Establishment of the active catalytic domain of human PDGFRbeta tyrosine kinase-based ELISA assay for inhibitor screening

Tyrosine kinases are emerging as frequent targets of primary oncogenic events and therefore represent an optimal focus of therapeutic intervention. In an effort towards therapeutic PDGFR inactivation, we expressed the catalytic domain of PDGFRbeta as a soluble active kinase using Bac-to-Bac expression system, and studied the correlations between PDGFRbeta activity and enzyme concentration, ATP concentration, substrate concentration and divalent cation type. And a convenient, effective and non-radioactive ELISA screening model is then established for identification of the potential inhibitors targeting PDGFRbeta kinase. Of 500 RTK target-based compounds, TKI-30 was identified as a small molecule potential inhibitor of PDGFRbeta (IC(50)=0.34 microM). Further studies indicated that TKI-30 blocked PDGF-BB-induced autophosphorylation of PDGFRbeta in a dose-dependent manner in Swiss 3T3 cells and human umbilical vein smooth muscle cells (HUVSMCs). Moreover, it dose-dependently suppressed the PDGF-BB-induced proliferation in HUVSMCs and tube formation of HUVEC. Our data collectively indicated that PDGFRbeta-based ELISA assay is a new method available for screening inhibitors targeting PDGFRbeta kinase and TKI-30 is a potential novel anti-cancer agent worthy of being further investigated.     

The Arabidopsis Nitrate Transporter NRT1.7, Expressed in Phloem,Is Responsible for Source-to-Sink Remobilization of Nitrate

Several quantitative trait locus analyses have suggested that grain yield and nitrogen use efficiency are well correlated with nitrate storage capacity and efficient remobilization. This study of the Arabidopsis thaliana nitrate transporter NRT1.7 provides new insights into nitrate remobilization. Immunoblots, quantitative RT-PCR, β-glucuronidase reporter analysis, and immunolocalization indicated that NRT1.7 is expressed in the phloem of the leaf minor vein and that its expression levels increase coincidentally with the source strength of the leaf. In nrt1.7 mutants, more nitrate was present in the older leaves, less ¹⁵NO₃⁻ spotted on old leaves was remobilized into N-demanding tissues, and less nitrate was detected in the phloem exudates of old leaves. These data indicate that NRT1.7 is responsible for phloem loading of nitrate in the source leaf to allow nitrate transport out of older leaves and into younger leaves. Interestingly, nrt1.7 mutants showed growth retardation when external nitrogen was depleted. We conclude that (1) nitrate itself, in addition to organic forms of nitrogen, is remobilized, (2) nitrate remobilization is important to sustain vigorous growth during nitrogen deficiency, and (3) source-to-sink remobilization of nitrate is mediated by phloem.     

Synthesis and biological evaluation of a novel 99mTc nitrido radiopharmaceutical with deoxyglucose dithiocarbamate,showing tumor uptake

The deoxyglucose dithiocarbamate (DGDTC) was synthesized and radiolabelled with [^99mTcN]²⁺ intermediate to form the ^99mTcN–DGDTC complex. The radiochemical purity of the ^99mTcN–DGDTC complex was over 90%, as measured by TLC and by HPLC, without any notable decomposition at room temperature over a period of 6 h. The partition coefficient and electrophoresis results indicated that this complex was hydrophilic and neutral. The biodistribution of ^99mTcN–DGDTC in mice bearing S 180 tumor showed that the complex accumulated in the tumor with high uptake and good retention. The tumor/blood and tumor/muscle ratios increased with time and reached 2.32 and 1.68 at 4 h post-injection, suggesting it would be a promising candidate for tumor imaging.     

Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.     

Isolation and characterization of polymorphic microsatellite loci from RAPD product in half-smooth tongue sole (Cynoglossus semilaevis) and a test of cross-species amplification

Eleven polymorphic microsatellite loci have been isolated and characterized from random amplified polymorphic DNA product in half-smooth tongue sole, Cynoglossus semilaevis. Twenty-one microsatellites were selected for designing microsatellite primers, of which 11 gave working primer pairs. They had between three and 12 alleles. Observed and expected heterozygosities varied from 0.53 to 0.93, and from 0.52 to 0.80, respectively. Five additional fish species assessed for cross-species amplification revealed between one and three positive amplifications and between zero and three polymorphic loci per species.     

New stemona alkaloids from the roots of Stemona sessilifolia

From the roots of Stemona sessilifolia, three new stemona-type alkaloids, namely stemosessifoine (1), isooxymaistemonine (2), and isomaistemonine (3), along with eight known alkaloids (bisdehydrostemoninine, isobisdehydrostemoninine, tuberostemonine, bisdehydrotuberostemonine, bisdehydrostemoninine, isobisdehydrostemoninine, stemoninine, and protostemonine), were isolated. Their structures were determined on the basis of extensive 2D-NMR spectroscopic-data analysis and by comparison with reported values in the literature. Compound 1 is a structurally unprecedented alkaloid, and it is depicted to be bioconverted from tuberostemonine as the precursor. Isooxymaistemonine (2) showed a positive effect on the human high-density lipoprotein (HDL) receptor gene CD36 and LIMP II analogous-1 (CLA-1) at the dosage of 10 microg/ml.     

Inhibition of integrin β3, a binding partner of kallistatin,leads to reduced viability,invasion and proliferation in NCI-H446 cells

Background

Kallistatin is a serine proteinase inhibitor and heparin-binding protein. It is considered an endogenous angiogenic inhibitor. In addition, multiple studies demonstrated that kallistatin directly inhibits cancer cell growth. However, the molecular mechanisms underlying these effects remain unclear.

Methods

Pull-down, immunoprecipitation, and immunoblotting were used for binding experiments. To elucidate the mechanisms, integrin β3 knockdown (siRNA) or blockage (antibody treatment) on the cell surface of small the cell lung cancer NCI-H446 cell line was used.

Results

Interestingly, kallistatin was capable of binding integrin β3 on the cell surface of NCI-H446 cells. Meanwhile, integrin β3 knockdown or blockage resulted in loss of antitumor activities induced by kallistatin. Furthermore, kallistatin suppressed tyrosine phosphorylation of integrin β3 and its downstream signaling pathways, including FAK/-Src, AKT and Erk/MAPK. Viability, proliferation and migration of NCI-H446 cells were inhibited by kallistatin, with Bcl-2 and Grb2 downregulation, and Bax, cleaved caspase-9 and caspase 3 upregulation.

Conclusions

These findings reveal a novel role for kallistatin in preventing small cell lung cancer growth and mobility, by direct interaction with integrin β3, leading to blockade of the related signaling pathway.

     

The Melibiose Transporter of Escherichia coli: CRITICAL CONTRIBUTION OF LYS-377 TO THE STRUCTURAL ORGANIZATION OF THE INTERACTING SUBSTRATE BINDING SITES*

We examine the role of Lys-377, the only charged residue in helix XI, on the functional mechanism of the Na⁺-sugar melibiose symporter from Escherichia coli. Intrinsic fluorescence, FRET, and Fourier transform infrared difference spectroscopy reveal that replacement of Lys-377 with either Cys, Val, Arg, or Asp disables both Na⁺ and melibiose binding. On the other hand, molecular dynamics simulations extending up to 200–330 ns reveal that Lys-377 (helix XI) interacts with the anionic side chains of two of the three putative ligands for cation binding (Asp-55 and Asp-59 in helix II). When Asp-59 is protonated during the simulations, Lys-377 preferentially interacts with Asp-55. Interestingly, when a Na⁺ ion is positioned in the Asp-55-Asp-59 environment, Asp-124 in helix IV (a residue essential for melibiose binding) reorients and approximates the Asp-55-Asp-59 pair, and all three acidic side chains act as Na⁺ ligands. Under these conditions, the side chain of Lys-377 interacts with the carboxylic moiety of these three Asp residues. These data highlight the crucial role of the Lys-377 residue in the spatial organization of the Na⁺ binding site. Finally, the analysis of the second-site revertants of K377C reveals that mutation of Ile-22 (in helix I) preserves Na⁺ binding, whereas that of melibiose is largely abolished according to spectroscopic measurements. This amino acid is located in the border of the sugar-binding site and might participate in sugar binding through apolar interactions.     

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations—MASS, RAREMETAL, MetaSKAT, and seqMeta—in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.