Effects of autologous stem cell transplantation on ventricular electrophysiology in doxorubicin-induced heart failure

To investigate whether stem cell transplantation affects ventricular electrophysiology in vivo, either autologous bone marrow mesenchymal stem cells or skeletal myoblast cells were transplanted via a catheter into a doxorubicin-treated failing heart. Four weeks after transplantation, electrophysiological investigation showed that transplantation of either cell type prolonged the local activation time and increased the activation time dispersion. In the stem cell transplantation groups, a positive correlation was demonstrated between activation time dispersion and the number of stem cell-derived cells in the pacing site. It is concluded that transplantation of either mesenchymal stem cells or skeletal myoblast cells might exacerbate abnormalities of local ventricular conduction in the doxorubicin-treated failing heart.     

Activation of IKK by TNFalpha requires site-specific ubiquitination of RIP1 and polyubiquitin binding by NEMO

The receptor interacting protein kinase 1 (RIP1) is essential for the activation of nuclear factor kappaB (NF-kappaB) by tumor necrosis factor alpha (TNFalpha). Here, we present evidence that TNFalpha induces the polyubiquitination of RIP1 at Lys-377 and that this polyubiquitination is required for the activation of IkappaB kinase (IKK) and NF-kappaB. A point mutation of RIP1 at Lys-377 (K377R) abolishes its polyubiquitination as well as its ability to restore IKK activation in a RIP1-deficient cell line. The K377R mutation of RIP1 also prevents the recruitment of TAK1 and IKK complexes to TNF receptor. Interestingly, polyubiquitinated RIP1 recruits IKK through the binding between the polyubiquitin chains and NEMO, a regulatory subunit of the IKK complex. Mutations of NEMO that disrupt its polyubiquitin binding also abolish IKK activation. These results reveal the biochemical mechanism underlying the essential signaling function of NEMO and provide direct evidence that signal-induced site-specific ubiquitination of RIP1 is required for IKK activation.     

An integrated approach to the prediction of domain-domain interactions

Background  

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.     

A new class of glycogen phosphorylase inhibitors

A new class of diacid analogues that binds at the AMP site not only are very potent but have approximately 10-fold selectivity in liver versus muscle glycogen phosphorylase (GP) in the in vitro assay. The synthesis, structure, and in vitro and in vivo biological evaluation of these liver selective glycogen phosphorylase inhibitors are discussed.     

枯草芽孢杆菌抗菌蛋白X98Ⅲ的纯化与性质

枯草芽孢杆菌（Bacillussubtilis）BS－98是一株能强烈抑制苹果轮纹病菌（Physalosporapiricola）等植物病原真菌的拮抗菌株。BS－98菌株培养液经硫酸铵分级盐析、SephadexG－100柱层析和DEAE－纤维素（DE32）柱层析后分离纯化出一种抗菌蛋白,命名为X98Ⅲ。蛋白电泳分析结果表明,此蛋白分子量为59000,等电点为4．50．醋酸纤维膜电泳后经特异染色证明X98Ⅲ含糖及胀。用DNS法测其含糖量为6％。此蛋白对热稳定,对蛋白酶部分敏感。氨基酸组分分析表明,该蛋白含11种不同氨基酸,尤富含谷氨酸和半胱氨酸等,而缺少天冬氨酸等。纯化后的X98Ⅲ对苹果轮纹病菌、芦笋茎枯病菌等有很强的抑制作用。X98Ⅲ的抑菌机理主要是溶解细胞壁,造成菌丝畸形、孢子不发芽或发芽异常。     

鉴别无色素性恶性黑色素瘤方法的探讨

无色素及少色素性恶黑鉴别诊断时,（１）组织化学铁反应,褪色素和黑色素银染对证实瘤细胞中黑色素是有帮助的,但不能从无色素性恶黑中检出黑色素。因此对无黑色素恶黑的诊断和鉴别诊断帮助不大。网状纤维染色良性病均有增加,而恶黑几乎没有增加或仅极少增加。因此网状纤维染色有助于良恶性的鉴别。（２）免疫组化Ｓ－１００１８例恶黑及６例良性痣均显示阳性,这对无色素恶黑的诊断是有价值的,但对色素痣的恶变帮助不大。１６例恶黑中１４例色素性与１０例无色素性恶黑ＨＭＢ４５均显示阳性,证明两者有共同抗原,总阳性率８７．５％,６例良性痣均为阴性,证明无ＨＭＢ４５抗原。结果提示ＨＭＢ４５免疫组化检测不仅对无色素及少色素性恶黑的诊断与鉴别诊断实用性大,还可以用于对恶黑与良性痣、良性痣恶变的鉴别。（３）本组４例无色素性恶黑电镜下均找到前黑色素小体。因此在其他方法诊断困难时,应用电镜检查对确诊具有决定性作用。     

Exogenous Jasmonic Acid Inhibits Cotton Fiber Elongation

Jasmonic acid (JA) is a well-characterized phytohormone that acts in various ways to influence plant development. Its role in cotton fiber development, however, has not yet been thoroughly explored. In this study, JA was proven to be an inhibitor of ovule and fiber development in vitro. Continuous exogenous JA application inhibited fiber elongation. This effect was dependent on development stage and dosage. Fibers and ovules at three different stages of development and different JA dosages were compared. The most serious suppression was detected when ovules 1?day before anthesis (–1?DPA) were cultured in medium with 2.5?μM JA. Genes related to trichome and fiber development responded differently to JA treatment between –1?DPA and 1?day post anthesis (1 DPA). JAs (JA and JA-Ile) quantification showed that JAs content was sharply decreased from –1?DPA to 5?DPA ovules, which indicated that JA was negatively associated with fiber elongation in vivo. In addition, gene expression analysis showed the same trend. Our results demonstrate that there was a negative relationship of JA with fiber elongation in vitro and in vivo. These results are meaningful for uncovering the mechanism of fiber elongation in cotton.     

The prediction of protein structural class using averaged chemical shifts

Knowledge of protein structural class can provide important information about its folding patterns. Many approaches have been developed for the prediction of protein structural classes. However, the information used by these approaches is primarily based on amino acid sequences. In this study, a novel method is presented to predict protein structural classes by use of chemical shift (CS) information derived from nuclear magnetic resonance spectra. Firstly, 399 non-homologue (about 15% identity) proteins were constructed to investigate the distribution of averaged CS values of six nuclei ((13)CO, (13)Cα, (13)Cβ, (1)HN, (1)Hα and (15)N) in three protein structural classes. Subsequently, support vector machine was proposed to predict three protein structural classes by using averaged CS information of six nuclei. Overall accuracy of jackknife cross-validation achieves 87.0%. Finally, the feature selection technique is applied to exclude redundant information and find out an optimized feature set. Results show that the overall accuracy increased to 88.0% by using the averaged CSs of (13)CO, (1)Hα and (15)N. The proposed approach outperformed other state-of-the-art methods in terms of predictive accuracy in particular for low-similarity protein data. We expect that our proposed approach will be an excellent alternative to traditional methods for protein structural class prediction.