FoxO1在胰岛β细胞代谢灵活性受损及失代偿进程中的作用

葡萄糖及脂肪酸是胰岛β细胞的关键代谢底物,葡萄糖刺激胰岛β细胞分泌胰岛素是维持机体血糖稳态平衡的关键。胰岛素抵抗发生时,β细胞对能量代谢底物的选择失调,加速胰岛β细胞由代偿到胰岛β细胞失代偿的进程,是肥胖胰岛素抵抗最终发展为2型糖尿病的始动因素。核转录因子FoxO1属于Fox家族成员,在胰腺内广泛表达,在β细胞的代谢,发育,增殖过程中发挥着重要的调节作用。鉴于FoxO1在维持胰岛β细胞功能中的关键作用,现着重对FoxO1在胰岛β细胞代谢灵活性受损及失代偿过程发生中的作用调节进行阐述。为其作为调控胰岛β细胞功能的关键靶点提供参考。     

Modification of olivomycin A at the side chain of the aglycon yields the derivative with perspective antitumor characteristics

A novel way of chemical modification of the antibiotic olivomycin A (1) at the side chain of the aglycon moiety was developed. Interaction of olivomycin A with the sodium periodate produced the key acid derivative olivomycin SA (2) in 86% yield. This acid was used in the reactions with different amines in the presence of benzotriazol-1-yl-oxy-trispyrrolidino-phosphonium hexafluorophosphate (PyBOP) or diphenylphosphoryl azide (DPPA) to give corresponding amides. Whereas olivomycin SA was two orders of magnitude less cytotoxic than the parent antibiotic, the amides of 2 demonstrated a higher cytotoxicity. In particular, N,N-dimethylaminoethylamide of olivomycin SA showed a pronounced antitumor effect against transplanted experimental lymphoma and melanoma and a remarkably high binding constant to double stranded DNA. The therapeutic effects of this derivative were achievable at tolerable concentrations, suggesting that modifications of the aglycon’s side chain, namely, its shortening to methoxyacetic residue and blocking of free carboxyl group, are straightforward for the design of therapeutically applicable derivatives of olivomycin A.     

The cytotoxicity of mevalonate, lovastatin and carminomycin with respect to MOLT-4 human malignant T-lymphoblasts in vitro]

It was shown in vitro that high concentrations of lovastatin, a competitive inhibitor of hydroxymethyl glutaryl coenzyme A (HMG-CoA) reductase inhibited human malignant cells MOLT-4. The activity of lovastatin in doses of 50-250 microM was dose-dependent. Addition of mevalonate in a concentration of 3 mM to the growth medium completely prevented the cytotoxic effect of 100 microM of lovastatin. At the same time, exogenous mevalonate did not decrease the cytotoxicity of the anthracycline antibiotic carminomycin. Moreover, in a high concentration (7 mM) mevalonate slowly but significantly inhibited the growth of the malignant target cells and the effect was added to the cytotoxic effect of carminomycin low concentrations (0.08 to 0.175 microgram/ml). The results and the literature data suggested that combination of mevalonate, HMG-CoA reductase inhibitors and anthracyclines could be useful in tumor chemotherapy. The suggestion needs further investigation.     

Design and analysis of quantitative differential proteomics investigations using LC-MS technology

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.