Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 235 records · Page 13Linked to original sources

bioRxiv2025

A comparison of adiponectin-deficient mice reveals the fundamental role of intracellular adiponectin

Adiponectin is an important adipokine with insulin-sensitizing, anti-inflammatory, and anti-fibrotic properties. The physiological roles of adiponectin have been studied using global adiponectin knockout (KO) mice. However, the reported phenotypes of adiponectin KO mice vary based on the mouse lines generated by different strategies and investigators. We performed a head-to-head comparison of the adiponectin KO mice that were generated in Dallas, Houston and Osaka. RNAseq revealed that the expression of the bioactive domain of adiponectin - the globular domain - was preserved in the Houston and Osaka KO mice. A complete adiponectin KO model, such as the Dallas KO mouse, exhibits a lower body weight, the highest adipocyte mitochondrial function and displays a susceptibility to DNA damage-mediated lung fibrosis. The reconstitution of globular adiponectin into the Dallas KO mice prompted an increase in body weight and a partial recapitulation of the Osaka KO model transcriptome signature. The intracellular globular adiponectin form is important, as we found that globular adiponectin enhances PPAR{gamma} activity by modulating the coregulators interacting with PPAR{gamma}. Overall, the residual expression of globular adiponectin regulates adipose tissue metabolism by altering PPAR{gamma} activity, highlighting an important novel role of intracellular adiponectin.

physiology↗

bioRxiv2025

SpatialMap: A Scalable Deep Learning Method for Cell Typing in Subcellular Spatial Transcriptomics

Subcellular imaging transcriptomics has offered unprecedented resolution of spatial mapping of gene expressions at subcellular level. However, accurate and robust cell type annotation at cellular level remains a challenge. Here we present SpatialMap, a deep weakly supervised cell typing framework that combines graph neural networks (GNN) with transfer learning to annotate cellular identities in subcellular spatial transcriptomics (ST). SpatialMap consistently outperforms state-of-the-art annotation methods on both simulated and real-world ST datasets. It demonstrates strong scalability across diverse tissue types and platforms, including NanoString CosMx, MERFISH and 10x Genomics Xenium. When applied to a mouse primary motor cortex dataset, SpatialMap successfully identified Micro, L4/5 IT, and L6 IT Car3 absent from the reference data, showcasing its capability to discover novel cell types. Furthermore, SpatialMap accurately annotated malignant cells in human liver cancer samples. Its design also enables seamless extension to cross-omics cell typing and cross-platform annotation within proteomics.

bioinformatics↗

bioRxiv2025

Genomic analysis identifies Campylobacter concisus genomospecies 2 as a novel species and proposes the name Campylobacter oralis

Campylobacter concisus comprises two distinct genomospecies (GS) based on core genome analysis. Here we conducted further analysis and demonstrated that C. concisus GS2 strains belong to a novel Campylobacter species. A total of 245 C. concisus genomes including 85 GS1 and 160 GS2 strains were analysed. DNA-DNA hybridization (DDH) values between strains of GS1 and GS2 C. concisus were from 42.8% to 66%. Average Nucleotide Identity (ANI) between GS1 and GS2 C. concisus strains were from 87.8% to 89.7%. The average GC contents of GS1 and GS2 C. concisus strains were 37.3% and 39.2% respectively. GS1 and GS2 specific genes have been identified. GS1 and GS2 C. concisus strains can also be differentiated based on core genome, 23S rRNA polymorphisms and protein profiles. Their morphology appears different under certain conditions. In conclusion, ANI and DDH values clearly support that C. concisus GS2 as a novel Campylobacter species, for which we propose the name Campylobacter oralis. The identification of C. concisus and C. oralis specific genes, along with differences in their protein profiles, morphology, core genome and 23S rRNA phylogeny, further support this classification.

microbiology↗

bioRxiv2025

A Dual-Compartment Scaffolding Role for RACK1 in Hepatic Glucagon Signaling and Gluconeogenesis

Background & AimsThe hepatic glucagon-PKA-CREB signaling axis plays a central role in regulating gluconeogenesis and maintaining glucose homeostasis during fasting. However, the mechanisms that govern the spatial coordination and substrate specificity of this pathway remain incompletely understood. This study determines the role of the scaffolding protein RACK1 (Receptor for Activated C Kinase 1) in orchestrating glucagon signaling to regulate hepatic gluconeogenesis. MethodsRACK1 was acutely deleted in mouse liver and primary hepatocytes. Metabolic phenotypes were assessed by glucose, pyruvate, and insulin tolerance tests, and hepatocyte glucose production assays. Protein interactions were analyzed with co-immunoprecipitation, GST pulldown, and proximity ligation assays. Subcellular localization and signaling events were studied by Western blot, confocal microscopy, and fractionation. Functional rescue was performed by hepatic expression of a constitutively active PKA catalytic subunit (PKAcW196R). ResultsAcute hepatic RACK1 deficiency caused fasting hypoglycemia, impaired gluconeogenesis, and improved glucose and pyruvate tolerance without affecting insulin signaling. RACK1 directly bound GCGR, PKA regulatory (RII) and catalytic (PKAc) subunits, and CREB, functioning as a dual-compartment scaffold assembling GCGR-PKA complexes at the plasma membrane and PKAc-CREB complexes in the nucleus. Loss of RACK1 impaired PKAc translocation, CREB phosphorylation, and gluconeogenic gene expression. These defects were rescued by PKAcW196R expresson. Overexpression of RACK1 WD1-2 and WD3-4 domains, which mediate PKA and GCGR interactions, similarly disrupted PKA signaling and gluconeogenesis. ConclusionRACK1 spatially organizes the glucagon-PKA-CREB axis, ensuring precise signal propagation and efficient hepatic gluconeogenesis, revealing a novel mechanism of compartmentalized signal regulation in glucose metabolism.

physiology↗

bioRxiv2025

Application of machine learning in the discovery of antimicrobial peptides: Exploring their potential for ulcerative colitis therapy

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with rising global prevalence, yet existing treatments are not universally effective. Antimicrobial peptides (AMPs), produced by the immune system, have diverse antimicrobial and immune-regulatory functions, making them promising candidates for UC therapy. Using machine learning, we developed a machine learning-based prediction model to identify novel AMPs. The predicted peptides demonstrated significant biological activity in vitro and in vivo. In a dextran sulfate sodium-induced UC mouse model, engineered AMPs notably improved UC-related parameters, such as body weight, disease activity index (DAI), and colon length. These effects were likely mediated by modulation of Akkermansia muciniphila. This study highlights the potential of machine learning-identified AMPs as future therapeutic candidates for UC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/660148v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@187dc6borg.highwire.dtl.DTLVardef@9800deorg.highwire.dtl.DTLVardef@1611c42org.highwire.dtl.DTLVardef@866406_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Murine peritoneal macrophages undergo female-specific remodeling with aging

Aging is a complex process characterized by a progressive decline in physiological functions. Immune function is strongly influenced by biological sex, affecting both innate and adaptive responses. Here, we investigated the effects of age and sex on mouse peritoneal immune cells and identified macrophages as the top affected cell type. Macrophages, as central components of the innate immune system, play critical roles in homeostasis maintenance and infection response. We found that aging induces sex-specific remodeling of mouse peritoneal macrophage omic landscapes, which is accompanied by female-specific age-related functional remodeling (i.e. decreased phagocysis, increased glycolysis). We show that age-related changes in circulating estrogen levels likely drive aspects of female-specific macrophage age-related changes, specifically age-related phagocytosis decline. By leveraging our multi-omic dataset, we identify transcription factors whose female-specific age-regulation may drive female-specific age-related changes in peritoneal macrophage phenotypes. Interestingly, Irf2 downregulation was sufficient to recapitulate aspects of female peritoneal macrophage aging phenotypes, including increased glycolysis. Mechanistically, decreased Irf2 expression leads to decreased binding to its target genes, including Hk3, which encodes hexokinase (the rate-limiting enzyme of glycolysis). Our findings support the notion that aging leads to female-specific remodeling of mouse peritoneal macrophages through hormone-dependent and -independent mechanisms.

immunology↗

bioRxiv2025

Predicting Disease-Specific Histone Modifications and Functional Effects of Non-coding Variants by Leveraging DNA Language Models

BackgroundEpigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimers disease (AD), where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to AD pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures. ResultsTo bridge this gap, we developed a novel large language model (LLM)-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on AD as a case study, we integrated epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn AD-associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts (MoE) architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of AD-relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, achieving mean area under receiver-operating curves (AUROCs) ranging from 0.7863 to 0.9142, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing AD-associated genetic variants, which show significant enrichment in disease-relevant pathways, supporting their potential functional role in AD pathogenesis. These findings suggest that differential modification loci identified by our model may represent key regulatory elements in AD. ConclusionsOur framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.

bioinformatics↗

bioRxiv2025

Mitochondrial calcium influx-driven bioenergetics in the dopaminergic system selectively enable drug reward

Drugs of abuse hijack the brains reward system, driving pathological dopamine surges that underlie compulsive behavior and addiction. However, directly targeting dopamine signaling for treatment risks disrupting natural reward processes. Here, we identify a bioenergetic mechanism that selectively promotes addiction-related dopamine release and behaviors. Opioids and methamphetamine, but not natural rewards, induce mitochondrial calcium (Ca2+) influx via the mitochondrial calcium uniporter (MCU) in dopaminergic terminals of the nucleus accumbens. Optogenetic stimulation reveals that this mitochondrial Ca2+ influx occurs exclusively during high-intensity dopaminergic neuronal activation. This Ca2+ influx drives rapid ATP production, compensating for energy deficits caused by neuronal hyperactivity and enabling sustained dopamine release. Genetic deletion or pharmacological inhibition of MCU in dopaminergic neurons selectively reduces drug-induced dopamine release and prevents addictive behaviors while sparing natural reward processing in mice. These findings uncover a distinct mitochondrial bioenergetic mechanism underlying drug reward and propose MCU as a promising therapeutic target for addiction treatment.

neuroscience↗

bioRxiv2025

Personalized whole-brain Ising models with heterogeneous nodes capture differences among brain regions

Multiple lines of research have studied how complex brain dynamics emerge from underlying connectivity by using Ising models as simplified neural mass models. However, limitations on parameter estimation have prevented their use with individual, high-resolution human neuroimaging data. Furthermore, most studies focus only on connectivity, ignoring node heterogeneity, even though real brain regions have different structural and dynamical properties. Here we present an improved approach to fitting Ising models to 360-region functional MRI data: derivation of an initial guess model from group data, optimization of simulation temperature, and two stages of Boltzmann learning, first with group data, then with individual data. Our implementation uses GPU acceleration to mitigate the high computational cost of this approach. We then analyze how data binarization threshold affects goodness-of-fit, the role of the external field in model behavior, consistency among models fitted to different scans of the same individual, and correlations between model parameters and features from structural MRI, including measures of myelination and cortical folding. We find that binarizing fMRI data at higher thresholds decreases correlation between model and data functional connectivity but increases the heterogeneity of node external fields and their correlations with structural features. A choice of threshold that achieves both goodness-of-fit and intrinsic heterogeneity of regions results in a model that better reflects the reality of the brain as a network of intrinsically heterogeneous nodes. By enabling personalized, biophysically interpretable modeling of structure-function mapping across the whole brain, this approach can aid understanding of individual differences in brain network organization and bridge the gap between the network-focused methodology of connectomics and the region-focused paradigm typical of translational research.

neuroscience↗

bioRxiv2025

Ultra-wide-field, deep, adaptive two-photon microscopy

Observing the activity patterns of large neural populations throughout the brain is essential for understanding brain function. However, capturing neural interactions across widely distributed brain regions from both superficial and deep cortical layers remains challenging with existing microscopy technologies. Here, we introduce a state-of-the-art two-photon microscopy system, ULTRA, capable of single-cell resolution imaging across an ultra-large field of view (FOV) exceeding 50 mm{superscript 2}, enabling deep and very wide field in vivo imaging. To demonstrate its capabilities, we conducted a series of experiments under multiple imaging conditions, successfully visualizing brain structures and neuronal activities spanning a spatial range of over 7 mm from superficial layers to depths of up to 900 m, while covering a volume of 45.24 mm3 in the mouse brain. This versatile imaging platform overcomes traditional spatial constraints, providing a powerful tool for comprehensive exploration of neuronal circuitry over extensive spatial scales with cellular resolution.

neuroscience↗

bioRxiv2025

prdm1a drives a fate switch between hair cells of different mechanosensory organs

Vertebrate mechanosensory hair cells (HCs) in the ear detect sound and gravitational forces. Additionally, fish have homologous lateral line HCs in the skin that detect water vibrations for orientation and predator avoidance. HCs in fish and other non-mammalian vertebrates regenerate to restore function after damage, but mammalian HCs lack this ability, causing deafness and vestibular defects. Experimental attempts at regeneration in mice result in incomplete differentiation of immature HCs. Despite differences in regeneration, the gene regulatory networks (GRNs) driving HC maturation are highly similar across vertebrates. Here, we show that the transcription factor prdm1a plays a key role in the HC fate GRN in the zebrafish lateral line. Mutating prdm1a respecifies lateral line HCs into ear HCs, altering morphology and transcriptome. Understanding how transcription factors control diverse HC fates in zebrafish is crucial for understanding the yet unsolved regeneration of diverse HCs in mammalian ears to restore hearing and balance.

developmental biology↗

bioRxiv2025

A caspase-1-cathepsin AND-gate probe for selectiveimaging of inflammasome activation

Caspase-1 is a key mediator of the inflammasome pathway, which is associated with several inflammatory disorders including obesity, diabetes mellitus (DM), cardiovascular diseases (CVDs), cancers and chronic respiratory diseases. Although substrate-based probes can be used to visualize the activity of caspase-1, none are selective enough for use as imaging agents. Here, we report the design and synthesis of a AND-gate substrate probe (Cas1-Cat-Cy7) that requires processing by both caspase-1 and cathepsins to produce a signal. Because both enzymes are only found together and active at the site of inflammasome activation, the resulting probe can be used to image caspase-1 mediated inflammation. We demonstrate that the probe produces selective signals in ex vivo biochemical and cellular assays and in a mouse model of acute inflammation.

biochemistry↗

bioRxiv2025

TIP-seq: A Single-cell Multiomics Approach for Simultaneous Transcriptome and Intracellular Protein Profiling

Single-cell multiomics technologies have significantly advanced our understanding of cellular heterogeneity; however, the concurrent profiling of mRNA transcripts and target proteins continues to pose a substantial challenge. To bridge the gap between single-cell sequencing and proteomic methods, we developed an innovative single-cell multiomics methodology, termed TIP-seq, which facilitates the simultaneous profiling of the transcriptome and intracellular proteins without prior cell manipulation. In TIP-seq, microfluidic technology is leveraged to integrate oligonucleotide-labeled antibodies with hydrogel beads, allowing for the capture of mRNAs and proteins from individual cells within droplets, thereby enabling high-throughput and precise dual-omics data acquisition. When applied to lung cell lines and non-small cell lung cancer (NSCLC) tissues, TIP-seq analysis revealed notable cellular heterogeneity and molecular dynamics, emphasizing the distinct immune cell interactions within NSCLC tissues. Key immune checkpoint interactions in NSCLC, such as SPP1-CD44, NECTIN2-TIGIT, and NECTIN2-CTLA4, along with functional alterations in tumor-associated dendritic cells (DCs) and T cells, were identified via TIP-seq, underscoring their pivotal roles in mediating immune suppression within the tumor microenvironment. Collectively, TIP-seq represents a powerful methodology for identifying novel therapeutic targets and biomarkers, thereby holding significant potential for the advancement of precision medicine in the treatment of lung cancer and other complex diseases.

biochemistry↗

bioRxiv2025

CSFeatures improves identification of cell type-specific differential features in single-cell and spatial omics data

Recent advancements in single-cell and spatial omics have enabled the analysis of gene expression patterns in cells and tissues with unprecedented precision. Genes that exhibit significant variation across different cell types or states are typically closely linked to cellular functional states and disease processes. However, current differential analysis methods often fail to adequately account for cell type specificity when identifying differentially expressed genes. CSFeatures effectively addresses this issue by comprehensively considering the expression level, the smoothness of expression distribution, and the proportion of gene expression in the target cell population as well as in each of the other cell populations. Through comprehensive experiments on eight single-cell RNA-seq datasets, it was demonstrated that CSFeatures can effectively identify cell type-specific genes that are highly expressed in the target cell population while being lowly expressed in all other populations. Moreover, it was validated that the underlying principle of CSFeatures can be generalized to other omics data, including single-cell ATAC-seq, spatial transcriptomics, and spatial ATAC-seq data. Furthermore, the differential features identified by CSFeatures are enriched in pathways, functional categories, and regulatory regions that are highly relevant to the specific functional states of the target cell population, providing insights into its gene regulation.

bioinformatics↗

bioRxiv2025

Machine Learning Matches Human Performance at Segmenting the Human Visual Cortex

A major problem in human visual neuroscience research is the localization of visual areas on the cortical surface. Currently available methods are capable of making detailed predictions about many areas, but human raters do not agree as well with these methods as they do with each other. Although highly accurate, human raters require substantial time and expertise that many researchers do not have. Additionally, human raters require functional data for drawing visual area boundaries that requires additional scan time, budget, and expertise to collect. Here, we train convolutional neural network (CNN) models to predict the boundaries and iso-eccentric regions of V1, V2, and V3 in both the Human Connectome Project dataset and the NYU Retinotopy dataset. CNNs trained to use the same functional data available to human raters predicted these boundaries with an accuracy similar to humans, while CNNs trained to use only anatomical data had a lower accuracy that was nonetheless higher than that of any currently available method. A comparison of the model accuracies when predicting eccentricity-based boundaries and polar angle-based boundaries suggests that eccentricity is substantially less closely tied to anatomical structure than polar angle and that the cortical magnification function, at least in terms of eccentricity, varies substantially between subjects. We further find that the fraction of V1, V2, and V3 that can be accurately parcellated into function regions using gray matter structural data alone is [~]75% ([~]80% of the inter-rater reliability of human experts), implying a much tighter coupling between structure and function in these areas than previously estimated. We conclude that machine learning techniques such as CNNs provide a powerful tool for mapping the brain with human accuracy and predict that such tools will become integral to neuroscience research going forward.

neuroscience↗

bioRxiv2025

Aligning Neural Population Patterns Facilitates Motor Learning Transfer

Motor learning transfer, the ability to apply skills acquired in one task to enhance performance in a related task, is driven by changes in neural ensemble activities. However, the long-term evolution of neural population dynamics during motor learning transfer remains unclear. Specifically, how do the neural patterns reorganize and stabilize over an extended learning period in the new task? To investigate the neural mechanisms in the motor cortex that enable such transfer, we employed a Brain-Machine Interface (BMI) paradigm in rats. In the experiment, rats first mastered a lever-pressing task before proceeding to a more complex but related lever-discrimination task. We analyzed neural ensemble activities by projecting them into a low-dimensional linear feature space that captures the most prominent dynamic structure. Within this space, we represented neural patterns as clusters and developed an iterative method to align similar clusters between the two tasks. Leveraging this alignment, we introduced a novel decoding approach, Clustering Alignment-based Transfer on Kernel Reinforcement Learning (CATKRL), which utilizes parameters learned from the initial task to enhance efficiency in the new task. Our results revealed that neural patterns for learned actions form distinct clusters with consistent shapes and centroid distances across tasks, and these patterns exhibit rotational evolution during learning. By integrating the cluster alignment mechanism into the RL decoder, we achieved a faster training speed with less data in the lever-discrimination task. Our results suggest that aligning neural pattern clusters can enhance BMI decoding efficiency by leveraging consistent neural representations. This approach not only provides valuable insights into the brain mechanisms underlying motor learning transfer but also holds promise for advancing multi-task learning in neuroprosthetics.

neuroscience↗

bioRxiv2025

Rapid and accurate protein structure database search using inverse folding model and contrastive learning

Protein structure database search has become increasingly challenging due to the growing number of experimental and computational structures. We introduce mTM-align2, a novel two-step approach for rapid and accurate protein structure database search. In the first step, protein structures are first transformed into embeddings using a pre-trained inverse folding model (ESM-IF) and 3D Zernike polynomials. The ESM-IF embeddings are further optimized through a contrastive learning network, which is trained on [~]7 million structure pairs. Structures with similar embeddings are returned on the fly in this step. The second step employs a rapid structure alignment program to refine top candidates, ensuring high precision and producing high-quality alignments. Extensive benchmarks reveal that mTM-align2 performs competitively compared to other leading methods, completing monomeric structure search in seconds with over 90% precision for the top 10 hits. The t-SNE visualization of the mTM-align2 embeddings for thousands of structures demonstrates that our embeddings are structurally informed, capturing the global structural features. It uncovers insights such as structure misclassifications and ambiguous structural class boundaries. A web server for mTM-align2 is accessible at https://yanglab.qd.sdu.edu.cn/mTM-align/.

bioinformatics↗

bioRxiv2025

Upstream open reading frame inactivation augments GATA4 translation and cardiomyocyte hypertrophy in mice

Upstream open reading frames (uORFs) are short peptide-encoding sequences located in the 5 untranslated region (5 UTR) of mRNAs, enabling translational repression of main (m)ORFs. While uORFs are found in [~]50% of mRNAs in humans, our understanding of their biological function remains limited. This study aims to elucidate the role of the uORF in the 5 UTR of the Gata4 (GATA binding protein 4) gene in cardiac biology by inactivating its start codon ({Delta}uORF) in the mouse genome. Our investigation reveals that mice with Gata4 uORF inactivation manifest spontaneous cardiac hypertrophy without apparent fibrosis as they age. Utilizing single-nucleus RNA sequencing (snRNA-seq), we uncovered significant transcriptional variations between wild-type (WT) and {Delta}ORF mice. Notably, mRNAs associated with sarcomeres and contractile functions show heightened expression levels, reflecting the hypertrophic phenotype. Notably, at least nine upregulated genes are GATA4-bound targets in mouse ventricles. Functional assessments of isolated primary adult cardiomyocytes confirmed enhanced hypertrophy and contractility in {Delta}ORF mice. Additionally, we employed single-nucleus transposase-accessible chromatin (snATAC)-seq to investigate changes in chromatin accessibility. Our results indicated increased accessibility within specific transcription-regulatory elements linked to elevated gene transcription. These putative cis-regulatory elements (pCREs) are significantly enriched in MEF2 (myocyte enhancer factor 2) binding motifs. In vitro luciferase reporter assays further supported the regulatory potential of three of these pCREs, highlighting their role in the transcriptional enhancement of three GATA4 target genes bound by MEF2 and GATA4. These findings illuminate the role of uORF in negatively regulating GATA4 protein expression and cardiomyocyte hypertrophy at the organismal level and provide a novel therapeutic target for cardiac pathogenesis.

biochemistry↗