Transcript and protein profiling analysis of OTA-induced cell death reveals the regulation of the toxicity response process in Arabidopsis thaliana

Ochratoxin A (OTA) is a toxic isocoumarin derivative produced by various species of mould which mainly grow on grain, coffee, and nuts. Recent studies have suggested that OTA induces cell death in plants. To investigate possible mechanisms of OTA phytotoxicity, both digital gene expression (DGE) transcriptomic and two-dimensional electrophoresis proteomic analyses were used, through which 3118 genes and 23 proteins were identified as being up- or down-regulated at least 2-fold in Arabidopsis leaf in response to OTA treatment. First, exposure of excised Arabidopsis thaliana leaves to OTA rapidly causes the hypersensitive reponse, significantly accelerates the increase of reactive oxygen species and malondialdehyde, and enhances antioxidant enzyme defence responses and xenobiotic detoxification. Secondly, OTA stimulation causes dynamic changes in transcription factors and activates the membrane transport system dramatically. Thirdly, a concomitant persistence of compromised photosynthesis and photorespiration is indicative of a metabolic shift from a highly active to a weak state. Finally, the data revealed that ethylene, salicylic acid, jasmonic acid, and mitogen-activated protein kinase signalling molecules mediate the process of toxicity caused by OTA. Profiling analyses on Arabidopsis in response to OTA will provide new insights into signalling transduction that modulates the OTA phytotoxicity mechanism, facilitate mapping of regulatory networks, and extend the ability to improve OTA tolerance in Arabidopsis

Ion selective separators based on graphene oxide for stabilizing lithium organic batteries

Ion selective membranes exist widely in the biological world and have been mimicked by scientists and engineers for the purpose of manipulating ion flow. For instance, polymers with sulfonate groups like Nafion are applied in proton exchange membrane fuel cells for facilitating proton transport whilst blocking other species. Herein, ion selective separators composed of graphene oxide (GO) and Super P (or graphene) are applied for stabilizing lithium organic batteries. The reconstructed GO sheets form numerous negatively charged nanochannels, which selectively allow the transport of lithium ions and reject the electroactive organic anions. Meanwhile, Super P (or graphene) on top of the coating layer functions as the upper current collector for reactivating the electroactive organic species. In this work, two typical carbonyl electrode materials with, respectively, two (anthraquinone, AQ) and four (perylene-3,4,9,10-tetracarboxylic dianhydride, PTCDA) carbonyl groups are applied as examples. Compared to the pristine Celgard separator, the ion selective separators enable significantly alleviated self-discharge, improved coulombic efficiency and cycling stability

Multimodal MRI-based classification of migraine: using deep learning convolutional neural network

Abstract Background Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number of migraineurs do not receive the accurate diagnosis when using traditional diagnostic criteria based on the guidelines of the International Headache Society. As such, there is substantial interest in developing automated methods to assist in the diagnosis of migraine. Methods To the best of our knowledge, no studies have evaluated the potential of deep learning technologies in assisting with the classification of migraine patients. Here, we used deep learning methods in combination with three functional measures (the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength) based on rs-fMRI data to distinguish not only between migraineurs and healthy controls, but also between the two subtypes of migraine. We employed 21 migraine patients without aura, 15 migraineurs with aura, and 28 healthy controls. Results Compared with the traditional support vector machine classifier, which has an accuracy of 83.67%, our Inception module-based convolutional neural network approach showed a significant improvement in classification output (over 86.18%). Our data also indicate that the Inception module-based CNN performs better than the AlexNet-based CNN (Inception module-based CNN reached an accuracy of 99.25%). Finally, we also found that regional functional correlation strength (RFCS) could be regarded as the optimum input out of the three indices (ALFF, ReHo, RFCS). Conclusions Overall, our study shows that combining the three functional measures of rs-fMRI with deep learning classification is a powerful method to distinguish between migraineurs and healthy individuals. Our data also highlight that deep learning-based frameworks could be used to develop more complicated models or systems to aid in clinical decision making in the future

A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks

Background. As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease. Method. In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture. In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM. Result. We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is 89.95% in fivefold cross-validation. Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively. Conclusion. It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness

Improving the Li-S battery performance by applying a combined interface engineering approach on the Li2S cathode

This journal is © The Royal Society of Chemistry. Lithium sulfide (Li2S) has been a promising candidate for Li-S battery cathode materials due to its high theoretical specific capacity and ability to be paired with safer lithium metal-free anodes. However, the low active material utilization and the short cycle life, which stem from the poor conductivity and the polysulfide dissolution-diffusion problem, hinder the further application of Li-S batteries. Herein, a combined interface engineering approach, which includes an ion-selective sulfonated poly(ether ether ketone) (SPEEK) membrane and a freestanding single wall carbon nanotube (SWCNT)/reduced graphene oxide (rGO) interlayer, has been demonstrated successfully to enhance the performance of Li-S batteries with a Li2S cathode. The SPEEK membrane is integrated into the Li2S cathode, functioning as both a barrier of the polysulfides and a highway for Li+ ions, due to its negatively charged ionic nanochannels which have been proved to mediate high-speed ion transport. With SPEEK, the cell presents high rate performance (above 400 mA h g-1 at 10 A g-1). Furthermore, to balance the conductivity of ions and electrons, a SWCNT/rGO interlayer is inserted between the cathode and the separator to improve the electronic conductivity, block the polysulfides, and provide space for sustained electrochemical reactions, resulting in improved capacity and life. With the interlayer, a high capacity of 620 mA h g-1 is retained after 80 cycles

iTRAQ Mitoproteome Analysis Reveals Mechanisms of Programmed Cell Death in Arabidopsis thaliana Induced by Ochratoxin A

Ochratoxin A (OTA) is one of the most common and dangerous mycotoxins in the world. Previous work indicated that OTA could elicit spontaneous HR-like lesions formation Arabidopsis thaliana, reactive oxygen species (ROS) play an important role in OTA toxicity, and their major endogenous source is mitochondria. However, there has been no evidence as to whether OTA induces directly PCD in plants until now. In this study, the presence of OTA in Arabidopsis thaliana leaves triggered accelerated respiration, increased production of mitochondrial ROS, the opening of ROS-dependent mitochondrial permeability transition pores and a decrease in mitochondrial membrane potential as well as the release of cytochrome c into the cytosol. There were 42 and 43 significantly differentially expressed proteins identified in response to exposure to OTA for 8 and 24 h, respectively, according to iTRAQ analysis. These proteins were mainly involved in perturbation of the mitochondrial electron transport chain, interfering with ATP synthesis and inducing PCD. Digital gene expression data at transcriptional level was consistent with the cell death induced by OTA being PCD. These results indicated that mitochondrial dysfunction was a prerequisite for OTA-induced PCD and the initiation and execution of PCD via a mitochondrial-mediated pathway

Establishing an ANO1-Based Cell Model for High-Throughput Screening Targeting TRPV4 Regulators

Transient receptor potential vanilloid 4 (TRPV4) is a widely expressed cation channel that plays an important role in many physiological and pathological processes. However, most TRPV4 drugs carry a risk of side effects. Moreover, existing screening methods are not suitable for the high-throughput screening (HTS) of drugs. In this study, a cell model and HTS method for targeting TRPV4 channel drugs were established based on a calcium-activated chloride channel protein 1 Anoctamin 1 (ANO1) and a double mutant (YFP-H148Q/I152L) of the yellow fluorescent protein (YFP). Patch-clamp experiments and fluorescence quenching kinetic experiments were used to verify that the model could sensitively detect changes in intracellular Ca2+ concentration. The functionality of the TRPV4 cell model was examined through temperature variations and different concentrations of TRPV4 modulators, and the performance of the model in HTS was also evaluated. The model was able to sensitively detect changes in the intracellular Ca2+ concentration and also excelled at screening TRPV4 drugs, and the model was more suitable for HTS. We successfully constructed a drug cell screening model targeting the TRPV4 channel, which provides a tool to study the pathophysiological functions of TRPV4 in vitro

SERPINE2 promotes liver cancer metastasis by inhibiting c‐Cbl‐mediated EGFR ubiquitination and degradation

Abstract Background Liver cancer is a malignancy with high morbidity and mortality rates. Serpin family E member 2 (SERPINE2) has been reported to play a key role in the metastasis of many tumors. In this study, we aimed to investigate the potential mechanism of SERPINE2 in liver cancer metastasis. Methods The Cancer Genome Atlas database (TCGA), including DNA methylation and transcriptome sequencing data, was utilized to identify the crucial oncogene associated with DNA methylation and cancer progression in liver cancer. Data from the TCGA and RNA sequencing for 94 pairs of liver cancer tissues were used to explore the correlation between SERPINE2 expression and clinical parameters of patients. DNA methylation sequencing was used to detect the DNA methylation levels in liver cancer tissues and cells. RNA sequencing, cytokine assays, immunoprecipitation (IP) and mass spectrometry (MS) assays, protein stability assays, and ubiquitination assays were performed to explore the regulatory mechanism of SERPINE2 in liver cancer metastasis. Patient‐derived xenografts and tumor organoid models were established to determine the role of SERPINE2 in the treatment of liver cancer using sorafenib. Results Based on the public database screening, SERPINE2 was identified as a tumor promoter regulated by DNA methylation. SERPINE2 expression was significantly higher in liver cancer tissues and was associated with the dismal prognosis in patients with liver cancer. SERPINE2 promoted liver cancer metastasis by enhancing cell pseudopodia formation, cell adhesion, cancer‐associated fibroblast activation, extracellular matrix remodeling, and angiogenesis. IP/MS assays confirmed that SERPINE2 activated epidermal growth factor receptor (EGFR) and its downstream signaling pathways by interacting with EGFR. Mechanistically, SERPINE2 inhibited EGFR ubiquitination and maintained its protein stability by competing with the E3 ubiquitin ligase, c‐Cbl. Additionally, EGFR was activated in liver cancer cells after sorafenib treatment, and SERPINE2 knockdown‐induced EGFR downregulation significantly enhanced the therapeutic efficacy of sorafenib against liver cancer. Furthermore, we found that SERPINE2 knockdown also had a sensitizing effect on lenvatinib treatment. Conclusions SERPINE2 promoted liver cancer metastasis by preventing EGFR degradation via c‐Cbl‐mediated ubiquitination, suggesting that inhibition of the SERPINE2‐EGFR axis may be a potential target for liver cancer treatment