway. Within acidified endolysosomes, C9 forms insoluble aggregates that are targeted for degradative aggrephagy in a process that activates NF-{kappa}B. For C9 aggrephagy to occur, ZFYVE21, a Rab5 effector, complexes with RNF34 to bridge C9 aggregates to LC3B+ aggresome membranes. We detect C9 aggregates in vivo, and we show that a ZFYVE21-RNF34 signaling axis is required for C9 aggrephagy and NF-{kappa}B -dependent EC activation in three separate mouse models. Mice with conditional loss of ZFYVE21 in ECs show reduced aggregraphy, resulting in attenuated systemic inflammation and reduced tissue injury following skin transplantation. Our data show that the C9 component of MACs forms intracellular aggregates with alarmin-like properties.

biochemistry↗

bioRxiv2026

HoloCell: A Generative Foundation Model for Holistic Cellular Modeling

Single-cell multi-omics technologies have recently advanced to enable the profiling of epigenomic, transcriptomic, and proteomic layers within individual cells, offering new opportunities to characterize cellular states as integrated biological systems. However, developing a unified framework that can seamlessly integrate diverse omics modalities and remain robust to heterogeneous modality missingness remains challenging. Existing methods are often designed for specific modalities or modality pairs, relying on dataset-specific training or paired measurements. Here we present HoloCell, to our knowledge the first generative foundation model for joint representation learning and generative modeling across all three major single-cell omics modalities, i.e., epigenomics, transcriptomics, and proteomics. HoloCell contains over 860 million parameters and is pretrained on the Human-Multi-Omics-Corpus, which comprises approximately 468 million single-cell profiles across these three omics layers, corresponding to over 425 billion tokens. HoloCell introduces a a simple yet biologically motivated hierarchical tokenization strategy that encodes cis-regulatory elements, genes, and proteins as structured tokens within a shared modeling framework. We evaluated HoloCell across single-omics representation learning, paired multi-omics integration, unpaired multi-omics alignment, and cross-modal generation via iterative diffusion and remasking, demonstrating its superior performance and flexibility across diverse omics tasks. From a representation perspective, HoloCell provides a unified digital mapping of cellular states across multiple omics layers, capturing cell heterogeneity as an integrated system. From a generation perspective, its iterative diffusion and remasking frame-work permits flexible generation orders beyond fixed left-to-right causality, enabling in silico simulation of multi-omics information flow. Together, these capabilities position HoloCell as a versatile foundation model toward the emerging concept of a virtual cell, offering both systematic characterization and generative simulation of cellular systems within a unified framework.

bioinformatics↗

bioRxiv2026

PRIME: scalable, robust inference of mechanistic cell states from multimodal single-cell counts via probability generating functions

Single-cell multiomic technologies can now quantify complementary RNA species within the same cell, creating an opportunity to move beyond descriptive clustering toward mechanistically interpretable cell states. Yet most current methods depend on heuristic integration steps and become computationally burdensome at scale, limiting their ability to robustly detect subtle kinetic differences across heterogeneous populations. Here we introduce PRIME, a scalable framework for mechanistic cell-state discovery from multimodal single-cell count data. PRIME embeds multimodal measurements in a probability generating function (PGF) space, where transcriptional dynamics are encoded compactly and compared efficiently. This representation enables robust inference of latent kinetic structure and supports rapid cell grouping with a power K-means backbone that remains stable under noise, sparsity, and multimodality. Across synthetic benchmarks and experimental multimodal datasets, PRIME consistently recovers cell populations distinguished by transcriptional kinetics, outperforms conventional integration-and-clustering pipelines in robustness, and yields interpretable parameters that link observed variability to underlying regulatory mechanisms. By providing a mathematically principled yet practical route from multimodal counts to kinetic cell states, PRIME empowers biologists to uncover dynamic transcriptional regimes, dissect regulatory heterogeneity, and connect cell identity to mechanism rather than markers.

systems biology↗

bioRxiv2026

Multi-Omics Characterization of Human Molecular Responses to Spaceflight Across Two Independent Missions

Human spaceflight has historically been led by government agencies, but the emergence of commercial organizations is enabling broader participation and new research opportunities. In this study, we present a comprehensive molecular characterization of early human responses to spaceflight, leveraging multi-omics data across the first three weeks of two commercial missions. Biospecimens from six individuals, four from the Axiom 2 mission (10 days) and two from Axiom 3 (21 days), were analyzed using single-cell and bulk RNA sequencing, alongside proteomic profiling. Individual and integrative analyses of these datasets reveal systemic changes in cell types, transcripts, and proteins related to immune regulation, osteoclast differentiation, NF-{kappa}B signaling, and blood homeostasis pathways. Importantly, several of the detected pathways align with physiological patterns observed in longer-duration missions. This work establishes a foundational resource for understanding early adaptation to spaceflight at the cellular and molecular levels, providing insights to reduce future space-travel health risks. HighlightsO_LIFirst integrated multi-omics analysis using single-cell, bulk RNA sequencing and proteomic profiling of early human spaceflight responses across two commercial missions (Ax-2 and Ax-3). C_LIO_LIDistinct PBMC clustering was observed across Ax-2 and Ax-3, and post-flight samples. It showed fewer monocytes, dendritic cells, and megakaryocytes with increased naive CD4 and cytotoxic T cells. C_LIO_LIMulti-omics integration identifies shared biological signatures, including osteoclast differentiation, metabolic stress, and coagulation changes. C_LIO_LIThese findings lay the foundation for developing countermeasures to protect immune, skeletal, and vascular health during spaceflight. C_LI

molecular biology↗

bioRxiv2026

Experimental Methods for CRISPR Enzyme Assays with Fluorescence Readout

Fluorescence-based CRISPR diagnostic assays have become a popular platform for nucleic acid detection due to their programmability, configurability, specificity, and compatibility with standard laboratory equipment. However, reported enzymatic kinetic rates and limits of detection for CRISPR trans-cleavage assays vary by several orders of magnitude across the literature. This variation in performance parameters is coupled with and exacerbated by inconsistent calibration, incomplete correction of measurement biases, and nonstandardized or incomplete data-analysis procedures. We present an experimental protocol and quantitative analysis framework for fluorescence-based enzyme assays using routine laboratory instrumentation, including thermocyclers and fluorescence microplate readers. Building on previous studies of CRISPR enzyme kinetics and fluorescence calibration, we describe procedures for flat-field and background correction; comprehensive fluorescence calibration including correction for inner-filter-effect; quantification and implications of reporter degradation; extraction of Michaelis-Menten kinetic parameters; and determination of assay limits of detection. We provide step-by-step experimental guidelines and open-source Python implementations for each stage of the workflow. Using representative Cas12 trans-cleavage datasets, we demonstrate that explicit fluorescence calibration and correction procedures substantially reduce systematic bias in measured kinetic rates and improve consistency between experiments. Our framework aims to establish standardized practices for quantitative fluorescence-based CRISPR assays and provides researchers with practical tools for reproducible kinetic characterization and rational assay design.

biochemistry↗

bioRxiv2026

Differentiation of Bacterial, Fungal, and Algal Communities on Coastal Concrete Versus Drainage Pipes and the Coexistence of Corrosion and Healing Potentials

Coastal concrete structures and drainage pipes are prone to microbially influenced deterioration. However, differences in microbial communities and their corrosion/healing potentials between these habitats remain unclear. Here, we compared bacterial(16S), fungal (ITS) and algal(18S) communities on coastal concrete(C) and drainage pipe(P) surfaces. Fungal and algal -diversity were significantly higher in P than in C, while bacterial diversity did not differ. {beta}-Diversity strongly separated bacterial and algal communities between habitats, but not fungi. A shared core "seed bank" of 575 bacterial, 520 fungal and 40 algal ASVs was identified. Students t-test revealed that P enriched oligotrophic degraders (Sphingomonas) and acid-producing fungi (Arxiella, Bisifusarium), whereas C selected for halotolerant EPS-producing bacteria (Tunicatimonas, Muricauda) and the extremotolerant alga Coelastrella. db-RDA linked these differences to salinity, NH4+-N, NO3--N, and COD. Functional prediction indicated a shift from metabolism pathways in C to signaling in P. Co-occurrence networks revealed cross-kingdom competition and within-kingdom cooperation, especially among algae. Importantly, both habitats harbored microorganisms with documented corrosion and healing potentials, but under natural conditions, net deterioration dominated, microbial healing is hardly to counteract the negative effects. This study provides a functional taxonomic framework for understanding and managing concrete microbiomes in coastal and sewer infrastructure. ImportanceConcrete is the most widely used construction material, and its deterioration in coastal and sewer environments poses significant economic and safety challenges. Microorganisms play a dual role in concrete durability -- they can both corrode and heal concrete, but the net outcome under natural conditions is poorly understood. Most studies have focused on bacteria alone, overlooking the contributions of fungi and algae, and few reports on the of coastal concrete which also exist the microbially influenced concrete corrosion (MICC) similar to sewer. Here, we simultaneously analyzed all three microbial kingdoms on coastal concrete and drainage pipes. We found that while concrete surfaces harbor a diverse "seed bank" of microorganisms potentially involved in both corrosion and healing, under natural conditions, net deterioration dominated, indicating microbial healing is hard to counteract the negative effects from the environment and microbial. Our work provides a functional framework to guide the development of microbiome-based strategies for enhancing concrete durability, such as activating rare healing taxa in drainage pipes or selecting for endogenous healers in marine environments.

microbiology↗

bioRxiv2026

Psilocybin reshapes cortical inhibition through selective interneuron recruitment

Psychedelics show therapeutic potential for treating psychiatric disorders. While studies have emphasized the roles of cortical pyramidal cells, GABAergic neurons also express serotonin receptors and are therefore likely targets of psychedelics. In this study, we determine the effect of psilocybin on the activity dynamics of major GABAergic cell types in the mouse medial frontal cortex. Psilocybin reduces the firing of somatostatin-expressing interneurons, but increases the activity of parvalbumin-expressing interneurons. This cell type-specific response is unlikely to involve vasoactive intestinal peptide-expressing interneurons. Instead, pharmacological blockade and conditional knockout experiments demonstrate that psilocybin acts on the 5-HT1A receptor at SST interneurons, which contributes to the drugs long-term behavioral effects. Collectively, the results reveal that the classic psychedelic psilocybin alters cortical inhibition in a cell type-specific manner.

neuroscience↗

bioRxiv2026

RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization

Messenger RNA (mRNA) vaccines offer promising therapeutics for combating various diseases, yet their inherent chemical instability hampers their long-term efficacy. Although several methods have been developed to predict mRNA degradation, they exhibit limited accuracy and lack the capability for rational sequence optimization. Here, we propose RNASTOP, a novel framework integrating deep learning with heuristic search to simultaneously predict and optimize mRNA chemical stability. RNASTOP achieves a 13% accuracy improvement over the top-performing model on the Stanford OpenVaccine competition dataset and demonstrates robust generalization in predicting full-length mRNA degradation. Applied to mRNA codon optimization, RNASTOP reduces the minimum free energy of the Varicella-Zoster Virus vaccine sequence by 75.73% while maintaining high translation efficiency. Overall, RNASTOP serves as a powerful tool for predicting and optimizing mRNA chemical stability, poised to expedite the development of mRNA therapeutics. The source code of RNASTOP can be accessed at https://github.com/xlab-BioAI/RNASTOP.

bioinformatics↗

bioRxiv2026

DNA-triggered AIM2 condensation orchestrates immune activation and regulation

The innate immune sensor AIM2 detects cytosolic DNA and initiates inflammatory responses, yet its activation mechanism remains incompletely understood. Here, we show that AIM2 undergoes liquid-liquid phase separation upon DNA binding, forming dynamic condensates both in vitro and in cells. These condensates serve as platforms for inflammasome and PANoptosome assembly, promoting immune activation across multiple pathways. Direct structural determination from condensates reveals the assembly of active-form ASC filaments. Mechanistically, liquid-phase condensation is governed by multivalent interactions involving different AIM2 domains, including previously uncharacterized regions and species-specific elements. In vitro and in vivo assays show that mutants specifically disrupting condensation impair immune complex assembly, cell death initiation, antimicrobial defense, and intestinal homeostasis. Moreover, AIM2-DNA condensates function as regulatory hubs targeted by host- and pathogen-derived factors to balance immune homeostasis or facilitate immune evasion. These findings establish liquid-phase condensation as a fundamental mechanism of AIM2 activation and a potential therapeutic target.

immunology↗

bioRxiv2026

Integrative Multi-Omics Analysis Identifies a Functional Enhancer Driving Tumorigenesis in Head and Neck Squamous Cell Carcinoma

Enhancer is a critical epigenetic feature in head and neck squamous cell carcinoma (HNSCC), yet the functional roles of individual enhancers remain poorly understood. Here, we conducted an integrative multi-omics analysis based on publicly available ATAC-seq, H3K27ac ChIP-seq, transcriptomic profiling, and genetic association datasets to systematically map HNSCC-associated enhancers and their target genes. Integrating ATAC-seq and H3K27ac ChIP-seq, we identified 20,362 enhancers and 18,040 enhancer-associated genes, highlighting widespread regulatory complexity. Functional characterization of the TERT-associated enhancer GH05J001312 (GRCh38/hg38: chr5:1312099-1317743) revealed strong transcriptional activity in HNSCC. CRISPR-mediated deletion of a core sequence significantly reduced TERT expression, impaired cellular proliferation in vitro, and suppressed tumor growth in vivo, confirming its role as a key cis-regulatory element. RNA-seq analysis of enhancer-edited cells uncovered 742 differentially expressed genes enriched in cancer-related pathways, including MAPK and IL-17 signaling, indicating a broad transcriptional impact. Collectively, our findings establish GH05J001312 as a functional enhancer driving oncogenic programs in HNSCC and suggest enhancer-targeted strategies as a potential therapeutic avenue.

cancer biology↗

bioRxiv2026

Endothelial MRG15 Is a Mechanosensitive Suppressor of Atherosclerosis

Disturbed blood flow induces early endothelial inflammation in atherosclerosis, yet the precise mechanisms of endothelial sensing of disturbed flow remain incompletely understood. In this study, we integrated proteomic profiling of human endothelial cells (ECs) subjected to disturbed flow with coronary artery disease risk related genes from large-scale genome-wide association studies (GWAS). We identified mortality factor 4-like protein 1 (MORF4L1, also called MRG15) as a critical modulator in the pathogenesis of atherosclerosis induced by disturbed flow. We show that the expression of MRG15 is markedly reduced in ECs of human aortic atherosclerotic plaques, as well as in ECs exposed to disturbed flow in mouse carotid artery and in cultured human umbilical vein endothelial cells (HUVECs). Endothelial-specific deletion of Mrg15 significantly worsened, while its overexpression attenuated endothelial inflammation and atherosclerotic lesions in turbulent blood flow- or Western diet-induced mouse atherosclerosis model. Single-cell transcriptomics showed that Mrg15 deficiency increased endothelial inflammation and intercellular adhesion molecule 1 (Icam1) expression, and enhanced integrin-mediated adhesion pathways. Mechanistically, MRG15 facilitated the recruitment of enhancer of zeste homolog 2 (EZH2) to maintain repressive histone H3 lysine 27 trimethylation (H3K27me3) marks on the promoters of ICAM1 and integrin subunit alpha 5 (ITGA5). Disturbed blood flow rapidly led to an elevation of protein neddylation, which subsequently induced neddylation-dependent degradation of MRG15 within endothelial cells. This degradation of MRG15 alleviated the transcriptional repression of ICAM1 and ITGA5, thereby enhancing monocyte adhesion to ECs. These findings highlight endothelial MRG15 as a mechanosensitive suppressor of atherosclerosis induced by disturbed flow. Consequently, MRG15 emerges as a promising novel therapeutic target for atherosclerosis.

molecular biology↗