Structural Basis for Reaction Mechanism and Drug Delivery System of Chromoprotein Antitumor Antibiotic C-1027 (BIOORGANIC CHEMISTRY-Bioactive Chemistry)

Antitumor antibiotic C-1027 that is regarded as a natural model of drug delivery system, consists of a carrier apoprotein (Apo) and an enediyne chromophore (Chr). We have compared three solution structures of the DNA-Chr complex, Apo-Chr complex, and free Chr determined by high-resolution NMR experiments. The guest molecule, C-1027 chromophore, showed two distinct binding modes fitted to binding sites of the hosts (target DNA and carrier Apo). The novel Chr interacts with DNA through its benzoxazolinate and aminosugar moieties, and also with Apo through the benzoxazolinate and macrocyclic moieties. The superposition of Chrs in these three states clearly revealed conformational deviation of the 16-membered macrocyclic moiety containing intra-chlorophenol ring. Ab initio calculations supported good correlation between the reactivity and the conformational alteration of Chr induced in hosts. The present results provide molecular basis and implication for the host-recognition mode, the reaction mechanism, and drug delivery system of chromoprotein C-1027

Efficient host excitation in thiosilicate phosphors of lanthanide(III)-doped Y4(SiS4)3

Lanthanide (Ln)-doped yttrium thiosilicate (Y1−x Ln x )4(SiS4)3 is synthesized, and its optical properties are studied. In (Y1−x Tb x )4(SiS4)3, the green photoluminescence band corresponding to the intra 4f transition of 5D4  →  7F5 appears at 545 nm and becomes the maximum for x  =  0.2 in the range x  =  0.01 to 1. The internal quantum efficiency is higher (11% for x  =  0.01) for the thiosilicate host excitation (360 nm) than for the direct excitation (1.6%) of the intra 4f transition of 5D4  ←  7F6 (489 nm). A time-resolved photoluminescence study shows that the luminescence of defect states of thiosilicate hosts decays faster (typically 10–30 ns) for higher Tb3+ concentration x. In addition, the rise time of Tb3+ photoluminescence is shorter (10–40 ns) for greater x. Energy transfer from the thiosilicate host to Tb3+ is discussed using these results. For all of (Y1−x Ln x )4(SiS4)3 (x  =  0.01, Ln  =  Pr, Nd, Dy, Er or Tm), the internal quantum efficiency is higher for the host excitation (11–21%) than for the direct excitation of intra 4f transitions (1.1–12%). A photoluminescence excitation study reveals broad host absorption in 300–400 nm for Ln luminescence. These results show the promising characteristics of the host absorption of (Y1−x Ln x )4(SiS4)3 phosphors and their optical properties

Broad luminescence of Ce3+ in multiple sites in (La,Ce,Y)6Si4S17

We have developed novel broad luminescent phosphors (La,Ce,Y)6Si4S17 including multiple substituted sites of Ce3+ . They have formed a triclinic structure (P  −  1) with three kinds of coordination structures around Ce3+ ions. The broad photoluminescence (PL) spectra at the range from 430 nm to 700 nm were observed, and they can be respectively decomposed into three PL bands. Moreover, the correlation between the three PL bands and the three substitution sites for Ce3+ was confirmed through the PL spectra at 78 K and Van Uitert\u27s universal equation

Novel inhibitor candidates of TRPV2 prevent damage of dystrophic myocytes and ameliorate against dilated cardiomyopathy in a hamster model

Transient receptor potential cation channel, subfamily V, member 2 (TRPV2) is a principal candidate for abnormal Ca²⁺-entry pathways, which is a potential target for therapy of muscular dystrophy and cardiomyopathy. Here, an in silico drug screening and the following cell-based screening to measure the TRPV2 activation were carried out in HEK293 cells expressing TRPV2 using lead compounds (tranilast or SKF96365) and off-patent drug stocks. We identified 4 chemical compounds containing amino-benzoyl groups and 1 compound (lumin) containing an ethylquinolinium group as candidate TRPV2 inhibitors. Three of these compounds inhibited Ca²⁺ entry through both mouse and human TRPV2, with IC₅₀ of less than 10 μM, but had no apparent effect on other members of TRP family such as TRPV1 and TRPC1. Particularly, lumin inhibited agonist-induced TRPV2 channel activity at a low dose. These compounds inhibited abnormally increased Ca²⁺ influx and prevented stretch-induced skeletal muscle damage in cultured myocytes from dystrophic hamsters (J2N-k). Further, they ameliorated cardiac dysfunction, and prevented disease progression in vivo in the same J2N-k hamsters developing dilated cardiomyopathy as well as muscular dystrophy. The identified compounds described here are available as experimental tools and represent potential treatments for patients with cardiomyopathy and muscular dystrophy

Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As biomedical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.htm

Robust prognostic prediction model developed with integrated biological markers for acute myocardial infarction

Commonly used prediction methods for acute myocardial infarction (AMI) were created before contemporary percutaneous coronary intervention was recognized as the primary therapy. Although several studies have used machine learning techniques for prognostic prediction of patients with AMI, its clinical application has not been achieved. Here, we developed an online application tool using a machine learning model to predict in-hospital mortality in patients with AMI. A total of 2, 553 cases of ST-elevation AMI were assigned to 80% training subset for cross validation and 20% test subset for model performance evaluation. We implemented random forest classifier for the binary classification of in-hospital mortality. The selected best feature set consisted of ten clinical and biological markers including max creatine phosphokinase, hemoglobin, heart rate, creatinine, systolic blood pressure, blood sugar, age, Killip class, white blood cells, and c-reactive protein. Our model achieved high performance: the area under the curve of the receiver operating characteristic curve for the test subset, 0.95: sensitivity, 0.89: specificity, 0.91: precision, 0.43: accuracy, 0.91 respectively, which outperformed common scoring methods. The freely available application tool for prognostic prediction can contribute to risk triage and decision-making in patient-centered modern clinical practice for AMI

Omeprazole- and Esomeprazole-associated Hypomagnesaemia: Data Mining of the Public Version of the FDA Adverse Event Reporting System

Objective: Case reports showing that proton-pump inhibitors (PPIs), omeprazole and esomeprazole, can cause hypomagnesaemia have been accumulating since 2006. In this study, the reports submitted to the Adverse Event Reporting System (AERS) of the US Food and Drug Administration (FDA) were evaluated to assess omeprazole and esomeprazole in terms of susceptibility to hypomagnesaemia

Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network

Femtosecond X-ray pulse lasers are promising probes for the elucidation of the multiconformational states of biomolecules because they enable snapshots of single biomolecules to be observed as coherent diffraction images. Multi-image processing using an X-ray free-electron laser has proven to be a successful structural analysis method for viruses. However, the performance of single-particle analysis (SPA) for flexible biomolecules with sizes ≤100 nm remains difficult. Owing to the multiconformational states of biomolecules and noisy character of diffraction images, diffraction image improvement by multi-image processing is often ineffective for such molecules. Herein, a single-image super-resolution (SR) model was constructed using an SR convolutional neural network (SRCNN). Data preparation was performed in silico to consider the actual observation situation with unknown molecular orientations and the fluctuation of molecular structure and incident X-ray intensity. It was demonstrated that the trained SRCNN model improved the single-particle diffraction image quality, corresponding to an observed image with an incident X-ray intensity (approximately three to seven times higher than the original X-ray intensity), while retaining the individuality of the diffraction images. The feasibility of SPA for flexible biomolecules with sizes ≤100 nm was dramatically increased by introducing the SRCNN improvement at the beginning of the various structural analysis schemes