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

Comparison of grain protein profiles of Brazilian cowpea (Vigna unguiculata) cultivars based on principal component analysis

This study aims to compare the grain protein profile of four Brazilian cowpea cultivars (BRS Aracê, BRS Itaim, BRS Pajeú, and BRS Xiquexique) by two-dimensional electrophoresis (2-DE) and principal component analysis (PCA). 2-DE efficiently separate cowpea protein profiles, showing high homogeneity among the four cultivars. In addition, the principal component analysis indicated that there is a difference in abundance of proteins among the cultivars. The cultivars BRS Aracê and BRS Xiquexique, both biofortified in iron and zinc, were separated from the cultivars BRS Itaim and BRS Pajeú. These results demonstrate that protein profiles can be used to discriminate cowpea varieties.

Graphical Abstract

Two-Dimensional Gel Electrophoresis Image Analysis

Gel-based proteomics is still quite widespread due to its high-resolution power; the experimental approach is based on differential analysis, where groups of samples (e.g., control vs diseased) are compared to identify panels of potential biomarkers. However, the reliability of the result of the differential analysis is deeply influenced by 2D-PAGE maps image analysis procedures. The analysis of 2D-PAGE images consists of several steps, such as image preprocessing, spot detection and quantitation, image warping and alignment, spot matching. Several approaches are present in literature, and classical or last-generation commercial software packages exploit different algorithms for each step of the analysis. Here, the most widespread approaches and a comparison of the different strategies are presented.

2D-DIGE and Fluorescence Image Analysis

2D-DIGE is still a very widespread technique in proteomics for the identification of panels of biomarkers, allowing to tackle with some important drawback of classical two-dimensional gel-electrophoresis. However, once 2D-gels are obtained, they must undergo a quite articulated multistep image analysis procedure before the final differential analysis via statistical mono- and multivariate methods. Here, the main steps of image analysis software are described and the most recent procedures reported in the literature are briefly presented.

Muscle Protein Profiles Used for Prediction of Texture of Farmed Salmon (Salmo salar L.)

A soft texture is undesired in Atlantic salmon as it leads to downgrading and reduced yield, yet it is a factor for which the cause is not fully understood. This lack of understanding highlights the need for identifying the cause of the soft texture and developing solutions by which the processing industry can improve the yield. Changes in muscle protein profiles can occur both pre- and postharvest and constitute an overall characterization of the muscle properties including texture. The aim of this study was to investigate this relationship between specific muscle proteins and the texture of the salmon fillet. Samples for 2D-gel-based proteomics were taken from the fillet above the lateral line at the same position as where the texture had been measured. The resulting protein profiles were analyzed using multivariate data analysis. Sixteen proteins were found to correlate to the measured texture, showing that it is possible to predict peak force based on a small subset of proteins. Additionally, eight of the 16 proteins were identified by tandem mass spectrometry including serum albumin, dipeptidyl peptidase 3, heat shock protein 70, annexins, and a protein presumed to be a titin fragment. It is contemplated that the identification of these proteins and their significance for the measured texture will contribute to further understanding of the Atlantic salmon muscle texture.

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.

A survey of computational tools for downstream analysis of proteomic and other omic datasets

Proteomics is an expanding area of research into biological systems with significance for biomedical and therapeutic applications ranging from understanding the molecular basis of diseases to testing new treatments, studying the toxicity of drugs, or biotechnological improvements in agriculture. Progress in proteomic technologies and growing interest has resulted in rapid accumulation of proteomic data, and consequently, a great number of tools have become available. In this paper, we review the well-known and ready-to-use tools for classification, clustering and validation, interpretation, and generation of biological information from experimental data. We suggest some rules of thumb for the reader on choosing the best suitable learning method for a particular dataset and conclude with pathway and functional analysis and then provide information about submitting final results to a repository.

Using 7 cm immobilized pH gradient strips to determine levels of clinically relevant proteins in wheat grain extracts

The aim of the work was to test a relatively simple proteomics approach based on phenol extraction and two-dimensional gel electrophoresis (2-DE) with 7 cm immobilized pH gradient strips for the determination of clinically relevant proteins in wheat grain. Using this approach, 157 2-DE spots were quantified in biological triplicate, out of which 55 were identified by matrix-assisted laser desorption/ionization - time of flight tandem mass spectrometry. Clinically relevant proteins associated with celiac disease, wheat dependent exercise induced anaphylaxis, baker's asthma, and food allergy, were detected in 24 2-DE spots. However, alcohol-soluble gliadins were not detected with this approach. The comparison with a recent quantitative study suggested that gel-based and gel-free proteomics approaches are complementary for the detection and quantification of clinically relevant proteins in wheat grain.

The inter-individual variability outperforms the intra-individual variability of differentially expressed proteins prior and post irradiation in lymphoblastoid cell lines

CONTEXT

Radio-sensitivity in normal tissue is characterized by heterogeneity throughout the population and the absence of pre-diagnostic biomarkers.

OBJECTIVE

We conducted a proteomic approach to search for radiation characteristic protein regulation.

MATERIALS AND METHODS

Cell lines were 10 Gy irradiated and analysed by 2D-DIGE after 24 h. RESULTS were analysed intra- and inter-individually. The principal component analysis and hierarchical clustering was applied to all datasets.

RESULTS

Differences in intra-individual spot abundance prior and post irradiation exactly show the separation of sample classes in two groups: sham-irradiated and irradiated. The inter-individual datasets clustered according to the cell line. The intra-individual differences on protein level after gamma-irradiation are very low, compared with the inter-individual differences among cell lines derived from the same tissue.

CONCLUSION

The application of 2-D DIGE may offer a realistic chance for a better molecular characterization of radio-sensitivity and for the discovery of candidate biomarkers.

The MS(E)-proteomic analysis of gliadins and glutenins in wheat grain identifies and quantifies proteins associated with celiac disease and baker's asthma

Precise content of gliadin (Glia) and glutenin (Glu) proteins in wheat grain are largely unknown despite their association with celiac disease, various allergies, and physical processing properties of wheat. Developing methods to quantitatively measure clinically relevant proteins could support advancement in understanding exposure thresholds and clinical study design. The aim of this study was to use a data-independent mass spectrometry (MS(E)) approach for quantifying gliadin and glutenin proteins in wheat grain. The biologically replicated analysis yielded concentrations for 34 gliadin and 22 glutenin proteins. The primary focus of this survey was on measuring celiac disease proteins and baker's asthma associated proteins along with the proteins associated with viscoelastic properties of wheat flour and grain texture. The technical coefficients of variation ranged from 0.12 to 1.39 and indicate that MS(E) proteomics is a reproducible quantitative method for the determination of gliadin and glutenin content in the highly complex matrix of protein extracts from wheat grain. This article is part of a Special Issue entitled: Translational Plant Proteomics.

A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels

Background

Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection.

Results

We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression.

Conclusions

Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D.

Time-dependent changes in protein expression in rainbow trout muscle following hypoxia

Adaptation to hypoxia is a complex process, and individual proteins will be up- or down-regulated in order to address the main challenges at any given time. To investigate the dynamics of the adaptation, rainbow trout (Oncorhynchus mykiss) was exposed to 30% of normal oxygen tension for 1, 2, 5 and 24 h respectively, after which muscle samples were taken. The successful investigation of numerous proteins in a single study was achieved by selectively separating the sarcoplasmic proteins using 2-DE. In total 46 protein spots were identified as changing in abundance in response to hypoxia using one-way ANOVA and multivariate data analysis. Proteins of interest were subsequently identified by MS/MS following tryptic digestion. The observed regulation following hypoxia in skeletal muscle was determined to be time specific, as only a limited number of proteins were regulated in response to more than one time point. The cellular response to hypoxia included regulation of proteins involved in maintaining iron homeostasis, energy levels and muscle structure. In conclusion, this proteome-based study presents a comprehensive investigation of the expression profiles of numerous proteins at four different time points. This increases our understanding of timed changes in protein expression in rainbow trout muscle following hypoxia.

Assessing variability in gel-based proteomic analysis of Nitrosomonas europaea

Proteomics offers a unique look at the way protein expression changes in response to stimuli, and "gel-based" methods that utilize two-dimensional gel electrophoresis (2-DE) are key technologies in such studies. However, the many steps involved can be technically complex, and the resulting data are subject to variability from both technical and biological sources. Designing 2-DE proteomic studies can be challenging, as a set of standard methods or experimental designs has not been established. This being the case, it is especially important to identify and control sources of variability. Statistically significant results can be obtained if the experimental design includes a sufficient number of replicate 2-DE gels, and if the replicate gels are similar enough to be analyzed in the same experiment. While three or four replicates are often sufficient for compensation of variability, the pilot study illustrated in this chapter showed that statistically significant expression differences could be detected for 90% of the spots matched if six replicate experiments were done.

Combined proteomic and transcriptomic analysis identifies differentially expressed pathways associated to Pinus radiata needle maturation

Needle differentiation is a very complex process that leads to the formation of a mature photosynthetic organ from pluripotent needle primordia. The proteome and transcriptome of immature and fully developed needles of Pinus radiata D. Don were compared to described changes in mRNA and protein species that characterize the needle maturation developmental process. A total of 856 protein spots were analyzed, defining a total of 280 spots as differential between developmental stages, from which 127 were confidently identified. A suppressive subtractive library (2048 clones, 274 non redundant contigs) was built, and 176 genes showed to be differentially expressed. The Joint data analysis of proteomic and transcriptomic results provided a broad overview of differentially expressed pathways associated with needle maturation and stress-related pathways. Proteins and genes related to energy metabolism pathways, photosynthesis, and oxidative phosphorylation were overexpressed in mature needles. Amino acid metabolism, transcription, and translation pathways were overexpressed in immature needles. Interestingly, stress related proteins were characteristic of immature tissues, a fact that may be linked to defense mechanisms and the higher growth rate and morphogenetic competence exhibited by these needles. Thus, this work provides an overview of the molecular changes affecting proteomes and transcriptomes during P. radiata needle maturation, having an integrative vision of the functioning and physiology of this process.

Abscisic acid and sucrose increase the protein content in date palm somatic embryos, causing changes in 2-DE profile

Various supplements (abscisic acid (ABA) or sucrose) were added to the initial embryo culture medium (M3) with the aim of improving the vigour of vitroplants deriving from date palm somatic embryogenesis. ABA (20 and 40 microM) and sucrose (90 g/l) applied for 4 and 2 weeks respectively increased embryo thickness, with no apparent difference in length. ABA (5-40 microM) increased embryo proliferation rate. Somatic embryos maintained in modified M3 (M3 supplemented with ABA and an increased sucrose concentration) contained a higher amount of protein than those maintained in initial M3 (no ABA, 30 g/l of sucrose), with a 1.5-1.7-fold increase depending on the compound and concentration assayed. The 1-D and 2-DE protein profiles showed qualitative and quantitative differences between the somatic embryos cultured in initial M3 (control) and in modified M3. Statistical analysis of spot intensity was performed by principal component analysis, yielding two accurate groups of samples and determining the most discriminating spots. Samples were also clustered using Euclidean distance with an average linkage algorithm. Thirty-four variable spots were identified using mass spectrometry analysis. Identified proteins were classified into the following functional categories: energy metabolism (five proteins); protein translation, folding and degradation (9); redox maintenance (5); cytoskeleton (3); storage protein (2); and with no assigned function as (10). While "up-regulation" of stress-related proteins and "down-regulation" of energy metabolism proteins were observed in somatic embryos matured in M3 supplemented with ABA, storage proteins (legumin) were "up-regulated" in somatic embryos matured in M3 supplemented with increased sucrose.

Proteomics of plant pathogenic fungi

Plant pathogenic fungi cause important yield losses in crops. In order to develop efficient and environmental friendly crop protection strategies, molecular studies of the fungal biological cycle, virulence factors, and interaction with its host are necessary. For that reason, several approaches have been performed using both classical genetic, cell biology, and biochemistry and the modern, holistic, and high-throughput, omic techniques. This work briefly overviews the tools available for studying Plant Pathogenic Fungi and is amply focused on MS-based Proteomics analysis, based on original papers published up to December 2009. At a methodological level, different steps in a proteomic workflow experiment are discussed. Separate sections are devoted to fungal descriptive (intracellular, subcellular, extracellular) and differential expression proteomics and interactomics. From the work published we can conclude that Proteomics, in combination with other techniques, constitutes a powerful tool for providing important information about pathogenicity and virulence factors, thus opening up new possibilities for crop disease diagnosis and crop protection.

Quantitation in mass-spectrometry-based proteomics

Mass-spectrometry-based proteomics, the large-scale analysis of proteins by mass spectrometry, has emerged as a new technology over the last decade and become routine in many plant biology laboratories. While early work consisted merely of listing proteins identified in a given organ or under different conditions of interest, there is a growing need to apply comparative and quantitative proteomics strategies toward gaining novel insights into functional aspects of plant proteins and their dynamics. However, during the transition from qualitative to quantitative protein analysis, the potential and challenges will be tightly coupled. Several strategies for differential proteomics that involve stable isotopes or label-free comparisons and their statistical assessment are possible, each having specific strengths and limitations. Furthermore, incomplete proteome coverage and restricted dynamic range still impose the strongest limitations to data throughput and precise quantitative analysis. This review gives an overview of the current state of the art in differential proteomics and possible strategies in data processing.

A statistical model to identify differentially expressed proteins in 2D PAGE gels

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.

Proteomic analysis by two-dimensional gel electrophoresis and starch characterization of Triticum turgidum L. var. durum cultivars for pasta making

Criteria for durum wheat quality are continuously evolving in response to market pressure and consumer's preference. Specific attributes of durum wheat for different end products require more rapid and objective means to grade and classify wheat parcels based on processing potential. A total of 10 durum wheat cultivars were compared for compositional, protein, and starch characteristics. Mean values for the gross composition differed for total protein, gluten, and starch. Two-dimensional electrophoresis (2DE) analysis showed the proteome diversity among the cultivars. As shown by the principal component analysis (PCA) applied to 2DE data of gliadin and glutenin fractions, cultivars differed mainly from the number of proteins and levels of protein expression. As determined by the rapid viscoanalyzer (RVA), swelling power, starch damage, amylose content, and starch pasting properties of 10 cultivars differed significantly. 2DE fingerprinting and amylose content seemed to distinguish specific cultivars being useful tools for selecting suitable durum wheat cultivars for pasta making.

Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples

One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified 'autoscaling' of the full data set based on within-group standard deviations is introduced and shown to be advantageous in revealing potential group-dependent proteins additional to those found by prefiltering.

Mass spectrometry analysis of gliadins in celiac disease

In recent years, scientific research on wheat gluten proteins has followed three main directions aimed at (1) finding relationships between individual genetic alleles coding for gliadins, high or low molecular weight glutenin subunits, and the viscoelastic dough properties of flour-derived products such as pasta and bread; (2) identifying prolamins and derived peptides involved in celiac disease, a pathological condition in which the small intestine of genetically predisposed individuals is reversibly damaged; and (3) developing and validating sensitive and specific methods for detecting trace amounts of gluten proteins in gluten-free foods for celiac disease patients. In this review, the main aspects of current and perspective applications of mass spectrometry and proteomic technologies to the structural characterization of gliadins are presented, with focus on issues related to detection, identification, and quantification of intact gliadins, as well as gliadin-derived peptides relevant to the biochemical, immunological, and toxicological aspects of celiac disease.