Absolute quantitation of 2-D protein spots

Global ubiquitinome profiling identifies NEDD4 as a regulator of Profilin 1 and actin remodelling in neural crest cells

The ubiquitin ligase NEDD4 promotes neural crest cell (NCC) survival and stem-cell like properties to regulate craniofacial and peripheral nervous system development. However, how ubiquitination and NEDD4 control NCC development remains unknown. Here we combine quantitative analysis of the proteome, transcriptome and ubiquitinome to identify key developmental signalling pathways that are regulated by NEDD4. We report 276 NEDD4 targets in NCCs and show that loss of NEDD4 leads to a pronounced global reduction in specific ubiquitin lysine linkages. We further show that NEDD4 contributes to the regulation of the NCC actin cytoskeleton by controlling ubiquitination and turnover of Profilin 1 to modulate filamentous actin polymerization. Taken together, our data provide insights into how NEDD4-mediated ubiquitination coordinates key regulatory processes during NCC development.

Here the authors combine multi-omics approaches to uncover a role for ubiquitination and the ubiquitin ligase NEDD4 in targeting the actin binding protein Profilin 1 to regulate actin polymerisation in neural crest cells.

Automatic analysis of 2D polyacrylamide gels in the diagnosis of DNA polymorphisms

Introduction

The analysis of polyacrylamide gels is currently carried out manually or automatically. In the automatic method, there are limitations related to the acceptable degree of distortion of lane and band continuity. The available software cannot deal satisfactorily with this type of situations. Therefore, the paper presents an original image analysis method devoid of the aforementioned drawbacks.

Material

This paper examines polyacrylamide gel images from Li-Cor DNA Sequencer 4300S resulting from the use of the electrophoretic separation of DNA fragments. The acquired images have a resolution dependent on the length of the analysed DNA fragments and typically it is M_G×N_G=3806×1027 pixels. The images are saved in TIFF format with a grayscale resolution of 16 bits/pixel. The presented image analysis method was performed on gel images resulting from the analysis of DNA methylome profiling in plants exposed to drought stress, carried out with the MSAP (Methylation Sensitive Amplification Polymorphism) technique.

Results

The results of DNA polymorphism analysis were obtained in less than one second for the Intel Core™ 2 Quad CPU Q9300@2.5GHz, 8GB RAM. In comparison with other known methods, specificity was 0.95, sensitivity = 0.94 and AUC (Area Under Curve) = 0.98.

Conclusions

It is possible to carry out this method of DNA polymorphism analysis on distorted images of polyacrylamide gels. The method is fully automatic and does not require any operator intervention. Compared with other methods, it produces the best results and the resulting image is easy to interpret. The presented method of measurement is used in the practical analysis of polyacrylamide gels in the Department of Genetics at the University of Silesia in Katowice, Poland.

Lipoproteomics: using mass spectrometry-based proteomics to explore the assembly, structure, and function of lipoproteins

Lipoproteins are centrally important in lipid transport, fuel metabolism, and cardiovascular disease. The prototypic lipoprotein has an outer shell of amphipathic lipids and proteins that solubilizes a hydrophobic lipid core. Lipoprotein-associated proteins have classically been viewed as structural elements and factors important in lipid metabolism. Recent mass spectrometric analyses reveal that the protein cargo of lipoproteins is much more diverse than previously appreciated, raising the possibility that lipoproteins play previously unsuspected roles in host defense mechanisms and inflammation. They further suggest that lipoprotein-associated proteins can identify humans at increased risk of cardiovascular disease. Here, we summarize recent developments in lipoproteomics, the proteomic analysis of lipoproteins. We also discuss the promises and challenges this powerful analytical strategy offers for expanding our understanding of the biology and structures of lipoproteins.

Mapping tissue-specific expression of extracellular proteins using systematic glycoproteomic analysis of different mouse tissues

Due to their easy accessibility, proteins outside of the plasma membrane represent an ideal but untapped resource for potential drug targets or disease biomarkers. They constitute the major biochemical class of current therapeutic targets and clinical biomarkers. Recent advances in proteomic technologies have fueled interest in analysis of extracellular proteins such as membrane proteins, cell surface proteins, and secreted proteins. However, unlike the gene expression analyses from a variety of tissues and cells using genomic technologies, quantitative proteomic analysis of proteins from various biological sources is challenging due to the high complexity of different proteomes and the lack of robust and consistent methods for analyses of different tissue sources, especially for specific enrichment of extracellular proteins. Since most extracellular proteins are modified by oligosaccharides, the population of glycoproteins therefore represents the majority of extracellular proteomes. Here, we quantitatively analyzed glycoproteins and determined the expression patterns of extracellular proteins from 12 mouse tissues using solid-phase extraction of N-linked glycopeptides and liquid chromatography-tandem mass spectrometry. We identified peptides enclosing 1231 possible N-linked glycosites from 826 unique proteins. We further determined the expression pattern of formerly N-linked glycopeptides and identified extracellular glycoproteins specifically expressed in each tissue. Furthermore, the tissue specificities of the overexpressed glycoproteins in a mouse skin tumor model were determined by comparing them to the quantitative protein expression from the different tissues. These skin tumor-specific extracellular proteins might serve as potential candidates for cell surface drug targets or disease-specific protein markers.

Correlation between protein and mRNA abundance in yeast

We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243-251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.