Team members

Matthieu FALQUE	IR INRA (team leader)
Carine REMOUE	AI CNRS
Xavier RAFFOUX	TR INRA
Céline RIDEL	TR INRA
Franck GAUTHIER	CDD INRA - ANR

Research Topics

The ACEP team is a transverse group which provides support to the other UMR teams on research projects related to molecular biology, genetic mapping, and meiotic recombination.

The team carries out analyses of DNA sequence polymorphism via molecular marker approaches or sequencing, and measurements of gene expression levels by real-time quantitative RT-PCT. It also provides a service for genetic mapping of candidate genes in maize, based on two populations of intermated recombinant lines, and develops data analysis tools for the construction and exploitation of genetic maps. Following on from genetic mapping activities, the ACEP team conducts in collaboration with the GQF team a research project on the genetic determinants of the number and distribution of meiotic crossing-overs, mainly based on modeling approaches.

The ACEP team also keeps a technological watch to meet the changing needs of the UMR for new molecular biology tools. It deals with the acquisition of new equipment and the development of  protocols. The team is also responsible for coordinating the use of the shared molecular biology facility, where staff from different teams of the UMR are working. The ACEP team regularly hosts intern students from L3 to M2 and trains staff from other teams (permanents, postdocs, docs, etc ...) to molecular biology experiments or genetic mapping. Safety and prevention in the laboratory, as well as quality insurance in research are also presently managed by members of the team.

Molecular Biology

1. Genotyping and analysis of DNA sequence polymorphism

- Microsatellite Markers
These markers are revealed on high-resolution agarose gels. Used for genetic mapping of maize, to genotype wheat populations (coll. DEAP), or to study genetic diversity of strains of yeast (coll. GQF).

- SNPs (single-nucleotide polymorphism)
These markers are revealed by the CAS-PCR method (Competitive Allele-Specific PCR). Used for genetic mapping of candidate genes in maize (Falque et al., 2005), or to genotype populations of wheat (coll. DEAP).

- Other methods used for genotyping
Some projects were carried out using markers like CAPS (Cleaved Amplified Polymorphic Sequences) or IDP (Insertion-Deletion Polymorphism) in wheat (coll. DEAP), AFLP (Amplified Fragment Length Polymorphism) in maize (coll. GQF) , SSAP (Sequence-Specific Amplified Polymorphsim) in rapeseed (coll. GEAR) or RFLP (Restriction Fragment Length Polymorphism) by Southern blot, used in the past for genetic mapping in maize.

- Analysis of DNA polymorphism by sequencing
The ACEP team frequently conducts studies of polymorphism by "vertical" sequencing of allelic series, in association genetics projects or to study genetic diversity studies in maize (coll. GEAR, GQMS) and wheat (coll. DEAP). In particular the study of the promoter of the Maize Asr1 gene (candidate for tolerance to water deficit) on a panel of maize lines (coll. GQMP), and study of the Asr gene family in maize (coll. GQMP).

2. Quantitative nalysis of gene expression levels

The ACEP team carries out gene expression analyses by real-time RT-PCR on wheat (candidate genes controlling flowering time, coll. DEAP), on sunflower (candidate genes for tolerance to salt stress, coll. GQF) and maize (study of ASR1 gene expression in maize in response to water deficit, coll. GQMP).

3. Other molecular biology projects .

Among other molecular biology activities of the team, we can note the study of the phenomenon of in vitro recombination during PCR and the construction of inserts for the development of fluorescent yeast strains (coll. GQF).

Genetic mapping

The team provides a service for mapping candidate genes in Maize for all teams of the UMR and their collaborators from other laboratories. Genotyping was done by RFLP until 2006 and since then only by PCR, using CAS-PCR (competitive allele-specific PCR) method. Polymorphisms are mapped on two populations of intermated recombinant inbred lines: IBM (Intermated B73xMo17) and LHRF (Intermated F2xF252). Under the project GENOPLANTE in collaboration with the company BIOGEMMA, we mapped 2000 loci of candidate genes, among which 1454 are in the public domain (Falque et al., 2005). Most software used in genetic mapping can not compute real distances in centimorgans from segregation data obtained on intermated populations such as IBM and LHRF, and lead to distances underestimated by a factor of 2-3. We developed the program IRILmap (Falque, 2005) for use in conjunction with MapMaker or any other mapping software working on recombinant inbred lines. IRILmap computes real centiMorgan distances taking into account the exact genetic structure of the intermated recombinant population.

Meiosis and recombination

This area of research is conducted by Matthieu Falque in collaboration with Olivier Martin, a physicist in the team GQF of the UMR and in close consultation with the team, "Meiosis and recombination" of INRA Versailles (Christine Mézard, Raphael Mercier) and Denise Zickler (IGM Orsay). We use modeling approaches to characterize (1) the number of crossovers produced during meiosis and (2) how these crossovers are distributed along the chromosomes, in order to better understand the underlying biological mechanisms.

As part of this research topic, we first participated in the analysis of interference by non-parametric approaches (based on the coefficient of coincidence) on Arabidopsis thaliana genetic mapping data  produced by our colleagues from Versailles (male and female meiosis studied separately Drouaud et al., 2007).

The use of the coefficient of coincidence posing too many problems to quantify interference, we then developed and validated on published mouse data a model of interference taking into account the phenomenon of " mandatory crossover" (Falque et al ., 2007).

In collaboration with Lorrie Anderson (University of Colorado), we then showed that there are two distinct pathways of crossover formation of in maize (one interfering, and the other not), and characterized these two pathways (Falque et al., 2009). For this study, we have developed a novel method to adjust any interference model to experimental data, even when calculating the likelihood is not possible (e.g. in the case of complex mechanical models).

More limitedly, we collaborate with other groups working on meiosis to develop statistical analysis tools to test specific hypotheses, for instance about the mode of distribution of crossovers on chromosome 3B of wheat (Saintenac et al., 2009 ) or about the distribution of MER3 foci in the fungus Sordaria macrospora (Storlazzi et al., 2010).

Development of software for recombination data analysis and genetic mapping.

1. IRILmap software (LIEN   http://moulon.inra.fr/index.php/en/scientific-output/software/irilmap)

Most software used in genetic mapping can not compute real distances in centiMorgans from segregation data obtained on intermated Recombginant Inbred (IRIL) populations such as IBM and LHRF, and lead to distances underestimated by a factor of 2-3 . We developed the program IRILmap (Falque, 2005) for use in conjunction with MapMaker or any other mapping software working on recombinant inbred lines, and to calculate distances taking into account the exact genetic structure of the intermated recombinant population.

2. ActionMap software (LIEN   http://moulon.inra.fr/index.php/en/tranverse-team/atelier-de-bioinformatique/projects/oldproject/73)

This software, developed in collaboration with the ABI team, automates the placement of new loci on a genetic map for implementing approaches of "bin-mapping”, to approximately place a large number of genes on a genetic map using a mapping population of modest size.

3. BioMercator software (LIEN http://moulon.inra.fr/index.php/en/tranverse-team/atelier-de-bioinformatique/projects/projets/74 )

This software, developed in collaboration with the ABI team, allows the projection of loci from one genetic map onto another, and meta-QTL analyses.

4. CODA software (Crossover Distribution Analyzer) (LIEN   http://moulon.inra.fr/index.php/en/scientific-output/software/coda)

This CODA (Crossover Distribution Analyzer) software implements the most recent crossover formation mathematical models including two-pathways models, and allows for characterizing the intensity of crossover interference, as well as the proportion of non-interfering crossovers.