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.     

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.     

Application of proteomics to understand the molecular mechanisms behind meat quality

The proteome is expressed from the genome, influenced by environmental and processing conditions, and can be seen as the molecular link between the genome and the functional quality characteristics of the meat. In contrast to traditional biochemical methods where one protein is studied at a time, several hundred proteins can be studied simultaneously. Proteomics is a promising and powerful tool in meat science and this is reflected by the increasing number of studies emerging in the literature using proteomics as the key tool to unleash the molecular mechanisms behind different genetic backgrounds or processing techniques of meat. Thus understanding the variations and different components of the proteome with regard to a certain meat quality or process parameter will lead to knowledge that can be used in optimising the conversion of muscles to meat. At present, there has been focus on development of techniques and mapping of proteomes according to genotypes and muscle types. In the future, focus should be more towards understanding and finding markers for meat quality traits. This review will focus on the methods used in the published proteome analyses of meat, with emphasis on the challenges related to statistical analysis of proteome data, and on the different topics of meat science that are investigated.     

Mechanisms of weight maintenance under high- and low-protein, low-glycaemic index diets

Scope: Weight maintenance after intended weight loss is a challenge in an obesogenic environment. In a large multicentre dietary intervention study (DiOGenes), it has recently been demonstrated that a high‐protein/low‐glycaemic index (HP/LGI) diet was slightly more efficient in maintaining weight loss than low‐protein/LGI or high‐GI (LP/LGI or HGI) diets. Here, we use a proteomic approach to assess the molecular mechanisms behind this positive effect. Methods and results: A subset of the most successful (weight loser, n=12) and unsuccessful (weight re‐gainer, n=12) individuals consuming the LGI diets with either high‐ or low‐protein content (HP or LP/LGI), following an initial calorie deficit run‐in weight loss phase, were analyzed at the plasma protein level. Proteomic analysis revealed 18 proteins regulated after 6 months of the dietary weight maintenance phase. Furthermore, 12 proteins were significantly regulated as a function of success rate under an HP diet, arising as candidate biomarkers of mechanisms of successful weight maintenance under an HP/LGI diet. Pregnancy‐zone protein (PZP) and protein S (PROS1) were revealed as novel biomarkers of weight maintenance showing opposite effects. Conclusion: Semantic network analysis of the 12 regulated proteins revealed that under an HP/LGI an anti‐atherogenic effect and alterations of fat metabolism were associated with the success of maintaining the initial weight loss.     

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。