Scientific-Technical StaffAnja Fritzsche*Ronny Kalis*Sandra NeuendorfStephanie PlaßmannKirstin RauAnke ThiemeFranziska Schiele*Martina ZenknerTechnical AssistantsNouhad Benlasfer*Anna Happe-Kramer*Daniela KleckersSusanne Köppen*Susanne RautenbergAlexandra RedelDana Rotte*Kati Scharf*Nancy SchugardtJan TimmCarsta Werner*Projekt Management(NGFN2 SMP Protein)Dr. Patrick Umbach*Project Management(NGFN-Plus/NeuroNet)Dr. Paul Schultze-Motel*aggregation intermediates into large amyloid structuresmight be a powerful therapeutic approach totreat protein misfolding diseases.In order to investigate whether the small moleculesthat modulate amyloid formation pathways in vitro andin vivo are useful for therapy development, a drug discoveryprogram for AD and HD was started in 2007 inthe framework of the GO-Bio initiative of the GermanFederal Ministry for Education and <strong>Research</strong>. The aim ofthis program is to promote the establishment of innovativestart-up companies out of the academic sector. Inthe first funding phase of the Go-Bio project, we havediscovered a number of novel drug candidates for bothAD and HD and developed them up to in vivo proof ofconcept in transgenic mouse models of the two diseases.At the moment, derivatisation and further in vivotesting are in progress in the framework of lead optimisation.Identification of proteins that modulate polyQmediatedhuntingtin aggregationHD is an inherited neurodegenerative disorder that iscaused by an expansion of a polyQ tract in the proteinhuntingtin, which leads to a characteristic accumulationof insoluble Htt aggregates in affected neuronsand eventually to cellular dysfunction and toxicity.However, the molecular pathways underlying brainspecific,polyQ-induced neurodegeneration in HD arestill unknown. Recently, a large number of interactionpartners were identified that associate with the N-terminaldomain of huntingtin, which harbours the aggregation-pronepolyQ tract. We hypothesized that perturbationof functional huntingtin protein complexes inneurons induces protein misfolding and neurotoxicity.To identify tissue-specific, dysregulated huntingtin proteininteractions, a bioinformatic approach was developed.By filtering publically available protein-proteininteraction (PPI) data with information from geneexpression studies of brain and non-brain tissues aswell as clinical case-control studies, a brain-specifichuntingtin PPI network was created, linking 14 potentiallydysregulated proteins directly or indirectly to thedisease protein. Analysis of published data confirmedthe predictive value of this network modelling strategy.Moreover, systematic investigations with in vitro andDrosophila model systems of HD demonstrated thatthe potentially dysregulated huntingtin interactionpartners influence polyQ-mediated protein misfoldingand neurodegeneration. The neuron-specific proteinCRMP1 e.g. is recruited to inclusion bodies with aggregatedhuntingtin protein in brains of HD transgenicmice and efficiently inhibits polyQ-mediated huntingtinexon 1 aggregation in cell free assays. Our resultsoffer a new strategy for identifying perturbed, tissuespecifichuman PPIs and modulators of protein misfoldingand aggregation (Bounab et al., 2009, submitted).Development of interactome databases andnovel quality standards for systematic proteininteraction studiesHuman protein interaction maps have become importanttools of biomedical research for the elucidation ofmolecular mechanisms and the identification of newmodulators of disease processes. We developed a comprehensiveinteractome database termed UnifiedHuman Interactome (UniHI). It provides researcherswith a comprehensive integrated platform to query andaccess human PPI data. Since its first release, UniHI hasconsiderably increased in size. The latest update ofUniHI includes over 250,000 interactions between~23,000 unique proteins collected from 14 majorsources. However, this wealth of data also poses newchallenges for researchers due to the size and complexityof interaction networks retrieved from the database.We therefore developed several new tools to query, analyzeand visualize human PPI networks. Most importantly,UniHI now allows the construction of tissue-specificinteraction networks and focused searches ofcanonical pathways. This will enable researchers to targettheir analysis and to prioritize candidate proteinsfor follow-up studies. UniHI 4 can be accessed athttp://www.mdc-berlin.de/unihi (Chaurasisa et al.,2008).184 Function and Dysfunction of the Nervous System
Project Management (GO-Bio)Dr. Annett BöddrichSigrid Schnögl*part of the period reportedAdministrative AssistantsErika PischSilke Spading*Several attempts have been made to systematicallymap PPI networks. However, it remains difficult toassess the quality and coverage of existing data sets. Incollaboration with the research group of Prof. MarcVidal from Harvard Medical School, Boston, we havedeveloped an approach to identify the most suitablequality parameters of currently available human interactomemaps. We found that high-throughput yeasttwo-hybrid (HT-Y2H) screening yields more accurateinteraction data for human proteins than non-systematicstudies of small numbers of individual interactions.This suggests that HT-Y2H screening is a powerfulmethod to map a significant portion of the humaninteractome. We estimated that the human interactomecontains ~130,000 binary interactions, most ofwhich remain to be mapped by systematic screenings.High quality data and accurate estimates of interactionnumbers are crucial to establish the magnitude of thetask of comprehensively mapping the human interactome(Venkatesan et al., 2009).Constructing directed protein interaction networksfor activated EGF/Erk signalingEpidermal growth factor (EGF) signaling through extracellular-signalregulated kinases (Erks) is a complexprocess involving a series of protein-protein interactions(PPIs) that propagate information from the plasmamembrane to transcription factors. To obtain aglobal view of EGF/Erk signaling and to predict potentialmodulators, we created a network connecting 1126proteins via 2626 PPIs using automated yeast twohybrid(Y2H) interaction mating. From this interactionmap, a network of activated signaling was generatedusing a naïve Bayesian classifier, in which informationon shortest PPI paths from membrane receptors totranscription factors was exploited to predictinput/output relationships between interacting proteins.Analysis of the resulting network model revealedregulatory motifs typical for information processingsystems. Moreover, it allowed predictions of potentialmodulators of EGF/Erk signaling, which were validatedin mammalian cell-based assays. This generic experimentaland computational approach provides a frameworkfor elucidating causal connections between proteinsand facilitates the identification of factors modulatingthe flow of information in signaling networks(Vinayagam et al., 2009, submitted).Selected PublicationsEhrnhoefer, DE, Bieschke, J, Boeddrich, A, Herbst, M, Masino, L, Lurz, R,Engemann, S, Pastore, A, Wanker, EE. (2008) Redirecting aggregationpathways: small molecule-mediated conversion of amyloidogenicpolypeptides into unstructured, off-pathway oligomers. Nat Struct MolBiol. 15(6), 558-66.Zolghadr, K, Mortusewicz, O, Rothbauer, U, Kleinhans, R, Goehler, H,Wanker, EE, Cardoso, MC and Leonhardt, H. (2008) A fluorescent twohybrid(F2H) assay for direct visualization of protein interactions in livingcells. Mol Cell Proteomics 7(11), 2279-87.Chaurasia, G, Malhotra, S, Russ, J, Schnögl, S, Hänig, C, Wanker, E andFutschik, M. (2009) UniHI 4: New tools for query, analysis andvisualization of the human protein-protein interactome. Nucleic AcidsRes. 37(Database issue), D657-60.Venkatesan, K, Rual, .J-F Vazquez, A, Stelzl, U, Lemmens, I, Hirozane-Kishikawa, T, Hao, T, Zenkner, M, Xin, X, Goh, K-I, Yildirim, MA, Simonis, N,Heinzmann, K, Gebreab, F, Sahalie, JM, Cevik, S, Simon, C, de Smet, A-S,Dann, E, Smolyar, A, Vinayagam, A, Yu, H, Szeto, D, Borick. H, Dricot, A,Klitgord, N, Murray, RR, Lin, C, Lalowski, M, Timm, J, Rau, K, Boone, C, Braun,P, Cusick, ME, Roth, FP, Hill, DE, Tavernier, J, Wanker, EE, Barabási, A-L andVidal, M. (2009) An empirical framework for binary interactome mapping.Nat Methods. 6(1), 83-90.Palidwor, GA, Shcherbinin, S, Huska, MR, Rasko, T, Stelzl, U, Arumughan, A,Foulle, R, Porras, P, Sanchez-Pulido, L, Wanker, EE, and Andrade-Navarro,MA. (2009) Detection of alpha-rod repeats using a neural network andapplication to huntingtin. PLoS Comput Biol. 5(3):e1000304.Function and Dysfunction of the Nervous System 185
- Page 1:
Research Report 2010MAX DELBRÜCK C
- Page 4 and 5:
ContentInhaltContentInhalt.........
- Page 6 and 7:
Surgical OncologyPeter M. Schlag...
- Page 11 and 12:
at the MDC. The role of the institu
- Page 13 and 14:
in discovering genes that contribut
- Page 16 and 17:
The ECRC offers research space and
- Page 18 and 19:
etween disciplines such as biology,
- Page 20 and 21:
approaches from bioinformatics/syst
- Page 23 and 24:
von Humboldt Foundation (AvH). The
- Page 25:
organization to a larger, multi-fac
- Page 28 and 29:
Cardiovascular and Metabolic Diseas
- Page 30 and 31:
electrical signals. More recent wor
- Page 32 and 33:
Basic Cardiovascular FunctionStruct
- Page 34 and 35:
Figure 2: SORLA and sortilin in neu
- Page 36 and 37:
Annette Hammes(Delbrück Fellow)Str
- Page 38 and 39:
Ingo L. MoranoStructure of the Grou
- Page 40 and 41:
Figure 3. Membrane resealing assay
- Page 42 and 43:
Michael GotthardtStructure of the G
- Page 44 and 45:
Structure of the GroupSalim Seyfrie
- Page 46 and 47:
Structure of the GroupFerdinand le
- Page 48 and 49:
Francesca M. SpagnoliStructure of t
- Page 50 and 51:
Structure of the GroupKai M. Schmid
- Page 52 and 53:
Genetics and Pathophysiology of Car
- Page 54 and 55:
Figure 2. Planariato experimentally
- Page 56 and 57:
Norbert HübnerStructure of the Gro
- Page 58 and 59:
Structure of the GroupGroup LeaderF
- Page 60 and 61:
Figure 2. Omega-3 fatty acids prote
- Page 62 and 63:
Structure of the GroupDominik N. M
- Page 64 and 65:
Rainer DietzStructure of the GroupG
- Page 66 and 67:
Figure 2. Cardiac-restricted ablati
- Page 68 and 69:
Ludwig ThierfelderStructure of the
- Page 70 and 71:
standing of the molecular and cellu
- Page 72 and 73:
Structure of the GroupThoralf Niend
- Page 74 and 75:
Michael BaderStructure of the Group
- Page 76 and 77:
Natriuretic peptide systemJens Butt
- Page 78 and 79:
Structure of the GroupZsuzsanna Izs
- Page 80 and 81:
Young-Ae LeeStructure of the GroupG
- Page 82 and 83:
Structure of the GroupMatthias Selb
- Page 84 and 85:
Matthew PoyStructure of the GroupGr
- Page 86 and 87:
Jana WolfStructure of the GroupGrou
- Page 88 and 89:
Structure of the GroupGroup LeaderD
- Page 91 and 92:
Cancer Research ProgramKrebsforschu
- Page 93 and 94:
are responsible for the emergence o
- Page 95 and 96:
tral component of the canonical Wnt
- Page 97 and 98:
lead to an aberrant constitutive ac
- Page 99 and 100:
How Notch- and TGFβ signaling casc
- Page 101 and 102:
tures of the chronic phase in human
- Page 103 and 104:
oped a new safeguard that is based
- Page 105 and 106:
Graduate StudentsSeda Cöl ArslanCa
- Page 107 and 108:
investigation, as is the cause of c
- Page 109 and 110:
Graduate StudentsÖzlem Akilli Özt
- Page 111 and 112:
onment, the (cancer) stem cell nich
- Page 113 and 114:
The pluripotent state of murine and
- Page 115 and 116:
In the morula of the early mouse em
- Page 117 and 118:
Graduate StudentsRami Hamscho*Qingb
- Page 119 and 120:
esis and granulopoiesis. We showed
- Page 121 and 122:
URE ∆/∆ mice regularly develope
- Page 123 and 124:
PD Dr. Wolfgang Walther (GroupLeade
- Page 125 and 126:
For the delivery of naked DNA into
- Page 127 and 128:
ACBDMyc and FoxO transcription fact
- Page 129 and 130:
Graduate StudentsKatrin BagolaHolge
- Page 131 and 132:
Heinemann, we could also identify t
- Page 133 and 134:
Above: Tip of chromosom3L showing t
- Page 135 and 136:
Graduate StudentsSarbani Bhattachar
- Page 137 and 138:
vesicle transport to the Golgi. Our
- Page 139 and 140:
acbFigure 1a: EHD2 is tubulatingpho
- Page 141 and 142:
KnowledgeProbabilitiesknownPossible
- Page 143 and 144:
Graduate and undergraduatestudentsU
- Page 145 and 146:
successive oncogenic mutations. We
- Page 147 and 148:
CXCR5 drives the development of ect
- Page 149 and 150:
Graduate and undergraduatestudentsW
- Page 151 and 152:
Graduate andUndergraduate StudentsM
- Page 153 and 154:
Angela MensenStefanie WittstockBjö
- Page 155 and 156:
deficient mice, both major effector
- Page 157 and 158:
ant of CD3 delta coding for a 45-me
- Page 159 and 160: Robert KudernatschLi-Min LiuAna Mil
- Page 161 and 162: Sebastian GüntherTechnical Assista
- Page 163 and 164: Graduate StudentsJana RolffAnnika W
- Page 165: with murine hepatocytes showed morp
- Page 168 and 169: Function and Dysfunction of the Ner
- Page 170 and 171: The Neuroscience Department also es
- Page 172 and 173: the coming years, Björn Schröder
- Page 174 and 175: mice. Further analysis of the funct
- Page 176 and 177: Signaling Pathways and Mechanisms i
- Page 178 and 179: Control Olig3 -/-ABFigure 2. Geneti
- Page 180 and 181: Thomas J. JentschStructure of the G
- Page 182 and 183: Figure 2. Cellular model for ionic
- Page 184 and 185: Structure of the GroupGroup LeaderF
- Page 186 and 187: paired-pulse facilitation, less dep
- Page 188 and 189: Gary R. LewinStructure of the Group
- Page 190 and 191: Model summarizing the three waves o
- Page 192 and 193: Structure of the GroupInes Ibañez-
- Page 194 and 195: Jochen C. MeierStructure of the Gro
- Page 196 and 197: Björn Christian SchroederStructure
- Page 198 and 199: Structure of the GroupJan Siemens(S
- Page 200 and 201: Structure of the GroupGroup LeaderD
- Page 202 and 203: Imaging of the Living BrainStructur
- Page 204 and 205: Pathophysiological Mechanisms of Ne
- Page 206 and 207: Figure 2. Iba1 positive microglia c
- Page 208 and 209: Erich E. WankerStructure of the Gro
- Page 212 and 213: Structure of the GroupJan Bieschke(
- Page 215 and 216: Berlin Institute of Medical Systems
- Page 217 and 218: etes, metabolic diseases and neurod
- Page 219 and 220: A number of MDC investigators have
- Page 221 and 222: Technical AssistantsClaudia Langnic
- Page 223 and 224: has become a standardized data flow
- Page 225: Phylogeny of cellulase genes from P
- Page 228 and 229: Experimental and Clinical Research
- Page 230 and 231: his patients, and a basic research
- Page 232 and 233: The ultrahigh field MR facility was
- Page 234 and 235: Structure of the GroupSimone Spuler
- Page 236 and 237: Ralph KettritzStructure of the Grou
- Page 238 and 239: Structure of the GroupJeanette Schu
- Page 240 and 241: Maik GollaschStructure of the Group
- Page 243 and 244: Technology PlatformsComputational B
- Page 245 and 246: projects/ard/] to detect repeats li
- Page 247 and 248: Simulation of line-scan images of C
- Page 249 and 250: Development of an MRM method for qu
- Page 251 and 252: mobility or turnover of the underly
- Page 253 and 254: Left: Inside view of a FACSAria2 (f
- Page 255 and 256: Examples fort the use of EM methods
- Page 257: Oviducts lined up in pre-implantati
- Page 260 and 261:
Academic Appointments 2008-2009Beru
- Page 262 and 263:
Buch which is part of the Excellenc
- Page 264 and 265:
“Bioinformatics in Quantitative B
- Page 266 and 267:
Delbrück FellowsDelbrück-Stipendi
- Page 268 and 269:
Yinth Andrea Bernal-Sierra, a PhD s
- Page 270 and 271:
Congresses and Scientific MeetingsK
- Page 272 and 273:
SeminarsSeminare2008Speaker Institu
- Page 274 and 275:
Speaker Institute TitleKiyoshi Mori
- Page 276 and 277:
2009Speaker Institute TitleDavid G.
- Page 278 and 279:
Speaker Institute TitleJuri Rappsil
- Page 280 and 281:
The Helmholtz AssociationDie Helmho
- Page 282 and 283:
The Berlin-Buch CampusDer Campus Be
- Page 284:
the MDC, the existing collaboration
- Page 287 and 288:
Prof. Dr. Gary R. LewinMDC Berlin-B
- Page 289 and 290:
Prof. Dr. Renato ParoCenter of Bios
- Page 291 and 292:
Staff CouncilThe Staff Council is i
- Page 293 and 294:
Type of Financing/Art der Finanzier
- Page 295 and 296:
Research Projects 2008-2009Forschun
- Page 297 and 298:
CIC-5 Regulation und Endocytose am
- Page 299 and 300:
MDCMAX-DELBRÜCK-CENTRUMFÜR MOLEKU
- Page 301 and 302:
Index 275Bröske, A. . . . . . . .
- Page 303 and 304:
Index 277Gross, V. . . . . . . . .
- Page 305 and 306:
Index 279Kur, E. . . . . . . . . .
- Page 307 and 308:
Index 281Piano, F. . . . . . . . .
- Page 309 and 310:
Index 283Smink, J. . . . . . . . .
- Page 312 and 313:
Campus MapCampusplanRobert-Rössle-
- Page 314:
How to find your way to the MDCDer