Ekaterini S. Bei

Comparative proteomic analysis of hypertrophic chondrocytes in osteoarthritis.

BackgroundOsteoarthritis (OA) is a multi-factorial disease leading progressively to loss of articular cartilage and subsequently to loss of joint function. While hypertrophy of chondrocytes is a physiological process implicated in the longitudinal growth of long bones, hypertrophy-like alterations in chondrocytes play a major role in OA. We performed a quantitative proteomic analysis in osteoarthritic and normal chondrocytes followed by functional analyses to investigate proteome changes and molecular pathways involved in OA pathogenesis.MethodsChondrocytes were isolated from articular cartilage of ten patients with primary OA undergoing knee replacement surgery and six normal donors undergoing fracture repair surgery without history of joint disease and no OA clinical manifestations. We analyzed the proteome of chondrocytes using high resolution mass spectrometry and quantified it by label-free quantification and western blot analysis. We also used WebGestalt, a web-based enrichment tool for the functional annotation and pathway analysis of the differentially synthesized proteins, using the Wikipathways database. ClueGO, a Cytoscape plug-in, is also used to compare groups of proteins and to visualize the functionally organized Gene Ontology (GO) terms and pathways in the form of dynamical network structures.ResultsThe proteomic analysis led to the identification of a total of ~2400 proteins. 269 of them showed differential synthesis levels between the two groups. Using functional annotation, we found that proteins belonging to pathways associated with regulation of the actin cytoskeleton, EGF/EGFR, TGF-β, MAPK signaling, integrin-mediated cell adhesion, and lipid metabolism were significantly enriched in the OA samples (p ≤10−5). We also observed that the proteins GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP, show distinct synthesis levels, characteristic for OA or control chondrocytes.ConclusionIn this study we compared the quantitative changes in proteins synthesized in osteoarthritic compared to normal chondrocytes. We identified several pathways and proteins to be associated with OA chondrocytes. This study provides evidence for further testing on the molecular mechanism of the disease and also propose proteins as candidate markers of OA chondrocyte phenotype.

On the Identification of Circulating Tumor Cells in Breast Cancer

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.

Biological interaction networks based on sparse temporal expansion of graphical models

Biological networks are often described as probabilistic graphs in the context of gene and protein sequence analysis in molecular biology. Microarrays and proteomics technology allow the monitoring of expression levels over thousands of biological units over time. In experimental efforts we are interested in unveiling pairwise interactions. Many graphical models have been introduced in order to discover associations from the expression data analysis. However, the small size of samples compared to the number of observed genes/proteins makes the inference of the network structure quite challenging. In this study we generate gene-protein networks from sparse experimental data using two methods, partial correlations and Kernel Density Estimation, in order to capture genetic interactions. Dynamic Gaussian analysis is used to match special characteristics to genes and proteins at different time stages utilizing the KDE method for expressing Gaussian associations with non-linear parameters.

Proteome Changes during Transition from Human Embryonic to Vascular Progenitor Cells.

Human embryonic stem cells (hESCs) are promising in regenerative medicine (RM) due to their differentiation plasticity and proliferation potential. However, a major challenge in RM is the generation of a vascular system to support nutrient flow to newly synthesized tissues. Here we refined an existing method to generate tight vessels by differentiating hESCs in CD34(+) vascular progenitor cells using chemically defined media and growth conditions. We selectively purified these cells from CD34(-) outgrowth populations also formed. To analyze these differentiation processes, we compared the proteomes of the hESCs with those of the CD34(+) and CD34(-) populations using high resolution mass spectrometry, label-free quantification, and multivariate analysis. Eighteen protein markers validate the differentiated phenotypes in immunological assays; nine of these were also detected by proteomics and show statistically significant differential abundance. Another 225 proteins show differential abundance between the three cell types. Sixty-three of these have known functions in CD34(+) and CD34(-) cells. CD34(+) cells synthesize proteins implicated in endothelial cell differentiation and smooth muscle formation, which support the bipotent phenotype of these progenitor cells. CD34(-) cells are more heterogeneous synthesizing muscular/osteogenic/chondrogenic/adipogenic lineage markers. The remaining >150 differentially abundant proteins in CD34(+) or CD34(-) cells raise testable hypotheses for future studies to probe vascular morphogenesis.

Searching for Significant Genes in Cancer Metastasis by Tissue Comparisons

DNA Microarrays allow scientists to simultaneously measure the expression levels of thousands of genes. However, an important need arises as to identify those genes closely associated with a particular state of interest, such as cancer, in order to discover useful biological information and efficiently classify new samples. The identification of marker genes is often based on the differential expression of groups of genes and/or their predictive potential manifested in classification experiments. Important aspects that need to be verified on both biological and statistical grounds are the actual problem considered and the algorithmic method selected. In this paper we consider the question of gene differentiation in cancer and how it can be explored through blood sample analysis. We study two algorithmic approaches based on support and relevance vector machines. The results indicate that the latter concept performs better in the specific biological environment, extracting meaningful biological concepts.

Temporal and Spatial Patterns of Gene Profiles during Chondrogenic Differentiation

Clustering analysis based on temporal profile of genes may provide new insights in particular biological processes or conditions. We report such an integrative clustering analysis which is based on the expression patterns but is also influenced by temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of pellet culture-conditioned human primary chondrocytes and human bone marrow-derived mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes. We further considered the induced biological processes and the gene-interaction networks formed within each cluster and discuss their biological significance. Our proposed methodology provides a consistent tool that facilitates both the statistical and biological validation of temporal profiles through spatial gene network profiles.

Nonparametric Network Design and Analysis of Disease Genes in Oral Cancer Progression

Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods.

Shape-influenced clustering of dynamic patterns of gene profiles

Statistical evaluation of temporal gene expression profiles plays an important role in particular biological processes and conditions. We introduce a clustering method for this purpose, which is based on the expression patterns but is also influenced by temporal changes. We compare the results of our platform with methods based on expression or the rank of temporal changes. The proposed platform is illustrated with a temporal gene expression dataset comprised of primary human chondrocytes and mesenchymal stem cells (MSCs). We derived three clusters in each cell type and compared the content of these classes in terms of temporal changes, which can support biological performance. For statistical evaluation we introduce a validity measure that takes under consideration these temporal changes and we also perform an enrichment analysis of three central genes in each cluster. Even though we can detect certain statistical similarities, these might be due to different biological processes. Our proposed platform contributes to both the statistical and biological validation of temporal profiles.

Stacking of Network Based Classifiers with Application in Breast Cancer Classification

In this study we present the use of existing biological knowledge in the form of biological networks for the construction of a two level classification scheme. At the first level base classifiers are built using a given list of candidate “biomarkers” and the topology of the biological network. In particular, the network structure is taken into account by a search strategy based on random walks for the selection of the genes used in these classifiers. At the second level, a meta-classifier is trained to combine in the best possible way the results of the base classifiers. The proposed approach therefore aims to strengthen the classification ability of the initial list of genes and provide more robust generalization guarantees. Our methodology is explained in full detail and promising results in Breast Cancer related scenarios are presented.

Extracting reliable gene expression signatures through Stable Bootstrap Validation.

Identification of candidate genes responsible for specific phenotypes, such as cancer, has been a major challenge in the field of bioinformatics. Given a DNA Microarray dataset, traditional feature selection methods produce lists of candidate genes which vary significantly under variations of the training data. That instability hinders the validity of research findings and raises doubts about the reliability of such methods. In this study, we propose a framework for the extraction of stable genomic signatures. The proposed methodology enforces stability at the validation step, independent of the feature selection and classification methods used. The statistical significance of the selected gene set is also assessed. The results of this study demonstrate the importance of stability issues in genomic signatures, beyond their prediction capabilities.