Biology subjects

Chen, M.

Publications and source records attributed to Chen, M..

At least 163 records · Page 9Linked to original sources

bioRxiv2025

Latrophilin mediates flow activation of Notch to control vascular endothelial phenotype and atherosclerosis

Fluid shear stress (FSS) is a major determinant of endothelial cell (EC) phenotype, with physio-logical laminar FSS promoting arterial identity and stability, and disturbed FSS promoting atherosclerosis. We previously identified the adhesion G protein-coupled receptor (GPCR) Latrophilin-2 (LPHN2) as a junctional protein required for FSS activation of the PECAM1/VE-cadherin/VEGFR2/PlexinD1 junctional mechanosensory pathway, which regulates EC alignment to laminar blood flow and promotes EC inflammatory activation and atherogenesis under disturbed flow. Here, we demonstrate that Lphn2 endothelial cell-specific knockout hyperlipidemic mice develop larger atherosclerotic plaques than controls--opposite from the previously reported Pecam1 knockout model. Transcriptomic analysis revealed that LPHN2 contributes more than half of flow-responsive gene expression and suppresses pro-inflammatory pathways. Critically, LPHN2 is essential for FSS-induced Notch1 activation to suppress inflammation, via a GPCR-independent mechanism. Active Notch correlates with physical association of LPHN2 with Notch1 activator, {gamma}-secretase, and Notch1 in response to flow. Both the Notch1 intracellular domain (NICD) and transmembrane domain (NTMD) contribute to the anti-inflammatory gene program, with the non-canonical NTMD signaling specifically suppressing the pro-inflammatory YAP pathway. Together with the accompanying paper showing that LPHN2 also mediates flow activation of the Smad1/5 pathway, these data identify LPHN2 as a central mediator of EC flow responses through multiple independent mechanisms in vascular biology and disease.

molecular biology↗

bioRxiv2025

The human transcription factor occupancy landscape viewed using high-resolution in situ base-conversion strand-specific single-molecule chromatin accessibility mapping

Chromatin accessibility profiling is a key tool for mapping the location of cis-regulatory elements (cREs) in the genome and tracking chromatin state dynamics during development, in response to various external and internal stimuli, and in disease contexts. Single-molecule footprinting (SMF) methods that rely on the labeling of individual accessible DNA bases have emerged in recent years as a powerful chromatin accessibility mapping approach, as they provide not just an average readout of accessibility over a given genomic position but also the distribution of accessibility states within a population at the level of individual original DNA molecules. However, methylation-based SMF approaches have often been limited either in their resolution or in labeling readout accuracy. We developed a deaminase-based high-resolution strand-specific single molecule footprinting approach that uses highly active sequence context-independent endogenous methylation-insensitive double-strand DNA (dsDNA) deaminases (CseDa01 and LbDa02 C[->]U/T-ssSMF), which convert accessible cytosines into uracils (which are converted to thymine after PCR amplification). We demonstrate the application of the method to mapping single-molecule accessibility states in human cell lines in both a short and a long-read format, and quantifying the occupancy states of individual transcription factors (TFs) as well as TF co-accessibility and strand-specific accessibility patterns.

genomics↗

bioRxiv2025

Cryogenic Electron Tomography Redefines Herpesvirus Capsid Assembly Intermediates Inside the Cell Nucleus.

Herpesviruses encapsulate their double-stranded DNA (dsDNA) genomes within an icosahedral nucleocapsid formed in the infected cell nucleus. Four biochemically purified nucleocapsids have been characterized, but their roles in herpesvirus replication remain controversial. The status of the capsid vertex-specific component (CVSC), essential for capsid stability and dsDNA packaging and retention, is also unclear. By integrating cryogenic focused ion beam milling with electron tomography and subtomogram averaging, we derived atomic models for all protein components, including the CVSC, across different herpesvirus capsid types within infected cell nuclei. Focused classification of pentonal vertex densities revealed differences in CVSC occupancy between genome-filled capsids and capsids lacking dsDNA, highlighting structural heterogeneity and providing insight into distinct capsid assembly stages in situ. These intra-nuclear findings redefine the maturation model of herpesvirus capsid assembly, advancing the understanding of herpesvirus replication, and demonstrate the effectiveness of in situ electron imaging by studying virus assembly within host cells.

microbiology↗

bioRxiv2025

Multitargeted Reduction of Inflammation and Atherosclerosis in Tet2-deficient CHIP via XPO1 Inhibition and Atf3 restoration

TET2 is the second most frequently mutated gene in clonal hematopoiesis of indeterminate potential (CHIP), driving hematopoietic stem cell clonal expansion and increasing the risk of myeloid malignancies. Affected individuals often develop atherosclerotic cardiovascular disease, exacerbated by hyperinflammatory TET2-mutant macrophages. Here, we show that the XPO1 nuclear export inhibitor eltanexor significantly reduces atherosclerotic plaque formation in a mouse model of Tet2-mutant CHIP. In addition, we investigated the mechanisms and gene expression pathways that underlie the proinflammatory phenotype that characterizes Tet2-mutant CHIP. Single-cell CITE-seq identified increased expression of multiple proinflammatory mediators in Tet2-mutant macrophages and in non-hematopoietic cells of the aortic wall, which was reduced by eltanexor treatment. Atf3, which encodes a core transcriptional modulator of inflammation, occupies and regulates the largest enhancer in wild-type macrophages. Tet2 loss diminished ATF3 binding to the regulatory loci of inflammatory mediators, which was restored upon XPO1 inhibition. These results provide new insights into drivers of heightened inflammation in TET2-mutant CHIP and highlight a novel therapeutic strategy for intervention.

immunology↗

bioRxiv2025

Histo-LCM-Hi-C reveals the 3D chromatin conformation of spatially localized rare cells in tissues at high resolution

It is now well established that an understanding of the chromatin structure is essential to delineate the mechanisms underlying genomic processes. However, while methods to obtain this information from cells in vitro are widely available, there is presently a significant lack of techniques that can acquire this data from cells in the tissue. Such a capability is critical to determine the dependence of the local tissue environment on cell functioning. Further, this ability is particularly necessary for cells that are a significant minority of the total tissue population, which are often obscured in data dominated by more abundant tissue cells. Here we have developed Histological Laser Capture Microdissection Hi-C (Histo-LCM-Hi-C) to enable the characterization of chromatin architecture of phenotype-defined, spatially localized cells within intact tissue sections from as few as about 300 cells. We demonstrate the effectiveness of this approach with the generation of the first 3D Hi-C map of the tissue-resident macrophages of the liver, the Kupffer cells (KC), which are a minor cell population in the normal liver. As expected, owing to their relative rarity, these KC maps are significantly different from those obtained from whole liver, revealing distant contacts between putative enhancers and genes involved in key KC functions as well as significant differences with that of in vitro induced bone-marrow derived macrophages. We anticipate that this method will prove to be an indispensable technique in the growing repertoire of methodologies used for the characterization of the genomic properties of cells within their native environment.

genomics↗

bioRxiv2025

Structural Basis for Cas9-Directed Spacer Acquisition in Type II-A CRISPR-Cas Systems

CRISPR-Cas systems confer prokaryotic adaptive immunity by integrating foreign DNA (prespacers) into host arrays. Type II-A systems employ Cas9 for protospacer adjacent motif (PAM) recognition and coordinate with Csn2 and the Cas1-Cas2 integrase during spacer acquisition, yet their structural basis remains unresolved. Here, we report cryo-EM structures of the Enterococcus faecalis Cas9-Csn2-Cas1-Cas2 supercomplex in apo and DNA-bound states. The apo-state structure (Cas92-Csn28-Cas18-Cas24) adopts a resting conformation, with Cas9 locked in a nuclease-inactive state and Cas1-Cas2 sterically blocked from prespacer loading. Upon DNA engagement, Cas9 undergoes a conformational transition, forming a prespacer catching complex that threads the DNA through Csn2s central channel. This architecture enables Cas9 to interrogate the PAM sequence while sliding along the DNA, with Cas9 and Csn2 jointly define a 30-bp DNA segment which matches the prespacer length. Subsequent dissociation of Cas9 triggers a structural reconfiguration of the Csn2-Cas1-Cas2 assembly. The PAM-proximal DNA becomes accessible, and Cas1-Cas2 relocates to bind to the exposed DNA, enabling further prespacer processing and directional integration. These findings reveal how Cas9 collaborates with Csn2 and Cas1-Cas2 to couple PAM recognition with prespacer selection, resolving the dynamic structural transitions that ensure fidelity during type II-A CRISPR adaptation.

molecular biology↗

bioRxiv2025

ImmunoFusion: A Unified Platform for Investigating RNA-seq-Derived Gene Fusions in Cancer and Immunotherapy

BackgroundGene fusions play a critical role in cancer development by persistently activating kinases or inactivating tumor suppressor genes, leading to altered signal transduction and gene expression regulation. However, their impact on treatment responses remains poorly understood. Although existing cancer databases catalog numerous fusion events or immune checkpoint blockade (ICB) studies, no unified platform integrates gene fusion data across cancer types while linking them to the tumor microenvironment (TME) and patient outcomes. Such integration is essential for elucidating how fusions shape immune responses and for developing improved biomarkers for personalized cancer therapies. MethodsTo address this gap, we constructed the Fusion Immune Atlas (ImmunoFusion), a platform integrating data from TCGA, TARGET, CPTAC, and 29 ICB cohorts (including ICB treatments and other treatment modalities) across diverse cancer types. We identified fusion events from raw RNA-seq data using Arriba and STAR-Fusion, and standardized fusion calls by adapting MetaFusion to the GRCh38 reference genome. Additionally, we curated clinical data and estimated TME signatures and cell fractions using the Immuno-Oncology Biological Research (IOBR) approach. ImmunoFusion was developed using the high-quality Rhino framework for Shiny applications, built on R. ResultsImmunoFusion (https://shiny.zhoulab.ac.cn/ImmunoFusion) encompasses 21,014 clinical samples, serving as a comprehensive RNA-seq-derived gene fusion database and analytical tool. It offers functionalities to investigate fusion breakpoints, confidence scores, frequency patterns, and associations with clinical variables. The platform also enables TME evaluation and interactive exploration of fusions for analyzing tumor immunophenotypes in cancer and immunotherapy contexts. As an illustration, analysis of MTAP fusions in lung cancer cohorts revealed their association with a metabolically depleted, "cold" (immune-suppressive) tumor environment and poorer patient outcomes, pinpointing MTAP fusions as a novel biomarker for treatment selection. ConclusionsImmunoFusion represents a significant advancement, delivering a unified platform to explore the influence of gene fusions on cancer and immune responses. It offers particular value in understanding fusion-driven immunotherapy outcomes, paving the way for more effective therapeutic strategies.

bioinformatics↗

bioRxiv2025

Advancing Ligand Binding Affinity Prediction with Cartesian Tensor-Based Deep Learning

0.We present PBCNet2.0, a cartesian tensor-based Siamese Neural Network for protein-ligand relative binding affinity prediction. Trained on 8.6 million protein-ligand complex structure pairs, PBCNet2.0 achieves zero-shot performance comparable to computationally intensive physics-based simulations. Our prioritization experiments show that PBCNet2.0 speeds up binding affinity optimization by 718% while reducing resource use by 41%. Through extensive retrospective experiments, we demonstrate that PBCNet2.0 intrinsically comprehends protein-ligand interactions, showing high sensitivity to intermolecular interactions and exceptional perception of spatial geometric information. Strikingly, PBCNet2.0 exhibits an emergent capability to predict affinity changes induced by binding residue variations, highlighting its potential for identifying resistance mutation. We prospectively validated these capabilities on two targets ENPP1 and ALDH1B1, where PBCNet2.0 successfully identified affinity shifts arising from subtle molecular interactions and conformational differences, and pinpointed critical binding residues with an 83% hit rate. This combination of computational efficiency, spatial geometric perception of binding site, and generalizable affinity prediction establishes PBCNet2.0 as a transformative tool for developing pharmacological probes for all human proteins.

molecular biology↗

bioRxiv2025

Central nervous system atlas of larval zebrafish constructed using the morphology of single excitatory and inhibitory neurons

Single-neuron morphology mapping is fundamental for deciphering brain architecture and function, yet in vertebrates it remains limited in spatial coverage and cell-type information. Here we present a nervous system-wide single-neuron morphology atlas of larval zebrafish with annotated excitatory/inhibitory identity and dendrite-axon polarity. The atlas comprises >20,000 reconstructed neurons from >13,400 animals, spanning the central and peripheral nervous system and representing [~]24.5% of the total excitatory and inhibitory neurons in the brain, all integrated into a common physical space with neuroanatomical parcellations. We define >500 excitatory and inhibitory neuronal morphotypes and construct putative inter-cell-type and interregional connectomes. Projection and morphotype analyses reveal distinct laterality and directionality biases and modular innervation patterns in excitatory and inhibitory neurons, while network analyses uncover fundamental connectivity motifs, hubs, and sensorimotor pathways. Accessible through custom-built interactive platform ZExplorer, this atlas provides a foundational resource for multimodal data integration, circuit analysis, computational modeling, and hypothesis-driven research.

neuroscience↗

bioRxiv2025

Marker genes for predicting cytokine release syndrome in vitro before CAR T cell infusion

Cytokine release syndrome (CRS) and neurotoxicity are common adverse events of the Chimeric antigen receptor (CAR) T cell therapy. Assessing the cytotoxicity associated biomarkers would be essential for therapy design to avoid developing severe toxicities. In this study, we re-analyzed previously published RNAseq results of CAR T cells before infusion and combined it with the clinical response post infusion. We observed that CAR T cells from patients who developed severe CRS displayed a higher expression of TCL6, HPCAL4, CCDC144B, and SIRPG, but lower levels of IL2, IL21, and HSPA1B when stimulated with anti-CAR19 idiotypic antibody in vitro. Interestingly, the upregulated gene SIRPG is positively correlated with CRS severity. In addition, without stimulation, CAR T cells from CRS group showed a higher levels of IFNAR1, IL7R, ZNF69, and USP32P1 but lower levels of CCL3, IL4, IL17A, IL23R, IL13, CD70, and IFNGR2. These results provided insights to evaluate the adverse events of CAR T products before treatments, which could be beneficial for designing therapy plans.

immunology↗

bioRxiv2025

CellReasoner: A reasoning-enhanced large language model for cell type annotation

We present CellReasoner, a lightweight, open-source large language model (LLM) tailored for single-cell type annotation. We introduced a compact training strategy that activates the reasoning capabilities of 7B-parameter LLMs using only 380 high-quality chain-of-thought exemplars. CellReasoner directly maps cell-level gene expression profiles to cell type labels, exhibiting robust zero- and few-shot generalization. The model further demonstrates expert-level, marker-by-marker reasoning, enabling structured, interpretable annotations and offering a practical solution for intelligent single-cell analysis.

bioinformatics↗

bioRxiv2025

Cross-Species Prediction of Histone Modifications in Plants via Deep Learning

The regulation of gene expression in plants is governed by complex interactions between cis-regulatory elements and epigenetic modifications such as histone marks. While deep learning models have achieved success in predicting regulatory features from DNA sequence, their cross-species generalizability in plants remains largely unexplored. In this study, we systematically evaluated the ability of deep learning models to predict histone modifications across plant species using a multi-stage framework based on the Sei architecture. We trained species-specific models for Arabidopsis (A. thaliana), rice (O. sativa), and maize (Z. mays), achieving high within-species predictive performance (AUROC > 0.94) and strong agreement between predictions and experimental ChIP-seq profiles. However, cross-species predictions showed reduced performance with increasing phylogenetic distance, highlighting limited model transferability between monocots and dicots. To improve generalization, we built family-level models by training on multiple species across the Poaceae and Brassicaceae families. These models demonstrated robust predictive power in completely unprofiled species--those entirely unused in training set--highlighting the models adaptability to novel plant genomes based solely on conserved regulatory syntax. In contrast, cross-family models produced inconsistent results, with reliable performance only in species sharing conserved regulatory features. Lastly, we developed an easy-to-use pipeline that predicts genome-wide chromatin signals directly from DNA sequences. Our findings demonstrate that phylogenetically informed model training significantly improves cross-species epigenomic prediction, offering a scalable computational strategy for functional annotation in non-model and agriculturally important plants.

bioinformatics↗

bioRxiv2025

AF3Score: A Score-Only Adaptation of AlphaFold3 for Biomolecular Structure Evaluation

Scoring biomolecular complexes remains central to structural modeling efforts. Recent studies suggest that AlphaFold (AF) - a revolutionary deep learning model for biomolecular structure prediction - has implicitly learned an approximate biophysical energy function. While many researchers highly rely on AF-derived scores for structure evaluation, existing AlphaFold2-based implementations require iterative refinement of the input structure, leading to biased scoring. To address this limitation, we adapted AlphaFold3 into a score-only model, AF3Score, by directly feeding input coordinates into the confidence head while bypassing the diffusion-based structure module. AF3Score demonstrates robust performance in structural quality assessment across diverse systems including monomeric proteins, protein-protein complexes, de novo designed binders, and fold-switching proteins. In benchmarking designed binder screening, AF3Score outperformed state-of-the-art methods for 8 out of 10 targets. Moreover, combining AF3Score with AlphaFold2-derived methods significantly improved the enrichment of experimentally validated binders, increasing the success rate from 15.2% to 31.6%. Additionally, AF3Score effectively identified stable conformations in fold-switching proteins, whereas AlphaFold predominantly predicted only the dominant fold. These findings highlight the broad applicability of AF3Score, from high-throughput screening in de novo binder design to filtering docking-generated poses and molecular dynamics (MD) trajectories.

bioinformatics↗

bioRxiv2025

Th9-endothelial cell crosstalk promotes inflammatory atherosclerotic cardiovascular disease

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death, and understanding its pathogenic drivers is critical for effective prevention and treatment. Inflammation has a critical role in ASCVD, and patients with inflammatory diseases are at increased risk. However, the key inflammatory mediator promoting ASCVD are incompletely understood, a major barrier when targeting inflammation to prevent ASCVD. Here, we found that interleukin-9 (IL-9) producing T helper cells (Th9) were significantly associated with ASCVD in patients with the autoimmune disease psoriasis. Th9 cells were poised to migrate to coronary vessels and were identified in atherosclerotic plaque. In vivo, murine inflammatory atherogenesis was prevented by IL-9 blockade and by IL-9 receptor (IL-9R) deletion in endothelial cells. In human arterial endothelial cells, IL-9R/STAT3 signaling promoted endothelial dysfunction, angiogenesis, and release of leukocyte chemoattractants. These findings suggest that in autoimmune diseases like psoriasis, Th9/IL-9 promote atherosclerosis by directly targeting endothelial cells, and that IL-9R/STAT3 signaling could be a promising therapeutic target for ASCVD. eTOC SummaryBaral et. al. investigate individuals who are at high risk for atherosclerosis due to underlying inflammatory disease and use mouse models of cardiovascular disease to uncover a role for interleukin-9-producing T helper 9 cells in the pathogenesis of inflammatory atherosclerosis.

immunology↗

bioRxiv2025

Metabolic modulation of intratumoral cholesterol with gut microbiota for the treatment of colorectal cancer

Excess cholesterol is positively correlated with colorectal cancer (CRC). Current therapeutic strategies for modulating cholesterol levels in CRC are limited and often come with complications. Here, we demonstrated that microbiome shunting of intestinal cholesterol to anticancer metabolites is a safe and applicable therapeutic approach for CRC. By screening major microbial metabolic products of cholesterol, we found that 4-cholesten-3-one (4-C-3) was selectively depleted in fecal samples and tumor tissues of patients with CRC. 4-C-3 exhibits strong antitumor effects on human CRC cell lines, patient-derived organoids, and patient-derived xenograft (PDX) models. Mechanistically, 4-C-3 suppresses CRC tumorigenesis by dually targeting the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homologue (KRAS). 4-C-3 directly binds to EGFR, blocking its signal transduction by inhibiting the binding of EGFR ligands. Additionally, 4-C-3 inhibits oncogenic KRAS variants, such as KRASG12D, by suppressing nucleotide exchange activity and effector engagement. By targeting both EGFR and KRAS, 4-C-3 reduces primary resistance to anti-EGFR therapies caused by KRAS mutations in CRC. As a proof-of-concept study, we showed that delivery of 4-C-3 by Oscillibacter ruminantium, a 4-C-3- producing commensal bacterium that is reduced in CRC patients, or a nonpathogenic Escherichia coli strain engineered to specifically convert intestinal cholesterol into 4-C-3, removed intratumoral cholesterol and led to rapid tumor regression in multiple models of CRC. These results suggest that microbial therapies for restoring intestinal cholesterol homeostasis represent a new therapeutic avenue for CRC.

cancer biology↗

bioRxiv2025

AWmeta empowers adaptively-weighted transcriptomic meta-analysis

Transcriptomic meta-analysis enhances biological veracity and reproducibility by integrating diverse studies, yet prevailing P-value or effect-size integration approaches exhibit limited power to resolve subtle signatures. We present AWmeta, an adaptively-weighted framework that unifies both paradigms. Benchmarking across 35 Parkinsons and Crohns disease datasets spanning diverse tissues and adaptively down-weighting underpowered studies, AWmeta yields higher-fidelity differentially expressed genes (DEGs) with markedly reduced false positives and establishes superior gene differential quantification convergence at both gene and study levels over state-of-the-art random-effects model (REM) and original studies. AWmeta requires fewer samples and DEGs from original studies to achieve substantial gene differential estimates, lowering experimental costs. We demonstrate AWmetas remarkable stability and robustness against external and internal perturbations. Crucially, AWmeta prioritizes disease tissue-specific mechanisms with higher functional coherence than those from REM and original studies. By bridging statistical rigor with mechanistic interpretability, AWmeta harmonizes heterogeneous transcriptomic data into actionable insights, serving as a transformative tool for precision transcriptomic integration.

bioinformatics↗

bioRxiv2025

Sialin2 Senses Nitrate to Activate Endosomal PI3K-AKT-NOS Signaling

Nitrate functions as a signaling molecule beyond its metabolic intermediate role. Despite progress in plants, the mechanisms underlying mammalian nitrate sensing and signaling remain unclear. The accompanying study identifies Sialin2--a proteolytic fragment of nitrate transporter Sialin--as a mammalian nitrate sensor mediating cellular responses. Here, we demonstrate that nitrate triggers endocytosis, inducing Sialin proteolysis and Sialin2 generation. Nitrate-induced Sialin2 scaffolds Lyn kinase with epidermal growth factor receptor (EGFR) at endosomes, activating phosphatidylinositol 3-kinase (PI3K)-AKT-nitric oxide synthase (NOS) pathway to stimulate localized nitric oxide (NO) production, enhancing angiogenesis and cell survival. In hypertensive rats, nitrate supplementation restores endothelial function and reduces blood pressure through AKT/eNOS-dependent signaling. Unlike the classical nitrate-nitrite-NO pathway, the Sialin2-PI3K-AKT-NOS axis confines NO synthesis to endosomal microdomains, enabling spatiotemporally precise vasodilation. By establishing Sialin2 as a mammalian nitrate sensor, this study unveils a novel paradigm in nitrogen homeostasis and provides targeted therapeutic strategies for vascular disorders.

cell biology↗

bioRxiv2025

Sialin2 Functions as a Mammalian Nitrate Sensor to Sustain Mitochondrial Homeostasis

Nitrogen homeostasis is fundamental for cellular physiology, yet mammalian nitrate (NO3-) sensing mechanisms remain elusive. Here, we identify Sialin2--a proteolytic fragment of the nitrate transporter Sialin generated by cathepsin B (CTSB) cleavage-- as the first mammalian nitrate sensor. Microscale thermophoresis (MST) reveals Sialin2 as a high-affinity nitrate sensor, while Cryo-Electron Microscopy (Cryo-EM) uncovers its structural basis for signaling. We show that Sialin2 localizes to mitochondria and scaffolds liver kinase B1 (LKB1)-AMP-activated protein kinase (AMPK) complexes to drive organelle-specific metabolic adaptation via spatiotemporally controlled AMPK activation, enhancing mitochondrial biogenesis, ATP production, and cell survival. Real-time tracking using the sCiSiNiS biosensor demonstrates nitrate signaling dynamics at physiological levels. This signaling axis redefines nitrate as a direct ligand activating receptor-like cascades, independent of classical nitric oxide synthesis. Our findings establish a paradigm of "inorganic salt signaling biology", wherein anions co-opt trafficking systems to achieve signaling specificity, offering therapeutic avenues for mitochondrial disorders.

cell biology↗