Protein profiling of low-density lipoprotein from obese subjects

Although obesity and high levels of low-density lipoprotein (LDL) are well-known risk factors for cardiovascular disease, the precise role(s) of different LDL constituents in obesity has not been explored. In the present study, we compared the LDL proteome of healthy control adults (body mass index<25) and obese subjects (body mass index>30). LDL was isolated by density-gradient ultracentrifugation and proteins were separated with 2-D PAGE, quantified, and identified by peptide mass fingerprinting using MALDI-TOF MS. A new LDL-associated protein was identified as transthyretin and found to be significantly more abundant in LDL from the obese subjects. In addition, LDL from the obese subjects contained relatively more α(1) -antitrypsin, apo J, apo C-II, than LDL from controls, and also more of an acidic isoform (pI/Mr; 5.2/23 100) of apo A-I. On the other hand, the relative amounts of apo A-IV and the major isoform of apo A-I (pI/Mr; 5.3/23 100) were significantly less in LDL from the obese subjects. Apo E was less and non-sialylated apo C-III more abundant in LDL from obese men than control men, while there were no such differences between LDL from obese and control women. These findings illustrate that obesity is not only associated with increased LDL-cholesterol levels but also with alterations in the LDL protein composition. The presence of transthyretin in LDL from obese subjects may reflect over-nutrition and affect the lipid metabolism in obesity.     

Region-based Statistical Analysis of 2D PAGE Images

A new comprehensive procedure for statistical analysis of two-dimensional polyacrylamide gel electrophoresis (2D PAGE) images is proposed, including protein region quantification, normalization and statistical analysis. Protein regions are defined by the master watershed map that is obtained from the mean gel. By working with these protein regions, the approach bypasses the current bottleneck in the analysis of 2D PAGE images: it does not require spot matching. Background correction is implemented in each protein region by local segmentation. Two-dimensional locally weighted smoothing (LOESS) is proposed to remove any systematic bias after quantification of protein regions. Proteins are separated into mutually independent sets based on detected correlations, and a multivariate analysis is used on each set to detect the group effect. A strategy for multiple hypothesis testing based on this multivariate approach combined with the usual Benjamini-Hochberg FDR procedure is formulated and applied to the differential analysis of 2D PAGE images. Each step in the analytical protocol is demonstrated by using an actual dataset. The effectiveness of the proposed methodology is shown using simulated gels in comparison with the commercial software packages PDQuest and Dymension. We also introduce a new procedure for simulating gel images.     

临床质谱学的最新进展:质谱成像方法及其应用

质谱成像技术将基质辅助激光解吸电离质谱的离子扫描与图像重建技术结合,直接分析生物组织切片,产生任意质荷比(m/z)化合物的二维或三维分布图。综述质谱成像原理、方法学及应用发展趋势。     

功能基因组学及其在辐射生物效应研究中的应用

功能基因组学是基因组学的重要组成部分,近年来已应用到辐射生物效应研究领域。本文介绍了功能基因组学的基本概念,主要研究内容、研究方法,以及在辐射生物效应研究领域中的应用。     

Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.