Supervisors info:
Κωνσταντίνος Βοργιάς, Καθηγητής, Τμήμα Βιολογίας, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών (Ε.Κ.Π.Α.)
Summary:
The processof agingis manifested through the gradual degeneration of the composition, physiology and function of cells, tissues and organs and is characterized by the inability of homeostasis systems to respond correctly to environmental stimuli. Proteostasis (protein homeostasis) plays an important role in maintaining this multilayer (cellular, tissue and organismic) homeostasis and depends on the balance between the synthesis, repair and degradation of cellular proteins.The proteasome is a multiprotein complex with major proteolytic activity.It has been shown to be dysfunctional in the course of aging and related diseases. In contrast, activation of the mechanism has been associated with an extension of both cellular and organismic life expectancy as well as delayed progression of various age-related diseases, such as Alzheimer's neurodegenerative disease.The present work aims at bioinformatic analysis of microarray transcriptional data related to activation of the proteolytic mechanism of the proteasome. Proteasomal activation will take place using natural and synthetic substances that have already been shown to: (a) induce proteasome activities and, (b) lead to prolong the cellular life expectancy of human fibroblasts as well as the life expectancy of the filamentous moth C. elegans. Total RNA will be harvested from human fibroblasts that will accept the effect of the substances under study (both natural and synthetic) in conditions that induce proteasome activity. Microarray analysis experiments will be performed on these samples.The Gene ARMADA software platform and the GRISSOM web portal (www.grissom.gr) developed by IBFXB's Metabolic Engineering and Bioinformatics software and used for the data processing and analysis of Illumina's clusters will be used. the analysis of a large number of gene and protein expression clusters. They are free software that will be used to pre-process the arrays, which includes noise filtering, normalization and background correction, as well as the primary selection of the most significantly differentiated genes. between the different phenotypic classes to be examined. The latter will utilize statistical selection and test methodologies supported by Gene ARMADA and the GRISSOM portal, such as parametric tests (t-test, ANOVA) and non-parametric tests (e.g., based on bootstrapping random sampling). data that produces more stringent statistical conclusions based on the False Discovery Rate).At the same time, hierarchical, k-means, Fuzzy C-means will be used to cluster data based on data, whilethe Gap Statistic method will automatically select the number of clusters. . Extracted gene lists based on statistical selection methods and clustering methods will power tools developed by the Metabolic Engineering and Bioinformatics Program that correlate gene lists of interest with semantic functional information
9related to the function of these genes/proteins. Specifically, the StRAnGER tools (www.grissom.gr/stranger/) and GORevEng use advanced combinations of statistical tests (hypergometric, x2 and Fischer control) with modern bootstrapping methods and graph similarity metrics. As a result, they link gene lists of interest to semantic information for statistically significant correlated functions or biochemical pathways that appear in the content of corresponding ontologies (Gene Ontology Terms, GOTs) or other well-defined forms of information organization (KEGG pathways).The ultimate goal will be to extract the broader molecular pathways/networks involved in proteasome activation and to identify pathways activated catastrophically by proteasome activation. The study of various substances/activators will reveal both common pathways that are involved in proteasome activation independently of the substance under study and unique pathways associated with proteasome activation through the action of a specific substance.
Keywords:
proteasome, ubiquitin, microarrays, phycocyanobilin, oleacin
