Biology subjects

Yu, J.

Publications and source records attributed to Yu, J..

At least 253 records · Page 14Linked to original sources

bioRxiv2024

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brains mechanisms of language processing within the context of the Chinese natural language.

neuroscience↗

bioRxiv2024

Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies

Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and cell types provide multiple perspectives and a more complete understanding of tissue architecture. Here, we introduce Proust, a scalable tool to predict discrete domains using spatial multi-omics data by combining the low-dimensional representation of biological profiles based on graph-based contrastive self-supervised learning. Our scalable method integrates multiple data modalities, such as RNA, protein, and H&E images, and predicts spatial domains within tissue samples. Through the integration of multiple modalities, Proust consistently demonstrates enhanced accuracy in detecting spatial domains, as evidenced across various benchmark datasets and technological platforms.

bioinformatics↗

bioRxiv2024

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

Structure-based drug design (SBDD) relies on accurate knowledge of protein structure and ligand-binding conformations. However, most of the static conformations obtained by advanced methods such as structural biology and de novo protein folding algorithms often dont meet the needs for drug design. We introduce PackDock, a flexible docking method that combines "conformation selection" and "induced fit" mechanisms in a two-stage docking pipeline. The core module of this method is PackPocket, which uses a diffusion model to explore the side-chain conformation space in ligand binding pockets, both with or without a ligand. We evaluate our method using several tests that reflect real-world application scenarios. (1) Side-chain packing and Re-docking experiments validate the ability of PackDock to predict accurate side-chain conformations and ligand conformations. (2) Cross-docking experiments with apo and non-homologous ligand-induced holo structures align with real docking scenarios, demonstrating PackDocks practical value. (3) Docking experiments with hypothetical models show that PackPocket can potentially conduct SBDD starting from protein sequence information only. Additionally, we found that PackDock can identify key amino acid conformation changes, which may provide insights for lead compound optimization. We demonstrate PackDock can accurately predict the complex conformations in various application scenarios, by combining the conformation selection theory and the induced fit theory, and by using the ability of PackPocket to accurately predict the side chain conformations in the pocket region. We believe this method can improve the usability of existing structures, providing a new perspective for the SBDD community.

bioinformatics↗

bioRxiv2024

From Planning Stage To FAIR Data: A Practical Metadatasheet For Biomedical Scientists

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

bioinformatics↗

bioRxiv2024

The EGF/EGFR-YAP1/TEAD2 axis-mediated BRAF inhibitor vemurafenib resistance upregulates STIM1 in melanoma

STIM1 is the endoplasmic reticulum (ER) Ca2+ sensor for store-operated entry (SOCE) and closely correlated to carcinogenesis and tumor progression. Previously we found that STIM1 is upregulated in melanoma cells resistant to the BRAF inhibitor vemurafenib, but the regulation mechanism is unknown. Here, we show that vemurafenib resistance upregulates STIM1 through an EGF/EGFR-YAP1/TEAD2 axis. Vemurafenib resistance can lead to the increase of EGF and EGFR levels to activate the EGFR signaling pathway. Reactivated EGFR signal promotes YAP1 nuclear localization to increase the expression of STIM1. Our finding not only demonstrates the mechanism by which vemurafenib resistance promotes STIM1 expression, but also provides combined targeting EGF/EGFR-YAP1/TEAD2-STIM1 to improve the therapeutic efficiency of BRAF inhibitor in melanoma patients.

cancer biology↗

bioRxiv2024

Integrative genomic analysis identifies unique immune environments associated with immunotherapy response in diffuse large B cell lymphoma

Most diffuse large B-cell lymphoma (DLBCL) patients treated with bispecific antibodies (BsAb) or chimeric antigen receptor (CAR) T cells fail to achieve durable treatment responses, underscoring the need for a deeper understanding of mechanisms that regulate the immune environment and response to treatment. Here, an integrative, multi-omic approach was employed to characterize DLBCL immune environments, which effectively segregated DLBCLs into four quadrants - termed DLBCL-immune quadrants (IQ) - defined by cell-of-origin and immune-related gene set expression scores. Recurrent genomic alterations were enriched in each IQ, suggesting that lymphoma cell-intrinsic alterations contribute to orchestrating unique DLBCL immune environments. In relapsed/refractory DLBCL patients, DLBCL-IQ assignment correlated significantly with clinical benefit with the CD20 x CD3 BsAb, mosunetuzumab, but not with CD19-directed CAR T cells. DLBCL-IQ provides a new framework to conceptualize the DLBCL immune landscape and uncovers the differential impact of the endogenous immune environment on outcomes to BsAb and CAR T cell treatment.

cancer biology↗

bioRxiv2024

Targeted LNPs deliver mRNA encoding IL-15 superagonists to balance efficacy and toxicity in cancer therapy

Interleukin-15 (IL-15) emerges as a promising immunotherapeutic candidate in oncology because of its pivotal role in modulating both innate and adaptive immunity. However, the therapeutic utility remains concern due to the unexpected toxicity. We propose here that the mRNA lipid nanoparticle (mRNA-LNP) system can balance the issue through targeted delivery to increase IL-15 concentration in the tumor area and reduce leakage into the circulation. Utilizing the Structure-driven TARgeting (STAR) platform, we acquired intellectual property LNP vectors for effective and selective mRNA delivery to local (LNPLocal) and to pulmonary (LNPLung). Then the promising IL-15 superagonists mRNAs were obtained through structural optimization and sequence screening, showing better activity compared with benchmarker N-803. Subsequently, the anti-tumor efficacy of IL-15 superagonists mRNAs were evaluated by intratumoural (i.t.) injection and intravenous (i.v.) injection via LNPLocal and LNPLung, respectively. As a result, such superagonists exhibited better anti-tumor activity, less systematic exposure, and less cytokine related risks than N-803. We finally verified the selective delivery and well tolerability of LNPLung in non-human primates (NHPs), confirming the potential for clinical application. This finding may open up new possibilities for the treatment of lung cancers and lung metastasis cancers.

bioengineering↗

bioRxiv2024

RBM7 deficiency promotes breast cancer metastasis by coordinating MFGE8 splicing switch and NF-kB pathway

Aberrant alternative splicing is well-known to be closely associated with tumorigenesis of various cancers. However, the intricate mechanisms underlying breast cancer metastasis driven by deregulated splicing events remain largely unexplored. Here, we unveiled that RBM7 is decreased in lymph node and distant organ metastases of breast cancer as compared to primary lesions and low expression of RBM7 is correlated with the reduced disease-free survival of breast cancer patients. Breast cancer cells with RBM7 depletion exhibited an increased potential for lung metastasis compared to scramble control cells. The absence of RBM7 stimulated breast cancer cell migration, invasion, and angiogenesis. Mechanistically, RBM7 controlled the splicing switch of MFGE8, favoring the production of the predominant isoform of MFGE8, MFGE8-L. This resulted in the attenuation of STAT1 phosphorylation and alterations in cell adhesion molecules. MFGE8-L exerted an inhibitory effect on the migratory and invasive capability of breast cancer cells, while the truncated isoform MFGE8-S, which lack the second F5/8 type C domain had the opposite effect. In addition, RBM7 negatively regulates the NF-{kappa}B cascade and an NF-{kappa}B inhibitor could obstruct the increase in HUVEC tube formation caused by RBM7 silencing. Clinically, we noticed a positive correlation between RBM7 expression and MFGE8 exon7 inclusion in breast cancer tissues, providing new mechanistic insights for molecular-targeted therapy in combating breast cancer.

cancer biology↗

bioRxiv2023

SurfDock is a Surface-Informed Diffusion Generative Model for Reliable and Accurate Protein-ligand Complex Prediction

In the field of structure-based drug design, accurately predicting the binding conformation of ligands to proteins is a long-standing objective. Despite recent advances in deep learning yielding various methods for predicting protein-ligand complex structures, these AI-driven approaches frequently fall short of traditional docking methods in practice and often yield structures that lack physical and chemical plausibility. To overcome these limitations, we present SurfDock, an advanced geometric diffusion network, distinguished by its ability to integrate multiple protein representations including protein sequence, three-dimensional structural graphs, and surface-level details into its equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, enabling precise optimization of molecular translations, rotations, and torsions for reliable binding poses generation. Complemented by a mixture density network for scoring using the same comprehensive representation, SurfDock achieves significantly improved docking success rates over all existing methods, excelling in both accuracy and adherence to physical constraints. Equipped with post-docking energy minimization as an optional feature, the plausibility of generated poses is further enhanced. Importantly, SurfDock demonstrates excellent generalizability to unseen proteins and extensibility to virtual screening tasks with state-of-the-art performance. We consider it a transformative contribution that could serve as an invaluable asset in structure-based drug design.

bioinformatics↗

bioRxiv2023

Early-stage idiopathic pulmonary fibrosis is characterized by bronchoalveolar accumulation of SPP1+ macrophages

Patients affected by idiopathic pulmonary fibrosis (IPF), a progressive chronic and eventually fatal lung disease with unknown cause, suffer from delayed diagnosis and limited personalized treatment options due to the lack of predictive and staging relevant disease markers. Prior studies focused on the cellular and transcriptomic changes found in lung tissue during terminal IPF-associated lung fibrosis. In clinical routine, bronchoscopy is applied for diagnosis and staging of suspected IPF affected individuals, thus providing us with a clinically applicable window, to study the cellular and transcriptional changes within affected individuals at the time of diagnosis. Here we study a cohort consisting of 11 IPF affected individuals and 11 healthy controls. We investigate their single cell transcriptomic profile alongside the surface phenotype of alveolar-resident immune cells. Single cell transcriptional analysis reveals accumulation of SPP1+ macrophages (SPP1+M{varphi}) within the alveolar space during early stage diagnosed IPF, in the absence of a decline in lung function. SPP1+M{varphi} were characterized by high expression of lipid storage and handling genes, accumulation of intracellular cholesterol and expression of proinflammatory cytokine expression. In silico developmental trajectory analysis revealed that SPP1+M{varphi} are likely derived from classical monocytes. Finally, to confirm the clinical diagnostic value of SPP1+ M{varphi}, we developed a flow cytometry panel to rapidly identify and test for the presence of SPP1+ M{varphi} in bronchoalveolar lavage samples. The new panel has confirmed the increased frequency of SPP1+ M{varphi} in early-IPF in an independent validation cohort. Taken together, we show that SPP1+ M{varphi} are associated with early IPF pathogenesis in the absence of a decline in lung function, thus providing a clinically valuable markers for diagnosis of early IPF in clinical practice. HighlightsO_LIIPF patients can be stratified into early and advanced disease groups based on clinical features. C_LIO_LISingle cell transcriptomic and high dimensional flow cytometry enabled mapping of the myeloid compartment across the continuum of idiopathic pulmonary fibrosis associated disease states. C_LIO_LISPP1+M{varphi} accumulated during early clinical stage of IPF. C_LIO_LIDevelopment of a clinically applicable SPP1M{varphi} identification panel. C_LI

immunology↗

bioRxiv2023

Highly Multiplexed Fluorescence Microscopy with Spectrally Tunable Semiconducting Polymer Dots

Current studies of biological tissues require visualizing diverse cell types and molecular interactions, creating a growing need for versatile techniques to simultaneously probe numerous targets. Traditional multiplexed imaging is limited to around five targets at once. Emerging methods utilizing sequential rounds of staining, imaging, and signal removal can probe tens of targets but require specialized hardware, time-consuming workflows, and face some challenges with sample distortion and artifacts. Here we present a new method for highly-multiplexed fluorescence microscopy using semiconducting polymer dots (Pdots) in a single round of staining and imaging. Pdots are small, bright, and photostable fluorescent probes with a wide range of tunable Stokes shifts (20-450 nm). Multiple series of Pdots with varying excitation wavelengths allow for fast (<1 minute) and single-round imaging of up to 21 targets in brain and kidney. This method is based on a simple immunofluorescence workflow, efficient use of spectral space, standard hardware, and straightforward analysis, making it widely applicable for bioimaging laboratories.

biochemistry↗

bioRxiv2023

Structural Basis of μ-Opioid Receptor-Targeting by a Nanobody Antagonist

The -opioid receptor (OR), a prototypical member of the G protein-coupled receptor (GPCR) family, is the molecular target of opioid analgesics such as morphine and fentanyl. Due to the limitations and severe side effects of currently available opioid drugs, there is considerable interest in developing novel modulators of OR function. Most GPCR ligands today are small molecules, however biologics, including antibodies and nanobodies, are emerging as alternative therapeutics with clear advantages such as affinity and target selectivity. Here, we describe the nanobody NbE, which selectively binds to the OR and acts as an antagonist. We functionally characterize NbE as an extracellular and genetically encoded {micro}OR ligand and uncover the molecular basis for {micro}OR antagonism by solving the cryo-EM structure of the NbE-{micro}OR complex. NbE displays a unique ligand binding mode and achieves {micro}OR selectivity by interactions with the orthosteric pocket and extracellular receptor loops. Based on a {beta}-hairpin loop formed by NbE that deeply inserts into the {micro}OR and centers most binding contacts, we design short peptide analogues that retain {micro}OR antagonism. The work illustrates the potential of nanobodies to uniquely engage with GPCRs and describes novel OR ligands that can serve as a basis for therapeutic developments.

molecular biology↗

bioRxiv2023

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

Precisely defining and mapping all cytosine positions and their clusters, known as CpG islands (CGIs), as well as their methylation status are pivotal for genome-wide epigenetic studies, especially when population-centric reference genomes are ready for timely application. Here we first align the two high-quality reference genomes, T2T-YAO and T2T-CHM13, from different ethnic backgrounds in a base-by-base fashion and compute their genome-wide density-defined and position-defined CGIs. Second, mapping some representative genome-wide methylation data from selected organs onto the two genomes, we find that there are about 4.7-5.8% sequence divergency of variable categories depending on quality cutoffs. Genes among the divergent sequences are mostly associated with neurological functions. Moreover, CGIs associated with the divergent sequences are significantly different with respect to CpG density and observed CpG/expected CpG (O/E) ratio between the two genomes. Finally, we find that the T2T-YAO genome not only has a greater CpG site coverage than that of the T2T-CHM13 genome when whole-genome bisulfite sequencing (WGBS) data from the European and American populations are mapped to each reference, but also show more hyper-methylated CpG sites as compared to the T2T-CHM13 genome. Our study suggests that future genome-wide epigenetic studies of the Chinese populations rely on both acquisition of high-quality methylation data and subsequent precision CGI mapping based on the Chinese T2T reference.

bioinformatics↗

bioRxiv2023

A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence Images

Due to the rapid increase and importance of multiplexed immunofluorescence (mIF) imaging data in spatial biology, there is a pressing need to develop efficient image-to-image search pipelines for both diagnostic and research purposes. While several image search methods have been introduced for conventional images and digital pathology, mIF images present three main challenges: (1) high dimension-ality, (2) domain-specificity, and (3) complex additional molecular information. To address this gap, we introduce the MIISS framework, a Multi-granularity mIF Image Similarity Search pipeline that employs self-supervised learning models to extract features from mIF image patches and an entropy-based aggregation method to enable similarity searches at higher, multi-granular levels. We then benchmarked various feature generation approaches to handle high dimensional images and tested them on various foundation models. We conducted evaluations using datasets from different tissues on both patch- and patient-level, which demonstrate the frame-works effectiveness and generalizability. Notably, we found that domain-specific models consistently outperformed other models, further showing their robustness and generalizability across different datasets. The MIISS framework offers an effective solution for navigating the growing landscape of mIF images, providing tangible clinical benefits and opening new avenues for pathology research.

bioinformatics↗

bioRxiv2023

A novel antagonist of the CCL5/CCR5 axis suppresses the tumor growth and metastasis of triple-negative breast cancer by CCR5-YAP1 regulation.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC) with a high mortality rate, and few effective therapeutic strategies are available. CCL5/CCR5 is an appealing immunotherapeutic target for TNBC. However, its signaling mechanism is poorly understood and its direct antagonists have not been reported. Here, we developed a high-throughput screening (HTS) assay for discovering its antagonists. Verteporfin was identified as a more selective and potent antagonist than the known CCR5 antagonist maraviroc. Without photodynamic therapy, verteporfin demonstrated significant inhibition on TNBC tumor growth through immune regulation, remarkable suppression of lung metastasis by cell-intrinsic mechanism, and a significant extension of overall survival in vivo. Mechanistically, CCR5 was found to be essential for expression of the key hippo effector YAP1. It promoted YAP1 transcription via HIF-1 and exerted further control over the migration of CD8+ T, NK, and MDSC immune cells through chemokines CXCL16 and CXCL8 which were identified from RNA-seq. Moreover, the CCR5-YAP1 axis played a vital role in promoting metastasis by modulating {beta}-catenin and core epithelial-mesenchymal transition transcription factors ZEB1 and ZEB2. It is noteworthy that the regulatory relationship between CCR5 and YAP1 was observed across various BC subtypes, TNBC patients, and showed potential relevance in fifteen additional cancer types. Overall, this study introduced an easy-to-use HTS assay that streamlines the discovery of CCL5/CCR5 axis antagonists. Verteporfin was identified as a specific molecular probe of this axis with great potentials as a therapeutic agent for treating sixteen malignant diseases characterized by heightened CCR5 and YAP1 levels.

cancer biology↗

bioRxiv2023

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a posteriori probability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an exemplar biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and automatically segmenting a cluster of cell nuclei into individual cell nuclei that increases the accuracy of classical watershed. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to extend classical watershed, particularly in the multiplexed imaging context. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue (GIT) dataset, which we anticipate will be useful for the broader research community. We demonstrate the efficacy of UNSEG on the GIT dataset, publicly available datasets, and on a range of practical scenarios. In these contexts, we also discuss the possibility of bias inherent in quantification of segmentation accuracy based on F1 score. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms.

bioinformatics↗

bioRxiv2023

The heparin-binding domain of VEGF165 directly binds to integrin αvβ3 and plays a critical role in signaling.

VEGF-A is a key cytokine in tumor angiogenesis and a major therapeutic target for cancer. VEGF165 is the predominant isoform and is the most potent angiogenesis stimulant. VEGFR2/KDR domains 2 and 3 (D2D3) bind to the N-terminal domain (NTD, residues 1-110) of VEGF165. Since removal of the heparin-binding domain (HBD, residues 111-165) markedly reduced the mitogenic activity of VEGF165, it has been proposed that the HBD plays a critical role in the mitogenicity of VEGF165. Integrin v{beta}3 has been shown to bind to VEGF165, but the role of integrin v{beta}3 in VEGF165 signaling are unclear. Here we describe that v{beta}3 specifically bound to the isolated HBD, but not to the NTD. We identified several critical amino acid residues in HBD for integrin binding (Arg-123, Arg-124, Lys-125, Lys-140, Arg-145, and Arg-149) by docking simulation and mutagenesis, and generated full-length VEGF165 that is defective in integrin binding by including mutations in the HBD. The full-length VEGF165 mutant defective in integrin binding (R123A/R124A/K125A/K140A/R145A/R149A) was defective in ERK1/2 phosphorylation, integrin {beta}3 phosphorylation, and KDR phosphorylation, although the mutation did not affect KDR binding to VEGF165. We propose a model in which VEGF165 induces KDR (through NTD)-VEGF165 (through HBD)-integrin v{beta}3 ternary complex formation on the cell surface and this process is critically involved in potent mitogenicity of VEGF165.

biochemistry↗

bioRxiv2023

APOE4 poses opposite effects of plasma LDL on white matter integrity in older adults

INTRODUCTIONAPOE4 is a strong genetic risk factor of Alzheimers disease and is associated with changes in metabolism. However, the interactive relationship between APOE4 and plasma metabolites on the brain remains largely unknown. MEHODSIn the UK Biobank, we investigated the moderation effects of APOE4 on the relationship between 249 plasma metabolites derived from nuclear magnetic resonance spectroscopy on whole-brain white matter integrity, measured by fractional anisotropy using diffusion magnetic resonance imaging. RESULTSThe increase in the concentration of metabolites, mainly LDL and VLDL, is associated with a decrease in white matter integrity (b= -0.12, CI= [-0.14, -0.10]) among older APOE4 carriers, whereas an increase (b= 0.05, CI= [0.04, 0.07]) among non-carriers, implying a significant moderation effect of APOE4 (b= -0.18, CI= [-0.20,-0.15]). DISCUSSIONThe results suggest that lipid metabolism functions differently in APOE4 carriers compared to non-carriers, which may inform the development of targeted interventions for APOE4 carriers to mitigate cognitive decline.

neuroscience↗