A PQL (protein quantity loci) analysis of mature pea seed proteins identifies loci determining seed protein composition.

Legume seeds are a major source of dietary proteins for humans and animals. Deciphering the genetic control of their accumulation is thus of primary significance towards their improvement. At first, we analysed the genetic variability of the pea seed proteome of three genotypes over 3 years of cultivation. This revealed that seed protein composition variability was under predominant genetic control, with as much as 60% of the spots varying quantitatively among the three genotypes. Then, by combining proteomic and quantitative trait loci (QTL) mapping approaches, we uncovered the genetic architecture of seed proteome variability. Protein quantity loci (PQL) were searched for 525 spots detected on 2-D gels obtained for 157 recombinant inbred lines. Most protein quantity loci mapped in clusters, suggesting that the accumulation of the major storage protein families was under the control of a limited number of loci. While convicilin accumulation was mainly under the control of cis-regulatory regions, vicilins and legumins were controlled by both cis- and trans-regulatory regions. Some loci controlled both seed protein composition and protein content and a locus on LGIIa appears to be a major regulator of protein composition and of protein in vitro digestibility.

Translational Genomics in Legumes Allowed Placing In Silico 5460 Unigenes on the Pea Functional Map and Identified Candidate Genes in Pisum sativum L.

To identify genes involved in phenotypic traits, translational genomics from highly characterized model plants to poorly characterized crop plants provides a valuable source of markers to saturate a zone of interest as well as functionally characterized candidate genes. In this paper, an integrated view of the pea genetic map was developed. A series of gene markers were mapped and their best reciprocal homologs were identified on M. truncatula, L. japonicus, soybean, and poplar pseudomolecules. Based on the syntenic relationships uncovered between pea and M. truncatula, 5460 pea Unigenes were tentatively placed on the consensus map. A new bioinformatics tool, http://www.thelegumeportal.net/pea_mtr_translational_toolkit, was developed that allows, for any gene sequence, to search its putative position on the pea consensus map and hence to search for candidate genes among neighboring Unigenes. As an example, a promising candidate gene for the hypernodulation mutation nod3 in pea was proposed based on the map position of the likely homolog of Pub1, a M. truncatula gene involved in nodulation regulation. A broader view of pea genome evolution was obtained by revealing syntenic relationships between pea and sequenced genomes. Blocks of synteny were identified which gave new insights into the evolution of chromosome structure in Papillionoids and Eudicots. The power of the translational genomics approach was underlined.

Optimizing TILLING populations for reverse genetics in Medicago truncatula.

Medicago truncatula has been widely adopted as a model plant for crop legume species of the Vicieae. Despite the availability of transformation and regeneration protocols, there are currently limited tools available in this species for the systematic investigation of gene function. Within the framework of the European Grain Legumes Integrated Project (http://www.eugrainlegumes.org), chemical mutagenesis was applied to M. truncatula to create two mutant populations that were used to establish a TILLING (targeting induced local lesions in genomes) platform and a phenotypic database, allowing both reverse and forward genetics screens. Both populations had the same M2 line number, but differed in their M1 population size: population 1 was derived from a small M1 population (one-tenth the size of the M2 generation), whereas population 2 was generated by single seed descent and therefore has M1 and M2 generations of equal size. Fifty-six targets were screened, 10 on both populations, and 546 point mutations were identified. Population 2 had a mutation frequency of 1/485 kb, twice that of population 1. The strategy used to generate population 2 is more efficient than that used to generate population 1, with regard to mutagenesis density and mutation recovery. However, the design of population 1 allowed us to estimate the genetically effective cell number to be three in M. truncatula. Phenotyping data to help forward screenings are publicly available, as well as a web tool for ordering seeds at http://www.inra.fr/legumbase.

Dissecting the proteome of pea mature seeds reveals the phenotypic plasticity of seed protein composition.

Pea (Pisum sativum L.) is the most cultivated European pulse crop and the pea seeds mainly serve as a protein source for monogastric animals. Because the seed protein composition impacts on seed nutritional value, we aimed at identifying the determinants of its variability. This paper presents the first pea mature seed proteome reference map, which includes 156 identified proteins (http://www.inra.fr/legumbase/peaseedmap/). This map provides a fine dissection of the pea seed storage protein composition revealing a large diversity of storage proteins resulting both from gene diversity and post-translational processing. It gives new insights into the pea storage protein processing (especially 7S globulins) as a possible adaptation towards progressive mobilization of the proteins during germination. The nonstorage seed proteome revealed the presence of proteins involved in seed defense together with proteins preparing germination. The plasticity of the seed proteome was revealed for seeds produced in three successive years of cultivation, and 30% of the spots were affected by environmental variations. This work pinpoints seed proteins most affected by environment, highlighting new targets to stabilize storage protein composition that should be further analyzed.

UTILLdb, a Pisum sativum in silico forward and reverse genetics tool.

The systematic characterization of gene functions in species recalcitrant to Agrobacterium-based transformation, like Pisum sativum, remains a challenge. To develop a high throughput forward and reverse genetics tool in pea, we have constructed a reference ethylmethane sulfonate mutant population and developed a database, UTILLdb, that contains phenotypic as well as sequence information on mutant genes. UTILLdb can be searched online for TILLING alleles, through the BLAST tool, or for phenotypic information about mutants by keywords.