Work in the Hatzopoulos lab focuses on two main areas:
Myocardial Infarction and the Path to Heart Failure
Myocardial infarction (MI), also known as a heart attack, strikes about 1.5 million people in the United States every year. MI occurs when a coronary artery is occluded usually after atherosclerotic plaque rupture and thrombosis. Because of the ensuing acute ischemia in the cardiac tissue downstream from the blocked blood vessel, cardiac myocytes begin to die within minutes. The extent of the injury depends on the location and duration of the blood flow obstruction. The widespread cell death triggers an immediate and massive inflammatory response that gradually clears out the injury site, leaving behind sparse tissue with enlarged capillaries. After cellular debris is removed, the gap fills with granulation tissue. This process starts a few days later and involves activation and proliferation of endothelial cells and infiltration of myofibroblasts. Angiogenesis leads to formation of new vessels in an attempt to restore blood supply, whereas myofibroblasts deposit collagen and other extracellular matrix proteins to reinforce the damaged ventricular wall. A week after infarction, the granulation tissue starts to mesh into a dense scar. Extensive cell death, inflammation, scar formation, together with the fact that the heart has a low regenerative capacity, this causes permanent loss of cardiac tissue leading to ventricular remodeling and heart fulure (HF).
Embryonic Stem (ES) cells and the Fungenes Database
The gold standard of pluripotent stem cells are the Embryonic Stem Cells. ES cells have practically unlimited self-renewal capacity in culture and the potential to differentiate into a wide variety of cell types including cardiac, endothelial, hematopoietic, neuronal, or insulin-producing β-cells, thus showing great promise for organ repair.They are also powerful ecperimental tools in the laboratory for drug discovery, studies on human and animal development, ageing, and diseaseses such as cancer.
Although the rich differentiation potential of ES cells offers an ample cellular source for experimental and clinical studies, also presents several practical problems. For example, the co-differentiation of multiple cell types creates challenges for cellular therapy that mostly requires monotypic cultures of specialized cells. Furthermore, the low yield of particular cell types necessitates elaborate manipulations to enrich for cells with desired phenotypes. For this reason, understanding the programs controlling self-renewal and differentiation of ES cells may lead to novel approaches to unlock their regenerative potential. In this arena, mouse DS cell offer an accessible and relevant model system because they grow and differentiate robustly and reproducibly, recapitulate events of early embryonic development, maintain a stable phenotype over many passages and are easy to genetically engineer.
In recent years, a number of genome-wide gene expression profiling approaches provided a wealth of information on the genetic make up of mouse and human ES cells. To fully profit from these resources, development of innovative bioinformatics tools are necessary to explore this knowledge and mine available data. One such useful resource is the FunGenES database that we built on gene expression data obtained in mouse ES cells within a European-wide consortium of stem cell laboratories.
The history of the FunGenES Consortium
The "Functional Genomics in Embryonic Stem Cells" Consortium (acronym FunGenES) was funded by the European Framework Programme 6 2003 to 2007. The consortium, which had 20 research groups including our laboratory (http://www.fungenes.org), analyzed gene expression patterns in ES cells before and after differentiation under a large number of diverse conditions. Data collection took place in coordinated fashion by streamlining experimental protocols among partners and focusing on a small number of independently-derived ES cell lines, namely the germ line-competent CGR8, E14TG2a and R1 cells. Most experiments were performed in at least six separate biological replicates and the total number of conditions included in the original analysis was 67. RNA samples were prepared with the same techniques and analyzed in a central facility using 258 Affymetrix Mouse 430 v.2 arrays. Detailed descriptions of the individual experimental settings have been previously published (Schulz et al., 2009 and references therein).
Main objectives of the FunGenES database
The coordinated collection of gene expression data facilitated their organization in an interactive database with a number of specific objectives. The first was to provide easy access to the bulk of the FunGenES data to Consortium partners and the scientific community. The second was to arrange expression profiles based on two fundamental parameter, i.e., timing of expression during ES cell differentiation and gene function (Time Cluster, Specific Gene Clusters). Third, the database was designed as reference tool for the expression pattern of any gene or group of genes in ES cells and their derivatives (Search your Gene Engine). The fourth goal of the database was to devise tools to search for transcripts that behave the same way, i.e., are co-induced or co-suppressed during ES cell growth and differentiation under a number of experimental conditions (Global Clusters).
The fifth objective was to depict gene-clustering results in interactive visual representations to enhance discovery of new mechanisms. Therefore, effort was put into creating tools such as the Expression Waves which assembles genes with characteristic expression profiles during ES cell differentiation in the same window as genes that respond in the opposite way and Pathway Animations that illustrate dynamic changes in the components of individual signaling and metabolic pathways viewed in time-related manner.
Sixth, to further enhance the discovery tools, the database was linked to external resources including the NCBI Entrez search engine (http://ww.ncbi.nlm.nih.gov/sites/gquery), iHOP (http://www.ihop-net.org), Ensembl (www.ensembl.org), Pubgene (http://www.pubgene.org) and String (http://string-db.org). There are also links to the Amazonia! (http://amazonia.transcriptome.eu), Hematopoietic Fingerprints (http://franklin.imgen.bem.tmc.edu/loligag) and SCGAP Urologic Epithelial Stem lls Project (http://scgap.systemsbiology.net) databases. In this way, the expression patterns of genes of interest during ES cell differentiation can be compared to their corresponding profiles in adult stem cells and tissues. Even more powerful, is the new Multi-Experiment Matrix (MEM) feature developed by the Bioinformatics team of Jaak Vilo that enable users to search for transcripts that follow similar patterns as a specific gene of interest within the broad collection of publicly available gene expression profiling data.
Finally, to enhance the discovery of new mechanisms, the g:Profiler tool provides functional annotation to assess the biological classification transcripts with specific expression patterns . Gene assignments in g:Profiler include GO categories , KEGG  and Reactome pathways , miRBase microRNA information , and TRANSFAC motifs . In addition to functional annotations, g:Profiler provides tools to sort different gene identifiers and find otrthologs from other organisms.
The database was designed and built by the group of Jaak Vilo in the U. of Talin-Estonia, Herbert Schulz in MDC Berlin, and our lab. It can be accessed with the link: http://biit.cs.ut.ee/fungenes. If you would like practical advice about how to use the FunGenES database, please send an email to: firstname.lastname@example.org. If you have questions about gene clustering methods, please contact Herbert Schulz at: email@example.com. If you are interested in the design and construction of the bioinformatics tools, please write to Jaak Vilo at: firstname.lastname@example.org)
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MeSH terms are retrieved from PubMed records. Learn more.
Key: MeSH Term KeywordAcetylation Age Factors BMP Bone Neoplasms cardiac function cardiomyocytes Cardiovascular Diseases Cell Proliferation cell therapy Cellular Senescence CHO Cells Coronary Circulation development DNA Primers Endothelial Cells endothelial cells gene regulation Genes, MHC Class II genomics heart Histones human embryonic stem cells induced-pluripotent stem cells Mice, Inbred C57BL mouse mouse embryonic stem cells myocardial infarction Notochord Protein Structure, Tertiary Proto-Oncogene Proteins c-mos regenerative medicine stem cell biology Stem Cell Niche Thiadiazoles vascular biology Virus Replication wnt Wnt Signaling Pathway zebrafish