Biology subjects

Zhang, W.

Publications and source records attributed to Zhang, W..

At least 505 records · Page 28Linked to original sources

bioRxiv2023

Towards establishing a rapid constant temperature detection method for canine parvovirus based on endonuclease activities

Canine parvovirus can cause a high morbidity and mortality rate in puppies, posing a significant threat to both pet dogs and the breeding industry. Rapid, accurate and convenient detection methods are important for the early intervention and treatment of canine parvovirus. In this study, we propose a visual CPV detection system called nucleic acid mismatch enzyme digestion (NMED). This system combines reverse transcription-loop mediated isothermal amplification (RT-LAMP), endonuclease for gene mismatch detection and colloidal gold lateral chromatography. We demonstrated that NMED can induce the binding of the amplicon from the sample to the specific labeling probe, which in turn triggers digestion by the endonuclease. The sensitivity and visual visibility of RT-LAMP were increased by combining of endonuclease and colloidal gold lateral chromatography assisted by a simple temperature-controlled device. The sensitivity of the NMED assay was 1 copy/L, which was consistent with qPCR. The method was validated with 10 clinical samples that potentially had CPV infection and achieved 100% accuracy. As a rapid, accurate and convenient molecular diagnostic method, NMED has great potential for application in the field of pathogenic microorganism detection.

molecular biology↗

bioRxiv2023

The somato-cognitive action network links diverse neuromodulatory targets for Parkinson's disease

The newly-recognized somato-cognitive action network (SCAN) is posited to be important in movement coordination. Functional disruptions in Parkinsons disease (PD) correspond with complex, non-effector-specific motor symptoms, indicating that SCAN dysfunction may underlie these symptoms. Along the same lines, the SCAN may link multiple neuromodulatory targets used for PD treatment. To investigate the role of the SCAN in PD, we leveraged resting-state precision functional mapping, analyzing data from 673 individuals across 6 independent datasets and 5 types of neuromodulation. Our findings revealed functional abnormalities within the SCAN in PD patients and the selective involvement of the SCAN in diverse neuromodulatory targets. Moreover, our data suggests causal links between SCAN connectivity changes and motor symptom alleviation following both invasive and non-invasive neuromodulation. Collectively, these findings underscore the critical role of the SCAN in the pathophysiology of PD and its brain stimulation treatments, and suggest the SCAN as a promising candidate target for neuromodulation.

neuroscience↗

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

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

bioinformatics↗

bioRxiv2023

Towards ultra-low-cost smartphone microscopy

The outbreak of COVID-19 exposed the inadequacy of our technical tools for home health surveillance, and recent studies have shown the potential of smartphones as a universal optical microscopic imaging platform for such applications. However, most of them use laboratory-grade optomechanical components and transmitted illuminations to ensure focus tuning capability and imaging quality, which keeps the cost of the equipment high. Here we propose an ultra-low-cost solution for smartphone microscopy. To realize focus tunability, we designed a seesaw-like structure capable of converting large displacements on one side into small displacements on the other (reduced to [~]9.1%), which leverages the intrinsic flexibility of 3D printing materials. We achieved a focus-tuning accuracy of [~] 5 {micro}m, which is 40 times higher than the machining accuracy of the 3D-printed lens holder itself. For microscopic imaging, we use an off-the-shelf smartphone camera lens as the objective and the built-in flashlight as the illumination. To compensate for the resulting image quality degradation, we developed a learning-based image enhancement method. We use the CycleGAN architecture to establish the mapping from smartphone microscope images to benchtop microscope images without pairing. We verified the imaging performance on different biomedical samples. Except for the smartphone, we kept the full costs of the device under 4 USD. We think these efforts to lower the costs of smartphone microscopes will benefit their applications in various scenarios, such as point-of-care testing, on-site diagnosis, and home health surveillance. Research highlightsWe propose a solution for ultra-low-cost smartphone microscopy. Utilizing the flexibility of 3D-printed material, we can achieve focusing accuracy of [~] 5 {micro}m. Such a low-cost device will benefit point-of-care diagnosis and home health surveillance.

bioengineering↗

bioRxiv2023

HyGAnno: Hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell ATAC sequencing (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. This calls for novel cell type annotation methods in scATAC-seq, to better explore cell type-specific gene regulatory mechanisms and provide a complementary epigenomic layer to scRNA-seq data. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno integrates genomewide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph that can be used to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was tested using large datasets and demonstrated the advantages of accurate cell annotation, interpretable cell embedding, robustness to noisy reference data, and adaptability to tumor tissues.

bioinformatics↗

bioRxiv2023

Warm temperature and mild water stress cooperatively promote root elongation

Warm temperatures have a dramatic effect on plant development. In shoots, stems elongate, and leaves are raised in a developmental programme called thermomorphogenesis. This results in enhanced leaf cooling capacity1. Thermomorphogenesis is tightly intertwined with light signalling pathways. The level of integration is so high that it has been proposed that shoot temperature sensing may have evolved from the co-option of an existing light signalling pathway during the colonisation of land by plants2. Roots also undergo thermomorphogenesis, but the mechanism by which this occurs is less well understood. Main root elongation is enhanced at warm temperatures, and this response is independent of many of the light and temperature signalling components of the shoot3. Roots develop in darkness and so it is a reasonable assumption that root temperature signalling is not through modulation of light signalling. It was recently speculated that due to the close correlation between warm temperature and soil moisture content, root temperature signalling could feasibly be related to water availability signals2. In this study we tested the interaction between temperature and water availability signalling in plant roots. We found that these environmental factors co-operatively enhance main root elongation. This interaction effect was dependent on SUCROSE NON-FERMENTING RELATED KINASE 2.2 (SnRK2.2) and SnRK2.3 and the E3 ubiquitin ligase CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1). We found that SnRK2.2 / 2.3 and COP1 have opposite effects on the stability of the transcription factor ELONGATED HYPOCOTYL 5 (HY5) in elongation zone hair cells. The stability of HY5 in these cell types generally corresponded to the degree of root elongation seen in each mutant background. Our study reveals several molecular components of root thermomorphogenesis and highlights the importance of an integrative approach to plant environmental signalling. Our results may have direct implications for agricultural land management, especially as global climates become more unpredictable.

plant biology↗

bioRxiv2023

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.

bioinformatics↗

bioRxiv2023

De novo design and optimization of aptamers with AptaDiff

Aptamers are single-stranded nucleic acid ligands, featuring high affinity and specificity to target molecules. Traditionally they are identified from large DNA/RNA libraries using in vitro methods, like Systematic Evolution of Ligands by Exponential Enrichment (SELEX). However, these libraries capture only a small fraction of theoretical sequence space, and various aptamer candidates are constrained by actual sequencing capabilities from the experiment. Addressing this, we proposed AptaDiff, the first in silico aptamer design and optimization method based on the diffusion model. Our Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data, leveraging motif-dependent latent embeddings from variational autoencoder, and can optimize aptamers by affinity-guided aptamer generation according to Bayesian optimization. Comparative evaluations revealed AptaDiffs superiority over existing aptamer generation methods in terms of quality and fidelity across four high-throughput screening data targeting distinct proteins. Moreover, Surface Plasmon Resonance (SPR) experiments were conducted to validate the binding affinity of aptamers generated through Bayesian optimization for two target proteins. The results unveiled a significant boost of 87.9% and 60.2% in RU values, along with a 3.6-fold and 2.4-fold decrease in KD values for the respective target proteins. Notably, the optimized aptamers demonstrated superior binding affinity compared to top experimental candidates selected through SELEX, underscoring the promising outcomes of our AptaDiff in accelerating the discovery of superior aptamers. Key PointsO_LIWe proposed AptaDiff, the first in silico aptamer design method based on the diffusion model. Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data. C_LIO_LIAptadiff can optimize aptamers through affinity-guided generation via Bayesian optimization within a motif-dependent latent space, and the affinity of the optimized aptamers to the target protein is better than the best experimental candidate from traditional SELEX screening. C_LIO_LIAptadiff consistently outperforms the current state-of-the-art method in terms of quality and fidelity across high-throughput screening data targeting distinct proteins. C_LI

bioinformatics↗

bioRxiv2023

TREVI: A Transcriptional Regulation-driven Variational Inference Model to Speculate Gene Expression Mechanism with Integration of Single-cell Multi-omics

Single-cell multi-omics technology enables the concurrent measurement of multiple molecular entities, making it critical for unraveling the inherent gene regulation mechanisms driving cell heterogeneity. However, existing multi-omics techniques have limitations in capturing the intricate regulatory interactions among these molecular components. In this study, we introduce TREVIXMBD (Transcriptional REgulation-driven Variational Inference), a novel method that integrates the well-established gene regulation structure with scRNA-seq and scATAC-seq data through an advanced Bayesian framework. TREVIXMBD models the generation of gene expression profiles in individual cells by considering the integrated influence of three fundamental biological factors: accessibility of cis-regulatory elements regions, transcription factor (TF) activities and regulatory weights. TF activities and regulatory weights are probabilistically represented as latent variables, which capture the inherent gene regulatory significance. Hence, in contrast to gene expression, TF activities and regulatory weights that depict the cell states from a more intrinsic perspective, can keep consistent across diverse datasets. TREVIXMBD exhibits superior performance when compared to baseline methods in a variety of biological analyses, including cell typing, cell development tracking, and batch effect correction, as validated through comprehensive benchmarking. Moreover, TREVIXMBD can reveal variations in TF-gene regulation relationships across cells. The pretrained TREVIXMBD model can work even when only scRNA-seq is available. Overall, TREVIXMBD introduces a pioneering biological-mechanism-driven framework for elucidating cell states at a gene regulatory level. The models structure is adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis.

bioinformatics↗

bioRxiv2023

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATEs prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

bioinformatics↗

bioRxiv2023

Neural Deterioration and Compensation in Visual Short-term Memory Among Individuals with Amnestic Mild Cognitive Impairment

Amnestic mild cognitive impairment (aMCI) is considered to carry a high risk of progression to Alzheimers Disease (AD) and it has been characterized by deficits in visual short-term memory (VSTM). However, the relationship between VSTM deficits and pathological brain damage in individuals with MCI has remained unknown. In the current study, we examined a group of 123 elder adults, including 55 with aMCI and 68 age-matched controls. Participants performed color change-detection VSTM tasks, and structural and functional magnetic resonance images were acquired during rest. Compared to the normal control (NC) group, individuals with aMCI exhibited poorer accuracy and longer reaction time in VSTM tasks, along with reduced VSTM capacity. Additionally, structural atrophy was observed in aMCI participants in several brain regions, including the left medial temporal lobe (MTL), the left thalamus, the right frontal pole (FP) and the right postcentral gyrus (postCG). Interestingly, VSTM accuracy and capacity were found to be associated with the volume of the left MTL in the NC group but not in the aMCI group, suggesting alterations in the relationship between VSTM and brain regions in aMCI. However, VSTM capacity was correlated with the volume of the right FP in both groups, suggesting potential compensatory mechanisms involving the prefrontal cortex in aMCI. Moreover, using the atrophic left MTL as a seed, functional connectivity to the right FP was significantly higher in aMCI compared to NC. Notably, this FP area showed overlap with the atrophic frontal areas in terms of structural abnormalities. Furthermore, for individuals with aMCI who had a larger left MTL, the compensatory involvement of the right FP in VSTM, as assessed by brain-behavior correlations, was diminished. In summary, the present study uncovered a mechanism involving MTL dysfunction and prefrontal compensation in aMCI when performing VSTM tasks. These findings may offer valuable insights into potential intervention targets for individuals with aMCI.

pathology↗

bioRxiv2023

ADEVO: Proof-of-concept of Adenovirus Directed EVOlution by random peptide display on the fiber knob

Directed evolution of viral vectors involves the generation of randomized libraries followed by artificial selection of improved variants. Directed evolution only yielded limited results in adenovirus vector (AdV) development until now, mainly due to insufficient complexities of randomized libraries. Clinical applications of AdVs as gene therapy or oncolytic vectors are still hampered by the predetermined tropism of natural types. To overcome this challenge, we hypothesized that the technology of randomized peptide insertions on the capsid surface can be incorporated into the AdV bioengineering toolbox for vector retargeting. Here we developed Adenovirus Directed EVOlution (ADEVO) protocols based on fiber knob peptide display. As a proof-of-concept, HAdV-C5-derived libraries were constructed following three distinct protocols and selected on A549-DCAR cells that lack the HAdV-C5 primary receptor, with the goal of identifying variants able to infect and lyse these tumor cells more efficiently. All protocols enabled the construction of high complexity libraries with up to 9.6x10^5 unique variants, an approximate 100-fold improvement compared to previously published AdV libraries. After selection, the most enriched variants did not display enhanced infectivity but rather more efficient replication and cell lysis. This warrants investigations into potential unsuspected involvement of the fiber protein in adenovirus replication. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/567388v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@187f7baorg.highwire.dtl.DTLVardef@1990555org.highwire.dtl.DTLVardef@12d3c60org.highwire.dtl.DTLVardef@d703da_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Integrative multiomics analysis of neointima proliferation in human saphenous vein: implications for bypass graft disease

IntroductionHuman saphenous veins (SV) are widely used as grafts in coronary artery bypass (CABG) surgery but often fail due to neointima proliferation (NP). NP involves complex interplay between vascular smooth muscle cells (VSMC) and fibroblasts. Little is known, however, regarding the transcriptomic and proteomic dynamics of NP. Here, we performed multi-omics analysis in an ex vivo tissue culture model of NP in human SV procured for CABG surgery. Methods and resultsHistological examination demonstrated significant elastin degradation and NP (indicated by increased neointima area and neointima/media ratio) in SV subjected to tissue culture. Analysis of data from 73 patients suggest that the process of SV adaptation and NP may differ according to sex and body mass index. RNA sequencing confirmed upregulation of pro-inflammatory and proliferation-related genes during NP and identified novel processes, including increased cellular stress and DNA damage responses, which may reflect tissue trauma associated with SV harvesting. Proteomic analysis identified upregulated extracellular matrix-related and coagulation/thrombosis proteins and downregulated metabolic proteins. Spatial transcriptomics detected transdifferentiating VSMC in the intima on the day of harvesting and highlighted dynamic alterations in fibroblast and VSMC phenotype and behavior during NP. Specifically, we identified new cell subpopulations contributing to NP, including SPP1+, LGALS3+ VSMC and MMP2+, MMP14+ fibroblasts. ConclusionDynamic alterations of gene and protein expression occur during NP in human SV. Identification of the human-specific molecular and cellular mechanisms may provide novel insight into SV bypass graft disease.

genomics↗

bioRxiv2023

Targeting CD74-positive macrophages improves neoadjuvant therapy in cervical cancer as revealed by single-cell transcriptomics analysis

Uterine cervical cancer (UCC) has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic strategy for cervical cancer. However, the efficacy of PD-1 (programmed cell death protein 1) blockade combined with neoadjuvant chemotherapy (NACT) and the ensuing alterations within the tumor microenvironment need further investigation. We conducted single-cell RNA sequencing using 46,950 cells from nine sequential human cervical cancer tissues representing different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and unveiled crucial factors involved in the combination therapy. Cell communication analysis revealed the inferred interaction strength decreased between T cell-cancer cells, while the communication between macrophage-cancer cells intensified after NACT therapy. We verified that macrophages are necessary for PD-1 blockade combination to exert antitumor effects in vitro. Detailed analysis unraveled the CD74-positive macrophages frequently interacted with the immunoreactive Epi 3 subgroup during neoadjuvant combination therapy. CD74 upregulation limited phagocytosis and stimulated M2 polarization. The CD74 blockade enhanced macrophage phagocytosis, resulting in decreased viability of cervical cancer cells in vitro and in vivo. This study unveils the dynamic microenvironment of UCC influenced by NACT and PD-1 blockade combination therapy, and targeting CD74 was a potential strategy to augment the therapy efficacy.

cancer biology↗

bioRxiv2023

FICTURE: Scalable segmentation-free analysis of sub-micron resolution spatial transcriptomics

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTUREs cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.

bioinformatics↗

bioRxiv2023

A Ralstonia solanacearum type III effector alters the actin and microtubule cytoskeleton to promote bacterial virulence in plants

Cellular responses to biotic stress frequently involve signaling pathways that are conserved across eukaryotes. These pathways include the cytoskeleton, a proteinaceous network that senses external cues at the cell surface and signals to interior cellular components. During biotic stress, dynamic cytoskeletal rearrangements serve as a platform from which early immune-associated processes are organized and activated. Bacterial pathogens of plants and animals use proteins called type III effectors (T3Es) to interfere with host immune signaling, thereby promoting virulence. We previously found that RipU, a T3E from the soilborne phytobacterial pathogen Ralstonia solanacearum K60 (Rs K60), co-localizes with the plant cytoskeleton. Here, we show that RipU from Rs K60 (RipUK60) physically associates with both actin and tubulin and disrupts actin and microtubule cytoskeleton organization. We find that pharmacological disruption of the tomato (Solanum lycopersicum) cytoskeleton promotes Rs K60 colonization. RipUK60 suppresses cell surface-triggered immune responses including flg22-mediated reactive oxygen species (ROS) production and callose deposition. Importantly, tomato plants inoculated with Rs K60 lacking RipUK60 ({Delta}ripUK60) had reduced wilting symptoms and significantly reduced root colonization when compared to plants inoculated with wild-type Rs K60. Collectively, our data suggest that Rs K60 uses the type III effector RipUK60 to remodel cytoskeletal organization, thereby promoting pathogen virulence.

plant biology↗

bioRxiv2023

Polygonum Cillinerve polysaccharide inhibits Transmissible gastroenteritis virus by regulating microRNA-181

Transmissible gastroenteritis virus (TGEV) is an important pathogen that can cause changes in the expression profile of cell miRNA. In this study, PCP could treat piglets infected with TGEV by in vivo experiment. High-throughput sequencing technology was used to detect that 9 miRNAs were up-regulated and 17 miRNAs were down-regulated during PCP inhibition of TGEV infection in PK15 cells. Meanwhile, miR-181 was related to the target genes of key proteins of apoptosis pathway. PK15 was treated with different PCP after transfection of miR-181 mimic or inhibitor. Real-time PCR was used to detect the effect of TGEV replication, electron microscopy, TEM and Hoechst fluorescence staining were used to detect the biological function of the treated cells, and western blot (WB) was used to detect the expression of key signaling factors cyt C, caspase 9 and P53 in the apoptotic signaling pathway. The results showed that compared with the control group, 250 g/mL PCP significantly inhibited the replication of TGEV gRNA and gene N (P < 0.01). PK15 cells in PCP groups (250 and 125 g/mL) had uniform cell morphology and a small number of floating cells by microscopic, no typical virus structure was observed in 250 g/mL PCP group under TEM, and apoptosis staining showed that 250 g/mL PCP significantly reduced the number of apoptotic cells. PCP may inhibit TGEV-induced apoptosis through Caspase-dependent mitochondrial pathway after transfection of miR-181. The above results provided a theoretical basis for further research on the mechanism of PCP anti-TGEV.

pharmacology and toxicology↗