Cross-linked SecA dimers are not functional in protein translocation

The ATPase SecA is involved in post-translational protein translocation through the SecY channel across the bacterial inner membrane. SecA is a dimer that can dissociate into monomers with translocation activity. Here, we have addressed whether dissociation of the SecA dimer is required for translocation. We show that a dimer in which the two subunits are cross-linked by disulfide bridges is inactive in protein translocation, translocation ATPase, and binding to a lipid bilayer. In contrast, upon reduction of the disulfide bridges, the resulting monomers regain these activities. These data support the notion that dissociation of SecA dimers into monomers occurs during protein translocation.     

Correlation analysis of Normalized Different Vegetation Index (NDVI) difference series and climate variables in the Xilingole steppe, China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system. It has been demonstrated by many researches that Normalized Different Vegetation Index (NDVI) time series from remotely sensed data, which provide effective information of vegetation conditions on a large scale with highly temporal resolution, have a good relation with meteorological factors. However, few of these studies have taken the cumulative property of NDVI time series into account. In this study, NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors. As a proxy of the vegetation growing process, NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors. This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series, and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale. By using the correlation analysis method, we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia. The results show that: (1) meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase; (2) the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities. In a typical steppe dominated by Leymus chinensis, temperature has higher correlation with NDVI difference than precipitation does, and in a typical steppe dominated by Stipa krylovii, the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference. In a typical steppe dominated by Stipa grandis, there is no significant difference between the two correlations. Precipitation is the key factor influencing vegetation growth in a desert steppe, and temperature has poor correlation with NDVI difference; (3) the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe, however, mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii; (4) the relationship between NDVI difference and temperature is becoming stronger with global warming. __________ Translated from Acta Phytoecologica Sinica, 2005, 29(5): 753–765 [译自: 植物生态学报]     

高效液相色谱法测定大黄素在Caco-2细胞中的摄取

目的:采用反相高效液相色谱法,观察大黄素在Caco-2细胞中的摄取特点。方法:将大黄素与Caco-2细胞共同孵育,收集细胞样品,液氮反复冻融。取细胞裂解液,加入甲醇提取,提取液采用HPLC进行分析。色谱分析柱为C18柱(250mm×4．6mm,5μm,Diamonsil),流动相组成为85%乙腈及15%水(含0．1%乙酸),流速1ml·min-1,进样量20μl,柱温25℃,3D模式采集数据。结果:检测Caco-2细胞中大黄素的工作曲线的回归方程为Y=0．278x 0．148(Y=0．9996,n=5),线性范围为0．037～4．8μmol·L-1,最低检测浓度为0．018μmol·L-1。当细胞中大黄素的浓度为0．05、2和8．5μg·ml-1时,回收率分别为(101．3±7．3)%、(96．7±3．0)%和(98．7±2．1)%(n=5);相应的日内标准偏差分别为0．25%、2．9%和1．4%;相应的日间标准偏差分别为2．3%、5．6%和6．3%。大黄素在Caco-2细胞中的摄取达峰时间为10分钟,峰浓度为108．56±11．57 nmol/L·mg·protein,10分钟后Caco-2细胞中大黄素的含量迅速下降。浓度处于2-50μM之间时,Caco-2细胞对大黄素的摄取量呈线性增加,浓度达50μM后,随着剂量的增加大黄素的摄取量变化不明显。结论:大黄素可被Caco-2细胞迅速摄取,随着剂量的增加,大黄素在Caco-2细胞中的摄取存在饱和现象。     

NMR assignment of a KlbA intein precursor from Methanococcus jannaschii

The backbone and side chain resonance assignments of a precursor of the KlbA intein from Methanococcus jannaschii have been determined, based on triple-resonance experiments with the uniformly [¹³C,¹⁵N]-labeled protein.     

1H, 13C, and 15N resonance assignment of the cAMP-regulated phosphoprotein endosulfine-alpha in free and micelle-bound states

¹³C, ¹⁵N, and ¹H chemical shift assignments are presented for the cAMP-regulated phosphoprotein endosulfine-alpha in its free and micelle-bound states. Secondary chemical shift analysis demonstrates formation of four helices in the micelle-bound state, which are not present in the absence of detergent.     

Exploring proteasome complexes by proteomic approaches

The ubiquitin proteasome system (UPS) represents a major pathway for intracellular protein degradation. Proteasome dependent protein quality control participates in cell cycle, immune response and apoptosis. Therefore, the UPS is in focus of therapeutic investigations and the development of pharmaceutical agents. Detailed analyses on proteasome structure and function are the foundation for drug development and clinical studies. Proteomic approaches contributed significantly to our current knowledge in proteasome research. In particular, 2-DE has been essential in facilitating the development of current models on molecular composition and assembly of proteasome complexes. Furthermore, developments in MS enabled identification of UPS proteins and their PTMs at high accuracy and high-throughput. First results on global characterization of the UPS are also available. Although the UPS has been intensively investigated within the last two decades, its functional significance and contribution to the regulation of cell and tissue phenotypes remain to be explored. This review recapitulates a variety of applied proteomic approaches in proteasome exploration, and presents an overview of current technologies and their potential in driving further investigations.     

Light-induced immobilisation of biomolecules as an attractive alternative to microdroplet dispensing-based arraying technologies

The present work shows how UV 'light-induced molecular immobilisation' (LIMI) of biomolecules onto thiol reactive surfaces can be used to make biosensors, without the need for traditional microdispensing technologies. Using 'LIMI,' arrays of biomolecules can be created with a high degree of reproducibility. This technology can be used to circumvent the need for often expensive nano/microdispensing technologies. The ultimate size of the immobilised spots is defined by the focal area of the UV beam, which for a diffraction-limited beam can be less than 1 microm in diameter. LIMI has the added benefit that the immobilised molecules will be spatially oriented and covalently bound to the surface. The activity of the sensor molecules is retained. Antibody sensor arrays made using LIMI demonstrated successful antigen binding. In addition, the pattern of immobilised molecules on the surface is not restricted to conventional array formats. The ultimate consequence of the LIMI is that it is possible to write complex protein patterns using bitmaps at high resolution onto substrates. Thus, LIMI of biomolecules provides a new technological platform for biomolecular immobilisation and the potential for replacing present microdispensing arraying technologies.     

Learning the drug target-likeness of a protein

Current drug discovery and development approaches rely extensively on the identification and validation of appropriate targets; for example, those with marketable and robust therapeutics. Wide-ranging efforts have been directed at this problem and various approaches have been developed to identify disease-associated genes as candidates. In this work, we show with statistical significance that successful drug targets, in addition to their linkage to disease, share common characteristics that are disease-independent. For example, marked differences in functional category, tissue specificity, and sequence variability are observed between known targets and average proteins. These results lead to an interesting hypothesis: potentially good drug targets shall have some desired properties, which we refer to as "drug target-likeness" that are beyond their disease-associations. Because of the limited availability of comprehensive protein characteristics data, we tried to learn the drug target-likeness property at the sequence level. Results show that a support vector machine model is able to accurately distinguish targets from nontargets entirely with sequence features. It is our hope that these encouraging results will invite future systematic proteomic scale experiments to gather necessary protein characteristics data for the accurate and predictive definition of "drug target-likeness", providing a new perspective toward understanding and pursuing effective therapeutics.     

Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data

This study assesses the ability of a novel family of machine learning algorithms to identify changes in relative protein expression levels, measured using 2-D DIGE data, which support accurate class prediction. The analysis was done using a training set of 36 total cellular lysates comprised of six normal and three cancer biological replicates (the remaining are technical replicates) and a validation set of four normal and two cancer samples. Protein samples were separated by 2-D DIGE and expression was quantified using DeCyder-2D Differential Analysis Software. The relative expression reversal (RER) classifier correctly classified 9/9 training biological samples (p<0.022) as estimated using a modified version of leave one out cross validation and 6/6 validation samples. The classification rule involved comparison of expression levels for a single pair of protein spots, tropomyosin isoforms and alpha-enolase, both of which have prior association as potential biomarkers in cancer. The data was also analyzed using algorithms similar to those found in the extended data analysis package of DeCyder software. We propose that by accounting for sources of within- and between-gel variation, RER classifiers applied to 2-D DIGE data provide a useful approach for identifying biomarkers that discriminate among protein samples of interest.     

Antibody microarray-based profiling of complex specimens: systematic evaluation of labeling strategies

Antibody microarrays have often had limited success in detection of low abundant proteins in complex specimens. Signal amplification systems improve this situation, but still are quite laborious and expensive. However, the issue of sensitivity is more likely a matter of kinetically appropriate microarray design as demonstrated previously. Hence, we re-examined in this study the suitability of simple and inexpensive detection approaches for highly sensitive antibody microarray analysis. N-hydroxysuccinimidyl ester (NHS)- and Universal Linkage System (ULS)-based fluorescein and biotin labels used as tags for subsequent detection with anti-fluorescein and extravidin, respectively, as well as fluorescent dyes were applied for analysis of blood plasma. Parameters modifying strongly the performance of microarray detection such as labeling conditions, incubation time, concentrations of anti-fluorescein and extravidin and extent of protein labeling were analyzed and optimized in this study. Indirect detection strategies whether based on NHS- or ULS-chemistries strongly outperformed direct fluorescent labeling and enabled detection of low abundant cytokines with many dozen-fold signal-to-noise ratios. Finally, particularly sensitive detection chemistry was applied to monitoring cytokine production of stimulated peripheral T cells. Microarray data were in accord with quantitative cytokine levels measured by ELISA and Luminex, demonstrating comparable reliability and femtomolar range sensitivity of the established microarray approach.