双依他尼酸乙醇胺对谷胱甘肽S-转硫酶mu的选择性抑制作用

谷胱甘肽S-转移酶(GST)的同工酶mu(GSTM)高表达与卵巢癌顺铂耐药有关.以GST非选择性抑制剂依他尼酸设计二价潜抑制剂双依他尼酸乙醇胺(aminoethanol di-ethacrynic acid,ADEA),测定ADEA及其与还原型谷胱甘肽(glutathione,GSH)加合物对GST同工酶亚型A1、P1...     

肺动脉内皮细胞与肺动脉平滑肌细胞增殖的相互调节

血管内皮细胞和血管平滑肌细胞在结构和功能上关系密切,二者的相互关系在血管舒缩和血管壁结构的调节中起重要作用。本文观察了培养的小牛肺动脉内皮细胞（ＰＡＥＣ）和肺动脉平滑肌细胞（ＰＡＳＭ）在细胞增殖方面的相互调节作用。混合培养的ＰＡＥＣ和ＰＡＳＭ细胞的３Ｈ－ＴｄＲ参入明显降低（Ｐ＜０．００１,与对照组相比）。无论向培养的ＰＡＥＣ和ＰＡＳＭ中分别加入ＰＡＳＭ和ＰＡＥＣ的条件培养基还是二者共培养时,均发现ＰＡＥＣ的３Ｈ－ＴｄＲ参入明显降低,而ＰＡＳＭ的３Ｈ－ＴｄＲ明显升高（Ｐ＜０．０５,与对照组相比）。流式细胞测定也发现共培养时ＰＡＥＣ的Ｇ１期细胞增多,Ｇ２／Ｍ期细胞减少;而ＰＡＳＭ的Ｇ１期细胞减少,Ｇ２／Ｍ期细胞增多。共培养的ＰＡＳＭ细胞内ｃＡＭＰ增加,ｃＧＭＰ含量降低;而ＰＡＥＣ细胞的ｃＡＭＰ和ｃＧＭＰ含量均降低（Ｐ＜０．０１,与对照组相比）。上述结果提示,ＰＡＥＣ和ＰＡＳＭ相互作用可能通过第二信使而调节它们本身的增殖     

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

Is There an Association between Work Stress and Diurnal Cortisol Patterns? Findings from the Whitehall II Study

Objective

The evidence on whether there is work stress related dysregulation of the hypothalamic-pituitary-adrenal axis is equivocal. This study assessed the relation between work stress and diurnal cortisol rhythm in a large-scale occupational cohort, the Whitehall II study.

Methods

Work stress was assessed in two ways, using the job-demand-control (JDC) and the effort-reward-imbalance (ERI) models. Salivary cortisol samples were collected six times over a normal day in 2002–2004. The cortisol awakening response (CAR) and diurnal cortisol decline (slope) were calculated.

Results

In this large occupational cohort (N = 2,126, mean age 57.1), modest differences in cortisol patterns were found for ERI models only, showing lower reward (β = −0.001, P-value = 0.04) and higher ERI (β = 0.002, P-value = 0.05) were related to a flatter slope in cortisol across the day. Meanwhile, moderate gender interactions were observed regarding CAR and JDC model.

Conclusions

We conclude that the associations of work stress with cortisol are modest, with associations apparent for ERI model rather than JDC model.     

Effects of age and size on critical swimming speed of juvenile Chinese sturgeon Acipenser sinensis at seasonal temperatures

Changes in the critical swimming speed (U_crit, cm s^?1) with ontogeny of 2·5–12·5 month‐old juvenile anadromous Chinese sturgeon Acipenser sinesis were measured in a modified Blazka‐type swimming tunnel. The absolute U_crit increased with length, mass and age; the relative U^′_crit (body lengths, s^?1), however, decreased. Juvenile A. sinesis did not display a parr–smolt transformation at the length or age threshold to tolerate full‐strength seawater.     

Differential Effector Engagement by Oncogenic KRAS

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