Biology subjects

Chen, T.

Publications and source records attributed to Chen, T..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Senescence-induced reparative fibroblasts enable scarless wound healing in aged murine skin.

It has been reported that elderly individuals exhibit reduced scarring during the wound healing process compared to younger adults. However, the underlying mechanisms responsible for this phenomenon remain poorly understood. Here, we revealed that aged mice exhibited more pronounced regenerative outcomes compared to young mice, characterized by increased hair follicle numbers and collagen fiber features closer to normal skin. Single-cell sequencing identified a reparative fibroblast subpopulation (Prss35+Fib) enriched in the aged group, which promotes regeneration through communication with epithelial cells, macrophages, and T cell subpopulations via PTN and EREG signaling. Spatial transcriptomics validated this communication pattern by elucidating cell proximity and locating the regenerative niche in the upper dermis. Finally, EREG treatment significantly enhanced regenerative outcomes in young mice, while the small wound model of aged mice, lacking the reparative fibroblast and EREG signaling, failed to achieve regeneration. Collectively, our findings advance the understanding of regenerative plasticity in aging and provide new insights for designing scarless therapeutic strategies.

cell biology↗

bioRxiv2025

The GSA Family in 2025: A Broadened Sharing Platform for Multi-Omics and Multimodal Data

The Genome Sequence Archive Family (GSA family) provides a suite of database resources for archiving, retrieving, and sharing multi-omics data for the global academic and industrial communities. It currently comprises four distinct database members: the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa/), the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), the open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/), and the Open Biomedical Imaging Archive (OBIA, https://ngdc.cncb.ac.cn/obia). Compared to the 2021 version, the GSA family has significantly expanded by introducing a new database member, the OBIA, and through comprehensive upgrades to the existing ones. Notable enhancements to the existing members include broadening the range of accepted data types, enhancing the quality control systems, fine-tuning the data retrieval system, and refining the data-sharing management mechanisms.

genomics↗

bioRxiv2025

Excavating Podocyte-Protective Targets of phloroglucinol-terpene hybrids Utilizing AI Models and Omics Analysis

Lupus nephritis (LN) is a multifactorial autoimmune inflammatory disorder, with podocyte dysfunction or structural abnormalities playing a pivotal role in its pathogenesis. Eucalyptus leaves, traditionally employed in folk medicine, have demonstrated notable efficacy in managing non-infectious inflammations. Eucarbwenstols D (R14) is a novel phloroglucinol-terpene that has been isolated from Eucalyptus robusta. To further refine potential targets, we utilized an AI scoring system and successfully identified the epidermal growth factor receptor (EGFR) as the novel target of R14. This discovery was built upon the foundation of transcriptome and connectivity mapping (CMap) analysis. Molecular docking and molecular dynamics simulations were employed to analyze the interaction between R14 and EGFR, surface plasmon resonance (SPR) and homogeneous time-resolved fluorescence (HTRF) assays were utilized to accurately quantify the binding affinity between R14 and EGFR. Western blot (WB) and immunofluorescence assays were then conducted to explore the downstream signaling pathways initiated by EGFR, specifically the PI3K/AKT/mTOR pathway. These assays demonstrated that R14 modulates the expression of apoptotic proteins Bax and Bcl-2 through this pathway, thereby exerting a protective effect on podocytes from undergoing apoptosis. The modulation of key podocyte proteins, nephrin and podocin, further substantiates this protective effect. Overall, we have applied a novel AI algorithm model that transcends bioinformatics analysis to precise identification of the novel podocyte target of R14. In this study, R14 was identified for the first time as a novel scaffold compound for EGFR inhibition. Moreover, R14 has demonstrated its capacity to inhibit podocyte apoptosis, thereby protecting these critical cells. This discovery establishes a robust theoretical foundation for the use of phloroglucinol-terpene hybrids (PTHs) in the treatment of LN.

cell biology↗

bioRxiv2025

scWGBS-GPT: A Foundation Model for Capturing Long-Range CpG Dependencies in Single-Cell Whole-Genome Bisulfite Sequencing to Enhance Epigenetic Analysis

Single-cell DNA methylomes are challenging to interpret because of sparse CpG coverage and the complexity of genome-wide sequences. We present scDNAm-GPT, a universal foundation model that uses context-aware CpG tokenization, a Mamba backbone, and cross-attention to capture both local and global DNA methylation patterns. Trained on over one million single cells from 35 human and mouse tissues, scDNAm-GPT enables accurate cell clustering, zero-shot prediction of CpG effects on gene expression, improved trajectory inference, and reference-free deconvolution of cell types from cell-free DNA. The model hierarchically learns regulatory features, and its attention maps highlight functionally relevant regions, demonstrating high biological interpretability. These results establish scDNAm-GPT as a scalable and generalizable framework for single-cell epigenomic analysis, offering new opportunities to dissect epigenetic regulation in health and disease. Code is available at GitHub (https://github.com/ChaoqiLiang/scDNAm-GPT).

bioinformatics↗

bioRxiv2025

Regulatory T and activated T cells in the chronic unpredictable mild stress mice: sex differences and effects of treatments

Background: Major depressive disorder (MDD) is associated with immune dysregulation, including T-cell activation and reduced regulatory T cells. Pharmacological treatments like fluoxetine, simvastatin, curcumin, and S-adenosylmethionine (SAMe) show promise in alleviating depressive symptoms, but their effects on immune responses are not fully understood. Methods: This study explored immune status in MDD using the chronic unpredictable mild stress (CUMS) mouse model. It examined how fluoxetine, simvastatin, curcumin, and SAMe affected immune responses and depressive behaviors. The study also investigated sex differences. Six groups, each with 12 mice (6 males and 6 females), were included: control, CUMS, and drug treatments. Male and female mice were housed separately in sex-specific cages throughout the experiment, with three mice per cage. Behavioral tests included sucrose preference, forced swimming, open field, and active avoidance. Immune cell markers were analyzed by flow cytometry. Results: CUMS mice showed immune imbalance, with Treg depletion (p = 0.047) and a higher CD154/CD152 ratio (p = 0.006). All treatments improved depressive-like behaviors (all p < 0.05), with SAMe normalizing the CD154/CD152 ratio (p = 0.009). Furthermore, female mice exhibited lower levels of Treg-related immune regulation and T-cell activation compared to males (both p < 0.001), along with a higher CD154/CD152 ratio (p = 0.008). Conclusion: This study highlights that curcumin, simvastatin, fluoxetine, and SAMe alleviate depressive-like behaviors, while SAMe also reduces the CD154/CD152 ratio. These findings reveal marked sex differences in T-cell activation and immune regulation, highlighting the importance of considering sex as a biological variable in CUMS research.

immunology↗

bioRxiv2025

Interferometric Ultra-High Resolution 3D Imaging through Brain Sections

Single-molecule super-resolution microscopy allows pin-pointing individual molecular positions in cells with nanometer precision. However, achieving molecular resolution through tissues is often difficult because of optical scattering and aberrations. We introduced 4Pi single-molecule nanoscopy for brain with in-situ point spread function retrieval through opaque tissue (4Pi-BRAINSPOT), integrating 4Pi single-molecule switching nanoscopy with dynamic in-situ coherent PSF modeling, single-molecule compatible tissue clearing, light-sheet illumination, and a novel quantitative analysis pipeline utilizing the highly accurate 3D molecular coordinates. This approach enables the quantification of protein distribution with sub-15-nm resolution in all three dimensions in complex tissue specimens. We demonstrated 4Pi-BRAINSPOTs capacities in revealing the molecular arrangements in various sub-cellular organelles and resolved the membrane morphology of individual dendritic spines through 50-{micro}m transgenic mouse brain slices. This ultra-high-resolution approach allows us to decipher nanoscale organelle architecture and molecular distribution in both isolated cells and native tissue environments with precision down to a few nanometers.

neuroscience↗

bioRxiv2025

Fluctuations in arousal reflect latent state transitions that facilitate behavioral optimization

People are often faced with surprising events that defy expectations. Such events elicit transient activity in the locus coeruleus/norepinephrine system and elevation of peripheral arousal markers including pupil dilation and the EEG P300, but the function of these signals remains unclear. We propose that they reflect latent state transitions that dynamically control the mental context governing learning and perception. We tested and confirmed five preregistered predictions of this theory using EEG and pupil measurements collected from people performing a color prediction and reproduction task. Task latent state transitions elicited pupil dilation and amplified event-related potentials including the P300. These EEG and pupil measures related to behavioral signatures of latent state updating, including reduced bias and bidirectional adjustment of learning, both across trials and individuals. Our findings support the theory that LC/NE-linked arousal systems optimize behavior by signaling environmental transitions to facilitate rapid adjustments of mental context.

neuroscience↗

bioRxiv2025

Generative latent diffusion language modeling yields anti-infective synthetic peptides

Generative artificial intelligence (AI) offers a powerful avenue for peptide design, yet this process remains challenging due to the vast sequence space, complex structure-activity relationships, and the need to balance antimicrobial potency with low toxicity. Traditional approaches often rely on trial-and-error screening and fail to efficiently navigate the immense diversity of potential sequences. Here, we introduce AMP-Diffusion, a novel latent diffusion model fine-tuned on antimicrobial peptide (AMP) sequences using embeddings from protein language models. By systematically exploring sequence space, AMP-Diffusion enables the rapid discovery of promising antibiotic candidates. We generated 50,000 candidate sequences, which were subsequently filtered and ranked using our APEX predictor model. From these, 46 top candidates were synthesized and experimentally validated. The resulting AMP-Diffusion peptides demonstrated broad-spectrum antibacterial activity, targeting clinically relevant pathogens--including multidrug-resistant strains--while exhibiting low cytotoxicity in human cell assays. Mechanistic studies revealed bacterial killing via membrane permeabilization and depolarization, and the peptides showed favorable physicochemical profiles. In preclinical mouse models of infection, lead peptides effectively reduced bacterial burdens, displaying efficacy comparable to polymyxin B and levofloxacin, with no detectable adverse effects. This study highlights the potential of AMP-Diffusion as a robust generative platform for designing novel antibiotics and bioactive peptides, offering a promising strategy to address the escalating challenge of antimicrobial resistance.

microbiology↗

bioRxiv2025

Receptor Allostery Promotes Context-Specific Sonic Hedgehog Signaling During Embryonic Development

Sonic Hedgehog (SHH) signaling functions in temporal- and context-dependent manners to pattern diverse tissues during embryogenesis. The signal transducer Smoothened (SMO) is activated by sterols, oxysterols, and arachidonic acid (AA) through binding pockets in its extracellular cysteine-rich domain (CRD) and 7-transmembrane (7TM) bundle. In vitro analyses suggest SMO signaling is allosterically enhanced by combinatorial ligand binding to these pockets but in vivo evidence of SMO allostery is lacking. Herein, we map an AA binding pocket at the top of the 7TM bundle and show that its disruption attenuates SHH and sterol-stimulated SMO induction. A knockin mouse model of compromised AA binding reveals that homozygous mutant mice are cyanotic, exhibit high perinatal lethality, and show congenital heart disease. Surviving mutants demonstrate pulmonary maldevelopment and fail to thrive. Neurodevelopment is unaltered in these mice, suggesting that context-dependent allosteric regulation of SMO signaling allows for precise tuning of pathway activity during cardiopulmonary development.

developmental biology↗

bioRxiv2025

A method for inducing a senescence trend in mesenchymal stem cells using D-galactose

The aging of the body is accompanied by a decline in tissue function, and this decline mainly stems from the aging of tissue stem cells. Therefore, they play a crucial role in the aging of the body.This study explores the biological characteristics of senescent hUCMSCs by establishing a replicative senescence model of hUCMSCs and a rapid senescence model induced by D - galactose (D-Gal), and analyzing the proliferative capacity, cell viability, cell cycle, pluripotency, and reactive oxygen species (ROS) levels of senescent hUCMSCs.

cell biology↗

bioRxiv2025

CryoNeRF: reconstruction of homogeneous and heterogeneous cryo-EM structures using neural radiance field

Cryogenic electron microscopy (cryo-EM) has become a widely used technique for determining the 3D structures of proteins. However, cryo-EM datasets often exhibit heterogeneity, with protein particle images from multiple conformations or compositional states. Here we propose CryoNeRF, a novel neural radiance fields (NeRF)-based cryo-EM reconstruction framework operating directly in Euclidean 3D space. CryoNeRF introduces a multi-resolution hash encoding and heterogeneity-aware cryo-EM encoder to model cryo-EM heterogenity. Extensive experiments demonstrate the stability and superior performance of CryoNeRF in both homogeneous and heterogeneous settings. On homogeneous datasets, CryoNeRF achieves 15.8% improvement over previous state-of-the-art methods. On both simulated and experimental heterogeneous datasets, CryoNeRF demonstrates exceptional capability in handling both conformational and compositional variations, which is consistent with previous experimental discoveries. Notably, CryoNeRF successfully distinguishes assembly states that even only account for 2% particles of the dataset in cases of compositional heterogeneity.

bioinformatics↗

bioRxiv2025

Catalytic degradation of circulating targets with FcRn-mediated cycling LYTACs

Circulating proteins are common targets for the discovery of occupancy-based inhibitors including monoclonal antibodies. Effective inhibition of target pathogenicity with blocking approaches, however, is often challenged by target parameters that lead to insufficient occupancy and/or incomplete pharmacology limited by only single site binding. Extracellular targeted protein degradation approaches, such as lysosomal targeting chimeras (LYTACs), offer an opportunity to minimize these challenges by an event-driven mechanism that selectively, thoroughly and irreversibly eliminates drivers of disease. First generation LYTACs, designed to traffic to the lysosome, show limited durability since the therapeutic is degraded along with the target protein of interest. Here we describe cataLYTACs, which overcome this limitation by combining stabilized asialoglycoprotein (ASGPR) ligands, pH-sensitive target binding and recycling via the neonatal Fc receptor (FcRn). These cataLYTACs degraded superstoichiometric levels of a target protein, IgE, in vitro and demonstrated deep and sustained clearance of human IgE in mouse models. In non-human primates, cataLYTACs resulted in >98% clearance of circulating endogenous IgE for 2 weeks and outperformed the standard of care blocking antibody, omalizumab (Xolair(R)), in both free IgE elimination and duration of action. CataLYTACs represent a new therapeutic modality for a wide range of disease states driven by circulating factors, with the potential for superior efficacy and duration of action compared to traditional inhibitors.

cell biology↗

bioRxiv2025

Structural basis of K11/K48-branched ubiquitin chain recognition by the human 26S proteasome

Beyond the canonical K48-linked homotypic polyubiquitination for proteasome-targeted proteolysis, K11/K48-branched ubiquitin (Ub) chains are involved in fast-tracking protein turnover during cell cycle progression and proteotoxic stress. Here, we report cryo-EM structures of human 26S proteasome in a complex with a K11/K48-branched Ub chain. The structures revealed a multivalent substrate recognition mechanism involving a hitherto unknown K11-linked Ub binding site at the groove formed by RPN2 and RPN10 in addition to the canonical K48-linkage binding site formed by RPN10 and RPT4/5 coiled-coil. Additionally, RPN2 recognizes an alternating K11-K48 linkage through a conserved motif similar to the K48-specific T1 binding site of RPN1. The insights gleaned from these structures explain the molecular mechanism underlying the recognition of the K11/K48 branched Ub as a priority signal in the ubiquitin-mediated proteasomal degradation.

biochemistry↗

bioRxiv2025

A novel broad-spectrum antibiotic targets multiple-drug-resistant bacteria with dual binding targets and no detectable resistance

The rapid emergence of difficult-to-treat multidrug-resistant pathogens, combined with the scarcity of antibiotics possessing novel mechanisms, poses a significant threat to global public health. Here, we integrated the synthetic-bioinformatic natural product approach with peptide optimization to unveil the antibiotic-producing potential of Paenibacillaceae bacteria. Our culture-independent approach led to the discovery of paenimycin, a novel 11-mer depsi-lipopeptide featuring an unprecedented dual-binding mechanism. By sequestering the phosphate and hydroxyl groups of lipid A in Gram-negative bacteria, as well as the phosphate groups of teichoic acids in Gram-positive bacteria, paenimycin exhibited potent and broad-spectrum efficacy against MDR pathogens in vitro and in vivo models. Remarkably, paenimycin demonstrates no detectable resistance, favorable pharmacokinetics and low nephrotoxicity, positioning it as a promising candidate for treating serve and urgent MDR infections.

biochemistry↗

bioRxiv2025

AAVone: A Cost-Effective, Single-Plasmid Solution for Efficient AAV Production with Reduced DNA Impurities

Currently, the most common approach for manufacturing GMP-grade adeno-associated virus (AAV) vectors involves transiently transfecting mammalian cells with three plasmids that carry the essential components for production. The requirement for all three plasmids to be transfected into a single cell and the necessity for high quantities of input plasmid DNA, limits AAV production efficiency, introduces variability between production batches, and increases time and labor costs. Here, we developed an all-in-one, single-plasmid AAV production system, called AAVone. In this system, the adenovirus helper genes (E2A, E4orf6, and VA RNA), packaging genes (rep and cap), and the vector transgene cassette are consolidated into a single compact plasmid with a 13-kb backbone. The AAVone system achieves a two- to four-fold increase in yields compared to the traditional triple-plasmid system. Furthermore, the AAVone system exhibits low batch-to-batch variation and eliminates the need for fine-tuning the ratios of the three plasmids, simplifying the production process. In terms of vector quality, AAVs generated by the AAVone system show similar in vitro and in vivo transduction efficiency, but a substantial reduction in sequences attributed to plasmid backbones and a marked reduction in non-functional snap-back genomes. In Summary, the AAVone platform is a straightforward, cost-effective, and highly consistent AAV production system - making it particularly suitable for GMP-grade AAV vectors.

bioengineering↗

bioRxiv2024

dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts

Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in imaging neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great potential for modeling the dFC patterns. However, these methods face challenges in effectively characterizing the modularity organization of brain networks and capturing varying dFC state patterns. To address these limitations, we propose dFCExpert, a novel method designed to learn robust representations of dFC patterns in fMRI data with modularity experts and state experts. Specifically, the modularity experts optimize multiple experts to characterize the brain modularity organization during graph feature learning process by combining GNN and mixture of experts (MoE), with each expert focusing on brain nodes within the same functional network module. The state experts aggregate temporal dFC features into a set of distinctive connectivity states using a soft prototype clustering method, providing insight into how these states support diverse brain functions and how they vary across brain conditions. Experiments on three large-scale fMRI datasets have demonstrated the superiority of our method over existing alternatives. The learned dFC representations not only enhance interpretability but also hold promise for advancing our understanding of brain function across a range of conditions, including development, sex difference, and Autism Spectrum Disorder. Our code is publicly available at MLDataAnalytics/dFCExpert1.

neuroscience↗

bioRxiv2024

ProCyon: A multimodal foundation model for protein phenotypes

Characterizing human proteins remains a major challenge: approximately 29% of human proteins lack experimentally validated functions and even well-annotated proteins often lack context-specific phenotypic insights. To enable universal modeling of protein phenotypes, we present PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW, a multimodal foundation model that utilizes protein sequence, structure, and natural language for generating and predicting protein phenotypes across diverse knowledge domains. PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW is trained on our novel dataset, PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW-IO_SCPLOWNSTRUCTC_SCPLOW, with 33 million protein phenotype instructions. On dozens of benchmarking tasks, PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW performs competitively against single-modal and multimodal models. Further, PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW conditionally retrieves proteins via mechanisms of action of small molecule drugs and disease contexts, and it generates candidate phenotypic descriptions for poorly characterized proteins, including those implicated in Parkinsons disease that were identified after PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOWs knowledge cutoff date. We experimentally confirm PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOWs predictions in multiple sclerosis using post-mortem brain RNA-seq, identifying novel MS genes and elucidating associated pathway mechanisms consistent with cortical pathology. PO_SCPLOWROC_SCPLOWCO_SCPLOWYONC_SCPLOW paves the way toward a general approach to generate functional insights into the human proteome.

bioinformatics↗

bioRxiv2024

SegCSR: Weakly-Supervised Cortical Surfaces Reconstruction from Brain Ribbon Segmentations

Deep learning-based cortical surface reconstruction (CSR) methods heavily rely on pseudo ground truth (pGT) generated by conventional CSR pipelines as supervision, leading to dataset-specific challenges and lengthy training data preparation. We propose a new approach for reconstructing multiple cortical surfaces using weak supervision from brain MRI ribbon segmentations. Our approach initializes a midthickness surface and then deforms it inward and outward to form the inner (white matter) and outer (pial) cortical surfaces, respectively, by jointly learning diffeomorphic flows to align the surfaces with the boundaries of the cortical ribbon segmentation maps. Specifically, a boundary surface loss drives the initialization surface to the target inner and outer boundaries, and an inter-surface normal consistency loss regularizes the pial surface in challenging deep cortical sulci. Additional regularization terms are utilized to enforce surface smoothness and topology. Evaluated on two large-scale brain MRI datasets, our weakly-supervised method achieves comparable or superior CSR accuracy and regularity to existing supervised deep learning alternatives.

neuroscience↗