Biology subjects

Wei, Z.

Publications and source records attributed to Wei, Z..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Magnetically Labeled iPSC-Derived Extracellular Vesicles Enable MRI/MPI-Guided Regenerative Therapy for Myocardial Infarction

Stem cell-derived extracellular vesicles (EVs) offer a promising cell-free approach for cardiovascular regenerative medicine. In this study, we developed magnetically labeled induced pluripotent stem cell-derived EVs (magneto-iPSC-EVs) encapsulated with superparamagnetic iron oxide (SPIO) nanoparticles for image-guided regenerative treatment of myocardial infarction, in which EVs that can be detected by both magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). iPSC-EVs were isolated, characterized per MISEV2023 guidelines, and loaded with SuperSPIO20 nanoparticles using optimized electroporation conditions (300 V, 2 x 10 ms pulses), achieving a high loading efficiency of 1.77 ng Fe/106 EVs. In vitro results show that magneto-iPSC-EVs can be sensitively detected by MPI and MRI, with a detectability of approximately 107 EVs. In a mouse myocardial ischemia-reperfusion model, intramyocardially injected magneto-iPSC-EVs (2 x 109) were imaged non-invasively by in vivo MPI for 7 days and ex vivo MRI, with the presence of magneto-iPSC-EVs confirmed by Prussian blue staining. Therapeutically, both native and magneto-iPSC-EVs significantly improved cardiac function, with a 37.3% increase in left ventricular ejection fraction and 61.0% reduction in scar size. This study highlights the potential of magneto-iPSC-EVs as a cell-free approach for cardiovascular regenerative medicine, offering both non-invasive imaging capabilities and therapeutic benefits for myocardial repair.

bioengineering↗

bioRxiv2025

The extensive diversity of nairoviruses reveals the complexity and high risk of tick-borne diseases in northeastern China

The increasing burden of tick-borne virus infections is becoming a global public health concern. In the current study, we identified nine tick-borne nairoviral species in northeastern China through metagenomic sequencing, which are phylogenetic classified into four groups: I) Wetland virus (WELV), II;) Songling virus (SGLV), Jian nairovirus (JANV), Yanbian nairo tick virus 1 (YBNTV-1), Shanxi tick virus 2 (SXTV-2), III;) Yezo virus (YEZV), and IV;) Beiji nairovirus (BJNV), Yichun nairovirus (YCNV), and Hunchun nairovirus (HCNV). These nairoviruses are influenced by both the species of ticks and their geographical distribution. Apart from the previously isolated pathogenic WELV, SGLV, YEZV, and BJNV, we successfully isolated JANV and YBNTV-1, which can replicate in Vero and HEK-293T cells. Two routes of nairovirus spread into northeastern China were evaluated. Group I and II viruses, vectored by Haemaphysalis sp. ticks, likely originated in Central or East China and spread via migratory birds along the Chinese coastline. Group III; and IV viruses, vectored by Ixodes persulcatus ticks, are believed to have originated in Europe and spread via rodents across the Eurasian continent. Our study reveals the extensive diversity and wide distribution of tick-borne nairoviruses in northeastern China, highlights the potential pathogenic risk of the newly discovered nairoviruses, and clarifies their origins and potential migration routes. The results will be of crucial significance for the tick-borne diseases control and prevention in China.

microbiology↗

bioRxiv2025

Spatiotemporal changes in soil properties predict plant health at continental scale

Abiotic and biotic soil properties are strong predictors of plant yield globally1-5, but they become unreliable over large areas when plant health is threatened by pathogen6-8. Here we present a novel approach to predict plant health based on spatiotemporal changes in soil chemical and biological properties. We first demonstrate that plant health and soil properties consistently respond to environmental change (organic fertilization) regardless of the soil type or geographical origin. Second, we experimentally show that trackable shifts in soil properties reliably explain soil suppressiveness to the Ralstonia solanacearum bacterial pathogen and that a scalable spatiotemporal model predicts plant health with 84% accuracy across multiple climatic zones and cropping systems. Our results suggest that this tight coupling between soil properties and plant health could facilitate the development of agricultural practices aimed at sustainably improving crop yields while safeguarding crop health.

ecology↗

bioRxiv2024

A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design

Antimicrobial peptides (AMPs) play a crucial role in developing novel antiinfective drugs due to their broad-spectrum antimicrobial activity and lower likelihood of causing bacterial resistance. However, laboratory synthesis of AMPs is tedious and time-consuming. Existing computational methods have limited capability in optimizing multiple desired properties simultaneously. Here, we propose a Multi-Property Optimizing Generative Adversarial Network (MPOGAN), a feedback-loop framework that iteratively learns from data with multiple desired properties. This approach enables de novo design of AMPs with potent antimicrobial activity, reduced cytotoxicity, and diversity. Through extensive computational tests, MPOGAN exhibits superior performance in optimizing multiple desired properties of generated AMPs. Ten of the most promising candidates are chemically synthesized, with nine showing potent antimicrobial activity against three bacterial strains and low cytotoxicity against eukaryotic cells. MPOGAN offers a powerful computational approach for effective multi-property optimization of AMPs, thereby advancing the field of AI-aided drug discovery.

bioinformatics↗

bioRxiv2024

A Watershed Algorithm GUI for Personalized fMRI-guided rTMS Target

Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimers disease-related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95% CI: 0.0-12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.

neuroscience↗

bioRxiv2024

Substrate utilization and cross-feeding synergistically determine microbiome resistance to pathogen invasion

Understanding how microbiomes resist pathogen invasion remains a key challenge in natural ecosystems. Here, we combined genome-scale metabolic models with synthetic community experiments to unravel the mechanisms driving pathogen suppression. We developed curated genome-scale models for each strain, incorporating 48 common resource utilization profiles to fully capture their metabolic capacities. Trophic interactions inferred from models accurately predicted pathogen invasion outcomes, achieving an F1 score of 96% across 620 invasion tests involving diverse microbial communities and nutrient environments. Importantly, considering both substrate and metabolite features provided a more holistic understanding of pathogen suppression. In particular, cross-feeding metabolites within the native community emerged as crucial yet often overlooked predictors of community resistance, disproportionally favoring native species over invaders. This study lays the foundation for designing disease-resistant microbiomes, with broad implications for mitigating pathogen exposure in diverse environments.

ecology↗

bioRxiv2024

Vascular responses of hypercapnia challenge in mice

Hypercapnia challenge with a few percent of carbon dioxide is popularly used in physiological studies to investigate the dynamic vascular properties. A typical hypercapnia experiment can be divided into four phases: baseline (regular air), transition (CO2 enriched), hypercapnia (CO2 enriched), and recovery (regular air). Cerebrovascular reactivity (CVR), denoting the percentage of functional changes between baseline and hypercapnia phases, can be measured to assess vascular health. In this study, we focus on the transition and recovery phases to track the built-up of cerebral blood flow (CBF) increase induced by CO2-enriched gas and recovery of CBF after returning to regular air. Dynamic features were compared with those of another potent vasodilatory agent, acetazolamide. Our results reveal that 5 min is sufficiently long to ensure 95% built-up of CBF increase under 5% CO2, but it takes much longer to recover to baseline.

physiology↗

bioRxiv2024

MdLRR-RLK1-MdATG3 module enhances the resistance of apples to abiotic stress via autophagy

LRR-RLKs play a key role in plant responses to stress, although their physiological functions under abiotic stress are not yet fully understood. MdLRR-RLK1 was found to regulate plant growth and development and to increase tolerance to salt and drought stress. MdLRR-RLK1 interacts with MdATG3, which ubiquitinates and degrades MdLRR-RLK1. MdATG3 enhances salt and drought tolerance through autophagy, interacting with MdATG8F/I-like in apple. These findings reveal the interaction between MdLRR-RLK1 and MdATG3, suggesting mechanisms that regulate apple growth and resistance to abiotic stress.

molecular biology↗

bioRxiv2024

Voltage imaging reveals circuit computations in the raphe underlying serotonin-mediated motor vigor learning

As animals adapt to new situations, neuromodulation is a potent way to alter behavior, yet mechanisms by which neuromodulatory nuclei compute during behavior are underexplored. The serotonergic raphe supports motor learning in larval zebrafish by visually detecting distance traveled during swims, encoding action effectiveness, and modulating motor vigor. We found that swimming opens a gate for visual input to cause spiking in serotonergic neurons, enabling encoding of action outcomes and filtering out learning-irrelevant visual signals. Using light-sheet microscopy, voltage sensors, and neurotransmitter/modulator sensors, we tracked millisecond-timescale neuronal input-output computations during behavior. Swim commands initially inhibited serotonergic neurons via GABA, closing the gate to spiking. Immediately after, the gate briefly opened: voltage increased consistent with post-inhibitory rebound, allowing swim-induced visual motion to evoke firing through glutamate, triggering serotonin secretion and modulating motor vigor. Ablating GABAergic neurons impaired raphe coding and motor learning. Thus, serotonergic neuromodulation arises from action-outcome coincidence detection within the raphe, suggesting the existence of similarly fast and precise circuit computations across neuromodulatory nuclei.

neuroscience↗

bioRxiv2024

A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies

While a subset of patients with metastatic melanoma achieves durable responses to immune checkpoint blockade (ICB) therapies, the majority ultimately exhibit either innate or acquired resistance to these treatments. However, the molecular mechanisms underlying resistance to ICB therapies remain elusive and are warranted to elucidate. Here, we comprehensively investigated the tumor and tumor immune microenvironment (TIME) of paired pre- and post-treatment tumor specimens from metastatic melanoma patients who were primary or secondary resistance to anti-CTLA-4 and/or anti-PD-1/PD-L1 therapies. Differentially expressed gene (DEG) analysis and single-sample gene set enrichment analysis (ssGSEA) with transcriptomic data identified cell cycle and c-MYC signaling as pathway-based resistance signatures. And weighted gene co-expression network analysis (WGCNA) revealed the activation of a cross-resistance meta-program involving key signaling pathways related to tumor progression in ICB resistant melanoma. Moreover, spatially-resolved, image-based immune monitoring analysis by using NanoStrings digital spatial profiling (DSP) and Cyclic Immunofluorescence (CyCIF) showed infiltration of suppressive immune cells in the tumor microenvironment of melanoma with resistance to ICB therapies. Our study reveals the molecular mechanisms underlying resistance to ICB therapies in patients with metastatic melanoma by conducting such integrated analyses of multi-dimensional data, and provides rationale for salvage therapies that will potentially overcome resistance to ICB therapies. Statement of translational relevanceThis study paves the way for the creation of innovative therapeutic strategies, aimed at subverting resistance to immune checkpoint blockade (ICB) therapies in metastatic melanoma patients. By unraveling the specific molecular mechanisms underlying resistance, scientists can design effective alternative treatments that target pathways such as pathways associated with cell cycle dysregulation and c-MYC signaling. Furthermore, through the application of advanced immune monitoring techniques such as NanoString Digital Spatial Profiling (DSP) and Cyclic Immunofluorescence (CyCIF), this study has significantly enriched our understanding of the tumor microenvironment. This enhanced characterization facilitates the discovery of potential biomarkers that may forecast a patients response to ICB treatment. Ultimately, these advancements could potentially refine patient outcomes and foster the development of more personalized cancer treatments in the future.

genetics↗

bioRxiv2024

m6AConquer: a Data Resource for Unified Quantification and Integration of m6A Detection Techniques

N6-methyladenosine (m6A) is the most prevalent RNA modification in mammalian cells and the most extensively studied epitranscriptomic mark. More than 10 m6A detection techniques have been proposed to measure m6A stoichiometry at either the site or peak level. However, these detection techniques are processed through heterogeneous pipelines, using different computational filters and reference features, leading to difficulties in fully harnessing data integration and analysis across orthogonal m6A detection techniques. Our m6AConquer (Consistent Quantification of External m6A RNA Modification Data) tackles this challenge by establishing a consistent multi-omics data-sharing standard, summarizing quantitative m6A data from 10 detection techniques using a unified reference feature set. Furthermore, we standardize site calling and m6A count matrix normalization procedures across platforms through a computational framework that accounts for over-dispersion in m6A levels. Available m6A detection techniques can be categorized into four types: antibody-assisted, chemical-assisted, enzyme-assisted, and direct-RNA sequencing. We leverage this categorization to develop a reproducibility-based integration framework that enables the reliable detection of high-confidence m6A sites confirmed across orthogonal techniques. Empirical evaluations report that both the site-calling and the integration framework outperform common alternatives, enhancing biological relevance. We apply interpretable machine learning models on our integrated high-confidence sites, and the result consistently identify proximity to intron-exon junctions as the driving predictor of m6A site coordinates across different techniques, demonstrating the high quality of the data curated in m6AConquer. m6AConquer webserver is freely accessible under: http://rnamd.org/m6aconquer. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/612173v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@f9d1a8org.highwire.dtl.DTLVardef@1339b41org.highwire.dtl.DTLVardef@115ececorg.highwire.dtl.DTLVardef@7580aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Rapid production of modified cattle and sheep from spermatid-like stem cells

Genome-modified ruminants, such as cattle and sheep, are important germplasm for agricultural applications. Haploid androgenetic stem cells (haSCs) can support the full-term development of embryos upon intracytoplasmic haSCs injection (iCHI) into intact oocytes. Thus, haSCs are invaluable resources for studying economic traits and significantly impact livestock breeding; however, ruminant haSCs have yet to be obtained. Here, we report the derivation of cattle and sheep haSCs using a recombinant FACE medium. These cells display characteristics of formative-state pluripotency and can differentiate into three germ layers, both in vitro and in vivo. Interestingly, the ectopic expression of protamine in haSCs converts their nuclei into a spermatid-like structure and synergistic enhancement of full-term development of iCHI embryos, named as Pro-iCHI approach. Furthermore, gene-modified cattle and sheep can be efficiently produced through the combinatorial use of the CRISPR and Pro-iCHI approach. Overall, Pro-iCHI provides an easy-to-manipulate agrobiotechnology tool for rapidly producing genetically modified livestock.

bioengineering↗

bioRxiv2024

Liprin-α/RIM complex regulates the dynamic assembly of presynaptic active zones via liquid-liquid phase separation

Presynaptic scaffold proteins, including liprin-, RIM, and ELKS, are pivotal to the assembly of the active zone and regulating the coupling of calcium signals and neurotransmitter release, yet the underlying mechanism remains poorly understood. Here, we determined the crystal structure of the liprin-2/RIM1 complex, revealing a multifaceted intermolecular interaction that drives the liprin-/RIM assembly. Neurodevelopmental disease-associated mutations block the formation of the complex. Disrupting this interaction in neurons impairs synaptic transmission and reduces the readily releasable pool of synaptic vesicles. Super-resolution imaging analysis supports a role for liprin- in recruiting RIM1 to the active zone, presumably by promoting the liquid-liquid phase separation (LLPS) of RIM1. Strikingly, the liprin-/RIM interaction modulates the competitive distribution of ELKS1 and voltage-gated calcium channels (VGCCs) in RIM1 condensates. Disrupting the liprin-/RIM interaction significantly decreased VGCC accumulation in the condensed phase and rendered release more sensitive to the slow calcium buffer EGTA, suggesting an increased physical distance between VGCC and vesicular calcium sensors. Together, our findings provide a plausible mechanism of the liprin-/RIM complex in regulating the coupling of calcium channels and primed synaptic vesicles via LLPS for efficient synaptic transmission and uncover the pathological implication of liprin- mutations in neurodevelopmental disorders.

neuroscience↗

bioRxiv2024

A single-molecule nanopore sequencing platform

Nanopore sequencing, a third-generation sequencing technology, has revolutionized the gene sequencing industry with its advantages of long reads, fast speed, real-time sequencing and analysis, and potential in detecting base modifications. This technology allows researchers to sequence longer DNA fragments in a single read, providing more comprehensive genomic information compared to previous methods. Nanopore sequencing operates on electrical signals generated by a nanopore embedded in a membrane separating two electrolyte-filled chambers. When single-stranded DNA (ssDNA) passes through the nanopore, it creates variations in the current that correspond to different DNA bases. By analyzing these current fluctuations with machine learning algorithms, the DNA sequence can be determined. In this study, we introduced several improvements to nanopore sequencing, including nanopore local chemistry sequencing, novel motor and pore proteins, chip design, and basecalling algorithms. Our new nanopore sequencing platform, CycloneSEQ, demonstrated long-duration sequencing (107 hours) on a single chip with high yield (>50 Gb). In human genomic DNA sequencing, CycloneSEQ was able to produce long reads with N50 33.6 kb and modal identity 97.0%. Preliminary findings on human whole-genome de novo assembly, variant calling, metagenomics sequencing, and single-cell RNA sequencing have further highlighted CycloneSEQs potential across different areas of genomics.

genomics↗

bioRxiv2024

Decoding critical targets and signaling pathways in EBV-mediated diseases using large language models

Epstein-Barr virus (EBV), a member of the gamma-herpesvirus, is the first identified human oncovirus and is associated with various malignancies. Understanding the intricate interactions between EBV antigens and cellular pathways is crucial to unravel the molecular mechanisms in EBV-mediated diseases. However, fully elucidating EBV-host interactions and the associated pathogenesis remains a significant challenge. In this study, we utilized LLMs to screen 36,105 EBV-relevant scientific publications, presenting a descriptive overview of the interactions between EBV antigens and host cellular pathways through comprehensive interaction networks. We described the critical roles of EBV antigens by constructing functional subsets of host proteins targeted by EBV antigens and illustrated the interactions using protein- protein interaction (PPI) networks. Furthermore, we developed an antigen-pathway network that highlights the connections with EBV-associated diseases, including DLBCL, BL, NPC, gastric cancer (GC), and post-transplant lymphoproliferative disorders (PTLD). Utilizing our dataset and public dataset, we validated the efficacy of using BLLF3-targeted TLR2-associated factors as marker genes for DLBCL. Next, we confirmed the co-expression of calcium pathway factors induced by LMP1 over- expression in BL. Finally, based on shared results suggesting that LMP1 actively regulates the glycolysis pathway, we identified and validated the correlation and co- expression of LMP1-induced PARP1, HIF1A, HOXB3, and key transcription-related factors, revealing the complete picture of LMP1s influence on the glycolysis pathway. Our study presents a comprehensive functional encyclopedia of the interactions between EBV antigens and host signaling pathways across various EBV-associated disease contexts, providing valuable insights for the development of therapeutic strategies.

microbiology↗

bioRxiv2024

Cost-effective DNA storage with DNA movable type

In the face of exponential data growth, DNA-based storage offers a promising solution for preserving big-data. However, most existing DNA storage methods, akin to traditional block printing, require costly chemical synthesis for each individual data file, adopting a sequential, one-time-use synthesis approach. To overcome these limitations, we introduce a novel, cost-effective "DNA-Movable-Type Storage" system, inspired by movable type printing. This system utilizes pre-fabricated DNA movable types-short, double-stranded DNA oligonucleotides encoding specific payload, address, and checksum data. These DNA-MTs are enzymatically ligated/assembled into cohesive sequences, termed "DNA movable type blocks", streamlining the assembly process with the automated BISHENG-1 DNA-MT inkjet printer. Using BISHENG-1, we successfully printed, assembled, stored and accurately retrieved 43.7 KB of data files in diverse formats (text, image, audio, and video) in vitro and in vivo, using only 350 DNA-MTs. Notably, each DNA-MT, synthesized once (2 OD), can be used up to 10,000 times, reducing costs to 122 $/MB-outperforming existing DNA storage methods. This innovation circumvents the need to synthesize entire DNA sequences encoding files from scratch, offering significant cost and efficiency advantages. Furthermore, it has considerable untapped potential to advance a robust DNA storage system, better meeting the extensive data storage demands of the big-data era.

bioinformatics↗

bioRxiv2024

A replicable and generalizable neuroimaging-based indicator of pain sensitivity across individuals

Developing neural indicators of pain sensitivity is crucial for revealing the neural basis of individual differences in pain and advancing individualized pain treatment. To identify reliable neural indicators of pain sensitivity, we leveraged six large and diverse functional magnetic resonance imaging (fMRI) datasets (total N=1046). We found replicable and generalizable correlations between nociceptive-evoked fMRI responses and pain sensitivity for laser heat, contact heat, and mechanical pains. These fMRI responses correlated more strongly with pain sensitivity than with tactile, auditory, and visual sensitivity. Moreover, we developed a machine learning model that accurately predicted not only pain sensitivity but also pain reduction from different interventions in healthy individuals. Notably, these findings were influenced considerably by sample sizes, requiring >200 for univariate correlation analysis and >150 for multivariate machine learning modelling. Altogether, we demonstrate the validity of decoding pain sensitivity from fMRI responses, thus facilitating interpretations of subjective pain reports and promoting more mechanistically informed investigation of pain physiology.

neuroscience↗

bioRxiv2024

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

The rapid evolution of single-cell sequencing technologies has facilitated precise transcriptomics profiling at the single-cell level, shedding light on the intricate heterogeneity within cellular populations. Despite these advances, the inherent diversity of cells and data challenges such as noise, batch effects, and sparsity, underscores the pressing need for a unified model to learn and represent cellular states effectively. Single-cell Large Language Models (LLMs) have been crafted to bridge this gap yet exhibit limited performance on human cells. This short-fall may stem from the confounding effects of training data from diverse species, partly because of limited cells for the single species. Here, we have compiled a dataset of approximately 100 million human cells sequenced by multiple technolo-gies from human single-cell datasets with various file types deposited in public databases and websites. Leveraging these extensive data cohorts, we developed CellFM, a robust single-cell foundation model with an impressive 800 million parameters, marking an eight-fold increase over the current largest single-species model. To ensure the training of CellFM on the MindSpore AI framework from Huawei, we have integrated RetNet, a Transformer architecture variant with lin-ear complexity for a balance between efficiency and performance, serving as the backbone of our model. Our comprehensive experiments have shown that CellFM outperforms existing models across diverse applications, such as cell annotation, perturbation prediction, and gene function prediction.

bioinformatics↗