Gianni Cesareni
e-mail: cesareni AT uniroma2.it
affiliation: Università di Roma Tor Vergata
research area(s): Molecular Biology, Computational Biology
Course:
Cell and Molecular Biology
University/Istitution: Università di Roma Tor Vergata
University/Istitution: Università di Roma Tor Vergata
Gianni Cesareni is a Full Professor of Genetics at the University of Rome Tor Vergata (Italy). After obtaining a degree in physics at the University of Rome La Sapienza he spent three years in Cambridge in the laboratory of Sidney Brenner. He then moved to the EMBL in Heidelberg where he led a group working on the mechanisms controlling plasmid DNA replication. Since 1989 he teaches and works in Rome. He is interested in the interplay between specificity and promiscuity in the protein interaction network mediated by protein recognition modules and on how this impact on signal transudction. Over the past couple of years a large fraction of the group has focused on the functional characterization of the human phosphatase family.
The main scientific focus of the group of Molecular Genetics is the elucidation and interpretation of the global protein interaction network with the goal of providing an understanding of signal transduction at system level. A large fraction (50%) of the effort is devoted to database design, data curation and development of tools for PPI data analysis. The informatic projects complement and support the experimental activities that focus on the pathways activated by growth factors and cytolines. The group initiated the MINT database that, together with the other major protein interaction databases, has contributed to developing a common standard for data representation (PSI-MI) and has signed an agreement for data exchange (IMEx). The group is supported by AIRC, Telethon and a couple of European projects.
1. S. Panni, L. Montecchi-Palazzi, L. Kiemer, A. Cabibbo, S. Paoluzi, E. Santonico, C. Landgraf, R. Volkmer-Engert, A. Bachi, L. CastagnoliG. Cesareni, Combining peptide recognition specificity and context information for the prediction of the 14-3-3-mediated interactome in S. cerevisiae and H. sapiens. Proteomics, 11, 128-143 (2011)
2. F. Leitner, A. Chatr-aryamontri, S. A. Mardis, A. Ceol, M. Krallinger, L. Licata, L. Hirschman, G. CesareniA. Valencia, The FEBS Letters/BioCreative II.5 experiment: making biological information accessible. Nat Biotechnol, 28, 897-899 (2010)
3. A. Ceol, A. Chatr Aryamontri, L. Licata, D. Peluso, L. Briganti, L. Perfetto, L. CastagnoliG. Cesareni, MINT, the molecular interaction database: 2009 update. Nucleic Acids Res, 38, D532-539 (2010)
4. R. Tonikian, X. Xin, C. P. Toret, D. Gfeller, C. Landgraf, S. Panni, S. Paoluzi, L. Castagnoli, B. Currell, S. Seshagiri, H. Yu, B. Winsor, M. Vidal, M. B. Gerstein, G. D. Bader, R. Volkmer, G. Cesareni, D. G. Drubin, P. M. Kim, S. S. SidhuC. Boone, Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins. PLoS Biol, 7, e1000218 (2009)
5. L. Salwinski, L. Licata, A. Winter, D. Thorneycroft, J. Khadake, A. Ceol, A. C. Aryamontri, R. Oughtred, M. Livstone, L. Boucher, D. Botstein, K. Dolinski, T. Berardini, E. Huala, M. Tyers, D. Eisenberg, G. CesareniH. Hermjakob, Recurated protein interaction datasets. Nat Methods, 6, 860-861 (2009)
6. F. Sacco, M. Tinti, A. Palma, E. Ferrari, A. P. Nardozza, R. Hooft van Huijsduijnen, T. Takahashi, L. CastagnoliG. Cesareni, Tumor suppressor density-enhanced phosphatase-1 (DEP-1) inhibits the RAS pathway by direct dephosphorylation of ERK1/2 kinases. J Biol Chem, 284, 22048-22058 (2009)
7. S. Gonfloni, L. Di Tella, S. Caldarola, S. M. Cannata, F. G. Klinger, C. Di Bartolomeo, M. Mattei, E. Candi, M. De Felici, G. MelinoG. Cesareni, Inhibition of the c-Abl-TAp63 pathway protects mouse oocytes from chemotherapy-induced death. Nat Med, 15, 1179-1185 (2009)
8. M. L. Miller, L. J. Jensen, F. Diella, C. Jorgensen, M. Tinti, L. Li, M. Hsiung, S. A. Parker, J. Bordeaux, T. Sicheritz-Ponten, M. Olhovsky, A. Pasculescu, J. Alexander, S. Knapp, N. Blom, P. Bork, S. Li, G. Cesareni, T. Pawson, B. E. Turk, M. B. Yaffe, S. BrunakR. Linding, Linear motif atlas for phosphorylation-dependent signaling. Sci Signal, 1, ra2 (2008)
9. A. Chatr-Aryamontri, A. Ceol, L. LicataG. Cesareni, Protein interactions: integration leads to belief. Trends Biochem Sci, 33, 241-242; author reply 242-243 (2008)
10. L. Kiemer, S. Costa, M. UeffingG. Cesareni, WI-PHI: a weighted yeast interactome enriched for direct physical interactions. Proteomics, 7, 932-943 (2007)
11. L. KiemerG. Cesareni, Comparative interactomics: comparing apples and pears? Trends Biotechnol, 25, 448-454 (2007)
12. H. Hermjakob, L. Montecchi-Palazzi, G. Bader, J. Wojcik, L. Salwinski, A. Ceol, S. Moore, S. Orchard, U. Sarkans, C. von Mering, B. Roechert, S. Poux, E. Jung, H. Mersch, P. Kersey, M. Lappe, Y. Li, R. Zeng, D. Rana, M. Nikolski, H. Husi, C. Brun, K. Shanker, S. G. Grant, C. Sander, P. Bork, W. Zhu, A. Pandey, A. Brazma, B. Jacq, M. Vidal, D. Sherman, P. Legrain, G. Cesareni, I. Xenarios, D. Eisenberg, B. Steipe, C. HogueR. Apweiler, The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data. Nat Biotechnol, 22, 177-183 (2004)
13. A. H. Tong, B. Drees, G. Nardelli, G. D. Bader, B. Brannetti, L. Castagnoli, M. Evangelista, S. Ferracuti, B. Nelson, S. Paoluzi, M. Quondam, A. Zucconi, C. W. Hogue, S. Fields, C. BooneG. Cesareni, A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science, 295, 321-324 (2002)
2. F. Leitner, A. Chatr-aryamontri, S. A. Mardis, A. Ceol, M. Krallinger, L. Licata, L. Hirschman, G. CesareniA. Valencia, The FEBS Letters/BioCreative II.5 experiment: making biological information accessible. Nat Biotechnol, 28, 897-899 (2010)
3. A. Ceol, A. Chatr Aryamontri, L. Licata, D. Peluso, L. Briganti, L. Perfetto, L. CastagnoliG. Cesareni, MINT, the molecular interaction database: 2009 update. Nucleic Acids Res, 38, D532-539 (2010)
4. R. Tonikian, X. Xin, C. P. Toret, D. Gfeller, C. Landgraf, S. Panni, S. Paoluzi, L. Castagnoli, B. Currell, S. Seshagiri, H. Yu, B. Winsor, M. Vidal, M. B. Gerstein, G. D. Bader, R. Volkmer, G. Cesareni, D. G. Drubin, P. M. Kim, S. S. SidhuC. Boone, Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins. PLoS Biol, 7, e1000218 (2009)
5. L. Salwinski, L. Licata, A. Winter, D. Thorneycroft, J. Khadake, A. Ceol, A. C. Aryamontri, R. Oughtred, M. Livstone, L. Boucher, D. Botstein, K. Dolinski, T. Berardini, E. Huala, M. Tyers, D. Eisenberg, G. CesareniH. Hermjakob, Recurated protein interaction datasets. Nat Methods, 6, 860-861 (2009)
6. F. Sacco, M. Tinti, A. Palma, E. Ferrari, A. P. Nardozza, R. Hooft van Huijsduijnen, T. Takahashi, L. CastagnoliG. Cesareni, Tumor suppressor density-enhanced phosphatase-1 (DEP-1) inhibits the RAS pathway by direct dephosphorylation of ERK1/2 kinases. J Biol Chem, 284, 22048-22058 (2009)
7. S. Gonfloni, L. Di Tella, S. Caldarola, S. M. Cannata, F. G. Klinger, C. Di Bartolomeo, M. Mattei, E. Candi, M. De Felici, G. MelinoG. Cesareni, Inhibition of the c-Abl-TAp63 pathway protects mouse oocytes from chemotherapy-induced death. Nat Med, 15, 1179-1185 (2009)
8. M. L. Miller, L. J. Jensen, F. Diella, C. Jorgensen, M. Tinti, L. Li, M. Hsiung, S. A. Parker, J. Bordeaux, T. Sicheritz-Ponten, M. Olhovsky, A. Pasculescu, J. Alexander, S. Knapp, N. Blom, P. Bork, S. Li, G. Cesareni, T. Pawson, B. E. Turk, M. B. Yaffe, S. BrunakR. Linding, Linear motif atlas for phosphorylation-dependent signaling. Sci Signal, 1, ra2 (2008)
9. A. Chatr-Aryamontri, A. Ceol, L. LicataG. Cesareni, Protein interactions: integration leads to belief. Trends Biochem Sci, 33, 241-242; author reply 242-243 (2008)
10. L. Kiemer, S. Costa, M. UeffingG. Cesareni, WI-PHI: a weighted yeast interactome enriched for direct physical interactions. Proteomics, 7, 932-943 (2007)
11. L. KiemerG. Cesareni, Comparative interactomics: comparing apples and pears? Trends Biotechnol, 25, 448-454 (2007)
12. H. Hermjakob, L. Montecchi-Palazzi, G. Bader, J. Wojcik, L. Salwinski, A. Ceol, S. Moore, S. Orchard, U. Sarkans, C. von Mering, B. Roechert, S. Poux, E. Jung, H. Mersch, P. Kersey, M. Lappe, Y. Li, R. Zeng, D. Rana, M. Nikolski, H. Husi, C. Brun, K. Shanker, S. G. Grant, C. Sander, P. Bork, W. Zhu, A. Pandey, A. Brazma, B. Jacq, M. Vidal, D. Sherman, P. Legrain, G. Cesareni, I. Xenarios, D. Eisenberg, B. Steipe, C. HogueR. Apweiler, The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data. Nat Biotechnol, 22, 177-183 (2004)
13. A. H. Tong, B. Drees, G. Nardelli, G. D. Bader, B. Brannetti, L. Castagnoli, M. Evangelista, S. Ferracuti, B. Nelson, S. Paoluzi, M. Quondam, A. Zucconi, C. W. Hogue, S. Fields, C. BooneG. Cesareni, A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science, 295, 321-324 (2002)
Project Title:
Project Title:
Functional mapping of gene products onto cell networks
The ability to address, on a large scale, the functional consequences of knocking down the expression of any gene of interest has considerably sped up gene annotation in complex eukaryotic systems. Typically, the consequences of interfering, by siRNA, large collections of genes, up to entire genomes, on any convenient phenotypic readout can be investigated by established approaches. Thus, genes may be associated to a function of interest if the alteration of their gene products perturbs the phenotypic readout. The mapping procedure, however, is low resolution because, given the intricacy of the gene interaction web in the cell, two genes affecting the same readout may map to different, distant signaling pathways.
The proposed project involves developing an approach that could map, at a high mechanistic detail, gene products onto complex pathways. The group has interest in the mapping the 300 human phosphatase gene products onto growth pathways that respond to cytokine, growth factors and nutrients. To this end the successful student will use high content phenotypic screening based on siRNA and automated fluorescence microscopy to monitor cell state after knocking down each of the phosphatase genes. Cell state is a “complex” phenotype defined by a combination of five readouts monitoring the activation of five key “sentinel” proteins chosen for their centrality in the pathways and for the robustness of the activation assay.
By modeling the available information on the growth pathways under consideration one can predict the effects of perturbing each node of interest on the cell state defined by the activation/inactivation pattern of the sentinel proteins.
Finally, by matching the experimentally determined cell states with the one predicted by the pathway model one can infer the pathway nodes that are likely to be affected by the phosphatase knock down.
The proposed project involves developing an approach that could map, at a high mechanistic detail, gene products onto complex pathways. The group has interest in the mapping the 300 human phosphatase gene products onto growth pathways that respond to cytokine, growth factors and nutrients. To this end the successful student will use high content phenotypic screening based on siRNA and automated fluorescence microscopy to monitor cell state after knocking down each of the phosphatase genes. Cell state is a “complex” phenotype defined by a combination of five readouts monitoring the activation of five key “sentinel” proteins chosen for their centrality in the pathways and for the robustness of the activation assay.
By modeling the available information on the growth pathways under consideration one can predict the effects of perturbing each node of interest on the cell state defined by the activation/inactivation pattern of the sentinel proteins.
Finally, by matching the experimentally determined cell states with the one predicted by the pathway model one can infer the pathway nodes that are likely to be affected by the phosphatase knock down.
Project Title:
Network analysis of the human interactome.
The successful candidate will develop tools for the analysis of the human physical and functional interactome. He/she will work in close collaboration with experimentalists and will develop strategies for the analysis and integration of high throughput experimental data produced in the wet lab.