Research topics

The research projects of team GQF aim at modelling the relationship between genetic polymorphism and phenotypic variation, in order to understand the adaptation of populations to new environments. Adaptation is defined as the increase of individual’s fitnesses as a response to environmental changes, which occurs thanks to phenotypics changes for life history traits. Those changes can be plastic, genetic or epigenetic. In order to understand the nature of phenotypic variations, as well as their evolutionary potential, we need to take into account:

(1) The multiplicity of the sources of genetic and phenotypic variations (Genome dynamics);

(2) The effects of genetic variations on phenotypic variation at different integrated levels. In particular, fluxes through metabolic networks are considered as model quantitative traits, which variation depend on the polymorphism of the genes that control the enzymes of the network (Genetics and evolution of biological networks ; Quantitative variations);

(3) The consequences of selection and other evolutionary pressures on phenotypic diversity, but also on genetic polymorphism and on the distribution of genetic polymorphism throughout the genome (evolutionary Forces).

We develop population genetics and quantitative genetics models. Different model organisms are used to test the predictions of the models (Saccharomyces cerevisiae, Zea mays ssp. mays, genus Helianthus, E. Coli), with different approaches, from statistical modelling to experimental evolution.

1. Genome Dynamics

1.1. Meiosis and modeling of crossovers and recombination

1.2. Spatial organization of the genome

1.3. The role of new mutations in adaptation : Maize DSE experiment for flowering time starting from inbred lines

2. Genetics and evolution of metabolic networks

2.1.Biological networks and robustness

2.2. Genetics and evolution of biological networks

3. Genetic bases of adaptation

3.1. Genetic diversity and domestication in S. cerevisiae

3.2. Genetics and evolution of life-history traits in yeast

Projects

Collaborations

E. coli metabolic diversity and modeling of metabolic networks:

• Claudine Medigue, Pierre-Yves Bourguignon, David Vallenet, Gilles Vieira, Genoscope, Evry, France ; Odile Bouvet, Eric Denamur, Bichat, Paris, France

Enzyme proteome, adaptation and heterosis in yeast:

• Monique Bollotin-Fukuhara, Université Paris -Sud, France ; Cécile Fairhead, université Paris-Sud, France ; Marina Bely et Isabelle Masneuf-Pomarède, Université Bordeaux 2, France; Philippe Marullo, Entreprise SARCO, France;  Sylvie Huet et  Christophe Giraud, Mathématiques et Informatiques appliquées, INRA Jouy-en-Josas, France.

Recombination :

• Raphael Mercier et Christine Mezard, INRA, Versailles, France ; Denise Zickler, Universite Paris-Sud, Orsay, France ; Pierre Sourdille et Cyril Saintenac, INRA, Clermont-Ferrand, France ; Lorinda Anderson, Colorado State University, USA

Reproduction:

• Sophie Nadot et Béatrice Albert, ESE, Université Paris XI, France; Pierre-Henri Gouyon et Emmanuelle Porcher, MNHN, Paris, France; Sylvain Billiard, Université de Lilles, France

Robustness of biological networks :

• Andreas Wagner, University of Zürich, Suisse.

Teaching at the university of Paris-Sud

Former team members

Selected publications

• Martin O.C. and Wagner A. 2008. Multifunctionality and robustness tradeoffs in model genetic circuits, Biophysical Journal 94, 2927-2937.
• Spor A., Wang S., Dillmann C., de Vienne D. and Sicard D. 2008. “Ant” and “Grasshopper” Life-History Strategies in Saccharomyces cerevisiae. PLoS ONE 3(2): e1579. doi:10.1371/journal.pone.0001579
• Kuang H., Van Eck H., Sicard D.,Michelmore R.W., and Nevo E. 2008. Evolution and Genetic Population Structure of Prickly Lettuce (Lactuca serriola) and Its RGC2 Resistance Gene Cluster. Genetics 178, 1547-1558.
• Nadot S., Furness C.A., Sannier J., Penet L., Triki-Teurtroy S., Albert B. and Ressayre A 2008. Phylogenetic comparative analysis of microsporogenesis in angiosperms with a focus on monocots. Am. J. Bot. 95, 1426-1436.
• Ciliberti S., Martin O.C. and Wagner A. 2007a. Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology, PLoS Comput Biol 3(2): e15.
• CilibertiS. , Martin O.C. and Wagner A. 2007b. Innovation and robustness in complex regulatory gene networks, PNAS> 104, 13591-6.
• Drouaud J., Mercier R, Chelysheva, L., Bérard A., Falque M., Martin O., Zanni V., Brunel, D. and Mézard C. 2007. Sex-specific crossover distribution and variations in interference level along Arabidopsis thaliana chromosome 4. PLoS Genet> 3(6): e106
• Falque M., Mercier R., Mézard C., de Vienne D., and O.C. Martin. 2007. Patterns of recombination rate along chromosomes : importance of interference and obligate chiasma, Genetics 176, 1453-1467.
• Manicacci D., Falque M., Le Guillou S., Piégu B., Henry A.M., Le Guillou M., Damerval C. and de Vienne D. 2007. Maize Sh2 gene is constrained by natural selection but escaped domestication. Journal of Evolutionary Biology 20, 503–516.
• Sicard D., Pennings P.S., Grandclément C., Acosta J., Kaltz O. and Shykoff, J.A.> 2007. Specialization and local adaptation of a fungal parasite on two host species as revealed by two fitness traits. Evolution 61, 27-41.
• Edelist C, Lexer C., Dillmann C., Sicard D. , and Rieseberg L.H. 2006. Microsatellite signature of ecological selection for salt tolerance in a wild sunflower hybrid species, Helianthus paradoxus. Molecular Ecology, 15, 4623-4634.
• Fievet J., Dillmann C., Curien G., and de Vienne D. 2006. Simplified modelling of metabolic pathways for flux prediction and optimization : lessons from an in vitro reconstruction of the upper part of glycolysis. iochem. J. 396: 17-26.
• Karrenberg S., Edelist C., Lexer C., Rieseberg L.H. 2006. Response to salinity in the homoploid hybrid species Helianthus paradoxus and its progenitors H. annuus and H. petiolaris. New Phytol. 170, 615-29.
• Martin, O.C., Hospital, F. 2006 Two and three-locus tests for linkage analysis using recombinant inbred lines. Genetics 173: 451-459.
• Penet L, Nadot S, Ressayre A, Forchioni A., Dreyer L., Gouyon P.H 2005 Multiple developmental pathways leading to a single morph: monosulcate pollen (examples from the Asparagales).Ann Bot (Lond). 2005 Jan;95(2):331-43
• Ressayre A., Dreyer L., Triky-Teurtroy S., Forchioni S. and Nadot S. 2005. Post-meiotic cytokinesis and pollen aperture ontogeny : comparison of development in for species differing in aperture pattern. Am. J. Bot. 92, 576-583
• Fiévet J, Dillmann C, Lagniel G, Davanture M, Negroni L, Labarre J, de Vienne D. 2004. Assessing factors for reliable quantitative proteomics based on two-dimensional electrophoresis. Proteomics, 4, 1939-1949.
• Lion S, Gabriel F, Bost B, Fievet J, Dillmann C, de Vienne D. 2004. An extension to the metabolic control theory taking into account correlations between enzyme concentrations. Eur. J. Biochem, 271, 1-17.
• Ressayre A, Godelle B, Raquin C, Gouyon PH. (2002). Aperture pattern ontogeny in angiosperms. J Exp Zool. 294:122-35.
• Bost B., de Vienne D., Moreau L., Hospital F., and Dillmann C. (2001). Genetic and non genetic bases for the L-shaped distribution of QTL effects. Genetics 157: 1773-1787.

Online Data

Below are links to downloadable text files containing data produced in the team.

Maize data

* Linkage and strong interactions with the genetic background at a locus associated with flowering time variation in maize inbred lines. Durand et al, manuscript submitted. [click here to data files]