Overproduction of mycotoxin biosynthetic enzymes triggers Fusarium toxisome-shaped structure formation via endoplasmic reticulum remodeling.

Mycotoxin deoxynivalenol (DON) produced by the Fusarium graminearum complex is highly toxic to animal and human health. During DON synthesis, the endoplasmic reticulum (ER) of F. graminearum is intensively reorganized, from thin reticular structure to thickened spherical and crescent structure, which was referred to as "DON toxisome". However, the underlying mechanism of how the ER is reorganized into toxisome remains unknown. In this study, we discovered that overproduction of ER-localized DON biosynthetic enzyme Tri4 or Tri1, or intrinsic ER-resident membrane proteins FgHmr1 and FgCnx was sufficient to induce toxisome-shaped structure (TSS) formation under non-toxin-inducing conditions. Moreover, heterologous overexpression of Tri1 and Tri4 proteins in non-DON-producing fungi F. oxysporum f. sp. lycopersici and F. fujikuroi also led to TSS formation. In addition, we found that the high osmolarity glycerol (HOG), but not the unfolded protein response (UPR) signaling pathway was involved in the assembly of ER into TSS. By using toxisome as a biomarker, we screened and identified a novel chemical which exhibited high inhibitory activity against toxisome formation and DON biosynthesis, and inhibited Fusarium growth species-specifically. Taken together, this study demonstrated that the essence of ER remodeling into toxisome structure is a response to the overproduction of ER-localized DON biosynthetic enzymes, providing a novel pathway for management of mycotoxin contamination.

Distinct characteristics of the gut virome in patients with osteoarthritis and gouty arthritis.

BACKGROUND/PURPOSE(S): The gut microbiota and its metabolites play crucial roles in pathogenesis of arthritis, highlighting gut microbiota as a promising avenue for modulating autoimmunity. However, the characterization of the gut virome in arthritis patients, including osteoarthritis (OA) and gouty arthritis (GA), requires further investigation. METHODS: We employed virus-like particle (VLP)-based metagenomic sequencing to analyze gut viral community in 20 OA patients, 26 GA patients, and 31 healthy controls, encompassing a total of 77 fecal samples. RESULTS: Our analysis generated 6819 vOTUs, with a considerable proportion of viral genomes differing from existing catalogs. The gut virome in OA and GA patients differed significantly from healthy controls, showing variations in diversity and viral family abundances. We identified 157 OA-associated and 94 GA-associated vOTUs, achieving high accuracy in patient-control discrimination with random forest models. OA-associated viruses were predicted to infect pro-inflammatory bacteria or bacteria associated with immunoglobulin A production, while GA-associated viruses were linked to Bacteroidaceae or Lachnospiraceae phages. Furthermore, several viral functional orthologs displayed significant differences in frequency between OA-enriched and GA-enriched vOTUs, suggesting potential functional roles of these viruses. Additionally, we trained classification models based on gut viral signatures to effectively discriminate OA or GA patients from healthy controls, yielding AUC values up to 0.97, indicating the clinical utility of the gut virome in diagnosing OA or GA. CONCLUSION: Our study highlights distinctive alterations in viral diversity and taxonomy within gut virome of OA and GA patients, offering insights into arthritis etiology and potential treatment and prevention strategies.

The CXCR2 chemokine receptor: A new target for gastric cancer therapy.

Gastric cancer (GC) is one of the most common malignant tumors in the world, and current treatments are still based on surgery and drug therapy. However, due to the complexity of immunosuppression and drug resistance, the treatment of gastric cancer still faces great challenges. Chemokine receptor 2 (CXCR2) is one of the most common therapeutic targets in targeted therapy. As a G protein-coupled receptor, CXCR2 and its ligands play important roles in tumorigenesis and progression. The abnormal expression of these genes in cancer plays a decisive role in the recruitment and activation of white blood cells, angiogenesis, and cancer cell proliferation, and CXCR2 is involved in various stages of tumor development. Therefore, interfering with the interaction between CXCR2 and its ligands is considered a possible target for the treatment of various tumors, including gastric cancer.

LC-MS/MS method for quick detection of vonoprazan fumarate in human plasma: Development, validation and its application to a bioequivalence study.

A liquid chromatography-tandem mass spectrometry method with vonoprazan fumarate-d4 as a stable isotope-labeled internal standard was developed and validated aiming at quantification of vonoprazan fumarate in human plasma for a bioequivalence study. Chromatographic separation was achieved by acetonitrile one-step protein precipitation using a gradient elution of 0.1% formic acid aqueous solution and acetonitrile with a run time of 3.65 min. Detection was carried out on a tandem mass spectrometer in multiple reaction monitoring mode via a positive electrospray ionization interface. The multiple reaction monitoring mode of precursor-product ion transitions for vonoprazan fumarate and vonoprazan fumarate-d4 were m/z 346.0 → 315.1 and 350.0 → 316.0, respectively. The linear range was 0.150-60.000 ng/ml. This method was fully validated with acceptable results in terms of selectivity, carryover, lower limit of quantification, calibration curve, accuracy, precision, dilution effect, matrix effect, stability, recovery and incurred sample reanalysis. A successful application of this method was realized in the bioequivalence study of vonoprazan fumarate tablet (20 mg) among healthy Chinese volunteers.

Quantification of cefaclor in human plasma using SIL-IS LC-ESI-MS/MS for pharmacokinetics study in healthy Chinese volunteers.

A steady, high-efficiency, and precise liquid chromatography-electrospray ionization-tandem mass spectrometry method was established and validated using cefaclor-d5 as the stable isotope-labeled internal standard for quantification of cefaclor in human plasma. One-step protein precipitation was applied to extract human plasma samples using methanol as precipitant. An Ultimate XB C18 column (2.1 × 50.0 mm, 5.0 µm) was used to achieve chromatographic separation. Mobile phases of gradient elution were an aqueous solution containing 0.1% formic acid (mobile phase A) and an acetonitrile solution containing 0.1% formic acid (mobile phase B). Electrospray ionization in positive-ion mode was applied to detect under multiple reaction monitoring mode. Target fragment ion pairs of cefaclor and stable isotope-labeled internal standard, respectively, were m/z 368.2 → 191.1 and m/z 373.2 → 196.1. Linear range of this method was between 20.0 and 10,000.0 ng/ml, with coefficient of determination (R2 ) >0.9900. Seven concentrations of quality control samples were used: 20.0 ng/ml (lower limit of quantitation), 60.0 ng/ml (low quality control), 650 ng/ml (middle quality control), 5000 ng/ml (arithmetic average middle quality control [AMQC]), 7500 ng/ml (high quality control), 10,000 ng/ml (upper limit of quantification), and 40,000 ng/ml (dilution quality control [DQC]). The method was validated for selectivity, lower limit of quantitation, linearity, accuracy, precision, recovery, matrix effect, dilution reliability, stability, carryover, and incurred sample reanalysis. This stable isotope-labeled internal standard liquid chromatography-electrospray ionization-tandem mass spectrometry approach has been successfully applied to study the pharmacokinetics of cefaclor dry suspension among healthy Chinese volunteers.

The calcium-calcineurin and high-osmolarity glycerol pathways co-regulate tebuconazole sensitivity and pathogenicity in Fusarium graminearum.

The calcium-calcineurin and high-osmolarity glycerol (HOG) pathways play crucial roles in fungal development, pathogenicity, and in responses to various environmental stresses. However, interaction of these pathways in regulating fungicide sensitivity remains largely unknown in phytopathogenic fungi. In this study, we investigated the function of the calcium-calcineurin signalling pathway in Fusarium graminearum, the causal agent of Fusarium head blight. Inhibitors of Ca2+ and calcineurin enhanced antifungal activity of tebuconazole (an azole fungicide) against F. graminearum. Deletion of the putative downstream transcription factor FgCrz1 resulted in significantly increased sensitivity of F. graminearum to tebuconazole. FgCrz1-GFP was translocated to the nucleus upon tebuconazole treatment in a calcineurin-dependent manner. In addition, deletion of FgCrz1 increased the phosphorylation of FgHog1 in response to tebuconazole. Moreover, the calcium-calcineurin and HOG signalling pathways exhibited synergistic effect in regulating pathogenicity and sensitivity of F. graminearum to tebuconazole and multiple other stresses. RNA-seq data revealed that FgCrz1 regulated expression of a set of non-CYP51 genes that are associated with tebuconazole sensitivity, including multidrug transporters, membrane lipid biosynthesis and metabolism, and cell wall organization. Our findings demonstrate that the calcium-calcineurin and HOG pathways act coordinately to orchestrate tebuconazole sensitivity and pathogenicity in F. graminearum, which may provide novel insights in management of Fusarium disease.

Construction of nursing-sensitive quality indicator system for cardiac rehabilitation of patients undergoing percutaneous coronary intervention based on structure-process-outcome model.

BACKGROUND: Coronary heart disease (CHD) is a cardiovascular disease with high mortality. At present, percutaneous coronary intervention (PCI) is considered as the main effective treatment for CHD due to less trauma, shorter course of treatment, and better curative effect. However, PCI alone is not a permanent cure, so cardiac rehabilitation (CR) is needed for a supplement. Nowadays, the evaluation of the nursing-sensitive quality of CR after PCI focuses on the outcomes of patients, lacks a complete evaluation indicator system, and is prone to problems such as nursing management imbalance. OBJECTIVE: A scientific, sensitive, comprehensive and practical nursing-sensitive quality indicator system based on the structure-process-outcome model was constructed to provide a reference for evaluating nursing-sensitive quality of CR after PCI. METHODS: Firstly, through literature analysis and semi-structured interview, the indicator system was collected, screened and determined. Then, the framework of the indicator system was established, and the draft of nursing-sensitive quality indicator system of CR after PCI was formed. Subsequently, the nursing-sensitive quality indicator system of CR after PCI was initially established using Delphi method. Finally, the specific weight was determined by analytic hierarchy process (AHP), and the nursing-sensitive quality indicator system of CR after PCI was established and perfected. RESULTS: Two rounds of expert consultations were separately given 15 questionnaires, and all these questionnaires were returned, with a questionnaire response rate of 100%. Such result indicated that experts were highly motivated. Besides, the authoritative coefficients for two rounds of expert consultations were 0.865 and 0.888, and the coordination coefficients were 0.491 and 0.522, respectively. Hence, the experts' authority and coordination were high and the results were reliable. After the second round of expert consultation, the nursing-sensitive quality indicator system of CR after PCI was established, eventually. This system consisted of 3 first-level indicators (structural indicator, process indicator and outcome indicator), 11 s-level indicators and 29 third-level indicators. CONCLUSION: A relatively complete and reliable nursing-sensitive quality indicator system of CR after PCI has been established in this study. Such system is scientific and reliable and can provide a reference for the evaluation of clinical teaching quality of CR after PCI.

Tools for fundamental analysis functions of TCR repertoires: a systematic comparison.

The full set of T cell receptors (TCRs) in an individual is known as his or her TCR repertoire. Defining TCR repertoires under physiological conditions and in response to a disease or vaccine may lead to a better understanding of adaptive immunity and thus has great biological and clinical value. In the past decade, several high-throughput sequencing-based tools have been developed to assign TCRs to germline genes and to extract complementarity-determining region 3 (CDR3) sequences using different algorithms. Although these tools claim to be able to perform the full range of fundamental TCR repertoire analyses, there is no clear consensus of which tool is best suited to particular projects. Here, we present a systematic analysis of 12 available TCR repertoire analysis tools using simulated data, with an emphasis on fundamental analysis functions. Our results shed light on the detailed functions of TCR repertoire analysis tools and may therefore help researchers in the field to choose the right tools for their particular experimental design.

Plant defense compound triggers mycotoxin synthesis by regulating H2B ub1 and H3K4 me2/3 deposition.

Fusarium graminearum produces the mycotoxin deoxynivalenol (DON) which promotes its expansion during infection on its plant host wheat. Conditional expression of DON production during infection is poorly characterized. Wheat produces the defense compound putrescine, which induces hypertranscription of DON biosynthetic genes (FgTRIs) and subsequently leads to DON accumulation during infection. Further, the regulatory mechanisms of FgTRIs hypertranscription upon putrescine treatment were investigated. The transcription factor FgAreA regulates putrescine-mediated transcription of FgTRIs by facilitating the enrichment of histone H2B monoubiquitination (H2B ub1) and histone 3 lysine 4 di- and trimethylations (H3K4 me2/3) on FgTRIs. Importantly, a DNA-binding domain (bZIP) specifically within the Fusarium H2B ub1 E3 ligase Bre1 othologs is identified, and the binding of this bZIP domain to FgTRIs depends on FgAreA-mediated chromatin rearrangement. Interestingly, H2B ub1 regulates H3K4 me2/3 via the methyltransferase complex COMPASS component FgBre2, which is different from Saccharomyces cerevisiae. Taken together, our findings reveal the molecular mechanisms by which host-generated putrescine induces DON production during F. graminearum infection. Our results also provide a novel insight into the role of putrescine during phytopathogen-host interactions and broaden our knowledge of H2B ub1 biogenesis and crosstalk between H2B ub1 and H3K4 me2/3 in eukaryotes.

Scale up of Transmembrane NADH Oxidation in Synthetic Giant Vesicles.

The transfer of electrons across and along biological membranes drives the cellular energetics. In the context of artificial cells, it can be mimicked by minimal means, while using synthetic alternatives of the phospholipid bilayer and the electron-transducing proteins. Furthermore, the scaling up to biologically relevant and optically accessible dimensions may provide further insight and allow assessment of individual events but has been rarely attempted so far. Here, we visualized the mediated transmembrane oxidation of encapsulated NADH in giant unilamellar vesicles via confocal laser scanning and time-correlated single photon counting wide-field microscopy. To this end, we first augmented phospholipid membranes with an amphiphilic copolymer in order to check its influence on the oxidation kinetics spectrophotometrically. Then, we scaled up the compartments and followed the process microscopically.

A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet.

LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. The capsule network represents the features by vectors, which can account for the direction of the features and the relative position between the features. Therefore, more detailed feature information can be extracted. ResNet protects the integrity of information by passing input information to the output directly, which can solve the problem of network degradation caused by information loss in the traditional CNN propagation process to a certain extent. Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. The experimental results show that ResCapNet proposed in this article `improve the performance of LiDAR classification.

The endocytic cargo adaptor complex is required for cell-wall integrity via interacting with the sensor FgWsc2B in Fusarium graminearum.

AP2 is a heterotetrameric clathrin adaptor complex that owns important roles in vesicle generation and cargo recognition. Cell-wall integrity (CWI) pathway is essential for fungal development, virulence, and adaptation to environment stresses. To date, the relationship between AP2 and CWI is largely unknown in phytopathogenic fungi. In this study, we identified the adaptor complex FgAP2 in Fusarium graminearum. The biological function analysis showed that FgAP2 complex contains FgAP2α, FgAP2ß, FgAP2σ, and FgAP2µ, and the subunit FgAP2µ, which is required for hyphal growth, conidiation, CWI, and virulence. Yeast two-hybrid showed that FgAP2µ interacts with the CWI sensor FgWsc2B. Consistently, western blotting analysis revealed that FgWsc2B positively regulates phosphorylation of FgMgv1, the MAP kinase of CWI. Moreover, the FgWsc2B deletion mutant exhibited defects in hyphal growth, virulence, and response to CWI damaging agents. Taken together, our data indicated that FgAP2µ is involved in CWI and virulence via interacting with FgWsc2B in F. graminearum.

Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters.

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification.

Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. However, many excellent CNNs have too many parameters due to depth and complexity. Meanwhile, traditional CNNs have spatial redundancy because different convolution kernels scan and store information independently. SqueezeNet replaces a part of 3 × 3 convolution kernels in CNNs with 1 × 1 convolution kernels, decomposes the original one convolution layer into two layers, and encapsulates them into a Fire module. This structure can reduce the parameters of network. Octave Convolution (OctConv) pools some feature maps firstly and stores them separately from the feature maps with the original size. It can reduce spatial redundancy by sharing information between the two groups. In this article, in order to improve the accuracy and efficiency of the network simultaneously, Fire modules of SqueezeNet are used to replace the traditional convolution layers in OctConv to form a new dual neural architecture: OctSqueezeNet. Our experiments, conducted using two well-known LiDAR datasets and several classical state-of-the-art classification methods, revealed that our proposed classification approach based on OctSqueezeNet is able to provide competitive advantages in terms of both classification accuracy and computational amount.

Transmembrane NADH Oxidation with Tetracyanoquinodimethane.

The design of efficient schemes for nicotinamide adenine dinucleotide (NAD) regeneration is essential for the development of enzymatic biotechnological processes in order to sustain continuous production. In line with our motivation for the encapsulation of redox cascades in liposomes to serve as microbioreactors, we developed a straightforward strategy for the interfacial oxidation of entrapped NADH by ferricyanide as an external electron acceptor. Instead of the commonly applied enzymatic regeneration methods, we employed a hydrophobic redox shuttle embedded in the liposome bilayer. Tetracyanoquinodimethane (TCNQ) mediated electron transfer across the membrane and thus allowed us to shortcut and emulate part of the electron transfer chain functionality without the involvement of membrane proteins. To describe the experimental system, we developed a mathematical model which allowed for the determination of rate constants and exhibited handy predictive utility.

Correlation of interleukin-17-producing effector memory T cells and CD4+CD25+Foxp3 regulatory T cells with the phosphate levels in chronic hemodialysis patients.

BACKGROUND AND OBJECTIVES: Hyperparathyroidism and hyperphosphatemia contribute to the inflammatory effects in chronic hemodialysis (HD) patients. Interleukin-17-producing CD4+ effector memory T (Th17) cells and CD4+CD25+Foxp3 regulatory T (Treg) cells both play critical roles in immune activation and inflammation. We investigated the relationship between the Treg and Th17 cells and the phosphate level in chronic HD patients. METHODS: 105 patients aged ≥35 years on chronic HD over 3 months were enrolled. The peripheral blood mononuclear cells were collected, cultured, and stimulated by phytohemagglutinin-L, phorbol myristate acetate, and ionomycin at different time points for T cell differentiation. RESULTS: The T cell differentiation was as follows: Th17 cells (mean±standard deviation (SD): 25.61%±10.2%) and Treg cells (8.45%±4.3%). The Th17 cell differentiation was positively correlated with the phosphate and albumin levels and negatively correlated with age. The Treg cell differentiation was negatively correlated with albumin level and age. In the nondiabetes group (n=53), the Th17 cell differentiation was predominantly correlated with the phosphate and iPTH (intact parathyroid hormone) levels as well as the dialysis vintage. CONCLUSION: Higher phosphate and iPTH levels and longer dialysis duration may increase Th17 cell differentiation, especially in the nondiabetic chronic HD patients.

One-Step Multiview Clustering via Adaptive Graph Learning and Spectral Rotation.

In graph based multiview clustering methods, the ultimate partition result is usually achieved by spectral embedding of the consistent graph using some traditional clustering methods, such as k -means. However, optimal performance will be reduced by this multistep procedure since it cannot unify graph learning with partition generation closely. In this article, we propose a one-step multiview clustering method through adaptive graph learning and spectral rotation (AGLSR). For every view, AGLSR adaptively learns affinity graphs to capture similar relationships of samples. Then, a spectral embedding is designed to take advantage of the potential feature space shared by different views. In addition, AGLSR utilizes a spectral rotation strategy to obtain the discrete clustering labels from the learned spectral embeddings directly. An effective updating algorithm with proven convergence is derived to optimize the optimization problem. Sufficient experiments on benchmark datasets have clearly demonstrated the effectiveness of the proposed method in six metrics. The code of AGLSR is uploaded at https://github.com/tangchuan2000/AGLSR.

Carbon dots-based dual-emission ratiometric fluorescent sensors for fluorescence and visual detection of hypochlorite and Cu2.

Carbon dots (CDs) with blue emission were synthesized by solvothermal method using hydroquinone and 5-aminoisphthalic acid as precursors. The strong oxidation of ClO- caused the fluorescence quenching of CDs at 405 nm, and synchronously generated a new emission peak at 500 nm. Furthermore, upon the addition of Cu2+ to CDs-ClO- system, the green fluorescence at 500 nm was quenched, while the blue emission at 405 nm remained unchanged, due to the complexation between Cu2+ and the amino group on the CDs surface. Meanwhile, the fluorescence color of system changed from blue to bright green and then to dark blue by sequentially increasing the concentrations of ClO- and Cu2+. The fluorescence signal of F500/F405 exhibited a linear relationship with the concentration of ClO- and Cu2+ in a certain range, respectively. Thus, a ratiometric fluorescence sensor based on the obtained CDs were developed to sequentially detect ClO- and Cu2+ with detection limits of 0.40 µM and 0.31 µM, respectively. Additionally, the CDs were mixed with polyvinyl alcohol hydrogel to form test strips, which were successfully used for visual detection of ClO- and Cu2+. Satisfactory results were also obtained in the analysis of ClO- and Cu2+ in actual water samples.

Hierarchical and dynamic graph attention network for drug-disease association prediction.

In the realm of biomedicine, the prediction of associations between drugs and diseases holds significant importance. Yet, conventional wet lab experiments often fall short of meeting the stringent demands for prediction accuracy and efficiency. Many prior studies have predominantly focused on drug and disease similarities to predict drug-disease associations, but overlooking the crucial interactions between drugs and diseases that are essential for enhancing prediction accuracy. Hence, in this paper, a resilient and effective model named Hierarchical and Dynamic Graph Attention Network (HDGAT) has been proposed to predict drug-disease associations. Firstly, it establishes a heterogeneous graph by leveraging the interplay of drug and disease similarities and associations. Subsequently, it harnesses the capabilities of graph convolutional networks and bidirectional long short-term memory networks (Bi-LSTM) to aggregate node-level information within the heterogeneous graph comprehensively. Furthermore, it incorporates a hierarchical attention mechanism between convolutional layers and a dynamic attention mechanism between nodes to learn embeddings for drugs and diseases. The hierarchical attention mechanism assigns varying weights to embeddings learned from different convolutional layers, and the dynamic attention mechanism efficiently prioritizes inter-node information by allocating each node with varying rankings of attention coefficients for neighbour nodes. Moreover, it employs residual connections to alleviate the over-smoothing issue in graph convolution operations. The latent drug-disease associations are quantified through the fusion of these embeddings ultimately. By conducting 5-fold cross-validation, HDGAT's performance surpasses the performance of existing state-of-the-art models across various evaluation metrics, which substantiates the exceptional efficacy of HDGAT in predicting drug-disease associations.