Threshold dissociation and molecular modeling of transition metal complexes of flavonoids

The relative threshold dissociation energies of a series of flavonoid/transition metal/auxiliary ligand complexes of the type [MII (flavonoid − H) auxiliary ligand]+ formed by electrospray ionization (ESI) were measured by energy-variable collisionally activated dissociation (CAD) in a quadrupole ion trap (QIT). For each of the isomeric flavonoid diglycoside pairs, the rutinoside (with a 1–6 inter-saccharide linkage) requires a greater CAD energy and thus has a higher dissociation threshold than its neohesperidoside (with a 1–2 inter-saccharide linkage) isomer. Likewise, the threshold energies of complexes containing flavones are higher than those containing flavanones. The monoglycoside isomers also have characteristic threshold energies. The flavonoids that are glycosylated at the 3-O- position tend to have lower threshold energies than those glycosylated at the 7-O- or 4′-O- position, and those that are C- bonded have lower threshold energies than the O- bonded isomers. The structural features that substantially influence the threshold energies include the aglycon type (flavanone versus flavone), the type of disaccharide (rutinose versus neohesperidose), and the linkage type (O- bonded versus C- bonded). Various computational means were applied to probe the structures and conformations of the complexes and to rationalize the differences in threshold energies of isomeric flavonoids. The most favorable coordination geometry of the complexes has a plane-angle of about 62°, which means that the deprotonated flavonoid and 2,2′-bipyridine within a complex do not reside on the same plane. Stable conformations of five cobalt complexes and five deprotonated flavonoids were identified. The conformations were combined with the point charges and helium accessible surface areas to explain qualitatively the differences in threshold energies for isomeric flavonoids

Dietary Modulation of Intestinal Microbiota: Future Opportunities in Experimental Autoimmune Encephalomyelitis and Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune disease that affects the functioning of the central nervous system (CNS). Recent studies on MS and its animal model, experimental autoimmune encephalomyelitis (EAE), have shown that the composition and abundance of microbes in the intestinal microbiota are an environmental risk factor for the development of MS and EAE. Changes in certain microbial populations in the gastrointestinal tract can cause MS in humans, but MS inflammation can be reduced or even prevented by introducing other commensal microbes that produce beneficial metabolites. Other risk factors for MS include the presence of an altered gut physiology and the interaction between the intestinal microbiota and the immune system. Metabolites including short-chain fatty acids (SCFAs), such as butyrate, are the primary signaling molecules produced by the intestinal microbiota that interact with the host immune system, suggesting an association between MS pathophysiology and gut microbiota. In addition, several host microRNAs present in the gut have been found to interact with the intestinal microbial community, these interactions may indirectly affect the neurological system. Increasing evidence has shown that regulation of the intestinal microbiota is an important approach for reducing MS inflammation. Thus, here we review the use of diet to alter the gut microbiota and its application in the treatment and prevention of MS

Arabidopsis

This paper aims to provide a method to represent the virtual Arabidopsis plant at each growth stage. It includes simulating the shape and providing growth parameters. The shape is described with elliptic Fourier descriptors. First, the plant is segmented from the background with the chromatic coordinates. With the segmentation result, the outer boundary series are obtained by using boundary tracking algorithm. The elliptic Fourier analysis is then carried out to extract the coefficients of the contour. The coefficients require less storage than the original contour points and can be used to simulate the shape of the plant. The growth parameters include total area and the number of leaves of the plant. The total area is obtained with the number of the plant pixels and the image calibration result. The number of leaves is derived by detecting the apex of each leaf. It is achieved by using wavelet transform to identify the local maximum of the distance signal between the contour points and the region centroid. Experiment result shows that this method can record the growth stage of Arabidopsis plant with fewer data and provide a visual platform for plant growth research

Draft Genome Sequence of \u3cem\u3eCercospora sojina\u3c/em\u3e Isolate S9, a Fungus Causing Frogeye Leaf Spot (FLS) Disease of Soybean

Fungi are the causal agents of many of the world\u27s most serious plant diseases causing disastrous consequences for large-scale agricultural production. Pathogenicity genomic basis is complex in fungi as multicellular eukaryotic pathogens. The fungus Cercospora sojina is a plant pathogen that threatens global soybean supplies. Here, we report the genome sequence of C. sojina strain S9 and detect genome features and predicted genomic elements. The genome sequence of C. sojina is a valuable resource with potential in studying the fungal pathogenicity and soybean host resistance to frogeye leaf spot (FLS), which is caused by C. sojina. The C. sojina genome sequence has been deposited and available at DDBJ/EMBL/GenBank under the project accession number AHPQ00000000

Heterologous expression and characterization of a malathion-hydrolyzing carboxylesterase from a thermophilic bacterium, Alicyclobacillus tengchongensis

A carboxylesterase gene from thermophilic bacterium, Alicyclobacillus tengchongensis, was cloned and expressed in Escherichia coli BL21 (DE3). The gene coded for a 513 amino acid protein with a calculated molecular mass of 57.82 kDa. The deduced amino acid sequence had structural features highly conserved among serine hydrolases, including Ser204, Glu325, and His415 as a catalytic triad, as well as type-B carboxylesterase serine active site (FGGDPENITIGGQSAG) and type-B carboxylesterase signature 2 (EDCLYLNIWTP). The purified enzyme exhibited optimum activity with β-naphthyl acetate at 60 °C and pH 7 as well as stability at 25 °C and pH 7. One unit of the enzyme hydrolyzed 5 mg malathion l(−1) by 50 % within 25 min and 89 % within 100 min. The enzyme strongly degraded malathion and has a potential use for the detoxification of malathion residues

Tumor Microenvironment-Activatable Nanoenzymes for Mechanical Remodeling of Extracellular Matrix and Enhanced Tumor Chemotherapy

Increased tissue stiffness is a hallmark of cancer and promotes tumor progression. It is hypothesized that decreased tumorous stress may aid or sensitize chemotherapies. To overcome extracellular matrix (ECM) stiffening and fulfill sensitized chemotherapy in one nanosystem, a reactive oxygen species-activatable nanoenzyme (SP-NE) based on a dendritic polyglycerol scaffold, integrating collagenase and paclitaxel (PTX) prodrug, is constructed. The dense and tough ECM is highly remitted by SP-NE in the tumor microenvironment (TME) mimicking gelatin hydrogel models, which causes cell shrinkage, disorders cytoskeletal constructions, and subsequently enhances chemotherapeutic efficacy. ECM softening via SP-NE downregulates mechanotransduction signaling pathways of integrin-focal adhesion kinase (FAK)-Ras homolog family member A (RhoA) implicated in cytoskeletal assembly, and integrin-FAK-phosphorylated extracellular signal regulated kinase (pERK 1/2) mediating mitosis. Notably, this programmed nanosystem in human breast MCF-7 tumor-bearing mice models displays a significant relief of ECM stress from 4300 to 1200 Pa and results in 87.1% suppression of tumor growth at a low PTX dosage of 3 mg kg(-1). The attenuated expression of the key players RhoA and pERK 1/2 involved in cellular mechano-sensing is further verified in vivo. This study thus provides a new and potential nanoplatform to selectively decrease TME stiffness for enhanced chemotherapy

Overexpression of \u3ci\u3eMsDREB1C\u3c/i\u3e Modulates Growth and Improves Forage Quality in Tetraploid Alfalfa (\u3ci\u3eMedicago sativa\u3c/i\u3e L.)

DREB has been reported to be involved in plant growth and response to environmental factors. However, the function of DREB in growth and development has not been elucidated in alfalfa (Medicago sativa L.), a perennial tetraploid forage cultivated worldwide. In this study, an ortholog of MtDREB1C was characterized from alfalfa and named MsDREB1C accordingly. MsDREB1C was significantly induced by abiotic stress. The transcription factor MsDREB1C resided in the nucleus and had self-transactivation activity. The MsDREB1C overexpression (OE) alfalfa displayed growth retardation under both long-day and short-day conditions, which was supported by decreased MsGA20ox and upregulated MsGA2ox in the OE lines. Consistently, a decrease in active gibberellin (GA) was detected, suggesting a negative effect of MsDREB1C on GA accumulation in alfalfa. Interestingly, the forage quality of the OE lines was better than that of WT lines, with higher crude protein and lower lignin content, which was supported by an increase in the leaf–stem ratio (LSR) and repression of several lignin-synthesis genes (MsNST, MsPAL1, MsC4H, and Ms4CL). Therefore, this study revealed the effects of MsDREB1C overexpression on growth and forage quality via modifying GA accumulation and lignin synthesis, respectively. Our findings provide a valuable candidate for improving the critical agronomic traits of alfalfa, such as overwintering and feeding value of the forage

A Retrospective Study of Progression-Free and Overall Survival in Pediatric Medulloblastoma Based on Molecular Subgroup Classification: A Single-Institution Experience

BackgroundMedulloblastoma (MB) has been classified into four core subgroups according to the transcriptional profile in recent years. However, some disagreement among researchers remains regarding the prognoses and most effective treatments of the different subgroups with different age distributions.ObjectiveThe objective of this study was to analyze MB prognosis in children population based on the classification of four molecular subgroups.MethodsFrom January 2011 to January 2013, 84 consecutive MB patients aged underwent tumor removal at Beijing Tiantan Hospital. A total of 55 patients who ranged in age from 4 to 18 years underwent detailed follow-up. Molecular subgrouping was performed using RT-PCR.ResultsThe 2-year progression-free survival (PFS) and overall survival (OS) rates for the entire cohort were 76.2 ± 5.8 and 81.8 ± 5.2%, respectively. Univariate analysis revealed that the Group 4 patients had a better survival (2-year OS, 90.6 ± 5.2%) than the SHH subgroup (P = 0.002) and Group 3 patients (P = 0.008). Only two of the 23 non-metastasized Group 4 patients relapsed, and chemotherapy did significantly affect these patients (PFS, P = 0.685). One out of five WNT patients had tumor relapse and died at last. Large cell/anaplastic (LC/A) histology and chemotherapy were independent risk factors in multivariate analysis.ConclusionIn our study, the non-metastasized Group 4 patients had an excellent prognosis. The SHH subgroup and Group 3 patients had worst prognoses. LC/A histology had a dismal prognosis in our cohorts, which warrants intensive treatment

AKT2 Blocks Nucleus Translocation of Apoptosis-Inducing Factor (AIF) and Endonuclease G (EndoG) While Promoting Caspase Activation during Cardiac Ischemia

The AKT (protein kinase B, PKB) family has been shown to participate in diverse cellular processes, including apoptosis. Previous studies demonstrated that protein kinase B2 (AKT2 − / − ) mice heart was sensitized to apoptosis in response to ischemic injury. However, little is known about the mechanism and apoptotic signaling pathway. Here, we show that AKT2 inhibition does not affect the development of cardiomyocytes but increases cell death during cardiomyocyte ischemia. Caspase-dependent apoptosis of both the extrinsic and intrinsic pathway was inactivated in cardiomyocytes with AKT2 inhibition during ischemia, while significant mitochondrial disruption was observed as well as intracytosolic translocation of cytochrome C (Cyto C) together with apoptosis-inducing factor (AIF) and endonuclease G (EndoG), both of which are proven to conduct DNA degradation in a range of cell death stimuli. Therefore, mitochondria-dependent cell death was investigated and the results suggested that AIF and EndoG nucleus translocation causes cardiomyocyte DNA degradation during ischemia when AKT2 is blocked. These data are the first to show a previous unrecognized function and mechanism of AKT2 in regulating cardiomyocyte survival during ischemia by inducing a unique mitochondrial-dependent DNA degradation pathway when it is inhibited.This work was supported by the National Natural Science Foundation of China, (Grant No. 81500179); the Natural Science Foundation of Jiangsu Province (Grant No. BK20150696); the Fundamental Research Funds for the Central Universities (Grant No. 2015PY005); the National Found for Fostering Talents of Basic Science (NFFTBS) (Grant No. J1310032); the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); the National High Technology Research and Development Program of China (863 Program, No.2015AA020314); and the National Natural Science Foundation of China (Grant No. 81570696 and No. 31270985); this work is also sponsored by Qing Lan Project

Identification of Biomarkers That Distinguish Chemical Contaminants Based on Gene Expression Profiles

Background: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. Results: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. Conclusions: Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical