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Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA–RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.

Background Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations. Result In this study, we develop a method (DWNN-RLS) to predict circRNA-disease associations based on Regularized Least Squares of Kronecker product kernel. The similarity of circRNAs is computed from the Gaussian Interaction Profile(GIP) based on known circRNA-disease associations. In addition, the similarity of diseases is integrated by the mean of GIP similarity and sematic similarity which is computed by the direct acyclic graph (DAG) representation of diseases. The kernels of circRNA-disease pairs are constructed from the Kronecker product of the kernels of circRNAs and diseases. DWNN (decreasing weight k-nearest neighbor) method is adopted to calculate the initial relational score for new circRNAs and diseases. The Kronecker product kernel based regularised least squares approach is used to predict new circRNA-disease associations. We adopt 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to assess the prediction performance of our method, and compare it with other six competing methods (RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP). Conlusion The experiment results show that DWNN-RLS reaches the AUC values of 0.8854, 0.9205 and 0.9701 in 5CV, 10CV and LOOCV, respectively, which illustrates that DWNN-RLS is superior to the competing methods RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP. In addition, case studies also show that DWNN-RLS is an effective method to predict new circRNA-disease associations.

Background Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

Dual oxidases (DUOXs) produce hydrogen peroxide by transferring electrons from intracellular NADPH to extracellular oxygen. They are involved in many crucial biological processes and human diseases, especially in thyroid diseases. DUOXs are protein complexes co-assembled from the catalytic DUOX subunits and the auxiliary DUOXA subunits and their activities are regulated by intracellular calcium concentrations. Here, we report the cryo-EM structures of human DUOX1-DUOXA1 complex in both high-calcium and low-calcium states. These structures reveal the DUOX1 complex is a symmetric 2:2 hetero-tetramer stabilized by extensive inter-subunit interactions. Substrate NADPH and cofactor FAD are sandwiched between transmembrane domain and the cytosolic dehydrogenase domain of DUOX. In the presence of calcium ions, intracellular EF-hand modules might enhance the catalytic activity of DUOX by stabilizing the dehydrogenase domain in a conformation that allows electron transfer. Dual oxidases (DUOXs), assembled from the catalytic DUOX and the auxiliary DUOXA subunits, produce hydrogen peroxide by transferring electrons from intracellular NADPH to extracellular oxygen in a calcium-activated manner. Here authors report the cryo-EM structures of human DUOX1-DUOXA1 complex in both high-calcium and low-calcium states.

Cancer-secreted exosomal miRNAs are emerging mediators of cancer-stromal cross-talk in the tumor environment. Our previous miRNAs array of cervical squamous cell carcinoma (CSCC) clinical specimens identified upregulation of miR-221-3p. Here, we show that miR-221-3p is closely correlated with peritumoral lymphangiogenesis and lymph node (LN) metastasis in CSCC. More importantly, miR-221-3p is characteristically enriched in and transferred by CSCC-secreted exosomes into human lymphatic endothelial cells (HLECs) to promote HLECs migration and tube formation in vitro, and facilitate lymphangiogenesis and LN metastasis in vivo according to both gain-of-function and loss-of-function experiments. Furthermore, we identify vasohibin-1 (VASH1) as a novel direct target of miR-221-3p through bioinformatic target prediction and luciferase reporter assay. Re-expression and knockdown of VASH1 could respectively rescue and simulate the effects induced by exosomal miR-221-3p. Importantly, the miR-221-3p-VASH1 axis activates the ERK/AKT pathway in HLECs independent of VEGF-C. Finally, circulating exosomal miR-221-3p levels also have biological function in promoting HLECs sprouting in vitro and are closely associated with tumor miR-221-3p expression, lymphatic VASH1 expression, lymphangiogenesis, and LN metastasis in CSCC patients. In conclusion, CSCC-secreted exosomal miR-221-3p transfers into HLECs to promote lymphangiogenesis and lymphatic metastasis via downregulation of VASH1 and may represent a novel diagnostic biomarker and therapeutic target for metastatic CSCC patients in early stages.

The present study was envisaged to investigate the chemical constituents and the intervention effects of Portulaca oleracea extract (POE) on acute alcoholic liver injury of rats. The chemical composition of POE was detected by high performance liquid chromatography (HPLC). Sixty male Wistar rats were divided into 6 groups: Normal control (NC) group, acute alcoholic liver injury model group (ALI), low, medium and high dose of POE (25, 50, 100 mg/kg) groups and bifendate (BF, 3.75 mg/kg) group. Each group was given by intragastrical administration for 7 days. Alcoholic liver injury was induced in the experimental model by administering 50% ethanol at 8 mL/kg and repeated administration after 6 h, for a period of 7 days. The results showed that pretreatment with POE significantly reduced the ethanol-elevated serum level of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and triglyceride (TG). The activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) in liver were enhanced followed by administration of POE, while the content of nitric oxide (NO) and malondialdehyde (MDA) was found to decrease. Hepatic content of tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) was also reduced by POE treatment. These results indicated that POE could increase the antioxidant capacity and relieve the inflammatory injury of the liver cells induced by ethanol. Meanwhile, in our study, POE reduced the expression of miR-122, acetyl coenzyme A carboxylase (ACC) 1 mRNA and protein and increased the expression of lipoprotein lipase (LPL) mRNA and protein in liver, which indicated that POE could improve the lipid metabolism disorder induced by ethanol. Our findings suggested that POE had protective effects on acute alcoholic liver injury of rats.

Sarcopenia is a progressive and widespread skeletal muscle disease that is related to an increased possibility of adverse consequences such as falls, fractures, physical disabilities and death, and its risk increases with age. With the deepening of the understanding of sarcopenia, the disease has become a major clinical disease of the elderly and a key challenge of healthy ageing. However, the exact molecular mechanism of this disease is still unclear, and the selection of treatment strategies and the evaluation of its effect are not the same. Most importantly, the early symptoms of this disease are not obvious and are easy to ignore. In addition, the clinical manifestations of each patient are not exactly the same, which makes it difficult to effectively study the progression of sarcopenia. Therefore, it is necessary to develop and use animal models to understand the pathophysiology of sarcopenia and develop therapeutic strategies. This paper reviews the mouse models that can be used in the study of sarcopenia, including ageing models, genetically engineered models, hindlimb suspension models, chemical induction models, denervation models, and immobilization models; analyses their advantages and disadvantages and application scope; and finally summarizes the evaluation of sarcopenia in mouse models.

ATP-sensitive potassium channels (K ATP ) are metabolic sensors that convert the intracellular ATP/ADP ratio to the excitability of cells. They are involved in many physiological processes and implicated in several human diseases. Here we present the cryo-EM structures of the pancreatic K ATP channel in both the closed state and the pre-open state, resolved in the same sample. We observe the binding of nucleotides at the inhibitory sites of the Kir6.2 channel in the closed but not in the pre-open state. Structural comparisons reveal the mechanism for ATP inhibition and Mg-ADP activation, two fundamental properties of K ATP channels. Moreover, the structures also uncover the activation mechanism of diazoxide-type K ATP openers. K ATP channels are energy sensors. Here, authors report the Cryo-EM structures of pancreatic K ATP in both the closed state and the pre-open state. These structures illuminate the mechanism of K ATP channel regulation by the intracellular nucleotides.

Mechanosensitive ion channels convert mechanical stimuli into a flow of ions. These channels are widely distributed from bacteria to higher plants and humans, and are involved in many crucial physiological processes. Here we show that two members of the OSCA protein family in Arabidopsis thaliana, namely AtOSCA1.1 and AtOSCA3.1, belong to a new class of mechanosensitive ion channels. We solve the structure of the AtOSCA1.1 channel at 3.5-Å resolution and AtOSCA3.1 at 4.8-Å resolution by cryo-electron microscopy. OSCA channels are symmetric dimers that are mediated by cytosolic inter-subunit interactions. Strikingly, they have structural similarity to the mammalian TMEM16 family proteins. Our structural analysis accompanied with electrophysiological studies identifies the ion permeation pathway within each subunit and suggests a conformational change model for activation.

The main purpose of the paper is to investigate the relationship between technological innovation and income inequality for China based on the financial Kuznets curve (FKC) hypothesis. The study uses time-series data from 1985 to 2019. We employ the Johansen cointegration, ARDL model and VECM Granger causality techniques to analyze the links between the variables. We also use the DOLS, FMOLS and CCR mechanisms to estimate the long-run parameters. The paper finds that the FKC is valid for China’s economy in the long run. Technological innovation positively affects the urban-rural income gap, while there is an inverted-U shaped between financial development and the urban-rural income gap. The relationship between financial development and the urban-rural income gap is bi-directional causality. Technological innovation and the urban-rural income gap cause each other. Empirical results suggest a twofold policy meaning: i) to further the financial system and ii) to eliminate the adverse impacts of technological innovations on income distribution.