Differential gene expression profiling of human bone marrow-derived mesenchymal stem cells during adipogenic development.

BACKGROUND: Adipogenesis is the developmental process by which mesenchymal stem cells (MSC) differentiate into pre-adipocytes and adipocytes. The aim of the study was to analyze the developmental strategies of human bone marrow MSC developing into adipocytes over a defined time scale. Here we were particularly interested in differentially expressed transcription factors and biochemical pathways. We studied genome-wide gene expression profiling of human MSC based on an adipogenic differentiation experiment with five different time points (day 0, 1, 3, 7 and 17), which was designed and performed in reference to human fat tissue. For data processing and selection of adipogenic candidate genes, we used the online database SiPaGene for Affymetrix microarray expression data. RESULTS: The mesenchymal stem cell character of human MSC cultures was proven by cell morphology, by flow cytometry analysis and by the ability of the cells to develop into the osteo-, chondro- and adipogenic lineage. Moreover we were able to detect 184 adipogenic candidate genes (85 with increased, 99 with decreased expression) that were differentially expressed during adipogenic development of MSC and/or between MSC and fat tissue in a highly significant way (p < 0.00001). Subsequently, groups of up- or down-regulated genes were formed and analyzed with biochemical and cluster tools. Among the 184 genes, we identified already known transcription factors such as PPARG, C/EBPA and RTXA. Several of the genes could be linked to corresponding biochemical pathways like the adipocyte differentiation, adipocytokine signalling, and lipogenesis pathways. We also identified new candidate genes possibly related to adipogenesis, such as SCARA5, coding for a receptor with a putative transmembrane domain and a collagen-like domain, and MRAP, encoding an endoplasmatic reticulum protein. CONCLUSIONS: Comparing differential gene expression profiles of human MSC and native fat cells or tissue allowed us to establish a comprehensive differential kinetic gene expression network of adipogenesis. Based on this, we identified known and unknown genes and biochemical pathways that may be relevant for adipogenic differentiation. Our results encourage further and more focused studies on the functional relevance of particular adipogenic candidate genes.

SiPaGene: A new repository for instant online retrieval, sharing and meta-analyses of GeneChip expression data.

BACKGROUND: Microarray expression profiling is becoming a routine technology for medical research and generates enormous amounts of data. However, reanalysis of public data and comparison with own results is laborious. Although many different tools exist, there is a need for more convenience and online analysis with restriction of access and user specific sharing options. Furthermore, most of the currently existing tools do not use the whole range of statistical power provided by the MAS5.0/GCOS algorithms. DESCRIPTION: With a current focus on immunology, infection, inflammation, tissue regeneration and cancer we developed a database platform that can load preprocessed Affymetrix GeneChip expression data for immediate access. Group or subgroup comparisons can be calculated online, retrieved for candidate genes, transcriptional activity in various biological conditions and compared with different experiments. The system is based on Oracle 9i with algorithms in java and graphical user interfaces implemented as java servlets. Signals, detection calls, signal log ratios, change calls and corresponding p-values were calculated with MAS5.0/GCOS algorithms. MIAME information and gene annotations are provided via links to GEO and EntrezGene. Users access via https protocol their own, shared or public data. Sharing is comparison- and user-specific with different levels of rights. Arrays for group comparisons can be selected individually. Twenty-two different group comparison parameters can be applied in user-defined combinations on single or multiple group comparisons. Identified genes can be reviewed online or downloaded. Optimized selection criteria were developed and reliability was demonstrated with the "Latin Square" data set. Currently more than 1,000 arrays, 10,000 pairwise comparisons and 500 group comparisons are presented with public or restricted access by different research networks or individual users. CONCLUSION: SiPaGene is a repository and a high quality tool for primary analysis of GeneChips. It exploits the MAS5.0/GCOS pairwise comparison algorithm, enables restricted access and user specific sharing. It does not aim for a complete representation of all public arrays but for high quality analysis with stepwise integration of reference signatures for detailed meta-analyses. Development of additional tools like functional annotation networks based on expression information will be future steps towards a systematic biological analysis of expression profiles.