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The interaction between Pleurotus citrinipileatus Sing. ergothioneine (PCEGT) and pancreatic lipase (PL) was studied by analyzing multiple spectroscopies including enzyme kinetics, ultraviolet absorption (UV), fluorescence spectroscopy, synchronous fluorescence spectroscopy and 3D fluorescence spectroscopy as well as the PCEGR-PL interaction mechanism by molecular docking in order to clarify its inhibitory mechanism. The results showed that the inhibition process of PCEGT on PL was reversible non-competitive with 15.29 mg/mL of the half-maximal inhibitory concentration (IC50). The UV spectrum indicated there was a π→π* transition produced by the peptide bond C=O group of PL as the mass concentration of PCEGT increased, which could reflect the reinforcement of polarity and hydrophilicity. Fluorescence spectra data revealed PCEGT could efficiently static-quench the intrinsic fluorescence of PL by changing its space structure. Based on the Lineweaver-Burk equation, the binding constants (Ka) under three different temperatures were obtained. Among them the Ka value was estimated to be 1.55×103 L/mol with approximately 1 binding site at 298 K. The thermodynamic parameters including enthalpy change and enthopy change calculated by Van't Hoff's law were to be −8.57 kJ/mol and 2.55 J/(mol·K), respectively. PCEGT might combine with the amino acid residues of PL via hydrogen bonding and electrostatic force. With the increase of the concentration of PCEGT, synchronous fluorescence intensity and 3D fluorescence spectra demonstrated that the failing hydrophobicity and incremental polarity of PL could be achieved evidencing by obvious redshifts of maximum absorption peaks (6 and 3 nm) and descent fluorescence intensities (32.2% and 25.6%). Molecular docking results further reinforced that PCEGT interacted with amino acids outside catalytic site of PL via hydrogen bonding, charge attraction and van der waals' force, resulting in the inhibition of PL. This study was beneficial for the deep understanding the hyperlipidemia of PCEGT from molecular mechanism and boosted the future health food processing of Pleurotus citrinipileatus Sing..

IntroductionEpithelioid glioblastoma (eGBM) is one of the rare glioblastoma (GBM) variants in the current World Health Organization (WHO) categorization of central nervous system (CNS) tumours. However, the diagnostic basis and molecular features of eGBM have not been clearly defined to date. In this study, we aimed to molecularly characterize these tumours.MethodsThe clinicopathological, molecular, and immunohistochemical characteristics of 12 cases of eGBM were investigated.ResultsThe tumours were found to be made up of epithelioid and rhabdoid cells when examined under a microscope. Six cases (50%) harboured the BRAF V600E mutation, and NF1 mutation was detected in 2 eGBM cases (16.7%). CDKN2A/B homozygous deletion was seen in 5 cases (41.7%). TP53 mutation was recognized in 2 instances (16.7%), and TERT promoter mutation was recognized in 5 cases (41.7%).DiscussioneGBM is characterized by high molecular heterogeneity and has molecular overlaps between low-grade gliomas. Moreover, rather than being a variant or entity, the biological significance of the "epithelioid" appearance may be reduced to a simply morphological pattern. In order to target the proper treatment to suitable patients, molecular stratification via genome-wide molecular profiling will be crucial.

In order to flexibly and completely suppress dynamic interference, a flexible and robust beamforming based on JADE is proposed in this paper. In addition, it is insensitive to the gain–phase errors of the array. Firstly, the actual steering vector with gain–phase errors is separated from the received snapshot data by the joint approximate diagonalization of eigenmatrix (JADE) algorithm. Secondly, the direction of arrival (DOA) of interference can be estimated from the separated actual steering vector by the correlation coefficient method. Thus, the actual interference steering vector with gain–phase errors can be selected by the correlation coefficient with the nominal steering vector constructed by the estimated DOA. Then, the interference covariance matrix can be reconstructed by the actual interference steering vector, and the interference power estimated by the Capon power spectral. Finally, according to the prior information of the interference, only the dynamic interference covariance matrix is tapered by the novel covariance matrix trap (CMT), which can flexibly broaden and deepen the null. Simulation results show that the depth of the proposed beamformer is more than 10 dB deeper than that of the traditional algorithm in the non-stationary interference. In addition, it can save at least 2 degrees of freedom compared to the traditional method.

The profound pharmacological properties of macrocyclic compounds have led to their development as drugs. In conformationally pre-organized ring structures, the multiple functions and stereochemical complexity provided by the macrocycle result in high affinity and selectivity of protein targets while maintaining sufficient bioavailability to reach intracellular locations. Therefore, the construction of macrocycles is an ideal choice to solve the problem of “undruggable” targets. Inspection of 68 macrocyclic drugs on the market showed that 10 of them were used to treat cancer, but this structural class still has been poorly explored within drug discovery. This perspective considers the macrocyclic compounds used for anti-tumor with different targets, their advantages and disadvantages, and the various synthetic methods of them.

This paper studies stability analysis of a class of integral delay systems with multiple exponential kernels. By using the multiple Jensen inequalities established recently and the Lyapunov-Krasovskii functional approach, some new sufficient stability conditions expressed by linear matrix inequalities (LMIs) are obtained. It is shown that the obtained stability conditions are always less conservative than the existing ones. Robust stability of this class of integral delay systems with parameter uncertainties is also investigated and some sufficient conditions expressed by LMIs are obtained. The effectiveness of the proposed methods is illustrated by some numerical examples.

BACKGROUND AND PURPOSE:It has been accepted that AMPK (Adenosine monophosphate-activated protein kinase) activation exhibits many beneficial effects on glucolipid metabolism. Lysophosphatidylcholine (LPC) is an important lysophospholipid which can improve blood glucose levels in diabetic mice and attenuate inflammation by activating AMPK signal pathway in macrophages. Synthetic alkylphospholipids (ALPs), such as miltefosine, is used as an alternate of LPC for the clinical application. Here, we investigated whether miltefosine could have an impact on hepatic steatosis and related metabolic disorders. EXPERIMENTAL APPROACH:Mice were fed with high fat diet (HFD) for 16 weeks to generate an obese model. Next, the obese mice were randomly divided into three groups: saline-treated and miltefosine-treated (2.5 or 5 mg/kg/d) groups. Miltefosine was intraperitoneally administrated into mice for additional 4 weeks plus HFD treatment. KEY RESULTS:It was shown that miltefosine treatment could substantially improve glucose metabolism, prevented hepatic lipid accumulation, and inhibited liver inflammation in HFD-fed mice by activating AMPK signal pathway. In vitro, miltefosine stimulated AMPKα phosphorylation both in time and dose dependent manner and decreased lipid accumulation in liver cells. When a specific AMPK inhibitor compound C was used to treat mice, the antagonistic effects of miltefosine on HFD-induced mouse hyperlipidaemia and liver steatosis were abolished. Treatment with miltefosine also dramatically inhibited the HFD-induced liver inflammation in mice. CONCLUSIONS AND IMPLICATIONS:Here we demonstrated that miltefosine might be a new activator of AMPK signal pathway in vivo and in vitro and be useful for treatment of hepatic steatosis and related metabolic disorders.

We establish the Poincaré-type inequalities for the composition of the homotopy operator, exterior derivative operator, and the projection operator with Lφ-norm applied to the nonhomogeneous A-harmonic equation in Lφ(Ω)-averaging domains.

Comprehensive Summary: A regiodivergent synthesis of azetidines, 5,6‐dihydro‐1,3‐oxazines and 2,3‐dihydro‐1,4‐oxazines has been achieved through palladium‐catalyzed tandem allylic substitution reaction. This protocol provides a variety of heterocycles in satisfactory yields with good to excellent regioselectivities under mild reaction conditions. [ABSTRACT FROM AUTHOR]

A new energetic metal-organic framework, [Ag2 (HOBTT)]n, was prepared by solvothermal method using 4, 5-bis (1-hydroxy-tetrazol)-1,2,3-triazole (H3OBTT) as ligand and Ag( I ) as metal center. The structure was characterized by single crystal X-ray diffraction (SXRD), powder X-ray diffraction (PXRD), infrared spectroscopy (IR) and elemental analysis (EA) . The thermal stability was studied by differential scanning calorimeter (DSC) and thermogravimetric (TG) analyzer. The non-isothermal kinetic parameters of thermal decomposition were calculated by Kissinger's method. The mechanical sensitivities of [Ag2 (HOBTT)]n were investigated by impact sensitivity meter and friction sensitivity meter. The GGA-PBE functional was used to calculate the detonation heat, and the detonation velocity and detonation pressure were obtained by Kamlet-Jacobs equation. The results show that [Ag2 (HOBTT)]n is a three-dimensional solvent-free framework, belonging to the orthorhombic crystal system and Fddd space group with crystal density of 3. 309 g/cm3 . The initial thermal decomposition temperature of [Ag2 (HOBTT)]n is 586. 6K, corresponding thermal decomposition kinetic parameters Ea = 255. 18kJ/mol and ln(/\/s-1 ) = 43. 30. Its impact sensitivity is greater than 40J, friction sensitivity is 108N, detonation velocity and detonation pressure are 9.73km/s and 56.38GPa, respectively, showing that [Ag2 (HOBTT)]n is a kind of promising energetic material with good thermal stability,moderate mechanical sensitivity,and excellent detonation performance. [ABSTRACT FROM AUTHOR]

Dominant mutations in cardiac transcription factor genes cause human inherited congenital heart defects (CHDs); however, their molecular basis is not understood. Interactions between transcription factors and the Brg1/Brm-associated factor (BAF) chromatin remodelling complex suggest potential mechanisms; however, the role of BAF complexes in cardiogenesis is not known. In this study, we show that dosage of Brg1 is critical for mouse and zebrafish cardiogenesis. Disrupting the balance between Brg1 and disease-causing cardiac transcription factors, including Tbx5, Tbx20 and Nkx2-5, causes severe cardiac anomalies, revealing an essential allelic balance between Brg1 and these cardiac transcription factor genes. This suggests that the relative levels of transcription factors and BAF complexes are important for heart development, which is supported by reduced occupancy of Brg1 at cardiac gene promoters in Tbx5 haploinsufficient hearts. Our results reveal complex dosage-sensitive interdependence between transcription factors and BAF complexes, providing a potential mechanism underlying transcription factor haploinsufficiency, with implications for multigenic inheritance of CHDs.

Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P

SNPLogic (http://www.snplogic.org) brings together single nucleotide polymorphism (SNP) information from numerous sources to provide a comprehensive SNP selection, annotation and prioritization system for design and analysis of genotyping projects. SNPLogic integrates information about the genetic context of SNPs (gene, chromosomal region, functional location, haplotypes tags and overlap with transcription factor binding sites, splicing sites, miRNAs and evolutionarily conserved regions), genotypic data (allele frequencies per population and validation method), coverage of commercial arrays (ParAllele, Affymetrix and Illumina), functional predictions (modeled on structure and sequence) and connections or established associations (biological pathways, gene ontology terms and OMIM disease terms). The SNPLogic web interface facilitates construction and annotation of user-defined SNP lists that can be saved, shared and exported. Thus, SNPLogic can be used to identify and prioritize candidate SNPs, assess custom and commercial arrays panels and annotate new SNP data with publicly available information. We have found integration of SNP annotation in the context of pathway information and functional prediction scores to be a powerful approach to the analysis and interpretation of SNP-disease association data.

BackgroundEpigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.ResultsWe propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.ConclusionOur proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.

Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls.Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single- SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods.