Biology subjects

Yang, R.

Publications and source records attributed to Yang, R..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Epidermal stem cell-derived extracellular vesicles induce fibroblasts mesenchymal-epidermal transition to alleviate hypertrophic scar via the miR-200s/ZEBs axis

Hypertrophic scar (HS) is a prevalent yet unresolved wound healing complication characterized by persistent hyperactive and proliferative fibroblasts, leading to excessive extracellular matrix (ECM) synthesis and collagen contraction. Our previous studies have identified epidermal stem cells (ESCs) as critical for wound healing and HS remodeling, with its extracellular vesicles (EVs) playing a vital role. However, the specific mechanisms remain unclear. In this study, we first discovered that ESC-EVs could effectively induce the mesenchymal-epidermal transition (MET) of HS fibroblasts (HSFs) and inhibit their biological activity. Furthermore, by next-generation sequencing and multiplexed CRISPR/Cas9 system, we elucidated that this therapeutic effect is mediated by the miR-200 family (miR-200s) encapsulated in ESC-EVs, which targeted and inhibited ZEB1 and ZEB2 in HSFs. This vital role and mechanism have been thoroughly validated in both in vitro cell experiments and in vivo rat tail HS (RHS) models. These findings not only shed light on a previously unidentified mechanism of ESC-EVs for HS, but also provide potential novel targets and strategies for its precise treatment.

cell biology↗

bioRxiv2025

Towards a Generative Paradigm for Large-scale Microbiome Analysis by Generative Language Model

Microbiome analysis has traditionally relied on taxonomic abundance tables, which, while effective, often constrain the exploration of deeper contextual relationships. In this study, we present MGM 2.0, a novel framework that applies advanced natural language processing (NLP) techniques to microbiome research. By reimagining microbiome samples as sentences and microbial species as words, MGM 2.0 enabled the extraction of nuanced patterns and relationships. The model demonstrated robust predictive performance in identifying exogenous species colonization (AUROC = 0.86). Additionally, through prompt-guided microbiome data generation, MGM 2.0 produced realistic microbial profiles conditioned on disease labels. The framework further revolutionized donor selection in fecal microbiota transplantation (FMT) by framing it as a sequence-to-sequence prediction task, enabling the prediction of post-transplantation community compositions and the identification of super donors for personalized treatments (average increase in C2R = 0.52). This innovative integration of NLP and microbiome science provides a versatile toolkit for predictive modeling, data generation, and personalized medicine. HighlightsO_LIIntroduced MGM 2.0, a generative language model utilizing sentence-like representation for microbiome analysis and generation. C_LIO_LIDemonstrated that sentence-like representation preserves sample distinctions, enabling accurate microbial sample classification tasks, such as colonization prediction. C_LIO_LIGenerated realistic, disease-specific microbiome profiles using a prompt-guided approach, validated by a novel "Microbiome Turing Test." C_LIO_LIApplied MGM 2.0 to fecal microbiota transplantation (FMT) donor selection, accurately predicting post-transplant community compositions and identifying potential "super donors" for personalized treatment strategies. C_LI

bioinformatics↗

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↗

bioRxiv2025

Microbial general model: Leveraging large language model for contextualized microbiome analysis

Microbial communities significantly impact medicine, biotechnology, and agriculture. Advanced sequencing technologies have generated extensive microbiome data, enabling the discovery of substantial evolutionary and ecological patterns. However, traditional supervised learning methods struggle to capture universal patterns in microbial community data, largely due to the large data heterogeneity and profound batch effects among samples, rendering it difficult to classify samples as well as detect biomarkers from millions of samples, not to say the intricate but important dynamic patterns from a variety of contextualized sceneries. In this study, we introduce the Microbial General Model (MGM), the first microbiome community foundation model pre-trained on a dataset of 263,302 microbiome samples using language modeling techniques. MGM demonstrated significant improvements in microbial community classification compared to traditional machine learning methods. Additionally, MGM has enabled contextualized classification, effectively overcomes cross-regional limitations, showing enhanced performance on intercontinental datasets through transfer learning. Furthermore, fine-tuning MGM on a longitudinal infant dataset revealed distinct keystone genera during development, with Bacteroides and Bifidobacterium exhibiting higher attention weights in vaginal deliveries, and Haemophilus in cesarean deliveries. Finally, through in silico modeling, the model also uncovered novel microbial dynamic patterns in a Crohns disease cohort following antibiotic treatment. In conclusion, by leveraging self-attention and autoregressive pre-training, MGM serves as a versatile model for various downstream microbiome tasks and holds significant potential for achieving contextualized aims. Key pointsO_LIThe Microbial General Model (MGM) is a foundation model with millions of parameters pre-trained on sub-million microbial community data. C_LIO_LIMGM outperforms traditional methods in various microbiome classification and prediction tasks, such as microbial community classification. C_LIO_LIMGM effectively captures the spatial and temporal dynamics of microbial communities. C_LIO_LIMGM could detect the effects of perturbation on microbial community through in silico experiments. C_LI

bioinformatics↗

bioRxiv2024

HiC2Self: self-supervised denoising for bulk and single-cell Hi-C contact maps

AO_SCPLOWBSTRACTC_SCPLOWHi-C is a chromosome conformation capture assay used to study 3D genome organization. The recent development of single-cell Hi-C technologies has further enabled the examination of 3D chromatin organization in individual cells, although these approaches often suffer from low-coverage libraries and data sparsity. Here we introduce HiC2Self, a self-supervised framework for denoising Hi-C contact maps that requires only low-coverage data as input. HiC2Self not only reconstructs key structures (such as TADs and significant loops) from bulk libraries, but its self-supervised training framework also allows it to easily reconstruct cell-type-specific Hi-C structures without the generalization challenges faced by supervised models. HiC2Self can also accurately reconstruct significant loops from Micro-C data at 1 kb resolution. Moreover, when applied to single-nucleus methyl-3C data, HiC2Self successfully reconstructs local TAD structures around specific genes at 10 kb resolution with as few as 50 cells. Finally, HiC2Self enables the examination of single-cell structures at 50 kb resolution in individual cells of the same cell type. HiC2Self thus provides a general tool for denoising bulk, pseudo-bulk, and single-cell 3D contact maps to enable downstream analyses.

genomics↗

bioRxiv2024

Blackbird: structural variant detection using synthetic and low-coverage long-reads

MotivationRecent benchmarks of structural variant (SV) detection tools revealed that the majority of human genome structural variations (SVs), especially the medium-range (50-10,000 bp) SVs cannot be re-solved with short-read sequencing, but long-read SV callers achieve great results on the same datasets. While improvements have been made, high-coverage long-read sequencing is associated with higher costs and input DNA requirements. To decrease the cost one can lower the sequence coverage, but the current long-read SV callers perform poorly with coverage below 10x. Synthetic long-read (SLR) technologies hold great potential for structural variant (SV) detection, although utilizing their long-range information for events smaller than 50 kbp has been challenging. ResultsIn this work, we propose a hybrid novel integrated alignment- and local-assembly-based algorithm, Blackbird, that uses SLR together with low-coverage long reads to improve SV detection and assembly. Without the need for a computationally expensive whole genome assembly, Blackbird uses a sliding window approach and barcode information encoded in SLR to accurately assemble small segments and use long reads for an improved gap closing and contig assembly. We evaluated Blackbird on simulated and real human genome datasets. Using the HG002 GIAB benchmark set, we demonstrated that in hybrid mode, Blackbird demonstrated results comparable to state-of-the-art long-read tools, while using less long-read coverage. Blackbird requires only 5x coverage to achieve F1 scores (0.835 and 0.808 for deletions and insertions) similar to PBSV (0.856 and 0.812) and Sniffles2 (0.839 and 0.804) using 10x Pacbio Hi-Fi long-read coverage.

bioinformatics↗

bioRxiv2024

MARVEL: Microenvironment Annotation by Supervised Graph Contrastive Learning

Recent advancements in in situ molecular profiling technologies, including spatial proteomics and transcriptomics, have enabled detailed characterization of the microenvironment at cellular and subcellular levels. While these techniques provide rich information about individual cells spatial coordinates and expression profiles, extracting biologically meaningful spatial structures from the data remains a significant challenge. Current methodologies often rely on unsupervised clustering followed by cell type annotation based on differentially expressed genes within each cluster and most of the time will require other information as the reference (e.g., HE-stained images). This is labor-intensive and demands extensive domain knowledge. To address these challenges, we propose a supervised graph contrastive learning framework, MARVEL. MARVEL is a supervised graph contrastive learning method that can effectively embed local microenvironments represented by cell neighbor graphs into a continuous representation space, facilitating various downstream microenvironment annotation scenarios. By leveraging partially annotated examples as strong positives, our approach mitigates the common issues of false positives encountered in conventional graph contrastive learning. Using real-world annotated data, we demonstrate that MARVEL outperforms existing methods in three key microenvironment-related tasks: transductive microenvironment annotation, inductive microenvironment querying, and the identification of novel microenvironments across different slices.

bioinformatics↗

bioRxiv2024

Structural basis of pseudoGTPase-mediated protein-protein interactions

GTPases regulate various cellular processes through conformational changes triggered by GTP or GDP binding. Recently, pseudoGTPases, the catalytically inactive counterparts of GTPases, have been identified across species from bacteria to human, although their functions and mechanisms remain unexplored. In this study, we demonstrate that the N-terminal region of the assembly chaperone AAGAB is a type i pseudoGTPase using biochemistry and X-ray crystallography. Furthermore, we discovered that the AAGAB pseudoGTPase domain (psGD) interacts with the {sigma} subunits of AP1 and AP2 adaptor complexes, heterotetrameric complexes involved in clathrin-mediated membrane trafficking. AAGAB psGD engages the {sigma} subunits via a unique interface distinct from the conventional GTPase interacting regions. Further biochemical and cell-based assays confirmed the crucial role of the newly identified interface in binding and membrane trafficking. Collectively, our results establish AAGAB pseudoGTPase domain as a critical protein-protein interaction module. These findings offer new insight into the structural basis and molecular mechanisms of pseudoGTPases. HighlightsO_LIAAGAB N-terminal region is a pseudoGTPase C_LIO_LIAAGAB pseudoGTPase domain interacts with and stabilizes AP1 and AP2 {sigma} subunits C_LIO_LIThe crystal structure of AAGAB psGD:AP1{sigma}3 is the first reported psGD complex structure, revealing a unique interface independent of guanine nucleotide regulation C_LIO_LIThe psGD:AP1{sigma}3 structure offers mechanistic insights into {sigma} subunits stabilization and protection through adaptor complex assembly C_LI

biochemistry↗

bioRxiv2024

A Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing

Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) can significantly distort transcriptome analyses, yet their detection and removal remain challenging due to limitations in existing basecalling models. We present Deep-Chopper, a genomic language model that precisely identifies and removes adapter sequences from base-called dRNA-seq long reads at single-base resolution, operating independently of raw signal or alignment information to effectively eliminate chimeric read artifacts. By removing these artifacts, DeepChopper substantially improves the accuracy of critical downstream analyses, such as transcript annotation and gene fusion detection, thereby enhancing the reliability and utility of nanopore dRNA-seq for transcriptomics research.

genomics↗

bioRxiv2024

All-trans retinoic acid-mediated ADAR1 degradation synergizes with PD-1 blockade to suppress pancreatic cancer

As a double-stranded RNA-editing enzyme and an interferon-stimulated gene, double-stranded RNA-specific adenosine deaminase (ADAR1) suppresses interferon signaling and contributes to immunotherapy resistance. Suppression of ADAR1 overcomes immunotherapy resistance in preclinical models, but has not yet been translated to clinical settings. By conducting a screening of a subset of the FDA-approved drugs, we found that all-trans retinoic acid (ATRA, also known as tretinoin) caused ADAR1 protein degradation through ubiquitin-proteasome pathways and concomitantly increased PD-L1 expression in pancreatic and breast cancers. In addition, the combination of ATRA and PD-1 blockade reprogrammed the tumor microenvironment and unleashed antitumor immunity and thereby impeded tumor growth in pancreatic cancer mouse models. In a pilot clinical trial, a higher dose of ATRA plus the anti-PD-1 antibody nivolumab prolonged median overall survival in patients with chemotherapy-resistant pancreatic cancer compared to a lower dose of the same regimen. In this study, ATRA was the first drug to be found to cause ADAR1 degradation. We propose translation of a promising 2-pronged antitumor strategy using ATRA and nivolumab to convert immunologically "cold" into "hot" tumors susceptible to immune checkpoint blockade.

cancer biology↗

bioRxiv2024

Data-Driven Image Analysis to Determine Antibody-Induced Dissociation of Cell-Cell Adhesion and Antibody Pathogenicity in Pemphigus Vulgaris

Pemphigus vulgaris (PV) is a blistering autoimmune disease that affects the skin and mucous membranes. The precise mechanisms by which PV antibodies induce a complete loss of cohesion of keratinocytes are not fully understood. But it is accepted that the process starts with antibody binding to desmosomal targets which leads to its disassembly and subsequent structural changes to cell-cell adhesions. In vitro immunofluorescence imaging of desmosome molecules has been used to characterize this initial phase, often qualitatively. However, there remains an untapped potential of image analysis in providing us more in-depth knowledge regarding ultrastructural changes after antibody binding. Currently, there is no such effort to establish a quantitative framework from immunofluorescence images in PV pathology. We take on this effort here in a comprehensive study to examine the effects of antibodies on key adhesion molecules and the cytoskeletal network, aiming to establish a correlation of ultrastructural changes in cell-cell adhesion with antibody pathogenicity. Specifically, we introduced a data-driven approach to quantitatively evaluate perturbations in adhesion molecules, including desmoglein 3, E-cadherin, as well as the cytoskeleton, following antibody treatment. We identify distinct immunofluorescence imaging signatures that mark the impact of antibody binding on the remodeling of the adhesion molecules and introduce a pathogenicity score to compare the relative effects of different antibodies. From this analysis, we showed that the biophysical response of keratinocytes to distinct PV associated antibodies is highly specific, allowing for accurate prediction of their pathogenicity. For instance, the high pathogenicity scores of the PVIgG and AK23 antibodies show strong agreement with their reported PV pathology. Our data-driven approach offers a more detailed framework for the action of autoantibodies in pemphigus and has the potential to pave the way for the development of effective novel diagnostic methods and therapeutic strategies. SIGNIFICANCEPemphigus vulgaris (PV) presents a critical unmet medical challenge due to its autoimmune-induced disruption of skin cell adhesion. Our study presents a data-driven approach to quantitatively analyze changes in adhesion molecules and the cytoskeleton upon exposure to various PV antibodies. By introducing a pathogenicity score, we pinpoint the specific impacts of different antibodies on various proteins, build association among these antibodies, and reveal the contribution of previously overlooked non-desmosomal antibodies, broadening the understanding of PV pathology. Although centered on PV, our method offers a versatile framework applicable for evaluating the effects of other antibodies and drugs, paving the way for new diagnostic tools for personalized medicine.

biophysics↗

bioRxiv2024

Structural insights into inhibitor mechanisms on immature HIV-1 Gag lattice revealed by high-resolution in situ single-particle cryo-EM

HIV-1 inhibitors, such as Bevirimat (BVM) and Lenacapavir (LEN), block the production and maturation of infectious virions. However, their mechanisms remain unclear due to the absence of high-resolution structures for BVM complexes and LENs structural data being limited to the mature capsid. Utilizing perforated virus-like particles (VLPs) produced from mammalian cells, we developed an approach to determine in situ cryo-electron microscopy (cryo-EM) structures of HIV-1 with inhibitors. This allowed for the first structural determination of the native immature HIV-1 particle with BVM and LEN bound inside the VLPs at high resolutions. Our findings offer a more accurate model of BVM engaging the Gag lattice and, importantly, demonstrate that LEN not only binds the mature capsid but also targets the immature lattice in a distinct manner. The binding of LEN induces a conformational change in the capsid protein (CA) region and alters the architecture of the Gag lattice, which may affect the maturation process. These insights expand our understanding of the inhibitory mechanisms of BVM and LEN on HIV-1 and provide valuable clues for the design of future inhibitors.

biochemistry↗

bioRxiv2024

Structural and molecular basis of FAN1 defects in promoting Huntington's disease

FAN1 is a DNA dependent nuclease whose proper function is essential for maintaining human health. For example, a genetic variant in FAN1, Arg507 to His hastens onset of Huntingtons disease, a repeat expansion disorder for which there is no cure. How the Arg507His mutation affects FAN1 structure and enzymatic function is unknown. Using cryo-EM and biochemistry, we have discovered that FAN1 arginine 507 is critical for its interaction with PCNA, and mutation of Arg507 to His attenuates assembly of the FAN1-PCNA on a disease-relevant extrahelical DNA extrusions formed within DNA repeats. This mutation concomitantly abolishes PCNA-FAN1-dependent cleavage of such extrusions, underscoring the importance of PCNA to the genome stabilizing function of FAN1. These results unravel the molecular basis for a specific mutation in FAN1 that dramatically hastens the onset of Huntingtons disease.

biochemistry↗

bioRxiv2024

Death in the taste bud: Morphological features of dying taste cells and engulfment by Type I cells

Taste buds comprise 50-100 epithelial derived cells, including glial-like cells (Type I) and two types of receptor cells (Types II and III). All of these taste cells are renewed throughout the life of an organism from a pool of uncommitted basal cells. Immature cells enter the bud at its base, maturing into one of the three mature cell types. How taste cells die and/or exit the bud, as well as the role of the glial-like cells in this process, remains unclear. Here we present morphological data obtained through Serial Blockface Scanning Electron Microscopy of murine circumvallate taste buds, revealing taste cells at the end of their life. Cells we identify as dying share morpholowgical features typical of apoptosis: swollen endoplasmic reticulum, large lysosomes, degrading organelles, distended outer nuclear membranes, heterochromatin reorganization, cell shrinkage, and cell and/or nuclear fragmentation. Most early stage dying cells have Type II cell morphologies, while a few display Type III cell features. Many dying cells maintain contacts with nerve fibers, but those postsynaptic fibers often appear to be detached from the main trunk of an afferent nerve. Dying cells, like mature Type II and III taste cells, are surrounded by glial-like Type I cells. In many instances Type I cells appear to be engulfing their dying neighbors, suggesting for them a novel, phagocytic role. Surprisingly, virtually no Type I cells display features of apoptosis, although they reportedly have the shortest residence time in taste buds. The ultimate fate of Type I cells therefore remains unknown. Table of Contents EntryO_ST_ABSMain PointsC_ST_ABSO_LIDying taste cells display morphologies consistent with apoptosis. C_LIO_LIGlial-like Type I cells engulf dying neighbors, perhaps acting as "undertakers." C_LIO_LIWe find no dying Type I cells--all early dying cells are taste transducing Type II or III cells. C_LI

neuroscience↗

bioRxiv2024

Cell-type-specific splicing of transcription regulators and Ptbp1 by Rbfox1/2/3 in the developing neocortex

How master splicing regulators crosstalk with each other and to what extent transcription regulators are differentially spliced remain unclear in the developing brain. Here, cell-type-specific RNA-Seq of the developing neocortex uncover that transcription regulators are enriched for differential splicing, altering protein isoforms or inducing nonsense-mediated mRNA decay. Transient expression of Rbfox proteins in radial glia progenitors induces neuronal splicing events preferentially in transcription regulators such as Meis2 and Tead1. Surprisingly, Rbfox proteins promote the inclusion of a mammal-specific alternative exon and a previously undescribed poison exon in Ptbp1. Simultaneous ablation of Rbfox1/2/3 in the neocortex downregulates neuronal isoforms and disrupts radial neuronal migration. Furthermore, the progenitor isoform of Meis2 promotes Tgfb3 transcription, while the Meis2 neuron isoform promotes neuronal differentiation. These observations indicate that transcription regulators are differentially spliced between cell types in the developing neocortex.

developmental biology↗

bioRxiv2024

Dynamins combine mechano-constriction and membrane remodeling to enable two-step mitochondrial fission via a snap-through instability.

Mitochondrial fission is controlled by dynamin proteins, the dysregulation of which is correlated with diverse diseases. Fission dynamins are GTP hydrolysis-driven mechanoenzymes that self-oligomerize into helical structures that constrict membrane to achieve fission, but details are not well understood. However, dynamins can also remodel membranes by inducing negative Gaussian curvature, the type of curvature required for completion of fission. Here, we examine how these drastically different mechanisms synergistically exert their influences on a membrane, via a mechanical model calibrated with small-angle X-ray scattering structural data. We find that free dynamin can trigger a "snap-through instability" that enforces a shape transition from an oligomer-confined cylindrical membrane to a drastically narrower catenoid-shaped neck within the spontaneous hemi-fission regime, in a manner that depends critically on the length of the confined tube. These results indicate how the combination of dynamin assembly, and paradoxically disassembly, can lead to diverse pathways to scission. TeaserDynamin mechano-constriction by assembly and curvature-driven instability by free monomers synergistically drive mitochondrial fission.

biophysics↗

bioRxiv2024

Two TAL effectors of Xanthomonas citri pv. malvacearum target GhSWEET15 as the susceptibility genes for bacterial blight of cotton

Bacterial Blight of Cotton (BBC) caused by Xanthomonas citri pv. malvacearum (Xcm) is an important and destructive disease affecting cotton plants. Transcription activator-like effectors (TALEs) released by the pathogen regulate cotton resistance to the susceptibility. In this study, we sequenced the whole genome of Xcm Xss-V2-18 and identified eight tal genes; seven on the plasmids and one on the chromosome. Deletion and complementation experiments of Xss-V2-18 tal genes demonstrated that Tal1b is required for full virulence on cotton. Transcriptome profiling coupled with TALE-binding element prediction revealed that Tal1b targets GhSWEET15A04/D04 and GhSWEET15D02 simultaneously. Expression analysis confirmed the independent inducibility of GhSWEET15A04/D04 and GhSWEET15D02 by Tal1b, whereas GhSWEET15A04/D04 is additionally targeted by Tal1. Moreover, GUS ({beta}-glucuronidase) and Xa10-mediated HR (hypersensitive response) assays indicated that the EBEs are required for the direct and specific activation of the candidate targets by Tal1 and Ta1b. These findings may advance our understanding of the dynamics between TALEs and EBEs, and decipher a simple and effective DNA-binding mechanism that could lead to the development of more efficient methods for gene editing and transgenic research.

pathology↗

bioRxiv2024

Sustained Strain Applied at High Rates Drives Dynamic Tensioning in Epithelial Cells

Epithelial cells experience long lasting loads of different magnitudes and rates. How they adapt to these loads strongly impacts tissue health. Yet, much remains unknown about the evolution of cellular stress in response to sustained strain. Here, by subjecting cell pairs to sustained strain, we report a bimodal stress response, where in addition to the typically observed stress relaxation, a subset of cells exhibits a dynamic tensioning process with significant elevation in stress within 100s, resembling active pulling-back in muscle fibers. Strikingly, the fraction of cells exhibiting tensioning increases with increasing strain rate. The tensioning response is accompanied by actin remodeling, and perturbation to actin abrogates it, supporting cell contractilitys role in the response. Collectively, our data show that epithelial cells adjust their tensional states over short timescales in a strain-rate dependent manner to adapt to sustained strains, demonstrating that the active pulling-back behavior could be a common protective mechanism against environmental stress.

biophysics↗