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Christine Brun

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Research interests


network.1.jpgOur main interest is to investigate protein and gene function from a network perspective. More specifically, we aim at taking advantage of genome- and proteome-wide data obtained without a priori knowledge to tackle biological questions related to protein and gene function. Our work is, therefore, two-sided:

• we develop dedicated in silico methods and tools

• we perform global and local interactome analyses


Graph partitioning methods for network analyses and function prediction

partition.jpgWe have, for several years, been involved in the development of graph partitioning methods for protein-protein interaction networks. These methods are mainly based on distance computation in both unweighted and weighted graphs: Prodistin (Genome Biol., 2003; Bioinformatics, 2006), ClasDens (BMC Bioinfo, 2004), OCG (Bioinformatics, 2012). Some of these methods have been implemented in Clust&See, a Cytoscape plugin for the identification, visualization and manipulation of network clusters(BioSystems, 2013). For this, we collaborate closely with the 'Mathematics Methods for Genomics' group at the Mathematics Institute of Marseille (I2M).


Ontology analyses and vizualisation tools

ontology.jpgInterpreting the functional meaning of the protein clusters obtained with the graph partitioning methods lead us to develop tools to analyse and visualize ontologies and classification trees. Whereas GOToolbox (Genome Biol, 2004) provides the user with statistics relative to the functional annotations of a gene/protein set, SimCT (Bioinformatics, 2009) visualizes relationships between biological objects annotated to an ontology, PrOnto (Front. Genetics, 2015) computes the functional dissimilarity between GO terms, and TreeDyn (BMC Bioinfo, 2006) is a tree editor.


Interactome Network analyses

Beyond the prediction of function for yet uncharacterized proteins (Genome Biol, 2003; 2004), we defined a scale of functional divergence for the evolution of the function of duplicated genes, a modular signalling network in drosophila (Genome Biol, 2004; BMC Sys Bio 2008) using graph partitioning methods. More recently, we highlighted functional integrative levels (PLosOne 2011) and multifunctional proteins (Bionformatics, 2012) by analysing the human interactome network.

Presently, our main projects aim at integrating omics data to protein network to better understand cancer progression (Mol Cell Poteomics, 2014), and identifying moonlighting proteins (Nature Com., 2015; Front Physiol, 2015) and genetic variants influencing multifactorial diseases with network approaches. 




A*MIDEX/Plan Cancer/ ANR Piribio/ ERASysbioAXA Research Fund/ Fondation pour la Recherche Médicale/ CNRS/ Inserm/ Aix-Marseille Université.



  • Christine Brun, Research Director CNRS
  • Diogo Ribeiro, PhD Student
  • Lionel Spinelli, Research Engineer
  • Andreas Zanzoni, Postdoc

Associated Member

  • Anaïs Baudot, Research Associate CNRS, Mathematics Institute of Marseille

Former members

  • Anaïs Baudot (PhD student), current position : Research Associate CNRS, Mathematics Institute of Marseille
  • Emmanuelle Becker (Postdoc), current position : Lecturer, Université de Rennes
  • Charles Chapple (Postdoc), curent position : Bioinformatics Engineer, Saphetor, Athens, Greece
  • Céline Guien (Master student), current position: Engineer, Inserm U910, Marseille
  • Charly Mathieu (Master student)
  • Pierre Mouren (Engineer CNRS)
  • Benoit Robisson (PhD student)
  • Ouissem Souiai (PhD student), current position : Lecturer, Medical Technology Institute & Pasteur Institute, Tunis, Tunisia
  • Guillaume Vinck, Engineer student




  • Zanzoni A & Brun C (2016) Integration of quantitative proteomics data and interaction networks: identification of dysregulated cellular functions during cancer progression. Methods (93):103-109. PMID: 26386316
  • Leca J, Martinez S., Lac S.,Nigri J., Secq V., Rubis M., Bressy C., Sergé A., Lavaut M.-N., Dusetti N., Loncle C., Roques J., Pietrasz D., Bousquet C., Garcia S., Granjeaud S., Ouaissi M., Bachet J.-B., Brun C., Iovanna J.L., Zimmermann P., Vasseur S. and Tomasini R. (2016) CAF-derived ANXA6+-extracellular vesicles support pancreatic cancer aggressiveness. Journal of Clinical Investigations. Accepted. 


  • Via A., Uyar B., Brun C. and Zanzoni A. (2015) How pathogens use linear motifs to perturb host cellular networks. Trends in Biochemical Sciences. 40(1):36-48. PMID: 25475989
  • Zanzoni A., Chapple C.E. and Brun C. (2015) Relationships between predicted moonlighting proteins, human diseases and comorbidities from a network perspective. Frontiers in Physiology. 6:171. PMID: 26157390
  • Baëza M, Viala S, Heim M, Dard A, Hudry B, Duffraisse M, Rogulja-Ortmann A, Brun C and Merabet S. (2015) Inhibitory activities of short linear motifs underlie Hox interactome specificity in vivo. Elife, 4:06034. PMID: 25869471
  • Chetouhi C, Panek J, Bonhomme L, ElAlaoui H, Texier C, Langin T, de Bekker C, Urbach S, Demettre E, Misse D, Holzmuller P, Hughes DP, Zanzoni A, Brun C and Biron DG. (2015). Cross-talk in host-parasite associations: what past and recent proteomics tools tell us? Infection, Genetics and Evolution, pii: S1567-1348(15)00140-9. PMID: 25913042
  • Chapple CE, Robisson B, Spinelli L, Guien C, Becker E and Brun C. (2015) Extreme multifunctional proteins identified from a human protein interaction network. Nature Communications, 6:7412. PMID: 26054620
  • Chapple CE, Herrmann C and Brun C. (2015) PrOnto database: GO term functional dissimilarity inferred from biological data. Frontiers in Genetics, 6:200. PMID: 26089836
  • Chapple CE & Brun C (2015) Redefining protein moonlighting. Oncotarget. 6(19):16812-3. PMID: 26220711
  • Didier G, Brun C and Baudot A. (2015) Identifying communities from multiplex biological networks. PeerJ, 3:e1525. PMID: 26713261


  • Souiai O, Guerfali F, Ben Miled S, Brun C and Benkahla A (2014) In silico prediction of protein-protein interactions in human macrophages. BMC Res Notes. 7 (1):157 PMID: 24636261
  • Panek J, El Alaoui H, Mone A, Urbach S, Demettre E, Texier C, Brun C, Zanzoni A, Peyretaillade E, Parisot N, Lerat E, Peyret P, Delbac F, Biron DG. (2014) Hijacking of host cellular functions by an intracellular parasite, the microsporidian Anncalia algerae. Plos One.  Jun 26;9(6):e100791. PMID: 24967735
  • Katsogiannou M., Andrieu C., Baylot V., Baudot A., Dusetti N.J., Gayet O., Finetti F., Garrido C., Birnbaum D., Bertucci F., Brun C.# and Rocchi P.# (2014) The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets. Mol Cell Proteomics. 13(12): 3585-3601. PMID: 25277244. # Equal contributions.


  • Spinelli L., Gambette P., Chapple C.E., Robisson B., Baudot A., Garreta H., Tichit L., Guénoche A. and Brun C. (2013) Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters. BioSystems. 113:91-95.
  • Robisson R., Guénoche A. and Brun C. (2013) Ajout d’incertitude dans les réseaux PPI et amélioration de la qualité biologique des partitions. Actes des Journées Ouvertes Biologie Informatique Mathématiques (JOBIM'2013), p241-250.
  • Mbodj A., Junion G., Brun C., Furlong E., and Thieffry D. (2013) Logical modelling of Drosophila signalling pathways. Mol BioSystems, 9: 2248-2258.


  • Becker E., Robisson B., Chapple C., Guénoche A. and Brun C. (2012) Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics. 28:84-90.
  • Furlan A, Lamballe F, Stagni V, Hussain A, Richelme S, Prodosmo A, Moumen A, Brun C, Barrantes ID, Simon C Arthur J, Koleske AJ, Nebreda AR, Barilà D, Maina F. (2012) Met acts through Abl to regulate p53 transcriptional outcomes and cell survival in the developing liver. J Hepatol. 57: 1292-1298. PMID: 22889954.
  • Bouveret E., and Brun C. (2012) Bacterial interactomes: from interactions to networks. Bacterial Networks- Methods Mol Biol. A. Toussaint, J. Van Helden and D. Thieffry (Eds), 804:15-33. PMID: 22144146
  • Baudot A., Souiai O., and Brun C. (2012) Network analysis and protein function prediction with the PRODISTIN Web Site. Bacterial Networks- Methods Mol Biol. A. Toussaint, J. Van Helden and D. Thieffry (Eds), 804:313-26. PMID : 22144161


  • Souiai O., Becker E., Prieto C., Benkahla A, De Las Rivas J. and Brun C. (2011) Functional integrative levels in the human interactome recapitulate organ organization. Plos One 6:e22051.
  • Merabet S., Litim-Mecheri I., Karlsson D., Dixit R., Saadaoui M., Monier B., Brun C., Thor S., Vijayraghavan K., Perrin L. and Graba Y. (2011) Insigths into Hox protein function from a large scale combinatorial analysis of protein domains. Plos Genetics 7: e1002302.


  • Pilot Storck F., Chopin E., Baudot A., Rual J.-F., Dobrokhotov P., Brun C., Robinson-Rechavi M., Hill D.E., Schaeffer L., Vidal M. and Goillot E. (2010) Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor. Mol. Cell. Proteomics 9:1578-93.


  • Becker E., Guénoche A. and Brun C. (2009) Systèmes de classes chevauchantes pour la recherche de protéines multifonctionnelles. Actes des Journées Ouvertes Biologie Informatique Mathématiques (JOBIM'2009), p. 49-54.


  • Baudot A., Angelelli J.B., Guénoche A., Jacq B. and Brun C. (2008) Defining a modular signalling network from the fly interactome. BMC Systems Biology 2: 45.
  • Angelelli J.B., Baudot A., Brun C., and Guénoche A. (2008) Two local dissimilarity measures for weighted graphs with application to protein interaction networks. Advances in Data Analysis and Classification 2: 3-16.
  • Baudot A. and Brun C. (2008) Outils bioinformatiques et ressources pour l'exploitation des données d'interactions protéine-protéine. Spectra Analyse 263: 40-45.


  • Baudot A., Martin D., Mouren P., Chevenet F., Guenoche A., Jacq B., and Brun C. (2006) PRODISTIN web site: a tool for the functional classification of proteins from interaction networks. Bioinformatics 22: 248-250.
  • Chevenet F., Brun C., Banuls AL., Jacq B.,  and Christen R. (2006) TreeDyn: towards dynamic graphics and annotations for analyses of trees. BMC Bioinformatics 7: 439.
  • Biron D.G., Brun C., Lefevre T., Lebarbenchon C., Loxdale H.D., Chevenet F., Brizard J.P., and Thomas F. (2006) The pitfalls of proteomics experiments without the correct use of bioinformatics tools. Proteomics 6: 5577-5596.


  • Formstecher E., Aresta S., Collura V., Hamberger A., Meil A., Trehin A., Reverdy C., Betin V., Maire S., Brun C., Jacq B., Arpin M., Bellaiche Y., Bellusci S., Benaroch P., Bornens M., Chanet R., Chavrier P., Delattre O., Doye V., Fehon R., Faye G., Galli T., Girault J.A., Goud B., de Gunzburg J., Johannes L., Junier M.P., Mirouse V., Mukherjee A., Papadopoulo D., Perez F., Plessis A., Rosbach M; Rossé C., Saule S., Stoppa-Lyonnet D., Vincent A., White M., Legrain P., Wojcik J., Camonis J., and Daviet L. (2005) Protein interaction mapping: a drosophila case study. Genome Res. 15: 376-384.


  • Brun C., Herrmann C., and Guénoche A. (2004) Clustering proteins from interaction networks for the prediction of cellular functions. BMC Bioinformatics 5: 95.
  • Baudot A., Jacq B., and Brun C. (2004) A scale of functional divergence for yeast duplicated genes revealed from the analysis of the protein-protein interaction network. Genome Biol. 5: R76.
  • Martin D., Brun C., Remy E., Mouren P., Thieffry D., and Jacq B. (2004) GOToolBox: Functional investigation of gene datasets based on the Gene Ontology. Genome Biol. 5: R101.
  • Hermjakob H., Montecchi-Palazzi L., Bader G., Wojcik J., Salwinski L., Ceol A., Moore S., Orchard S., Sarkans U., von Mering C., Roechert B., Poux S., Jung E., Mersch H., Kersey P., Lappe M., Li Y., Zeng R., Rana D., Nikolski M., Husi H., Brun C., Shanker K., Grant S. G. N., Sander C., Bork P., Zhu W., Pandey A., Brazma A., Jacq B., Vidal M., Sherman D., Legrain P., Cesareni G., Xenarios I., Eisenberg D., Steipe B., Hogue C. and Apweiler R. (2004) The HUPO PSI Molecular Interaction Format - A community standard for the representation of protein interaction data. Nature Biotech. 22: 177-183.
  • Brun C., Baudot A., Guénoche A., and Jacq B. (2004) The use of protein-protein interaction networks for genome wide protein function comparisons and predictions. Principles and Practices - Methods in Proteome and Protein Analysis. R.M. Kamp, J.J. Calvete, and T. Choli-Papadopoulou (Eds.), Springer-Verlag Berlin Heidelberg, p103-124.
  • Brun C., Baudot A., and Jacq B. (2004) Functional classification of proteins based on protein interaction data. Encyclopedia of Genomics, Proteomics and Bioinformatics. M. Dunn, L. Jorde, P. Little, S. Subramaniam (Eds), John Wiley & Sons Limited, Part 3.3.


  • Brun C., Chevenet F., Martin D., Wojcik J., Guénoche A., and Jacq B. (2003) Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol. 5: R6.
  • Brun C., Guénoche A., and Jacq B. (2003) Approach of the functional evolution of duplicated genes in Saccharomyces cerevisiae using a new classification method based on protein-protein interaction data. J. Struct. Funct. Genomics 3: 213-224.

Former life