Work in the Hatzopoulos lab focuses on two main areas:

  1. To optimize heart tissue repair in order to reduce infarct size after myocardial infarction.
  2. To investigate the potential of stem cells to regenerate cardiac tissue after injury.

 

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 [30].  Gene assignments in g:Profiler include GO categories [31], KEGG [32] and Reactome pathways [33], miRBase microRNA information [34], and TRANSFAC motifs [35].  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: antonis.hatzopoulos@vanderbilt.edu. If you have questions about gene clustering methods, please contact Herbert Schulz at: heschulz@mdc-berlin.de.  If you are interested in the design and construction of the bioinformatics tools, please write to Jaak Vilo at: vilo@ut.ee)

Publications

Featured publications

  1. Effect of the use and timing of bone marrow mononuclear cell delivery on left ventricular function after acute myocardial infarction: the TIME randomized trial. Traverse JH, Henry TD, Pepine CJ, Willerson JT, Zhao DX, Ellis SG, Forder JR, Anderson RD, Hatzopoulos AK, Penn MS, Perin EC, Chambers J, Baran KW, Raveendran G, Lambert C, Lerman A, Simon DI, Vaughan DE, Lai D, Gee AP, Taylor DA, Cogle CR, Thomas JD, Olson RE, Bowman S, Francescon J, Geither C, Handberg E, Kappenman C, Westbrook L, Piller LB, Simpson LM, Baraniuk S, Loghin C, Aguilar D, Richman S, Zierold C, Spoon DB, Bettencourt J, Sayre SL, Vojvodic RW, Skarlatos SI, Gordon DJ, Ebert RF, Kwak M, Moyé LA, Simari RD, Cardiovascular Cell Therapy Research Network (CCTRN) (2012) JAMA 308(22): 2380-9
    › Primary publication · 23129008 (PubMed) · PMC3652242 (PubMed Central)
  2. Egr-1 induces DARPP-32 expression in striatal medium spiny neurons via a conserved intragenic element. Keilani S, Chandwani S, Dolios G, Bogush A, Beck H, Hatzopoulos AK, Rao GN, Thomas EA, Wang R, Ehrlich ME (2012) J Neurosci 32(20): 6808-18
    › Primary publication · 22593050 (PubMed) · PMC3752065 (PubMed Central)
  3. Effect of transendocardial delivery of autologous bone marrow mononuclear cells on functional capacity, left ventricular function, and perfusion in chronic heart failure: the FOCUS-CCTRN trial. Perin EC, Willerson JT, Pepine CJ, Henry TD, Ellis SG, Zhao DX, Silva GV, Lai D, Thomas JD, Kronenberg MW, Martin AD, Anderson RD, Traverse JH, Penn MS, Anwaruddin S, Hatzopoulos AK, Gee AP, Taylor DA, Cogle CR, Smith D, Westbrook L, Chen J, Handberg E, Olson RE, Geither C, Bowman S, Francescon J, Baraniuk S, Piller LB, Simpson LM, Loghin C, Aguilar D, Richman S, Zierold C, Bettencourt J, Sayre SL, Vojvodic RW, Skarlatos SI, Gordon DJ, Ebert RF, Kwak M, Moyé LA, Simari RD, Cardiovascular Cell Therapy Research Network (CCTRN) (2012) JAMA 307(16): 1717-26
    › Primary publication · 22447880 (PubMed) · PMC3600947 (PubMed Central)
  4. Discovering small molecules that promote cardiomyocyte generation by modulating Wnt signaling. Ni TT, Rellinger EJ, Mukherjee A, Xie S, Stephens L, Thorne CA, Kim K, Hu J, Lee E, Marnett L, Hatzopoulos AK, Zhong TP (2011) Chem Biol 18(12): 1658-68
    › Primary publication · 22195568 (PubMed) · PMC3645312 (PubMed Central)
  5. Effect of intracoronary delivery of autologous bone marrow mononuclear cells 2 to 3 weeks following acute myocardial infarction on left ventricular function: the LateTIME randomized trial. Traverse JH, Henry TD, Ellis SG, Pepine CJ, Willerson JT, Zhao DX, Forder JR, Byrne BJ, Hatzopoulos AK, Penn MS, Perin EC, Baran KW, Chambers J, Lambert C, Raveendran G, Simon DI, Vaughan DE, Simpson LM, Gee AP, Taylor DA, Cogle CR, Thomas JD, Silva GV, Jorgenson BC, Olson RE, Bowman S, Francescon J, Geither C, Handberg E, Smith DX, Baraniuk S, Piller LB, Loghin C, Aguilar D, Richman S, Zierold C, Bettencourt J, Sayre SL, Vojvodic RW, Skarlatos SI, Gordon DJ, Ebert RF, Kwak M, Moyé LA, Simari RD, Cardiovascular Cell Therapy ResearchNetwork (2011) JAMA 306(19): 2110-9
    › Primary publication · 22084195 (PubMed) · PMC3600981 (PubMed Central)
  6. Tfap2a and Foxd3 regulate early steps in the development of the neural crest progenitor population. Wang WD, Melville DB, Montero-Balaguer M, Hatzopoulos AK, Knapik EW (2011) Dev Biol 360(1): 173-85
    › Primary publication · 21963426 (PubMed) · PMC3236700 (PubMed Central)
  7. Continuous antagonism by Dkk1 counter activates canonical Wnt signaling and promotes cardiomyocyte differentiation of embryonic stem cells. Rai M, Walthall JM, Hu J, Hatzopoulos AK (2012) Stem Cells Dev 21(1): 54-66
    › Primary publication · 21861760 (PubMed) · PMC3245675 (PubMed Central)
  8. The feelgood mutation in zebrafish dysregulates COPII-dependent secretion of select extracellular matrix proteins in skeletal morphogenesis. Melville DB, Montero-Balaguer M, Levic DS, Bradley K, Smith JR, Hatzopoulos AK, Knapik EW (2011) Dis Model Mech 4(6): 763-76
    › Primary publication · 21729877 (PubMed) · PMC3209646 (PubMed Central)
  9. Preconditioned endothelial progenitor cells reduce formation of melanoma metastases through SPARC-driven cell-cell interactions and endocytosis. Defresne F, Bouzin C, Grandjean M, Dieu M, Raes M, Hatzopoulos AK, Kupatt C, Feron O (2011) Cancer Res 71(14): 4748-57
    › Primary publication · 21616936 (PubMed)
  10. Nfatc1 coordinates valve endocardial cell lineage development required for heart valve formation. Wu B, Wang Y, Lui W, Langworthy M, Tompkins KL, Hatzopoulos AK, Baldwin HS, Zhou B (2011) Circ Res 109(2): 183-92
    › Primary publication · 21597012 (PubMed) · PMC3132827 (PubMed Central)