Function in Cell Growth, Differentiation & Development · blood samples based on DNA methylation at only three specific CpG sites. These genomic regions can be analyzed relatively

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2013Helmholtz-Institute for Biomedical EngineeringRWTH Aachen University

Gene Function

in Cell Growth, Differentiation & DevelopmentDirectorUniv.-Prof. Dr. rer. nat.Martin Zenke

RWTH Aachen University HospitalPauwelsstrasse 30, 52074 Aachen

Helmholtz Institute for Biomedical EngineeringPauwelsstrasse 20, 52074 Aachen

Phone: +49-241-80 80760 (Office) +49-241-80 80759 (Secretary)Fax: +49-241-80 82008Email: [email protected]: http://www.molcell.de http://www.stemcellfactory.de

StaffOffergeld, Andrea, Administrative AssistantSous, Renate, Administrative Assistant (part time)

Aydin, Gülcan, BSc, TechnicianCelikkin, Nehar, MSc StudentChauvistré, Heike, MSc, PhD StudentChrist, Anette, MSc, PhD StudentDiebold, Sebastian, MSc StudentHieronymus, Thomas, PhD, Group LeaderHübel, Jessica, MSc, PhD StudentKlisch, Theresa, MSc StudentKüstermann, Caroline, MSc StudentLeidl, Quentin, BSc StudentLin, Qiong, MSc, PhD StudentMaxeiner, Sebastian, MSc StudentMitzka, Saskia, TechnicianQin, Jie, MSc, PhD StudentSchalla, Carmen, TechnicianSechi, Antonio, PhD, Group LeaderSeré, Kristin, PhD, Group LeaderSontag, Stephanie, MSc, PhD StudentTran, Phuong Thanh, MSc StudentWanek, Paul, BSc, TechnicianWang, Xiaoying, MSc, PhD StudentWeynands, Corinna, MSc, PhD StudentWolf, Michael, MSc Student

Stem Cell Biology and Cellular EngineeringUniv.-Prof. Dr. med., Dr. rer. nat.Wolfgang Wagner

Helmholtz Institute for Biomedical EngineeringPauwelsstrasse 20, 52074 Aachen

Phone: +49-241-80 88611 (Office)Fax: +49-241-80 82008Email: [email protected]

Abagnale, Giulio, MSc, PhD StudentFrobel, Joana, MSc, PhD StudentGötzke, Roman, MSc StudentHemeda, Hatim, MSc, PhD, PostdocJätzold, Sandra, TechnicianJoussen, Sylvia, TechnicianKalz, Jana, MSc StudentLohmann, Michael, MD StudentPieper, Lisa, MSc StudentRaic, Annamarija, MSc StudentReck, Kristina, MSc StudentSchellenberg, Anne, MSc, PhD StudentWalenda, Gudrun, MSc, PhD StudentWalenda, Thomas, MSc, PhD, PostdocWeidner, Carola, MSc, PhD Student

Computational Biology Research GroupDr. rer. nat. Ivan Gesteira Costa Filho

Interdisciplinary Center for Clinical Research (IZKF) AachenHelmholtz Institute for Biomedical Engineering Centre of Medical Technology (MTZ) Pauwelsstr. 19 52074 AachenPhone: +49-241-80 80270Email [email protected]: http://www.costalab.org

Hänzelmann, Sonja, MSc, PhD, PostdocGusmao, Eduardo G., MSc, PhD StudentAllhoff, Manuel, MSc, PhD StudentKuo, Joseph, MSc Student

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Introduction

Our research focuses on elucidating genetic and epigene-tic regulation of stem cells, including hematopoietic stem cells (HSC), mesenchymal stem cells (MSC) and embry-onic stem cells (ES cells). A particular focus is on the mo-lecular mechanisms driving the differentiation of HSC into specific lineages, such as antigen presenting dendritic cells (DC).A further emphasis is on the engineering of stem cells from somatic cells, including the generation of induced pluripotent stem cells (iPS cells). Given the enormous po-tential of engineered stem cells and their differentiated progeny, our studies are expected to provide the basis for novel biomedical applications and cell-based therapies.Further projects concentrate on (i) recapitulating condi-tions of the stem cell niche with biomaterials and specific factors and (ii) tracking of stem cells and immune cells in vivo with magnetic nanoparticles. This involves also stud-ies on the cell-to-cell communication and cell migration.Pluripotent stem cells are immortal and escape normal processes of aging (Koch et al., 2013). Thus, we study the underlying mechanisms of cellular ageing and how cellular aging can be measured molecularly. Our studies build on a strong expertise in bioinformatics and computational biology. Therefore methods are devel-oped and applied for genetic and epigenetic analysis of cell differentiation and disease, including predictions and associations.

Fig. 1: To-wards deci-phering gene networks during cell differentia-tion (A) using genome wide gene expres-sion data (B), chromatin data (C) and bioinformat-ics (D).

Induced Pluripotent Stem Cells

iPS cells are engineered stem cells with properties very similar to ES cells. The molecular characteris-tics of iPS cells and their generation from somatic cells are now being increasingly better understood. We found that knockdown of the polycomb group protein Ezh2 severely impairs iPS cell generation (Fig. 2). Ezh2 is a protein involved in chromatin re-modelling and our findings emphasize the impact of chromatin architecture on reprogramming and iPS cell generation (Ding et al., 2013).

Fig. 2: Knockdown of Ezh2 expres-sion by shRNA (shEzh2-1 and Ezh2-2) impairs iPS cell generation. Al-kaline phosphatase staining of iPS cell colonies (day 10). Control, untreated cells; shLuc, lu-ciferase shRNA control.

iPS cells allow the generation of tissue-, disease- and pa-tient-specific cells and thus introduce a new dimension to disease modelling, drug development and regenera-tive medicine. The StemCellFactory consortium has built a production facility for patient-specific iPS cells, com-prising automation, standardization and parallelization of all required cell culture steps, and the prototype is cur-rently being tested (Fig. 3). The StemCellFactory con-sortium combines leading experts in stem cell research and engineering sciences in North Rhine Westphalia, based in Aachen, Bonn, Leverkusen and Münster and Herzogenrath (www.stemcellfactory.de).

Fig. 3: iPS cell colony picking unit of StemCellFactory.

Dendritic Cell Development Requires Histone Deacetylase Activity

DC are the most potent antigen-presenting cells of our immune system. They develop from multipotent hematopoietic progenitors (MPP) in two steps of cell fate decision (Fig. 4A). In step 1, MPP progress into common dendritic cell progenitors (CDP). In step 2, CDP further differentiate into classical DC

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(cDC) and plasmacytoid DC (pDC). Cell fate deci-sions are mediated by epigenetic changes, such as histone acetylation. The level of histone acetylation is regulated by histone acetyltransferases (HAT) and histone deacetylases (HDAC), which add and re-move acetyl groups, respectively (Fig. 4B).

Fig. 4: (A) Development of MPP into DC. Step1: DC com-mitment. Step 2: DC differentiation. (B) Level of histone acetylation is determined by the balanced activity of HAT and HDAC.

We studied the impact of histone acetylation on DC de-velopment by employing the HDAC inhibitor trichostatin A (TSA). We observed that DC commitment of MPP into CDP (step 1) was attenuated by HDAC inhibition (Fig. 5A). Furthermore, we found that differentiation of CDP into DC was restrained (step 2) and that pDC development was specifically blocked (Fig. 5B). Thus, our work demonstrates that chromatin modifiers, such as HDAC, are required for efficient DC development (Chauvistré et al., 2013).

Fig. 5: DC development requires HDAC activity (Chauvistré et al., 2013). (A) MPP/CDP cultures are treated with TSA and analyzed by flow cytometry. CDP are progressively lost with increasing concentrations TSA. (B) HDAC inhibition during DC differentiation results in reduced numbers of CD11c+ DC and lack of pDC.

Magnetic Nanoparticle-labelling of Cells and Tracking by MRI

Cellular therapies using stem cells and immune cells, such as DC, are increasingly applied in clinical trials. Accurate delivery of cells to target organs is crucial for the success of such cell-based therapies. Engineered magnetic nanoparticles (MNP) are currently emerging as promising tools in numerous medical applications, including the development as contrast agents for mag-netic resonance imaging (MRI). We demonstrate tailor-ing of MNP properties using layer-by-layer assembly of polyelectrolytes (PE). Such PE-coated MNPs proved to be effective cell-labelling agents for DC (Schwarz et al., Nanomedicine, 2012; in collaboration with W. Richtering, Institute of Physical Chemistry and J. E. Wong, Chemical Process Engineering (AVT.CVT), Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany; U. Himmelreich and M. Hodenius, Biomedical NMR Unit/MoSAIC, Faculty of Biomedical Sciences and Laboratory of BioNanoColloids, Interdisciplinary Research Centre, Katholic University Leuven, Belgium; M. Hoehn, In vivo NMR Research Group, MPI for Neurological Research, Cologne, Germany). Currently, we investigate PE-coated and lipid-shell MNPs for label-ling of HSC derived from mouse bone marrow or human umbilical cord blood (Hodenius et al., Nanotechnology, 2012).

Fig. 6: (A) Cell labelling with synthetic and biogenic MNP for cell tracking by MRI. (B) Electron micrographs of lipid-shell MNP, magnetosomes and polyelectrolyte MNP (PEI-MNP) after uptake into HSC (left). T2*-weighted 3D gradient echo MRI of MNP-labelled HSC in agarose phan-toms to determine cell detection limits (right).

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8) to uncover regulatory gene networks for DC develop-ment by combining gene expression data, sequence-based motif matching and data from chromatin immunoprecipi-tation (ChIP) deep sequencing (ChIP-seq).

Additionally, we develop methodologies to detect cell specific open chromatin regions to reduce the search space for transcription factor binding sites (Gusmao et al., 2013). This strategy greatly enhances the accuracy of cell specific binding site detection (Fig. 9). These methods are implemented in the Regulatory Genomics Toolbox (http://reg-gen.googlecode.com).

Fig. 9: Example of histone modification and DNase hyper-sensitive sequencing to detect open chromatin regions (footprints). Another line of research is the use of statistical methods and machine learning methods to deduce mechanism of disease from patient specific DNA sequences and gene expression profiles. We have investigated the benefit of distinct proximity indices in the task of clustering patient specific expression profiles (Jaskowiak et al., 2013). Gene Set Variance Analysis allows transforming gene expres-sion data into pathway space, thereby improving down-stream analysis of cancer gene expression (Hänzelmann et al., 2013).In Araujo et al., 2013 we propose a methodology of com-bining gene networks for improving the association of sin-gle nucleotide polymorphisms (SNP) from genome-wide associations. We could detect novel associations in distinct stages of Alzheimer’s disease progression. Moreover, we developed an approach to detect sequencing errors from Illumina technology, which avoids the miscalling SNP in DNA sequencing studies (Allhoff et al., 2013).

Acknowledgements

This work was supported by • GermanResearchFoundation(DFG) • GermanFederalMinistryof Education andResearch

(BMBF) • EuropeanUnion(EU) • InterdisciplinaryCentre forClinicalResearchAachen

(IZKF Aachen) • AachenInstituteforAdvancedStudyinComputational

Engineering Science (AICES) • Stem Cell Network NRW, Ministry of Innovation,

Science and Research of the State North Rhine-Westphalia

• ExcellenceInitiativeoftheGermanFederalandStateGovernments

Epigenetic Tracks of Aging

Aging affects all tissues of our organism and there is a grow-ing perception that this process is associated with tightly regulated epigenetic modifications. These modifications – such as DNA methylation and histone modifications – in-fluence the DNA conformation without changing the DNA sequence. Age-associated DNA methylation changes are tissue specific and tightly regulated.

We have invented a method to estimate the donor age of blood samples based on DNA methylation at only three specific CpG sites. These genomic regions can be analyzed relatively cost effective by pyrosequencing. Based on the three DNA methylation levels the age is predicted with a mean absolute deviation of about 5 years (Fig. 7). Lifestyle parameters, gender and certain blood diseases affect these predictions – hence, our “Epigenetic-Aging-Signature” seems to reflect the biological age of blood rather than the chronological age (Weidner et al., 2013; patent pending).

Fig. 7: Epigenetic-Aging-Signature. The donor age of blood samples is estimated by epi-genetic analysis of three genomic re-gions (predicted age). This method provides a simple biomarker to esti-mate the biologi-cal age of blood.

Computational Biology of Cell Differentiation and Gene Regulation

We develop methods for analyzing epigenetic signatures to investigate gene regulation during cell differentiation anddisease. We propose a computational framework (Fig.

Fig. 8 Strategy of integration of gene expression data, sequence-based motif matching and ChIP-seq data for inference of cell specific regulatory networks.

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H., Zenke, M., Saric, T., and Wagner, W. (2013). Pluripotent stem cells escape from senescence-associated DNA methylation changes. Genome Res. 23, 248-259

[17] Koch, C.M., and Wagner, W. (2013). Epigenetic biomarker to de-termine replicative senescence of cultured cells. Methods Mol. Biol. 1048, 309-321.

[18] Lin, Q., Wagner, W., and Zenke, M. (2013). Analysis of genome-wide DNA methylation profiles by BeadChip technology. Methods Mol. Biol.1049, 21-33.

[19] Linta, L., Stockmann, M., Lin, Q., Lechel, A., Proepper, C., Boeck-ers, T.M., Kleger, A., and Liebau, S. (2013). Microarray-based com-parisons of ion channel expression patterns: Human keratinocytes to reprogrammed hiPSCs to differentiated neuronal and cardiac progeny. Stem Cells Int. 784629.

[20] Ludwig, A., Saffrich, R., Eckstein, V., Bruckner, T., Wagner, W., Ho, A.D., and Wuchter, P. (2014). Functional potentials of human hemat-opoietic progenitor cells are maintained by mesenchymal stromal cells and not impaired by plerixafor. Cytotherapy 16, 111-121.

[21] Qin, J., and Zenke, M. (2013). Genetic modification of MSC for pharmacological screening. In: Essentials of Mesenchymal Stem Cell Biology and Its Clinical Translation, edited by R. C. Zhao, Springer publishers, in press.

[22] Schellenberg, A., Hemeda, H., and Wagner, W. (2013). Tracking of replicative senescence in mesenchymal stem cells by colony-forming unit frequency. Methods Mol. Biol. 976, 143-154.

[23] Schmeckebier, S., Mauritz, C., Katsirntaki, K., Sgodda, M., Puppe, V., Duerr, J., Schubert, S., Schmiedl, A., Lin, Q., Palecek, J., Draeger, G., Ochs, M., Zenke, M., Cantz, T., Mall, M., and Martin, U. (2013) Keratinocyte growth factor and dexamethasone plus elevated cAMP levels synergistically support pluripotent stem cell differentiation into alveolar epithelial type II cells. Tissue Eng. Part A 19, 938-951.

[24] Schuh, A., Konschalla, S., Kroh, A., Schober, A., Marx, N., Sönmez, T. T., Zenke, M., Sasse, A., and Liehn, E. A. (2013). Effect of SDF-1α on endogenous mobilized and transplanted stem cells in regen-eration after myocardial infarction. Curr. Pharm. Des., Jun 18, Epub ahead of print.

[25] Seré, K., Felker, P., Hieronymus, T., and Zenke, M. (2013). TGFb1 microenvironment determines dendritic cell development. OncoIm-munology 2, e23083.

[26] Shao, K., Koch, C., Gupta, M. K., Lin, Q., Lenz, M., Laufs, S., Denek-ke, B., Schmidt, M., Linke, M., Hennies, H.-C., Hescheler, J., Zenke, M., Zechner, U., Saric, T., and Wagner, W. (2013). Induced pluripo-tent mesenchymal stromal cell clones retain donor-derived differ-ences in DNA methylation profiles. Mol. Ther. 21, 240-250.

[27] Walenda, G., Abnaof, K., Joussen, S., Meurer, S., Smeets, H., Rath, B., Hoffmann, K., Fröhlich, H., Zenke, M., Weiskirchen, R., and Wagner, W. (2013). TGFb1 does not induce senescence of multipo-tent mesenchymal stromal cells and has similar effects in early and late passages. PLoS ONE 8, e77656.

[28] Weidgang, C. E., Russell, R., Tata, P. T., Kühl, S. J., Illing, A., Müller, M., Lin, Q., Brunner, C., Boeckers, T. M., Bauer, K., Kartikasari, A. E. R., Guo, Y., Radenz, M., Bernemann, C., Weiß, M., Seufferlein, T., Zenke, M., Iacovino, M., Kyba, M., Schöler, H. R., Kühl, M., Liebau, S., and Kleger, A. (2013) TBX3 directs cell fate decision toward mes-endoderm. Stem Cell Reports 1, 248-265.

[29] Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D. O., Jöckel, K.-H., Erbel, R., Mühleisen, T. W., Zenke, M., Brümmendorf, T. H., and Wagner, W. (2013). Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biology, in press.

[30] Weidner, C. I., Walenda, T., Lin, Q., Wölfler, M. M., Denecke, B., Costa, I. G., Zenke, M., and Wagner, W. (2013). Hematopoietic stem and progenitor cells acquire distinct DNA hypermethylation during in vitro culture. Sci. Rep. , Nov 28. Epub ahead of print.

[16] Wessels, I., Rosenkranz, E., Ventura Ferreira, M., Neuss, S., Zenke, M., Rink, L., and Uciechowski P. (2013). Activation of IL-1β and TNFα genes is mediated by the establishment of permissive chro-matin structures during monopoiesis. Immunobiology 218, 860-866.

[31] Willmann, C., Hemeda, H., Lenz, M., Qin, J., Sontag, S., Wanek, P., Denecke, B., Zenke, M., and Wagner, W. (2013). To clone or not to clone? Induced pluripotent stem cells can be generated in bulk culture. PLoS ONE 8, e65324.

Patent applicationsMethod for determining a human individual’s predisposition to contract a malignant disease (Epimutation of DNMT3A/DNMT3B); 2013; EP 13167411.1; Wagner W, Jost E, Walenda T, Weidner C, Brümmendorf TH.

• START-Program of the Faculty of Medicine, RWTHAachen

• Else-Kröner-FreseniusFoundation • DonationbyU.Lehmann • StemCellFactoryisco-fundedbytheEuropeanUnion

(European Regional Development Fund - Investing in your future) and the German Federal State North Rhine-Westphalia (NRW)

Selected References in 2013 [1] Allhoff, M., Schonhuth, A., Martin, M., Costa, I.G., Rahmann, S., and

Marschall, T. (2013). Discovering motifs that induce sequencing er-rors. BMC Bioinformatics 14 Suppl 5, S1-9.

[2] Araujo, G., Costa, I. G., Souza, M. R. B., and Oliveira, J. R. O. (2013). Random forests and gene networks for association of SNPs to Alzheimer`s disease. Lecture Notes in Bioinformatics 8213, 104-115.

[3] Chauvistré, H., Küstermann, C., Rehage, N., Klisch, T., Mitzka, S., Felker, P., Rose-John, S., Zenke, M., and Seré, K. M. (2013). Den-dritic cell development requires histone deacetylase activity. Eur. J. Immunol., in revision.

[4] Didie, M., Christalla, P., Rubart, M., Muppala, V., Döker, S., Uns-öld, B., El-Armouche, A., Rau, T., Eschenhagen, T., Schwoerer, A. P., Ehmke, H., Schumacher, U., Fuchs, S., Lange, C., Becker, A., Wen, T., Scherschel, J. A., Soonpaa, M. H., Yang, T., Lin, Q., Zen-ke, M., Han, D.-W., Schöler, H. R., Rudolph, C., Steinemann, D., Schlegelberger, B., Kattman, S., Witty, A., Keller, G., Field, L. J., and Zimmermann, W.-H. (2013). Parthenogenetic stem cells for tissue engineered heart repair. J. Clinic. Invest. 123, 1285-1298.

[5] Ding, X., Wang, X., Sontag, S., Qin, J., Wanek, P., Lin, Q., and Zenke, M. (2013). The polycomb protein Ezh2 impacts on iPS cell genera-tion. Stem Cells Dev., Dec 10, Epub ahead of print.

[6] Duarte Campos, D. F., Vogt, M., Lindner, M., Kirsten, A., Weber, M., Megens, R. T., Pyta, J., Zenke, M., Zandtvoort, M. T., Fischer, H. (2013). Two-photon scannling microscopy as a usefull tool for imaging and evaluating macrophage-, IL-4 activated macrophage- and osteoclast-based in vitro degradation of b-tricalcium phosphate bone substitute material. Microsc. Res. Tech., Nov 26. Epub ahead of print.

[7] Ferreira, M. S., Schneider, R. K., Wagner, W., Jahnen-Dechent, W., Labude, N., Bovi, M., Piroth, D. M., Knüchel, R., Hieronymus, T., Müller, A. M., Zenke, M., and Neuss, S. (2013). 2D polymer-based cultures expand cord blood-derived hematopoietic stem cells and support engraftment in NSG mice. Tissue Eng. Part C Methods 19, 25-38.

[8] Gusmao, E. G., Dieterich, C., Zenke, M., and Costa, I. G. (2013). Detection of active transcription factor binding sites with the com-bination of DNase hypersensitivity and histone modifications. Bioin-formations, in revision.

[9] Hänzelmann, S., Castelo, R., and Guinney, J. (2013). GSVA: gene set variation alaysis for microarray and RNA-Seq data. BMC Bioinforma-tics 14, 7-21.

[10] Hemeda, H., Giebel, B., and Wagner, W. (2013). Evaluation of hu-man platelet lysate versus fetal bovine serum for culture of mesen-chymal stromal cells. Cryotherapy, in press.

[11] Hemeda, H., Kalz, J., Walenda, G., Lohmann, M., and Wagner, W. (2013). Heparin concentration is critical for cell culture with human platelet lysate. Cytotherapy 15, 1174-1181.

[12] Hoss, M., Apel, C., Dhanasingh, A., Suschek, C. V., Hemmrich, K., Salber, J., Zenke, M. and Neuss, S. (2013). Integrin a4 impacts on differential adhesion of preadipocytes and stem cells on synthetic polymers. J. Tissue Eng. Reg. Med. 7, 312-323.

[13] Hoss,M.,Šarić,T.,Denecke,B.,Peinkofer,G.,Bovi,M.,Groll,J.,Ko,K., Salber, J., Halbach, M., Schöler, H. R., Zenke, M., and Neuss, S. (2013). Expansion and differentiation of germline-derived pluripo-tent stem cells on biomaterials. Tissue Eng., Part A 19, 1067-1080.

[14] Jaskowiak, P.A., Campello, R.J., and Costa, I.G. (2013). Proximity measures for clustering gene expression microarray data: a valida-tion methodology and a comparative analysis. IEEE/ACM Trans Com-put Biol Bioinform 10, 845-857.

[15] Jost, E., Lin, Q., Weidner, C. I., Wilop, S., Hoffmann, M., Walenda, T., Schemionek, M., Herrmann, O., Zenke, M., Brümmendorf, T. H., Koschmieder, S., and Wagner, W. (2013). Epimutation of DNMT3A is associated with poor prognosis in acute myeloid leukemia. Leuke-mia, in press.

[16] Koch, C. M., Reck, K., Shao, K., Lin, Q., Joussen, S., Ziegler, P., Walenda, G. Drescher, W., Opalka, B., May, T., Brümmendorf, T.

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Cell Biology

Team

Foto 1: Lab out in The Hague, The Netherlands.

Foto 2: Anette Christ (4th from left) receives first Joint PhDof RWTH Aachen University and Maastricht University.

Foto 3: Professor W. Wagner (right) receives Vision4 Life Sciences Price for Regenerative Medicine together with Professor K. De Greef, Ant-werpen, Belgium (left); Professor A. Ramon, President of ITERA (center).

Documents

Function in Cell Growth, Differentiation & Development · blood samples based on DNA methylation at only three specific CpG sites. These genomic regions can be analyzed relatively