Normalization and analysis of residual variation in two-dimensional gel electrophoresis for quantitative differential proteomics

Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) across batches of samples and varying rates of “missing not at random” (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

Proteome-based identification of apolipoprotein A-IV as an early diagnostic biomarker in liver fibrosis

Hepatic fibrosis may ultimately result in organ failure and death, a reality compounded by the fact that most drugs for liver fibrosis appear to be effective only if given as a prophylactic or early treatment. In a dimethylnitrosamine-induced liver fibrotic model, aspartate aminotransferase/alanine aminotransferase levels could not precisely distinguish the differences between the initial stage of liver fibrosis and normal control, whereas histological examination indicated that dimethylnitrosamine treatment for two weeks has resulted in hepatic fibrogenesis. Comprehensive proteomics identified 12 proteins mainly associated with the interleukin 6-stimulated inflammatory pathway. Coordinately, cytokine profiles showed that dimethylnitrosamine administration would stimulate various signaling pathways leading to liver fibrosis. Of note, apolipoprotein A4 in serum samples obtained from patients in the early stage of liver fibrosis were significantly increased compared to the healthy controls (p<0.001) while the area under curve was 0.966. Moreover, increased apolipoprotein A4 significantly enhanced transforming growth factor beta 1-induced alpha smooth muscle actin expression. In this regard, overexpression of apolipoprotein A4 in early stage of liver fibrosis might magnify and imply the progression of hepatic fibrosis. These findings suggest that apolipoprotein A4 upregulation may correlate with hepatic fibrosis staging and that apolipoprotein A4 together with current biomarker can increase the sensitivity and specificity for the early detection of liver fibrosis in a high-throughput manner.

Vascular Endothelial Growth Factor (VEGF) Promotes Assembly of the p130Cas Interactome to Drive Endothelial Chemotactic Signaling and Angiogenesis

p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and reconstruction of protein interaction (PPI) networks with the aid of multiple PPI databases. VEGF enriched the p130Cas interactome in proteins involved in actin cytoskeletal dynamics and cell movement, including actin-binding proteins, small GTPases and regulators or binders of GTPases. Detailed studies showed that p130Cas association of the GTPase-binding scaffold protein, IQGAP1, plays a key role in VEGF chemotactic signaling, endothelial polarization, VEGF-induced cell migration, and endothelial tube formation. These findings indicate a cardinal role for assembly of the p130Cas interactome in mediating the cell migratory response to VEGF in angiogenesis, and provide a basis for further studies of p130Cas in cell movement.

Tuber proteome comparison of five potato varieties by principal component analysis

BACKGROUND

Data analysis of omics data should be performed by multivariate analysis such as principal component analysis (PCA). The way data are clustered in PCA is of major importance to develop some classification systems based on multivariate analysis, such as soft independent modeling of class analogy (SIMCA). In a previous study a one-class classifier based on SIMCA was built using microarray data from a set of potatoes. The PCA grouped the transcriptomic data according to varieties. The present work aimed to use PCA to verify the clustering of the proteomic profiles for the same potato varieties.

RESULTS

Proteomic profiles of five potato varieties (Biogold, Fontane, Innovator, Lady Rosetta and Maris Piper) were evaluated by two-dimensional gel electrophoresis (2-DE) performed on two immobilized pH gradient (IPG) strip lengths, 13 and 24 cm, both under pH range 4-7. For each strip length, two gels were prepared from each variety; in total there were ten gels per analysis. For 13 cm strips, 199-320 spots were detected per gel, and for 24 cm strips, 365-684 spots.

CONCLUSION

All four PCAs performed with these datasets presented clear grouping of samples according to the varieties. The data presented here showed that PCA was applicable for proteomic analysis of potato and was able to separate the samples by varieties. © 2016 Society of Chemical Industry.

Image Pretreatment Tools II: Normalization Techniques for 2-DE and 2-D DIGE

Gel electrophoresis is usually applied to identify different protein expression profiles in biological samples (e.g., control vs. pathological, control vs. treated). Information about the effect to be investigated (a pathology, a drug, a ripening effect, etc.) is however generally confounded with experimental variability that is quite large in 2-DE and may arise from small variations in the sample preparation, reagents, sample loading, electrophoretic conditions, staining and image acquisition. Obtaining valid quantitative estimates of protein abundances in each map, before the differential analysis, is therefore fundamental to provide robust candidate biomarkers. Normalization procedures are applied to reduce experimental noise and make the images comparable, improving the accuracy of differential analysis. Certainly, they may deeply influence the final results, and to this respect they have to be applied with care. Here, the most widespread normalization procedures are described both for what regards the applications to 2-DE and 2D Difference Gel-electrophoresis (2-D DIGE) maps.

Proteomics and systems biology for understanding diabetic nephropathy

Like many diseases, diabetic nephropathy is defined in a histopathological context and studied using reductionist approaches that attempt to ameliorate structural changes. Novel technologies in mass spectrometry-based proteomics have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. The goal is to translate these new definitions into improved patient outcomes through diagnostic, prognostic, and therapeutic tools. Here, we review progress made in studying the proteomics of diabetic nephropathy and provide an introduction to the informatics tools used in the analysis of systems biology data, while pointing out statistical issues for consideration. Novel bioinformatics methods may increase biomarker identification, and other tools, including selective reaction monitoring, may hasten clinical validation.

COMPARISON OF TWO ACADEMIC SOFTWARE PACKAGES FOR ANALYZING TWO-DIMENSIONAL GEL IMAGES

One of the key limitations for proteomic studies using two-dimensional (2D) gel is the lack of automatic, fast, robust, and reliable methods for detecting, matching, and quantifying protein spots. Although there are commercial software packages for 2D gel image analysis, extensive human intervention is still needed for spot detection and matching, which is time-consuming and error-prone. Moreover, the commercial software packages are usually expensive and non–open source. Thus, it is very beneficial for researchers to have free software that is fast, fully automatic, and robust. In this paper, we review and compare two recently developed and publicly available software packages, RegStatGel and Pinnacle, for analyzing 2D gel images. These two software packages share some common features and also have some fundamental difference in the aspects of spot detection and quantification. Based on our experience, RegStatGel is much better in terms of spot detection and matching. It also contains more advanced statistical tools and is more user-friendly. In contrast, Pinnacle is quite sensitive to background noise and relies on external statistical software packages for statistical analysis.

Quantitative gel electrophoresis: new records in precision by elaborated staining and detection protocols

Gel electrophoresis (GE) is a very common analytical technique for proteome research and protein analysis. Despite being developed decades ago, there is still a considerable need to improve its precision. Using the fluorescence of Colloidal Coomassie Blue -stained proteins in near-infrared (NIR), the major error source caused by the unpredictable background staining is strongly reduced. This result was generalized for various types of detectors. Since GE is a multi-step procedure, standardization of every single step is required. After detailed analysis of all steps, the staining and destaining were identified as the major source of the remaining variation. By employing standardized protocols, pooled percent relative standard deviations of 1.2-3.1% for band intensities were achieved for one-dimensional separations in repetitive experiments. The analysis of variance suggests that the same batch of staining solution should be used for gels of one experimental series to minimize day-to-day variation and to obtain high precision.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

Identification of diagnostic urinary biomarkers for acute kidney injury

Acute kidney injury (AKI) is an important cause of death among hospitalized patients. The 2 most common causes of AKI are acute tubular necrosis (ATN) and prerenal azotemia (PRA). Appropriate diagnosis of the disease is important but often difficult. We analyzed urine proteins by 2-dimensional gel electrophoresis from 38 patients with AKI. Patients were randomly assigned to a training set, an internal test set, or an external validation set. Spot abundances were analyzed by artificial neural networks to identify biomarkers that differentiate between ATN and PRA. When the trained neural network algorithm was tested against the training data, it identified the diagnosis for 16 of 18 patients in the training set and all 10 patients in the internal test set. The accuracy was validated in the novel external set of patients where conditions of 9 of 10 patients were correctly diagnosed including 5 of 5 with ATN and 4 of 5 with PRA. Plasma retinol-binding protein was identified in 1 spot and a fragment of albumin and plasma retinol-binding protein in the other. These proteins are candidate markers for diagnostic assays of AKI.

Evaluating the performance of new approaches to spot quantification and differential expression in 2-dimensional gel electrophoresis studies

Spot detection and quantification for 2-DE are challenging and important tasks to fully extract the proteomic information from these data. Traditional analytical methods have significant weaknesses, including spot mismatching and missing data, which require time-consuming manual editing to correct, dramatically decreasing throughput and compromising the objectivity and reproducibility of the analysis. To address this issue, we developed Pinnacle, a novel, quick, automatic, noncommercial method that borrows strength across gels in spot detection and has been shown to yield more precise spot quantifications than traditional methods. New commercial software, notably SameSpots, has also recently been developed as an improvement over traditional workflows. In this paper, we briefly describe Pinnacle and compare its performance to SameSpots in spot detection, spot quantification precision, and differential expression. Our analysis is performed in a rigorous fashion that, unlike other comparisons in the literature, summarizes performance across all spots detected on the gels, and we manually optimize SameSpots results while simply running Pinnacle with standard settings and no manual editing. While both methods showed marked improvement over a commercially available traditional method PG240, Pinnacle consistently yielded spot quantifications with greater validity and reliability, avoided spot delineation problems, and detected more differentially expressed proteins than SameSpots, and represents a significant noncommercial alternative for 2-DE processing.

Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae

Background

The industrially important yeast Saccharomyces cerevisiae is able to grow both in the presence and absence of oxygen. However, the regulation of its metabolism in conditions of intermediate oxygen availability is not well characterised. We assessed the effect of oxygen provision on the transcriptome and proteome of S. cerevisiae in glucose-limited chemostat cultivations in anaerobic and aerobic conditions, and with three intermediate (0.5, 1.0 and 2.8% oxygen) levels of oxygen in the feed gas.

Results

The main differences in the transcriptome were observed in the comparison of fully aerobic, intermediate oxygen and anaerobic conditions, while the transcriptome was generally unchanged in conditions receiving different intermediate levels (0.5, 1.0 or 2.8% O₂) of oxygen in the feed gas. Comparison of the transcriptome and proteome data suggested post-transcriptional regulation was important, especially in 0.5% oxygen. In the conditions of intermediate oxygen, the genes encoding enzymes of the respiratory pathway were more highly expressed than in either aerobic or anaerobic conditions. A similar trend was also seen in the proteome and in enzyme activities of the TCA cycle. Further, genes encoding proteins of the mitochondrial translation machinery were present at higher levels in all oxygen-limited and anaerobic conditions, compared to fully aerobic conditions.

Conclusion

Global upregulation of genes encoding components of the respiratory pathway under conditions of intermediate oxygen suggested a regulatory mechanism to control these genes as a response to the need of more efficient energy production. Further, cells grown in three different intermediate oxygen levels were highly similar at the level of transcription, while they differed at the proteome level, suggesting post-transcriptional mechanisms leading to distinct physiological modes of respiro-fermentative metabolism.

Quantitative proteome analysis in cardiovascular physiology and pathology. I. Data processing

Methodological evaluation of the proteomic analysis of cardiovascular-tissue material has been performed with a special emphasis on establishing examinations that allow reliable quantitative analysis of silver-stained readouts. Reliability, reproducibility, robustness and linearity were addressed and clarified. In addition, several types of normalization procedures were evaluated and new approaches are proposed. It has been found that the silver-stained readout offers a convenient approach for quantitation if a linear range for gel loading is defined. In addition, a broad range of a 10-fold input (loading 20-200 microg per gel) fulfills the linearity criteria, although at the lowest input (20 microg) a portion of protein species will remain undetected. The method is reliable and reproducible within a range of 65-200 microg input. The normalization procedure using the sum of all spot intensities from a silver-stained 2D pattern has been shown to be less reliable than other approaches, namely, normalization through median or through involvement of interquartile range. A special refinement of the normalization through virtual segmentation of pattern, and calculation of normalization factor for each stratum provides highly satisfactory results. The presented results not only provide evidence for the usefulness of silver-stained gels for quantitative evaluation, but they are directly applicable to the research endeavor of monitoring alterations in cardiovascular pathophysiology.

A transcriptomic analysis of the stress induced by capture-release health assessment studies in wild dolphins (Tursiops truncatus)

The health of wild bottlenose dolphins (Tursiops truncatus) is typically evaluated by the study of animals that are captured and released back into the wild after examination. The impact of such studies on gene expression in peripheral blood cells was investigated using microarray and quantitative polymerase chain reaction methods. Significantly increased expression was observed in two major classes of genes: (i) energy metabolism, and (ii) responsiveness to stress and trauma, the latter effect suggesting the initiation of an acute-phase response. The value of data obtained in capture/release studies may need to be weighed against the potential physiological impacts of such studies.

Pinnacle: a fast, automatic and accurate method for detecting and quantifying protein spots in 2-dimensional gel electrophoresis data

MOTIVATION

One of the key limitations for proteomic studies using 2-dimensional gel electrophoresis (2DE) is the lack of rapid, robust and reproducible methods for detecting, matching and quantifying protein spots. The most commonly used approaches involve first detecting spots and drawing spot boundaries on individual gels, then matching spots across gels and finally quantifying each spot by calculating normalized spot volumes. This approach is time consuming, error-prone and frequently requires extensive manual editing, which can unintentionally introduce bias into the results.

RESULTS

We introduce a new method for spot detection and quantification called Pinnacle that is automatic, quick, sensitive and specific and yields spot quantifications that are reliable and precise. This method incorporates a spot definition that is based on simple, straightforward criteria rather than complex arbitrary definitions, and results in no missing data. Using dilution series for validation, we demonstrate Pinnacle outperformed two well-established 2DE analysis packages, proving to be more accurate and yielding smaller coefficiant of variations (CVs). More accurate quantifications may lead to increased power for detecting differentially expressed spots, an idea supported by the results of our group comparison experiment. Our fast, automatic analysis method makes it feasible to conduct very large 2DE-based proteomic studies that are adequately powered to find important protein expression differences.

AVAILABILITY

Matlab code to implement Pinnacle is available from the authors upon request for non-commercial use.

An open-source representation for 2-DE-centric proteomics and support infrastructure for data storage and analysis

BACKGROUND

In spite of two-dimensional gel electrophoresis (2-DE) being an effective and widely used method to screen the proteome, its data standardization has still not matured to the level of microarray genomics data or mass spectrometry approaches. The trend toward identifying encompassing data standards has been expanding from genomics to transcriptomics, and more recently to proteomics. The relative success of genomic and transcriptomic data standardization has enabled the development of central repositories such as GenBank and Gene Expression Omnibus. An equivalent 2-DE-centric data structure would similarly have to include a balance among raw data, basic feature detection results, sufficiency in the description of the experimental context and methods, and an overall structure that facilitates a diversity of usages, from central reposition to local data representation in LIMs systems.

RESULTS & CONCLUSION

Achieving such a balance can only be accomplished through several iterations involving bioinformaticians, bench molecular biologists, and the manufacturers of the equipment and commercial software from which the data is primarily generated. Such an encompassing data structure is described here, developed as the mature successor to the well established and broadly used earlier version. A public repository, AGML Central, is configured with a suite of tools for the conversion from a variety of popular formats, web-based visualization, and interoperation with other tools and repositories, and is particularly mass-spectrometry oriented with I/O for annotation and data analysis.

2D-PAGE maps analysis

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.

Avian proteomics: advances, challenges and new technologies

Proteomics is defined as an analysis of the full complement of proteins of a cell or tissue under given conditions. Avian proteomics, or more specifically chicken proteomics, has focussed on the study of individual tissues and organs of interest to specific researchers. Researchers have looked at skeletal muscle and growth, and embryonic development and have performed initial studies in avian disease. Traditional proteomics involves identifying and cataloguing proteins in a cell and identifying relative changes in populations between two or more states, be that physiological or disease-induced states. Recent advances in proteomic technologies have included absolute quantification, proteome simplification and the ability to determine the turnover of individual proteins in a global context. This review discusses the current developments in this relatively new field, new technologies and how they may be applied to biological questions, and the challenges faced by researchers in this ever-expanding and exciting field.

Diabetes-induced changes in the renal cortical proteome assessed with two-dimensional gel electrophoresis and mass spectrometry

To understand the spectrum of proteins affected by diabetes and to characterize molecular functions and biological processes they control, we analyzed the renal cortical proteome of db/db mice using 2-DE combined with MALDI-TOF, MALDI-TOF/TOF, and LC-MS/MS. This approach yielded 278 high confidence identifications whose expression levels were significantly increased or decreased >two-fold by diabetes, of which 170 mapped to gene identifiers representing 147 nonredundant proteins. Gene Ontology classification demonstrated that 80% of these proteins modulated physiological functions, 55% involved metabolism, approximately 25% involved carboxylic and organic acid metabolism, 14% involved biosynthesis or catabolism, and 12% involved fatty acid metabolism. Predominant molecular functions were catalytic (61%), oxidoreductase (20%), and transferase (17%) activities, and nucleotide and ATP binding (11-15%). Twenty eight percent of the proteins identified as significantly altered by diabetes were mitochondrial proteins. The top-ranked network described by Ingenuity Pathway Analysis indicated PPARalpha was the most common node of interaction for the numerous enzymes whose expression levels were influenced by diabetes. These differentially regulated proteins create a foundation for a systems biology exploration of molecular mechanisms underlying the pathophysiology of diabetic nephropathy.

Statistics for proteomics: Experimental design and 2-DE differential analysis

Proteomics relies on the separation of complex protein mixtures using bidimensional electrophoresis. This approach is largely used to detect the expression variations of proteins prepared from two or more samples. Recently, attention was drawn on the reliability of the results published in literature. Among the critical points identified were experimental design, differential analysis and the problem of missing data, all problems where statistics can be of help. Using examples and terms understandable by biologists, we describe how a collaboration between biologists and statisticians can improve reliability of results and confidence in conclusions.

Urine biomarkers predict the cause of glomerular disease

Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 x 10(-8). Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, alpha-1 microglobulin, zinc alpha-2 glycoprotein, alpha-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.

A dolphin peripheral blood leukocyte cDNA microarray for studies of immune function and stress reactions

A microarray focused on stress response and immune function genes of the bottlenosed dolphin has been developed. Random expressed sequence tags (ESTs) were isolated and sequenced from two dolphin peripheral blood leukocyte (PBL) cDNA libraries biased towards T- and B-cell gene expression by stimulation with IL-2 and LPS, respectively. A total of 2784 clones were sequenced and contig analysis yielded 1343 unigenes (archived and annotated at ). In addition, 52 dolphin genes known to be important in innate and adaptive immune function and stress responses of terrestrial mammals were specifically targeted, cloned and added to the unigene collection. The set of dolphin sequences printed on a cDNA microarray comprised the 1343 unigenes, the 52 targeted genes and 2305 randomly selected (but unsequenced) EST clones. This set was printed in duplicate spots, side by side, and in two replicates per slide, such that the total number of features per microarray slide was 19,200, including controls. The dolphin arrays were validated and transcriptomic profiles were generated using PBL from a wild dolphin, a captive dolphin and dolphin skin cells. The results demonstrate that the array is a reproducible and informative tool for assessing differential gene expression in dolphin PBL and in other tissues.

Insights into the immune transcriptome of the shrimp Litopenaeus vannamei: tissue-specific expression profiles and transcriptomic responses to immune challenge

Infectious disease constitutes a major obstacle to the sustainability of shrimp aquaculture worldwide and a significant threat to natural populations of shrimp and other crustacea. The study of the shrimp immune system, including the response to viral infection, has been hampered by a relative lack of molecular genetic information and of tools suitable for high-throughput assessment of gene expression. In this report, the generation of a cDNA microarray encompassing 2,469 putative unigenes expressed in gills, circulating hemocytes, and hepatopancreas of Litopenaeus vannamei is described. The unigenes printed on the microarray were derived from the analyses of 7,021 expressed sequence tags obtained from standard cDNA libraries as well as from libraries generated by suppression subtractive hybridization, after challenging shrimp with a variety of immune stimuli. The general utility of the cDNA microarray was demonstrated by interrogating the array with labeled RNA from four different shrimp tissues (gills, hemocytes, hepatopancreas, and muscle) and by analyzing the transcriptomic response of shrimp to a lethal challenge with white spot syndrome virus. Our results indicate that white spot syndrome virus infection upregulates (in the hepatopancreas) genes encoding known and potential antimicrobial effectors, while some genes involved in protection from oxidative stress were found to be downregulated by the virus.

Quantitative analysis of representative proteome components and clustering of Helicobacter pylori clinical strains

BACKGROUND

Several Helicobacter pylori proteins have been reported to be associated with severe symptoms of gastric disease. However, expression levels of most of these disease-associated proteins require further evaluation in order to clarify their relationships with gastric disease patterns. Representative proteome components of 71 clinical isolates of H. pylori were analyzed quantitatively to determine whether the protein expression levels were associated with gastric diseases and to cluster clinical isolates.

METHODS

After two-dimensional electrophoresis (2-DE) of H. pylori isolates, spot intensities were analyzed using pdquest 2-D Gel Analysis Software. The intensities of 10 representative protein spots, identified by peptide fingerprinting using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) or peptide sequencing using quadrupole TOF MS, were subjected to the nonparametric Mann-Whitney test and hierarchical agglomerative cluster analysis. The relationship between clusters and gastric diseases was analyzed by the chi-squared test.

RESULTS

Although the spot intensities of the 10 representative proteins were highly variable within each gastric disease group, the expression levels of CagA, UreB, GroEL, EF-Tu, EF-P, TagD, and FldA showed some significant differences among the gastric disease patterns. On the basis of the 10 target protein intensities, hierarchical agglomerative cluster analysis generated a dendrogram with clusters indicative of chronic gastritis/gastric cancers and gastric/duodenal ulcers.

CONCLUSION

These results indicated that quantitative analysis of proteome components is a feasible method for examining disease-associated proteins and clustering clinical strains of H. pylori.

Modeling pneumococcal resistance to penicillin in southern Sweden using artificial neural networks

In recent decades, penicillin-resistant pneumococci (PRP) have emerged and spread rapidly between and within countries over the world. In this study we developed an iterative artificial neural network (ANN) model to describe and predict the spread of PRP in space and time as a function of antibiotic consumption and a number of different confounders. Retrospective data from 1997 to 2000 on an international epidemic PRP clone (serotype 9V) and antibiotic consumption data from Southern Sweden were used to train the ANN models and data from 2001 to 2003 for evaluation of the model predictions. Five different ANN models were trained, each with independent topology optimization for alternative sets of input variables to find the most descriptive model. The model containing all variables was the only one performing better than the reference linear models, as assessed by the correlation between predictions and observations. The inability to identify a smaller subset of most predictive parameters may reflect either diffuse causal mechanisms or just the absence of critical experimental indicators from the dataset. The iterative ANN model identified is useful to predict future data. The sensitivity analysis of the model suggests that past incidence has a small effect on the number of PRP cases.

Examination of 2-DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique

The Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) pilot studies have generated over 200 2-D gels from eight participating laboratories. This data includes 67 single-channel and 60 DIGE gels comparing 30 whole frozen C57/BL6 female mouse brains, ten each at embryonic day 16, postnatal day 7 (juvenile) and postnatal day 54-56 (adult); and ten single-channel and three DIGE gels comparing human epilepsy surgery of the temporal front lobe with a corresponding post-mortem specimen. The samples were generated centrally and distributed to the participating laboratories, but otherwise no restrictions were placed on sample preparation, running and staining protocols, nor on the 2-D gel analysis packages used. Spots were characterised by MS and the annotated gel images published on a ProteinScape web server. In order to examine the resultant differential expression and protein identifications, we have reprocessed a large subset of the gels using the newly developed RAIN (Robust Automated Image Normalisation) 2-D gel matching algorithm. Traditional approaches use symbolic representation of spots at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in spot modelling and matching. With RAIN, image intensity distributions, rather than selected features, are used, where smooth geometric deformation and expression bias are modelled using multi-resolution image registration and bias-field correction. The method includes a new approach of volume-invariant warping which ensures the volume of protein expression under transformation is preserved. An image-based statistical expression analysis phase is then proposed, where small insignificant expression changes over one gel pair can be revealed when reinforced by the same consistent changes in others. Results of the proposed method as applied to the HUPO BPP data show significant intra-laboratory improvements in matching accuracy over a previous state-of-the-art technique, Multi-resolution Image Registration (MIR), and the commercial Progenesis PG240 package.

Challenges Related to Analysis of Protein Spot Volumes from Two-Dimensional Gel Electrophoresis As Revealed by Replicate Gels

Assumptions that need to be considered prior to statistical analysis of protein spot volumes from two-dimensional gel electrophoresis (2-DE) data are studied using replicate gels of the same sample. The most important observation is that the data tables of protein spot volumes from 2-DE images contain a large number of missing values, which are not consistent with the presence or absence of the proteins. This implies both loss of information and problems for the subsequent statistical analysis. Challenges with 2-DE protein spot volumes are viewed in light of multiple gel comparisons and multivariate data analysis.

mGrid: a load-balanced distributed computing environment for the remote execution of the user-defined Matlab code

BACKGROUND

Matlab, a powerful and productive language that allows for rapid prototyping, modeling and simulation, is widely used in computational biology. Modeling and simulation of large biological systems often require more computational resources then are available on a single computer. Existing distributed computing environments like the Distributed Computing Toolbox, MatlabMPI, Matlab*G and others allow for the remote (and possibly parallel) execution of Matlab commands with varying support for features like an easy-to-use application programming interface, load-balanced utilization of resources, extensibility over the wide area network, and minimal system administration skill requirements. However, all of these environments require some level of access to participating machines to manually distribute the user-defined libraries that the remote call may invoke.

RESULTS

mGrid augments the usual process distribution seen in other similar distributed systems by adding facilities for user code distribution. mGrid's client-side interface is an easy-to-use native Matlab toolbox that transparently executes user-defined code on remote machines (i.e. the user is unaware that the code is executing somewhere else). Run-time variables are automatically packed and distributed with the user-defined code and automated load-balancing of remote resources enables smooth concurrent execution. mGrid is an open source environment. Apart from the programming language itself, all other components are also open source, freely available tools: light-weight PHP scripts and the Apache web server.

CONCLUSION

Transparent, load-balanced distribution of user-defined Matlab toolboxes and rapid prototyping of many simple parallel applications can now be done with a single easy-to-use Matlab command. Because mGrid utilizes only Matlab, light-weight PHP scripts and the Apache web server, installation and configuration are very simple. Moreover, the web-based infrastructure of mGrid allows for it to be easily extensible over the Internet.

Prediction of urinary protein markers in lupus nephritis

BACKGROUND

Lupus nephritis is divided into six classes and scored according to activity and chronicity indices based on histologic findings. Treatment differs based on the pathologic findings. Renal biopsy is currently the only way to accurately predict class and activity and chronicity indices. We propose to use patterns of abundance of urine proteins to identify class and disease indices.

METHODS

Urine was collected from 20 consecutive patients immediately prior to biopsy for evaluation of lupus nephritis. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) class of lupus nephritis, activity, and chronicity indices were determined by a renal pathologist. Proteins were separated by two-dimensional gel electrophoresis. Artificial neural networks were trained on normalized spot abundance values.

RESULTS

Biopsy specimens were classified in the database according to ISN/RPS class, activity, and chronicity. Nine samples had characteristics of more than one class present. Receiver operating characteristic (ROC) curves of the trained networks demonstrated areas under the curve ranging from 0.85 to 0.95. The sensitivity and specificity for the ISN/RPS classes were class II 100%, 100%; III 86%, 100%; IV 100%, 92%; and V 92%, 50%. Activity and chronicity indices had r values of 0.77 and 0.87, respectively. A list of spots was obtained that provided diagnostic sensitivity to the analysis.

CONCLUSION

We have identified a list of protein spots that can be used to develop a clinical assay to predict ISN/RPS class and chronicity for patients with lupus nephritis. An assay based on antibodies against these spots could eliminate the need for renal biopsy, allow frequent evaluation of disease status, and begin specific therapy for patients with lupus nephritis.

AGML Central: web based gel proteomic infrastructure

SUMMARY

AGML Central is a web-based open-source public infrastructure for dissemination of two-dimensional Gel Electrophoresis (2-DE) proteomics data in AGML format (Annotated Gel Markup Language). It includes a growing collection of converters from proprietary formats such as those produced by PDQUEST (BioRad), PHORETIX 2-D (Nonlinear Dynamics) and Melanie (GenBio SA). The resulting unifying AGML formatted entry, with or without the raw gel images, is optionally stored in a database for future reference. AGML Central was developed to provide a common platform for data dissemination and development of 2-DE data analysis tools. This resource responds to an increasing use of AGML for 2-DE public source data representation which requires automated tools for conversion from proprietary formats. Conversion and short-term storage is made publicly available, permanent storage requires prior registering. A JAVA applet visualizer was developed to visualize the AGML data with cross-reference links. In order to facilitate automated access a SOAP web service is also included in the AGML Central infrastructure.

AVAILABILITY

http://bioinformatics.musc.edu/agmlcentral.