Transcriptional suppression of breast cancer resistance protein (BCRP) by wild-type p53 through the NF-κB pathway in MCF-7 cells

AbstractBreast cancer resistance protein (BCRP) has been shown to confer multidrug resistance, but the mechanisms of its regulation are poorly understood. Here, we investigate the effects of wild-type and mutant p53, and nuclear factor kappa-B (NF-κB) (p50) on BCRP promoter activity in MCF-7 cells. Our results demonstrated that wild-type p53 markedly suppressed BCRP activity and enhanced the chemosensitivity of cells to mitoxantrone, whereas mutant p53 had little inhibitory effect. After inhibition of NF-κB, similar results were obtained. Following knockdown of endogenous p53, BCRP and p50 expressions were increased, and the chemosensitivity of the cells to mitoxantrone was decreased. We conclude that wild-type p53 acts as a negative regulator of BCRP gene transcription

Correction to: Mining a stroke knowledge graph from literature

From Springer Nature via Jisc Publications RouterHistory: registration 2021-11-30, collection 2021-12, pub-electronic 2021-12-08, online 2021-12-08Publication status: Publishe

Mining a stroke knowledge graph from literature

From Springer Nature via Jisc Publications RouterHistory: collection 2021-05, received 2021-06-13, accepted 2021-07-06, registration 2021-07-09, pub-electronic 2021-07-29, online 2021-07-29Publication status: PublishedFunder: National High-level Personnel for Defense Technology Program; Grant(s): (2017-JCJQ-ZQ-013), and NSF 61902405Funder: the national key r&d project by ministry of science and technology of china; Grant(s): 2018YFB1003203Funder: the open fund from the State Key Laboratory of High Performance Computing; Grant(s): No. 201901-11Funder: National Science Foundation of China; Grant(s): U1811462Abstract: Background: Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the “Western” biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases. Results: To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine. Conclusions: Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Strok

HFN : Heterogeneous feature network for multivariate time series anomaly detection

As the key step of anomaly detection for multivariate time-series (MTS) data, learning the relations among different variables has been explored by many approaches. However, most existing approaches overlook the heterogeneity among variables, that is, different types of variables (continuous numerical variables, discrete categorical variables or hybrid variables) may have different edge distributions. In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS. Specifically, we first combine the embedding similarity subgraph generated by sensor embedding and the feature value similarity subgraph generated by sensor values to construct a time-series heterogeneous graph, which fully utilizes the rich heterogeneous mutual information among variables. Then, a prediction model containing nodes and channel attentions is jointly optimized to obtain better time-series representations. This approach fuses the state-of-the-art technologies of heterogeneous graph structure learning (HGSL) and representation learning. Experimental results on four sensor datasets from real-world applications demonstrate that our approach achieves more accurate anomaly detection compared to baseline methods, laying a foundation for the rapid positioning of anomalies

STGAT-MAD: Spatial-Temporal Graph Attention Network for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations and heterogeneity. This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGATMAD). The core of our framework is to coherently capture the feature and temporal correlations among multivariate time-series data with stackable STGAT networks. Meanwhile, a multi-scale input network is exploited to capture the temporal correlations in different time-scales. Experiments on a new wind turbine dataset (built and released by us) and three public datasets show that our method detects anomalies more accurately than baseline approaches and provide interpretability through observing the attention score among multiple sensors and different times

RNA-Seq reveals the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango (Mangifera indica L.) peel

IntroductionFlavonoids are important water soluble secondary metabolites in plants, and light is one of the most essential environmental factors regulating flavonoids biosynthesis. In the previous study, we found bagging treatment significantly inhibited the accumulation of flavonols and anthocyanins but promoted the proanthocyanidins accumulation in the fruit peel of mango (Mangifera indica L.) cultivar ‘Sensation’, while the relevant molecular mechanism is still unknown.MethodsIn this study, RNA-seq was conducted to identify the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango peel.ResultsBy weighted gene co-expression network analysis (WGCNA), 16 flavonoids biosynthetic genes were crucial for different flavonoids compositions biosynthesis under bagging treatment in mango. The higher expression level of LAR (mango026327) in bagged samples might be the reason why light inhibits proanthocyanidins accumulation in mango peel. The reported MYB positively regulating anthocyanins biosynthesis in mango, MiMYB1, has also been identified by WGCNA in this study. Apart from MYB and bHLH, ERF, WRKY and bZIP were the three most important transcription factors (TFs) involved in the light-regulated flavonoids biosynthesis in mango, with both activators and repressors. Surprisingly, two HY5 transcripts, which are usually induced by light, showed higher expression level in bagged samples.DiscussionOur results provide new insights of the regulatory effect of light on the flavonoids biosynthesis in mango fruit peel

PathNER: A tool for systematic identification of biological pathway mentions in the literature

BACKGROUND: Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature. RESULTS: We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase. CONCLUSIONS: In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/