Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor

BACKGROUND

Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process.

RESULTS

We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data.

CONCLUSION

Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment.

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis

Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line. In the present report, the phenotypic expression of GRANTA-519 and MAVER-1 cell lines has been compared and evaluated by a proteomic approach, exploiting 2-D map analysis. By univariate statistical analysis (Student's t-test, as commonly used in most commercial software packages), most of the protein spots were found to be identical between the two cell lines. Thirty spots were found to be unique for the GRANTA-519, whereas another 11 polypeptides appeared to be expressed only by the MAVER-1 cell line. A number of these spots could be identified by MS. These data were confirmed and expanded by multivariate statistical tools (principal component analysis and soft-independent model of class analogy) that allowed identification of a larger number of differently expressed spots. Multivariate statistical tools have the advantage of reducing the risk of false positives and of identifying spots that are significantly altered in terms of correlated expression rather than absolute expression values. It is thus suggested that, in future work in differential proteomic profiling, both univariate and multivariate statistical tools should be adopted.

Evaluation of an in vitro model of hepatic inflammatory response by gene expression profiling

The body's response to biochemical stress involves coordinated changes in the expression of several sets of genes that regulate its return to homeostasis. Although several cell culture systems have been utilized for studying such complex physiological events in vitro, their assessment has been limited to biochemical assays on individual genes and proteins, limiting interpretation of the results in a systems context. Advances in genomics provide an opportunity to provide a more comprehensive assessment. In this study, we have used DNA microarrays to profile gene expression dynamics during interleukin 6-stimulated inflammation in hepatocytes maintained in a stable, collagen double-gel in vitro model system. The observed expression profile was also compared with that obtained from rat liver tissue after burn injury to determine the extent and nature of responses captured by the in vitro system. Our results indicate that several aspects of the in vivo hepatic inflammatory response can be captured by the in vitro system at the molecular systems level. Statistical analysis of the mRNA profiles was also used to characterize the temporal response in each model system and demonstrate similar behavior. A small panel of molecules involved in the hepatic acute-phase response was also profiled, using quantitative kinetic polymerase chain reaction, to confirm these observations. These results indicate the utility of the stable hepatocyte culture system for expression profiling of inflammatory states and for providing insights into the interplay of changes in gene expression during complex physiological states.

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.

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease. Application to lymphomas

Two-dimensional (2D) electrophoresis is the most wide spread technique for the separation of proteins in biological systems. This technique produces 2D maps of high complexity, which creates difficulties in the comparison of different samples. The method proposed in this paper for the comparison of different 2D maps can be summarised in four steps: (a) digitalisation of the image; (b) fuzzyfication of the digitalised map in order to consider the variability of the two-dimensional electrophoretic separation; (c) decoding by principal component analysis of the previously obtained fuzzy maps, in order to reduce the system dimensionality; (d) classification analysis (linear discriminant analysis), in order to separate the samples contained in the dataset according to the classes present in said dataset. This method was applied to a dataset constituted by eight samples: four belonging to healthy human lymph-nodes and four deriving from non-Hodgkin lymphomas. The amount of fuzzyfication of the original map is governed by the sigma parameter. The larger the value, the more fuzzy theresulting transformed map. The effect of the fuzzyfication parameter was investigated, the optimal results being obtained for sigma = 1.75 and 2.25. Principal component analysis and linear discriminant analysis allowed the separation of the two classes of samples without any misclassification.

Cluster analysis of an extensive human breast cancer cell line protein expression map database

In the current study, the protein expression maps (PEMs) of 26 breast cancer cell lines and three cell lines derived from normal breast or benign disease tissue were visualised by high resolution two-dimensional gel electrophoresis. Analysis of this data was performed with ChiClust and ChiMap, two analytical bioinformatics tools that are described here. These tools are designed to facilitate recognition of specific patterns shared by two or more (a series) PEMs. Both tools use PEMs that were matched by an image analysis program and locally written programs to create a match table that is saved in an object relational database. The ChiClust tool uses clustering and subclustering methods to extract statistically significant protein expression patterns from a large series of PEMs. The ChiMap tool calculates a differential value (either as percentage change or a fold change) and represents these graphically. All such differentials or just those identified using ChiClust can be submitted to ChiMap. These methods are not dependent on any particular commercial image analysis program, and the whole software package gives an integrated procedure for the comparison and analysis of a series of PEMs. The ChiClust tool was used here to order the breast cell lines into groups according to biological characteristics including morphology in vitro and tumour forming ability in vivo. ChiMap was then used to highlight eight major protein feature-changes detected between breast cancer cell lines that either do or do not proliferate in nude mice. Mass spectrometry was used to identify the proteins. The possible role of these proteins in cancer is discussed.

Characterisation of global protein expression by two-dimensional electrophoresis and mass spectrometry: proteomics of Toxoplasma gondii

The development of tools for the analysis of global gene expression is vital for the optimal exploitation of the data on parasite genomes that are now being generated in abundance. Recent advances in two-dimensional electrophoresis (2-DE), mass spectrometry and bioinformatics have greatly enhanced the possibilities for mapping and characterisation of protein populations. We have employed these developments in a proteomics approach for the analysis of proteins expressed in the tachyzoite stage of Toxoplasma gondii. Over 1000 polypeptides were reproducibly separated by high-resolution 2-DE using the pH ranges 4-7 and 6-11. Further separations using narrow range gels suggest that at least 3000-4000 polypeptides should be resolvable by 2-DE using multiple single pH unit gels. Mass spectrometry was used to characterise a variety of protein spots on the 2-DE gels. Peptide mass fingerprints, acquired by matrix-assisted laser desorption/ionisation-(MALDI) mass spectrometry, enabled unambiguous protein identifications to be made where full gene sequence information was available. However, interpretation of peptide mass fingerprint data using the T. gondii expressed sequence tag (EST) database was less reliable. Peptide fragmentation data, acquired by post-source decay mass spectrometry, proved a more successful strategy for the putative identification of proteins using the T. gondii EST database and protein databases from other organisms. In some instances, several protein spots appeared to be encoded by the same gene, indicating that post-translational modification and/or alternative splicing events may be a common feature of functional gene expression in T. gondii. The data demonstrate that proteomic analyses are now viable for T. gondii and other protozoa for which there are good EST databases, even in the absence of complete genome sequence. Moreover, proteomics is of great value in interpreting and annotating EST databases.

Quantitative and reproducible two-dimensional gel analysis using Phoretix 2D Full

Quantitative two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) is used to determine changes in individual protein levels in complex protein mixtures. To provide reliable data, the software used for 2-D gel image analysis must provide a linear response over a wide dynamic range of data output. Here, we show that Phoretix 2D Full analysis of 2-D gels stained with colloidal Coomassie Brilliant Blue G-250 can provide a linear measure of changes in protein quantity. We show using a complex mixture of Arabidopsis thaliana proteins, that this is true for essentially all focused proteins, in a data output range greater than three orders of magnitude. An analysis of the factors that affect errors in the results demonstrated that reproducibility of the data is significantly improved by user seeding, whereas it is reduced by use of the background subtraction algorithms.

Compression wood-responsive proteins in developing xylem of maritime pine (Pinus pinaster ait.)

When a conifer shoot is displaced from its vertical position, compression wood (CW) is formed on the under side and can eventually return the shoot to its original position. Changes in cell wall structure and chemistry associated with CW are likely to result from differential gene/protein expression. Two-dimensional polyacrylamide gel electrophoresis of differentiating xylem proteins was combined with the physical characterization of wooden samples to identify and characterize CW-responsive proteins. Differentiating xylem was harvested from a 22-year-old crooked maritime pine (Pinus pinaster Ait.) tree. Protein extracted from different samples were revealed by high-resolution silver stained two-dimensional polyacrylamide gel electrophoresis and analyzed with a computer-assisted system for single spot quantification. Growth strain (GS) measurements allowed xylem samples to be classified quantitatively from normal wood to CW. Regression of lignin and cellulose content on GS showed that an increase in the percentage of lignin and a decrease of the percentage of cellulose corresponded to increasing GS values, i.e. CW. Of the 137 studied spots, 19% were significantly associated with GS effect. Up-regulated proteins included 1-aminocyclopropane-1-carboxylate oxidase (an ethylene forming enzyme), a putative transcription factor, two lignification genes (caffeic O-methyltransferase and caffeoyl CoA-O-methyltransferase), members of the S-adenosyl-L-methionine-synthase gene family, and enzymes involved in nitrogen and carbon assimilation (glutamine synthetase and fructokinase). A clustered correlation analysis was performed to study simultaneously protein expression along a gradient of gravistimulated stressed xylem tissue. Proteins were found to form "expression clusters" that could identify: (a) Gene product under similar control mechanisms, (b) partner proteins, or (c) functional groups corresponding to specialized pathways. The possibility of obtaining regulatory correlations and anticorrelations between proteins provide us with a new category of homology (regulatory homology) in tracing functional relationships.

Proteomics in human disease: cancer, heart and infectious diseases

In recent years, genomics has increased the understanding of many diseases. Proteomics is a rapidly growing research area that encompasses both genetic and environmental factors. The protein composition represents the functional status of a biological compartment. The five approaches presented here resulted in the detection of disease-associated proteins. Calgranulin B was upregulated in colorectal cancer, and hepatoma-derived aldose reductase-like protein was reexpressed in a rat model during hepatocarcinogenesis. In these two investigations, attention was focused on one protein, obviously differing in amount, directly after two-dimensional electrophoresis (2-DE). Additional methods, such as enzyme activity measurements and immunohistochemistry, confirmed the disease association of the two candidates resulting from 2-DE subtractive analysis. The following three investigations take advantage of the holistic potential of the 2-DE approach. The comparison of 2-DE patterns from dilated cardiomyopathy patients with those of controls revealed 25 statistically significant intensity differences, from which 12 were identified by amino acid analysis, Edman degradation or matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). A human myocardial 2-DE database was constructed, containing 3300 protein spots and 150 identified protein species. The number of identified proteins was limited by the capacity of our group, rather than by the principle of feasibility. Another field where proteomics proves to be a valuable tool in identifying proteins of importance for diagnosis is proteome analysis of pathogenic microorganisms such as Borrelia burgdorferi (Lyme disease) and Toxoplasma gondii (toxoplasmosis). Sera from patients with early or late symptoms of Lyme borreliosis contained antibodies of various classes against about 80 antigens each, containing the already described antigens OspA, B and C, flagellin, p83/100, and p39. Similarly, antibody reactivity to seven different marker antigens of T. gondii allowed differentiation between acute and latent toxoplasmosis, an important diagnostic tool in both pregnancy and immunosuppressed patients.

Proteomics in human disease: cancer, heart and infectious diseases

In recent years, genomics has increased the understanding of many diseases. Proteomics is a rapidly growing research area that encompasses both genetic and environmental factors. The protein composition represents the functional status of a biological compartment. The five approaches presented here resulted in the detection of disease-associated proteins. Calgranulin B was upregulated in colorectal cancer, and hepatoma-derived aldose reductase-like protein was reexpressed in a rat model during hepatocarcinogenesis. In these two investigations, attention was focused on one protein, obviously differing in amount, directly after two-dimensional electrophoresis (2-DE). Additional methods, such as enzyme activity measurements and immunohistochemistry, confirmed the disease association of the two candidates resulting from 2-DE subtractive analysis. The following three investigations take advantage of the holistic potential of the 2-DE approach. The comparison of 2-DE patterns from dilated cardiomyopathy patients with those of controls revealed 25 statistically significant intensity differences, from which 12 were identified by amino acid analysis, Edman degradation or matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). A human myocardial 2-DE database was constructed, containing 3300 protein spots and 150 identified protein species. The number of identified proteins was limited by the capacity of our group, rather than by the principle of feasibility. Another field where proteomics proves to be a valuable tool in identifying proteins of importance for diagnosis is proteome analysis of pathogenic microorganisms such as Borrelia burgdorferi (Lyme disease) and Toxoplasma gondii (toxoplasmosis). Sera from patients with early or late symptoms of Lyme borreliosis contained antibodies of various classes against about 80 antigens each, containing the already described antigens OspA, B and C, flagellin, p83/100, and p39. Similarly, antibody reactivity to seven different marker antigens of T. gondii allowed differentiation between acute and latent toxoplasmosis, an important diagnostic tool in both pregnancy and immunosuppressed patients.