Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.

BACKGROUND: With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. METHODS: In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. RESULTS: The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. CONCLUSIONS: The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Effect of Chestnut (Castanea Mollissima Blume) Bur Polyphenol Extract on Shigella dysenteriae: Antibacterial Activity and the Mechanism.

Shigella dysenteriae is a highly pathogenic microorganism that can cause human bacillary dysentery by contaminating food and drinking water. This study investigated the antibacterial activity of chestnut bur polyphenol extract (CBPE) on S. dysenteriae and the underlying mechanism. The results showed that the minimum inhibitory concentration (MIC) of CBPE for S. dysenteriae was 0.4 mg/mL, and the minimum bactericidal concentration (MBC) was 1.6 mg/mL. CBPE treatment irreversibly disrupted cell morphology, decreased cell activity, and increased cell membrane permeability, cell membrane depolarization, and cell content leakage of S. dysenteriae, indicating that CBPE has obvious destructive effects on the cell membrane and cell wall of S. dysenteriae. Combined transcriptomic and metabolomics analysis revealed that CBPE inhibits S. dysenteriae by interfering with ABC protein transport, sulfur metabolism, purine metabolism, amino acid metabolism, glycerophospholipid metabolism, and some other pathways. These findings provide a theoretical basis for the prevention and treatment of S. dysenteriae infection with extract from chestnut burs.

Enhancement of Potential Health Benefits of Insoluble Dietary Fiber from the Fruiting Body of Medicinal Mushroom Schizophyllum commune (Agaricomycetes) through Superfine Pulverization.

The objective of the current study is to reveal the influence of particle size of ground Schizophyllum commune and its extracted dietary fiber (DF) on physicochemical and antioxidant properties. Sch. commune powder and the extracted DF was ground by regular and superfine grinding, and their particle sizes were determined using laser diffraction particle size analyzer. The results indicated that superfine grinding could significantly pulverize DF particles to micro-scale; the particle size distribution presented a Gaussian distribution. The soluble DF in Sch. commune was increased effectively with superfine grinding. Sub-micronized insoluble DF showed increased total phenolic content (TPC) and ferric reducing antioxidant power (FRAP). Moreover, with particle size reduction, the oil binding capacity (OBC), nitrite ion absorption capacity (NIAC), cation-exchange capacity (CEC), cholesterol absorption capacity (CAC), and Pb ion adsorption capacity were significantly (p < 0.05) increased and the water retention capacity (WRC), swelling capacity (SC) and Cu, Zn ions adsorption capacity had no significant changes. A kind of health beneficial DF with higher soluble DF content, OBC, NIAC, CEC, CAC, Pb ion adsorption capacity and antioxidant activity (TPC and FRAP) was obtained through superfine grinding. Sch. commune DF could be potentially used as an ingredient for functional food and nutraceuticals.

Anti-inflammatory effect of flavonoids from chestnut flowers in lipopolysaccharide-stimulated RAW 264.7 macrophages and acute lung injury in mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Chestnut flowers were one of the by-products during chestnut industrial processing. Chestnut (Castanea mollissima Blume) flower is rich in flavonoids and has been used as a traditional medicine to treat a variety of diseases including respiratory disorders for a long history. AIM OF THE STUDY: The present study aims to investigate the potential anti-inflammatory effect of flavonoids from chestnut flower (FCF) in lipopolysaccharide (LPS)-treated RAW 264.7 cells and stimulated acute lung injury (ALI) in mice. MATERIALS AND METHODS: HPLC-ESI-MS/MS was applied to identify flavonoids from Chestnut flower. The ROS content in cells and lung tissue was measured by flow cytometry. The malondialdehyde (MDA) content, superoxide dismutase (SOD) activity and glutathione (GSH) content in cells and bronchoalveolar lavage fluid (BALF) was analyzed by photometry. Furthermore, the level of pro-inflammatory factors was analyzed by ELISA, and the expression of inflammatory gene mRNA by fluorescence quantitative PCR. H&E staining was used to evaluate the degree of lung tissue injury in mice. MPO activity was used to measure the degree of neutrophil infiltration. Total protein content was detected by BCA method. RESULTS: A total of forty-nine flavonoids compounds were tentatively identified in FCF by mass spectrometry analysis. The results of cell experiment suggested that FCF could alleviate oxidative injury via increasing SOD activity and GSH content, as well as inhibiting the production of intracellular ROS and MDA. FCF exerted its protective effect by suppressing the expression of both inducible nitric oxide synthase (iNOS) and cycooxygenase 2 (COX-2) to inhibit the synthesis of pro-inflammatory factors and cytokines, including NO, PGE2, TNF-α, IL-6 and IL-1ß. Besides, FCF treatment could alleviate the thickening of alveolar wall and pulmonary congestion in LPS-treated ALI mice, and significantly inhibit the activity of myeloperoxidas (MPO) and the expression of cytokines in BALF. CONCLUSIONS: FCF could ameliorate inflammation and oxidative stress in LPS-treated inflammation, resulting in an overall improvement in both macroscopic and histological parameters.

Compositional characterization of Pyrus ussuriensis Maxim and their antioxidant activities and induction of apoptosis in Bel-7402 cell.

In this work, a comparison study on active sites (the total phenolic, total flavonoids, and total triterpenes contents) and antioxidant activities (DPPH, ABTS, and FRAP assays) of different fractions from Pyrus ussuriensis Maxim was evaluated. Moreover, the inhibition capability on human hepatocarcinoma cells Bel-7402 cells and the mechanism was also discussed. Results showed that the ethyl acetate fraction significantly scavenged DPPH and ABTS+ radicals, exhibited ferric ion reducing antioxidant, and inhibited Bel-7402 cells proliferation. In addition, oleanolic acid was the dominant compound act on the Bel-7402 cells in the extract and it induced apoptosis by the caspase pathway and induced cell cycle arrest at the G0/G1 phase by inhibiting the cyclin D1/CDK4 pathway. The extracts of P. ussuriensis Maxim were confirmed to have anti-oxidative and antiproliferative effects against Bel-7402 cell in vitro. PRACTICAL APPLICATIONS: Fruits and vegetables which contain high levels of antioxidants can help to reduce the risk of chronic diseases such as cancer. Pyrus ussuriensis Maxim is a kind of edible and medical fruit with multiple bioactivities, whereas the capability to anti-lung cancer activity has not been investigated. The extracts of P. ussuriensis Maxim were revealed to have anti-oxidative and antiproliferative effects against Bel-7402 cell in vitro. Accordingly, it is the first time to verify that oleanolic acid was the main chemical components of P. ussuriensis with antitumor potential.

Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks.

MOTIVATION: Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibility states. However, lacking the confidence score for predicted values has limited their applications. Here we investigated whether or not we can make a reasonable prediction of absolute errors for predicted backbone torsion angles, Cα-atom-based angles and torsion angles, solvent accessibility, contact numbers and half-sphere exposures by employing deep neural networks. RESULTS: We found that angle-based errors can be predicted most accurately with Spearman correlation coefficient (SPC) between predicted and actual errors at about 0.6. This is followed by solvent accessibility (SPC∼0.5). The errors on contact-based structural properties are most difficult to predict (SPC between 0.2 and 0.3). We showed that predicted errors are significantly better error indicators than the average errors based on secondary-structure and amino-acid residue types. We further demonstrated the usefulness of predicted errors in model quality assessment. These error or confidence indictors are expected to be useful for prediction, assessment, and refinement of protein structures. AVAILABILITY AND IMPLEMENTATION: The method is available at http://sparks-lab.org as a part of SPIDER2 package. CONTACT: yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins.

MOTIVATION: Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cß (HSEß) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEß. RESULTS: This study developed a novel method for predicting both HSEα and HSEß (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEß (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEß (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction. AVAILABILITY AND IMPLEMENTATION: The method is available at http://sparks-lab.org CONTACT: yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.