Application of three-way principal component analysis to the evaluation of two-dimensional maps in proteomics

Proteomic Analysis of the Effect of Extremely Low-Frequency Electromagnetic Fields (ELF-EMF) With Different Intensities in SH-SY5Y Neuroblastoma Cell Line

Introduction: During the last 3 decades, human is exposed to extremely low frequency electromagnetic fields (ELF-EMF) emitted by power lines and electronic devices. It is now well accepted that ELF-EMF are able to produce a variety of biological effects, although the molecular mechanism is unclear and controversial. Investigation of different intensities effects of 50 Hz ELF-EMF on cell morphology and protein expression is the aim of this study. Methods: SH-SY5Y human neuroblastoma cell line was exposed to 0.5 and 1 mT 50 Hz (ELF-EMF) for 3 hours. Proteomics techniques were used to determine the effects of these fields on protein expression. Bioinformatic and statistical analysis of proteomes were performed using Progensis SameSpots software. Results: Our results showed that exposure to ELF-EMF changes cell morphology and induces a dose-dependent decrease in the proliferation rate of the cells. The proteomic studies and bioinformatic analysis indicate that exposure to 50 Hz ELF-EMF leads to alteration of cell protein expression in both dose-dependent and intensity dependent manner, but the later is more pronounced. Conclusion: Our data suggests that increased intensity of ELF-EMF may be associated with more alteration in cell protein expression, as well as effect on cell morphology and proliferation.

Differential Analysis of 2-D Maps by Pixel-Based Approaches

Two approaches to the analysis of 2-D maps are available: the first one involves a step of spot detection on each gel image; the second one is based instead on the direct differential analysis of 2-D map images, following a pixel-based procedure. Both approaches strongly depend on the proper alignment of the gel images, but the pixel-based approach allows to solve important drawbacks of the spot-volume procedure, i.e., the problem of missing data and of overlapping spots. However, this approach is quite computationally intensive and requires the use of algorithms able to separate the information (i.e., spot-related information) from the background. Here, the most recent pixel-based approaches are described.

Biomarker discovery from the top down: Protein biomarkers for efficient virus transmission by insects (Homoptera: Aphididae) discovered by coupling genetics and 2-D DIGE

Yellow dwarf viruses cause the most economically important virus diseases of cereal crops worldwide and are vectored by aphids. The identification of vector proteins mediating virus transmission is critical to develop sustainable virus management practices and to understand viral strategies for circulative movement in all insect vectors. Previously, we applied 2-D DIGE to an aphid filial generation 2 population to identify proteins correlated with the transmission phenotype that were stably inherited and expressed in the absence of the virus. In the present study, we examined the expression of the DIGE candidates in previously unstudied, field-collected aphid populations. We hypothesized that the expression of proteins involved in virus transmission could be clinically validated in unrelated, virus transmission-competent, field-collected aphid populations. All putative biomarkers were expressed in the field-collected biotypes, and the expression of nine of these aligned with the virus transmission-competent phenotype. The strong conservation of the expression of the biomarkers in multiple field-collected populations facilitates new and testable hypotheses concerning the genetics and biochemistry of virus transmission. Integration of these biomarkers into current aphid-scouting methodologies will enable rational strategies for vector control aimed at judicious use and development of precision pest control methods that reduce plant virus infection.

Proteome analysis in cardiovascular pathophysiology using Dahl rat model

Dahl salt-sensitive (DS) and salt-resistant (DR) inbred rat strains represent a well established animal model for cardiovascular research. Upon prolonged administration of high-salt-containing diet, DS rats develop systemic hypertension, and as a consequence they develop left ventricular hypertrophy, followed by heart failure. The aim of this work was to explore whether this animal model is suitable to identify biomarkers that characterize defined stages of cardiac pathophysiological conditions. The work had to be performed in two stages: in the first part proteomic differences that are attributable to the two separate rat lines (DS and DR) had to be established, and in the second part the process of development of heart failure due to feeding the rats with high-salt-containing diet has to be monitored. This work describes the results of the first stage, with the outcome of protein expression profiles of left ventricular tissues of DS and DR rats kept under low salt diet. Substantial extent of quantitative and qualitative expression differences between both strains of Dahl rats in heart tissue was detected. Using Principal Component Analysis, Linear Discriminant Analysis and other statistical means we have established sets of differentially expressed proteins, candidates for further molecular analysis of the heart failure mechanisms.

An Integrated Strategy in Two-Dimensional Electrophoresis Analysis Able to Identify Discriminants Between Different Clinical Conditions

Two-dimensional gel electrophoresis (2DE) is an indispensable tool in proteomics for the analysis of protein expression in complex biological systems such as cells and tissues. However, the automatic extraction of information from gel images is still a challenging task. In this paper we propose a strategy that represents a computational procedure of support to the discrimination of different clinical conditions associated with the samples. The analyzed gel images were acquired within the framework of a study of peripheral neuropathies: twenty-four 2DE maps generated from cerebrospinal fluid (16 pathologic and 8 control subjects) were processed. Quantitative features were defined to describe each image and treated with a method of dimensionality reduction. The informativeness of the descriptors allowed us to see the gel of the data set as items in a three-dimensional space, segregating according to the clinical conditions. Moreover, information with prognostic value was obtained for a single outsider gel of a patient who was included in a clinical subgroup at the first diagnosis but whose disease progressed with clinical features belonging to a different clinical subgroup. The method developed may represent an effective tool of classification that can be used repeatedly to capture the essential impression from separation images.

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.

Multivariate analysis of 2-DE protein patterns--practical approaches

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.

Improvements in the search for potential biomarkers by proteomics: Application of principal component and discriminant analyses for two-dimensional maps evaluation

In this study, we evaluated if the application of multivariate analysis on the data obtained from two-dimensional protein maps could mean an improvement in the search for protein markers. First, we performed a classical proteomic study of the differential expression of serum N-glycoproteins in colorectal cancer patients. Then, applying principal component analysis (PCA) we assessed the utility of the 2-D protein pattern and certain subsets of spots as a tool to distinguish control and case samples, and tested the accuracy of the classification model by linear discriminant analysis (LDA). On the other hand we looked for altered spots by univariate statistics and then analysed them as a cluster by PCA and LDA. We found that those proteins combined presented a theoretical sensitivity and specificity of 100%. Finally, the spots with known protein identity were analysed by multivariate methods, finding a subgroup that behaved as the most obvious candidates for further validation trials.

A novel data mining method to identify assay-specific signatures in functional genomic studies

Background:

The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies.

Results:

The proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data – a study of classification of human tissue samples and a study of E. coli cultures with different medium formulations.

Conclusion

We have developed a PCA-based method that effectively identifies assay-specific signatures in ranked groups of genes from the full data set in a more efficient and simplistic procedure than current approaches. Although this work demonstrates the ability of the PM to identify assay-specific signatures in DNA microarray experiments, this approach could be useful in areas such as proteomics and metabolomics.

A Comprehensive Two-Dimensional Retention Time Alignment Algorithm To Enhance Chemometric Analysis of Comprehensive Two-Dimensional Separation Data

A comprehensive two-dimensional (2D) retention time alignment algorithm was developed using a novel indexing scheme. The algorithm is termed comprehensive because it functions to correct the entire chromatogram in both dimensions and it preserves the separation information in both dimensions. Although the algorithm is demonstrated by correcting comprehensive two-dimensional gas chromatography (GC x GC) data, the algorithm is designed to correct shifting in all forms of 2D separations, such as LC x LC, LC x CE, CE x CE, and LC x GC. This 2D alignment algorithm was applied to three different data sets composed of replicate GC x GC separations of (1) three 22-component control mixtures, (2) three gasoline samples, and (3) three diesel samples. The three data sets were collected using slightly different temperature or pressure programs to engender significant retention time shifting in the raw data and then demonstrate subsequent corrections of that shifting upon comprehensive 2D alignment of the data sets. Thirty 12-min GC x GC separations from three 22-component control mixtures were used to evaluate the 2D alignment performance (10 runs/mixture). The average standard deviation of first column retention time improved 5-fold from 0.020 min (before alignment) to 0.004 min (after alignment). Concurrently, the average standard deviation of second column retention time improved 4-fold from 3.5 ms (before alignment) to 0.8 ms (after alignment). Alignment of the 30 control mixture chromatograms took 20 min. The quantitative integrity of the GC x GC data following 2D alignment was also investigated. The mean integrated signal was determined for all components in the three 22-component mixtures for all 30 replicates. The average percent difference in the integrated signal for each component before and after alignment was 2.6%. Singular value decomposition (SVD) was applied to the 22-component control mixture data before and after alignment to show the restoration of trilinearity to the data, since trilinearity benefits chemometric analysis. By applying comprehensive 2D retention time alignment to all three data sets (control mixtures, gasoline samples, and diesel samples), classification by principal component analysis (PCA) substantially improved, resulting in 100% accurate scores clustering.

Selection of Biomarkers by a Multivariate Statistical Processing of Composite Metabonomic Data Sets Using Multiple Factor Analysis

We introduce a statistical approach for integrating data from several analytical platforms. We illustrate this approach using (1)H-(13)C Heteronuclear Multiple Bond Connectivity nuclear magnetic resonance spectroscopy ((1)H-(13)C HMBC NMR) and Pyrolysis Metastable Atom Bombardment Time-of-Flight mass spectrometry (Py-MAB-TOF-MS) to perform metabolic fingerprinting on cattle treated with anabolic steroids. Multiple factor analysis (MFA) integrates complementary aspects from NMR and MS data into a unique metabolic signature describing the biomarkers related to the dose-response. This work also indicates that, from a practical point of view, metabonomics and other "-omics" biotechnologies can benefit significantly from a generalized multi-platform integrative approach using multiple factor analysis.

Application of Relative Warp Analysis to the Evaluation of Two-Dimensional Gels in Proteomics: Studying Isoelectric Point and Relative Molecular Mass Variation

We propose a geometric-morphometrics method (relative warp analysis) to be used in proteomic comparisons. This approach was applied to a dataset from a comparison between 5 controls and 5 patients with colorectal cancer disease published elsewhere. The spots in the 2-D maps were used as landmarks in a morphometric study, and the differences in shape (spot distribution) among them were obtained. The shape variables were used to compare controls and patients. These components mostly ignore random or experimental effects affecting all the proteins in any of the two dimensions studied. Furthermore, the method allows the researcher to find those proteins which contributed the most to the local shape component detected. Applying this approach, we detected variations in the position (isoelectric point and/or relative molecular mass) of some spots that may reflect differences in the amino acidic sequence or post-translational modifications.

Towards a new conceptual approach to ‘parasitoproteomics’

Many parasitologists are betting heavily on proteomic studies to explain biochemical host-parasite interactions and, thus, to contribute to disease control. However, many "parasitoproteomic" studies are performed with powerful techniques but without a conceptual approach to determine whether the host genomic responses during a parasite infection represent a nonspecific response that might be induced by any parasite or any other stress. In this article, a new conceptual approach, based on evolutionary concepts of immune responses of a host to a parasite, is suggested for parasitologists to study the host proteome reaction after parasite invasion. Also, this new conceptual approach can be used to study other host-parasite interactions such as behavioral manipulation.

Numerical approaches for quantitative analysis of two-dimensional maps: A review of commercial software and home-made systems

The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.

Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods

BACKGROUND

The adrenal gland is the election organ forming primary neuroblastoma (NB) tumours, the most common extracranial solid tumours of infancy and childhood.

METHODS

Samples of adrenal gland belonging to healthy and diseased nude mouse were analysed by 2D gel-electrophoresis. The resulting 2D-PAGE maps were digitized by PDQuest and investigated by principal component analysis (PCA).

RESULTS

The analysis of the loadings of the first principal component (PC) permitted the evaluation of the spots characterising each class of samples. Moreover, the soft-independent model of class analogy (SIMCA) method confirmed the separation of the samples in the two classes and allowed the identification of the modelling and discriminating spots. Very good correlation was found between the data obtained by analysis of 2D maps via the commercial software PDQuest and the present PCA analysis. In both cases, the comparison between such maps showed up- and down-regulation of 84 polypeptide chains, out of a total of 700 spots detected by a fluorescent stain, Sypro Ruby. Spots that were differentially expressed between the two groups were analysed by matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry and 14 of these spots were identified so far.

Correlation of Mass Spectrometry Identified Bacterial Biomarkers from a Fielded Pyrolysis-Gas Chromatography-Ion Mobility Spectrometry Biodetector with the Microbiological Gram Stain Classification Scheme

A pyrolysis-gas chromatography-ion mobility spectrometry (Py-GC-IMS) briefcase system has been shown to detect and classify deliberately released bioaerosols in outdoor field scenarios. The bioaerosols included Gram-positive and Gram-negative bacteria, MS-2 coliphage virus, and ovalbumin protein species. However, the origin and structural identities of the pyrolysate peaks in the GC-IMS data space, their microbiological information content, and taxonomic importance with respect to biodetection have not been determined. The present work interrogates the identities of the peaks by inserting a time-of-flight mass spectrometry system in parallel with the IMS detector through a Tee connection in the GC module. Biological substances producing ion mobility peaks from the pyrolysis of microorganisms were identified by their GC retention time, matching of their electron ionization mass spectra with authentic standards, and the National Institutes for Standards and Technology mass spectral database. Strong signals from 2-pyridinecarboxamide were identified in Bacillus samples including Bacillus anthracis, and its origin was traced to the cell wall peptidoglycan macromolecule. 3-Hydroxymyristic acid is a component of lipopolysaccharides in the cell walls of Gram-negative organisms. The Gram-negative Escherichia coli organism showed significant amounts of 3-hydroxymyristic acid derivatives and degradation products in Py-GC-MS analyses. Some of the fatty acid derivatives were observed in very low abundance in the ion mobility spectra, and the higher boiling lipid species were absent. Evidence is presented that the Py-GC-ambient temperature and pressure-IMS system generates and detects bacterial biochemical information that can serve as components of a biological classification scheme directly correlated to the Gram stain reaction in microorganism taxonomy.