Protecs, a comprehensive and powerful storage and analysis system for OMICS data, applied for profiling the anaerobiosis response of Staphylococcus aureus COL.

Broad functional genomic studies call for comprehensive and powerful data repositories for storage of genome sequences, experimental information, protein identification data, protein properties and expression values. The better such data repositories can integrate and display complex data in a clear and structured way the more biologically meaningful conclusions or novel hypotheses can be derived from extensive omics data sets. This work presents the web accessible database system Protecs and how it was used to support analysis of 50 samples drawn from four Staphylococcus aureus cultivations under anaerobiosis. Protecs incorporates findings from visualization science, e.g. micro charts and heat maps in the user interface. Its integrated tools for expression data analysis in combination with TIGR Multi Experiment Viewer were used to highlight similar gene expression profiles in single or multiple experiments based on the continuously updated S. aureus master gel. Raw data analysis results are available online at www.protecs.uni-greifswald.de. Our meta-study revealed that S. aureus responds in different anaerobiotic experimental setups (growth without oxygen; growth without oxygen but with supplemental pyruvate and uracil; growth without oxygen but with NO(3)(-); growth without oxygen but with NO(3)(-) and without functional nreABC genes) with a general anaerobiosis response. Among others, this response is characterized by an induction of fermentation enzymes (PflB, Ldh1, SACOL0135, SACOL0660) as well as the response regulator SrrA. Interestingly, especially genes with a high codon adaptation index highly overlap with anaerobically induced genes.

Improved image analysis workflow for 2-D gels enables large-scale 2-D gel-based proteomics studies--COPD biomarker discovery study.

2-D gel electrophoresis has been used for more than three decades to study the protein complement of organisms, tissues, and cells. Three issues are holding back large-scale proteomics studies: low-throughput, high technical variation, and study designs lacking statistical power. We identified image analysis as the central factor connecting these three issues. By developing an improved image analysis workflow we shortened project timelines, decreased technical variation, and thus enabled large-scale proteomics studies that are statistically powered. Rather than detecting protein spots on each gel image and matching spots across gel images, the improved workflow is based on aligning images first, then creating a consensus spot pattern and finally propagating the consensus spot pattern to all gel images for quantitation. This results in a data table without gaps. As an example we show here a study aimed at discovering circulating biomarkers for chronic obstructive pulmonary disease (COPD). Eight candidate biomarkers were identified by comparing plasma from 24 smokers with COPD and 24 smokers without COPD. Among the candidates are proteins such as plasma retinal-binding protein (RETB) and fibrinogen that had previously been linked to the disease and are frequently monitored in COPD patients, as well as other proteins such as apolipoprotein E (ApoE), inter-alpha-trypsininhibitor heavy chain H4 (ITIH4), and glutathione peroxidase.

The state of the art in the analysis of two-dimensional gel electrophoresis images.

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field.

2-DE profiling of GDNF overexpression-related proteome changes in differentiating ST14A rat progenitor cells.

Targeted differentiation of neural progenitor cells (NPCs) is a challenge for treatment of neurodegenerative diseases by cell replacement therapy and cell signalling manipulation. Here, we applied a proteome profiling approach to the rat striatal progenitor model cell line ST14A in order to elucidate cellular differentiation processes. Native cells and cells transfected with the glial cell line-derived neurotrophic factor (GDNF) gene were investigated at the proliferative state and at seven time points up to 72 h after induction of differentiation. 2-DE combined with MALDI-MS was used to create a reference 2-DE-map of 652 spots of which 164 were identified and assigned to 155 unique proteins. For identification of protein expression changes during cell differentiation, spot patterns of triplicate gels were matched to the 2-DE-map. Besides proteins that display expression changes in native cells, we also noted 43 protein-spots that were differentially regulated by GDNF overexpression in more than four time points of the experiment. The expression patterns of putative differentiation markers such as annexin 5 (ANXA5), glucosidase II beta subunit (GLU2B), phosphatidylethanolamine-binding protein 1 (PEBP1), myosin regulatory light chain 2-A (MLRA), NASCENT polypeptide-associated complex alpha (NACA), elongation factor 2 (EF2), peroxiredoxin-1 (PRDX1) and proliferating cell nuclear antigen (PCNA) were verified by Western blotting. The results reflect the large rearrangements of the proteome during the differentiation process of NPCs and their strong modification by neurotrophic factors like GDNF.

Visual analysis of gel-free proteome data.

We present a visual exploration system supporting protein analysis when using gel-free data acquisition methods. The data to be analyzed is obtained by coupling liquid chromatography (LC) with mass spectrometry (MS). LC-MS data have the properties of being nonequidistantly distributed in the time dimension (measured by LC) and being scattered in the mass-to-charge ratio dimension (measured by MS). We describe a hierarchical data representation and visualization method for large LC-MS data. Based on this visualization, we have developed a tool that supports various data analysis steps. Our visual tool provides a global understanding of the data, intuitive detection and classification of experimental errors, and extensions to LC-MS/MS, LC/LC-MS, and LC/LC-MS/MS data analysis. Due to the presence of randomly occurring rare isotopes within the same protein molecule, several intensity peaks may be detected that all refer to the same peptide. We have developed methods to unite such intensity peaks. This deisotoping step is visually documented by our system, such that misclassification can be detected intuitively. For differential protein expression analysis, we compute and visualize the differences in protein amounts between experiments. In order to compute the differential expression, the experimental data need to be registered. For registration, we perform a nonrigid warping step based on landmarks. The landmarks can be assigned automatically using protein identification methods. We evaluate our methods by comparing protein analysis with and without our interactive visualization-based exploration tool.

2-DE proteome analysis of a proliferating and differentiating human neuronal stem cell line (ReNcell VM).

The proteome of a proliferating human stem cell line was analyzed and then utilized to detect stem cell differentiation-associated changes in the protein profile. The analysis was conducted with a stable human fetal midbrain stem cell line (ReNcell VM) that displays the properties of a neural stem cell. Therefore, acquisition of proteomic data should be representative of cultured human neural stem cells (hNSCs) in general. Here we present a 2-DE protein-map of this cell line with annotations of 402 spots representing 318 unique proteins identified by MS. The subsequent proteome profiling of differentiating cells of this stem cell line at days 0, 4 and 7 of differentiation revealed changes in the expression of 49 identified spots that could be annotated to 45 distinct proteins. This differentiation-associated expression pattern was validated by Western blot analysis for transgelin-2, proliferating cell nuclear antigen, as well as peroxiredoxin 1 and 4. The group of regulated proteins also included NudC, ubiquilin-1, STRAP, stress-70 protein, creatine kinase B, glial fibrillary acidic protein and vimentin. Our results reflect the large rearrangement of the proteome during the differentiation process of the stem cells to terminally differentiated neurons and offer the possibility for further characterization of specific targets driving the stem cell differentiation.

Using standard positions and image fusion to create proteome maps from collections of two-dimensional gel electrophoresis images.

Databases for two-dimensional protein gels pose new challenges in extracting meaningful information from large numbers of experiments. In order to create expression profiles, positions of corresponding protein spots across all gel images have to be established. In larger gel sets errors may accumulate rapidly during this spot matching process, effectively limiting the number of samples available for data mining. Here we present a novel approach for organizing spot data based on the concept of a standard position for a protein species. Standard positions are meaningful average positions that are determined using all occurrences of a protein species. They can be extended to spots that are not annotated via interpolation. The standard position of a spot can serve as a unifying index across all gels in a database, thus allowing creation and analysis of expression profiles that span the whole collection. The standard position gives a much more accurate estimation of a spot's position on a gel than can be obtained using theoretical isoelectric point and molecular weight. Positional indexing is a complement to a priori identifications (e.g. by mass spectrometry or Edman degradation). Moreover it can be used in advance to select spots that are worth identifying because they show relevant expression profiles. Furthermore, we show how to combine all spots that occur on any of the gels into one synthetic but nevertheless realistic-looking image. This composite image is produced such that all spots have their standard positions. It can serve as a proteome reference map for an organism. As an application, we have computed a reference map from 23 gel images of Bacillus subtilis, using an enhanced prerelease version of the gel analysis software Delta2D (DECODON, Greifswald, Germany).