# Recent Advances inMedicago truncatula Genomics

Authors: Jean-Michel Ané; Hongyan Zhu; Julia Frugoli
Journal: International Journal of Plant Genomics
 (2008)

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## Abstract

Legume rotation has allowed a consistent increase in crop yield and consequently in human population since the antiquity. Legumes will also be instrumental in our ability to maintain the sustainability of our agriculture while facing the challenges of increasing food and biofuel demand.Medicago truncatula and Lotus japonicus have emerged during the last decade as two major model systems for legume biology. Initially developed to dissect plant-microbe symbiotic interactions and especially legume nodulation, these two models are now widely used in a variety of biological fields from plant physiology and development to population genetics and structural genomics. This review highlights the genetic and genomic tools available to the M. truncatula community. Comparative genomic approaches to transfer biological information between model systems and legume crops are also discussed.


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## Body

## 1. Introduction

Legumes are usually defined by their typical flower
structure and the ability of many of them to form root nodules in presence of
symbiotic bacteria named rhizobia. With more than 18 000 species, legumes are
found from the artic circle to the tropics and include many crops of agronomic
importance for grain production, pasture, and forestry [1, 2]. The ability of more than 88%
of legumes to obtain nitrogen from the air through root nodules was probably a
major determinant in this evolutionary, ecological, and economical success [3]. Interestingly, the study of
symbiotic associations with rhizobia as well as with arbuscular mycorrhizal
(AM) fungi also drove the development of two model legumes: Medicago truncatula Gaertner and Lotus japonicus (Regel) K. Larsen.WhileM. truncatula is an annual medic from the Trifolieae tribe and a close relative of alfalfa
and clovers, L. japonicus belongs to
the Loteae and is more distant from cultivated cool season legumes than M. truncatula. This phylogenetic
distance to economically important crops is critical in the choice of M. truncatula by many researchers and
support by numerous funding agencies. The use of both model legumes allows
unique comparative genomic studies within the legume family as well as the
comparison between two patterns of root nodule development: indeterminate with
a persistent nodule meristem in the case of M.
truncatula and determinate in L.
japonicus. Unfortunately, these two models belong to the same cool season
legumes (Galegoid clade), whereas soybean and common bean are tropical season
legumes (Phaseolid clade). Soybean is therefore proposed as a third model
legume for both its own economic weight and the phylogenetic proximity to other
important crops [4, 5].Research efforts on model legumes and especially onM. truncatula encompass a broad range of
fields in plant biology from population biology [6–8] and plant development [9–16] to plant pathology [17–22], insect resistance [23–27], and biotechnology production
[28]. The goal of this review is
to provide an overview of the natural characteristics and genetic and genomic
tools that make M. truncatula such a
desirable experimental system for a growing number of plant biologists. We will
highlight how information gained from M.
truncatula can be transferred to other legumes crops through comparative
genomics and we will share our vision of how M. truncatula can allow us to reach the goal of sustainable
well-being through sustainable food and biofuel production.


## 2.Medicago truncatula as a Model Legume

Natural attributes ofM. truncatula that make it a valuable genetic model include its annual habit
and rapid life cycle, its diploid (2n=16) and autogamous nature, its prolific
seed production, and a relatively small genome of about 550 Mb. Jemalong A17 has
been selected by the research community as a reference line for most genetic
and genomic approaches and is derived from the major commercial cultivar. M. truncatula is native to the
Mediterranean basin and is found in a wide range of habitats. It is therefore
not surprising to find a high level of variation among and within natural
populations [29]. Using microsatellite
markers, a publicly available core-collection of 346 inbred lines was developed
and thus represents the breadth of this natural diversity [30]. M. truncatula is used as a fodder crop in ley-farming systems in
Australia, and a large and diverse collection is
housed at the South Australian Research and Development
Institute (SARDI) [8].Like many higher plants,M. truncatula forms symbiotic associations with a wide array of
arbuscular mycorrhizal (AM) fungi. As a legume, M. truncatula is also able to develop root nodules with Sinorhizobium meliloti, which is one of
the best-characterized rhizobium species at the genetic level [31]. Cultivation-independent techniques have been used to
sample the diversity of microbes associated with M. truncatula roots at various developmental stages and they reveal
an extremely dynamic genetic structure of its rhizosphere [32].Mutagenesis approaches using ethyl methane sulfonate
(EMS), gamma rays, and fast neutron bombardment (FNB) have
generated large mutant populations ofM.
truncatula from which mutants affected in symbiotic as well as
developmental pathways have been identified [33–37]. T-DNA and Tnt1 mutagenesis have been developed
recently to generate tagged mutants for forward and reverse genetics purposes
[38–40].Several protocols have been optimized to transformM. truncatula using Agrobacterium tumefaciens [41–45]. These protocols are
particularly efficient for the R108 and Jemalong 2HA lines but the regeneration
efficiency still needs to be improved for Jemalong A17. This moderate
efficiency as well as the time required for the regeneration steps is driving
the preference of the Medicago community towards Tnt1 versus T-DNA
for gene tagging approaches [46] as well as the search for
alternative transformation systems.Hairy root transformation viaAgrobacterium rhizogenes proved to be a rapid and efficient
transformation system allowing the generation of transgenic roots in 2-3 weeks.
Such “hairy roots” can be infected by rhizobia or AM fungi with symbiotic
phenotypes indistinguishable from nontransgenic roots and are therefore an
ideal system for plant-microbe symbiosis studies [47]. The development of DsRed as
a visual reporter reduced the need for Kanamycin or Basta selection systems
which were significantly decreasing nodulation efficiency. This hairy root
transformation system is now used routinely to express protein fusions or RNA
interference (RNAi) constructs [48–50]. The possibility to
regenerate transgenic plants from hairy roots of the R108 line has been
reported recently. This flexible approach should allow a rapid initial
screening of phenotypes on hairy roots and a subsequent regeneration of
transgenic plants if necessary [41]. An interesting ex vitro procedure that eliminates
the need for labor-intensive in vitro
culture will undoubtedly increase the throughput of hairy root transformations
to a level compatible with genomic studies [51].TheMedicago community has therefore identified many ecotypes and developed a wide range of
mutants and transgenic lines. A current goal of the International Medicago truncatula steering committee
is to address the need for a stock center able to maintain, amplify, and
distribute these lines to an ever growing community.


## 3. Mapping the Genome ofMedicago truncatula

Genetic and cytogenetic tools have been instrumental to the
development of a “gene rich” genome sequence forM. truncatula. This project also required several bacterial artificial
chromosomes (BAC) libraries that were developed using HindIII and EcoRI partial
digests as well as a robust physical map (Figure 1).Figure 1
Integration of different maps and libraries to generate and
annotate the genomic sequence ofM. truncatula. Expressed sequence tags (EST) are used to generate genetic
markers and to identify BAC clones in gene-rich regions as well as for gene
identification. Repeats identified via genome sequencing and comparison with
other species can be mapped via FISH on chromosome spreads.Genetic maps have been developed from F2 populations and a
wide array of genetic markers such as CAPS, AFLPs, RAPDs, and microsatellites
(SSRs) [52–54]. One of them, based on a
Jemalong A17 A17×A20  F2 population, is currently used as a reference for the
genome sequencing project (http://www.medicago.org/genome/map.php).
Unfortunately, these F2 populations are either based on a limited amount of
genomic DNA or require a labor-intensive vegetative propagation of F2
individuals. In order to provide sustainable tools to the community, genetic
maps based on recombinant inbred lines (RILs) and highly
polymorphic microsatellite markers are developed and will undoubtedly represent
the future reference for M. truncatula genetics
(T. Huguet, personal communication).Cytogenetic maps based on fluorescencein situ hybridization (FISH) with interphase or metaphase
chromosomes provide a quick access to the chromosomal location of BAC clones
and repeated sequences [55–57]. Obtaining pachytene
chromosomes is more labor intensive than metaphase chromosomes but provides an
unequalled resolution all along the chromosome and particularly in euchromatic
regions [58]. Information from such
cytogenetic tools was instrumental for comparative genomics and map-based
cloning projects but also allowed the determination that M. truncatula heterochromatin was mostly localized in
pericentromeric regions. Genetic and cytogenetic markers corresponding to the
borders of these regions have been developed [57, 59, 60]. Based on this unique
chromosomal structure, it is therefore possible to predict through the genetic
map if a BAC clone belongs to a euchromatic or a heterochromatic region. This
observation as well as the possibility to select EST-rich BAC clones led the M. truncatula community to initiate the
sequencing of euchromatic (gene-rich) regions via a BAC-by-BAC strategy (http://www.medicago.org/genome/).Four
centers share the sequencing effort of the 8 chromosomes: Bruce Roe et al. at the University of Oklahoma, Chris Town et al. at The Institute for Genomic Research (TIGR), Jane
Rogers et al. at the Sanger
Centre, and Francis Quétier  et al.  at the Genoscope. A physical
map grouping and ordering of BAC clones was developed by the laboratory of
Douglas R. Cook by combiningHindIII
digestion fingerprints with BAC-end sequence data through the FPC software [52, 61, 62]. More than 1370 FPC contigs
cover 466 Mbp (93% of the genome) and are used to determine the minimum tiling
path of gene-rich regions for whole genome sequencing [52].As of
February 2007, 188 Mb of genome sequence from 1950 BAC clones are publicly
available. About 10% of this information is redundant due to the overlap of BAC
clones necessary to create a tiling path and more than 300 gaps between contigs
need to be filled. These gaps are sized by FISH and covered with contigs by
long-range PCR or classical chromosome walking [62].Integration
of genetic, cytogenetic, physical, and sequence maps allowed the development of
pseudochromosomes and greatly facilitated comparative mapping [52, 58–60]. Annotating pseudochromosome
sequences is classically achieved through gene prediction programs and
comparison with EST databases (Figure 1). The IMGAG (International Medicago Genome Annotation Group) has
developed a unique automated pipeline to predict gene structures and functions [63]. More than 25 000 genes have
been predicted so far and techniques to test these predictions need to be
developed.Oligonucleotides
covering the entire sequence of pseudochromosomes can be printed on glass
slides to generate tiling arrays. These arrays can be used for a wide range of
applications from gene identification and detection of alternative splicing to comparative
genome hybridization (CGH) and chromatin immunoprecipitation on chips (ChIP
chips) [64–67].


## 4. Systems Analysis

## 4.1. Transcriptomics

Large-scale EST sequencing is essential for functional
genomics studies, permitting the direct identification of large gene
collections and setting the stage for further analysis, such as those using DNA
microarray technology. Several large EST projects have been completed [68–71].
The analysis of the almost 200 000 ESTs isolated from many different libraries constructed from diverse
stages and treatments that came out of these projects is facilitated by
searchable databases such as MtDB2 [72] and the TIGR Gene Index
(http://www.tigr.org).Both microarray and macroarray analyses of gene expression
changes during symbiosis have been published [73–78]. These experiments ranged
from analysis of a few thousand genes on filters during AM symbiosis [73] to almost
10 000 genes compared between wild type and nonnodulating mutants [76, 77] or between fix-mutants [79]. A dual symbiosis chip
containing 10 000 M. truncatula genes
and the entire S. meliloti prokaryotic genome allows side by side analysis of both partners in the
symbiosis [80], and an Affymetrix chip with bioinformatically optimized oligonucleotides
representing 48 000 genes is available
(http://www.affymetrix.com/support/technical/datasheets/medicago_datasheet.pdf).
As genome sequencing continues, following the expression of all M. truncatula genes under varying
conditions should soon be possible. Affymetrix placed probe sets for 1850 M. sativa transcripts on these chips to
facilitate the study of closely related species such as M. sativa. The use of M. truncatula arrays for analysis of M. sativa (crop alfalfa) gene expression
has proven effective [81, 82].Other
effective genomic approaches to transcriptional analysis utilized to date inM. truncatula include suppressive subtractive
hybridization (SSH) and serial analysis of gene
expression
(SAGE). In SSH, suppressive PCR is used to both normalize the abundance of
transcripts in individual libraries and enrich for transcripts unique to the
library by subtracting sequences common to several libraries, with rare
sequences being enriched up to 1000 folds [83].
This method has been used to identify AM specific transcripts [84]
and transcripts specifically involved in the S. meliloti symbiosis [85].
SAGE is a method for comprehensive analysis of
gene expression patterns using short sequence tags obtained from a unique
position within each transcript (10–14 bp) to uniquely identify a transcript.
The expression level of the corresponding transcript is determined by
quantifying of the number of times a particular tag is observed [86]. Although no publications have arisen yet, a project applying
SAGE to M. truncatula is underway at
the Center for Medicago Genomics Research at the Nobel Foundation (http://www.noble.org/medicago/GEP.html).


## 4.2. Proteomics

Another complementary approach to identify import gene
products involved in interesting processes is to look at changes in the protein
complement of a genome that vary by cell or treatment. In order for proteomic
approaches to be useful in a system, a large sequence resource is necessary to
match the sequences of peptides generated in tryptic digests to their proteins
of origin. The growing sequence resource inM.
truncatula allows identification of proteins by their mass spectra, making
proteomics an effective approach for M.
truncatula and proteomics approaches have become quite popular. A
comprehensive review of considerations important in proteomics technology and
applications in M. truncatula and Arabidopsis was recently published [87, 88]. Because small peptides have
been shown to have roles in plant signaling, proteomics has been applied to
identifying small protein/peptide components of certain M. truncatula tissues [89]. Proteomic approaches have
also been applied to analyses of seed development [14, 16], pathogen interactions [90], symbiosome membranes [91], AM membranes [92], root microsomes [93], and other organ, tissue, and
treatment-specific approaches [11, 94–100].Most of the genes cloned thus far in the initial signal
transduction pathway for nodulation are kinases [101]; suggesting global analysis
of phosphoproteins is a way to identify important genes involved in signal
transduction in M. truncatula. Unfortunately,
phosphoproteins involved in cellular signaling are generally present in low
abundance, creating new challenges for proteomics. By making adjustments the
basic proteomics procedures, such as adding an enrichment step, a proof of
concept experiment in M. truncatula phosphoproteomics, gives a taste of the potential of this approach [102].


## 4.3. Metabolomics

Alfalfa produces a number of
secondary metabolites of great interest because of their contributions to human
health and animal forage quality. The
principle behind metabolomics is that metabolic profiling on a genomic scale
offers a view of the metabolic status of an organism, which can lend insight to
the study gene function or whole plant biology [103].
Successful attempts to link proteomics, transcriptomics, and metabolomics for
cell cultures in M. truncatula have
emerged from these studies [104, 105].Metabolomics is a new and evolving science, and requires
specialized equipment and multifaceted technical strategies. The
Nobel Foundation employs a strategy that utilizes sequential or selective
extraction followed by parallel analyses. The parallel analyses achieve a
comprehensive view of the metabolome with high-performance liquid
chromatography (HPLC), capillary electrophoresis (CE), gas chromatography (GC),
mass spectrometry (MS), and various combinations of the above techniques such
as GC/MS, LC/MS, and CE/MS. In addition to studying biological responses to
biotic and abiotic elicitors inM.
truncatula cellcultures, these
techniques are being applied to the study of natural variants in M. truncatula, M. truncatula development, lignin biosynthesis, and legume-insect
interactions.Perhaps the most daunting aspect of metabolomic experiments
is the analysis of the data. Early on, it became obvious that metabolomics
required a standard similar to MIAME (minimum information about a microarray experiment)
to allow comparison of data. A framework for the description of plant
metabolomic experiments and their results has recently been developed. ArMet
(architecture for metabolomics) is published and in accepted use [106, 107].


## 4.4. Phenomics

As more and more researchers useM. truncatula as a model, the need for a standardized method of
describing phenotypes becomes acute. Since the timing and structure of
vegetative and floral development in M.
truncatula differ from Arabidopsis,
adoption of standards such as those used for Arabidopsis [108] is inappropriate.
Additionally, M. truncatula symbioses
with AM fungi and Sinorhizobium meliloti add another dimension to developmental processes that require a standardized
description of process stages and plant anatomy.To date, a few attempts have been made to develop a
standardized language for comparison. Vegetative growth parameters were
carefully measured to provide a benchmark in [109], but the use of a glasshouse
environment rather than a controlled light and temperature regime rendered the
data not universally applicable. Likewise, flower development and response to
vernalization have been documented in the same way [109], again in a glasshouse so the
light intensity was uncontrolled. These experiments are progress toward a
controlled standard for comparison of mutant phenotypes such as “late
flowering” or “increased internodal distance.” Precision in phenotypic
descriptions will be critical to genome scale mutant hunts.There is no plant structural GO ontology terms for
nodulation or nodule structures in the Plant Ontology Consortium site as of the
February 2007 release (http://www.plantontology.org).
The present plant ontology system provides terms for growth and developmental
stages, as well as organs and tissues of Arabidopsis,
maize and rice, but none of these plants nodulates, creating a problem for
using GO annotation in M. truncatula.


## 4.5. Bioinformatics

All of the “omics” scale tools discussed above necessitate
strong bioinformatics infrastructure for the species. A good place to begin is
theMedicago Consortium website: http://www.medicago.org. In addition to a
handbook of protocols for everything from growing and transforming M. truncatula to naming genes, links
from this page lead to informatics tools such as ENSEMBL which allow a real
time view of the annotation of the genome, tools allowing browsing of the
genome for markers, genes, the location of BACs, the status of the sequencing
project or the sequence status of any individual BAC. Users can also view the
contigs assembled for sequencing, and make comparisons to other legumes through
the legume information system [110] and the consensus legume database
(www.legumes.org). Tools are also
available through links from the medicago.org website for examining ESTs (TIGR,
MtDB2, MENS), and in the future, examining microarray data. In silico approaches in M. truncatula have led to important
insights, such as the identification of a large family of small legume-specific
transcripts with
conserved cysteine motifs whose
function continues to be investigated [111–113].But as genome
scale biology is applied, the need to synthesize transcriptomics data,
proteomics data, metabolomics data, and more becomes as important as the
availability of informatics tools to analyze these data individually. Several
steps in this direction have occurred within theM. truncatula community. Some of these integrated solutions are
focused around a process, such as gene expression in mycorrhizal symbiosis [114]. Because of the
nearly complete genome sequence and the cooperative nature of the sequencing
and annotation of the genome, comprehensive integration of various data sources
has been necessary from the beginning. Cannon et al. [115] provide a nice
summary of the available sequence-based resources and how they interact. A
freely available database of biochemical pathway data for M. truncatula (MedicCyc) contains more than 250 pathways with
related genes, enzymes, and metabolites [116]. This provides
the ability to not only visualize metabolomics data and integrate them with
functional genomics data, but  also allow
comparison of M. truncatula pathways
to those in other plants using the compatible AraCyc and RiceCyc databases.


## 5. Reverse Genetics

Reverse genetics approaches which identify mutants in a gene
of interest based on sequence differences are critical genomic tools in a model
system. A range of approaches are available, including retrotransposon tagging,
T-DNA tagging, TILLING for EMS mutations, PCR screening for fast neutron
mutations, and RNA-induced gene silencing (RNAi) [46]. Each method has advantages
and disadvantages, and the choice of which method(s) to use will depend on the
purpose of the investigator. In M.
truncatula, RNAi, TILLING, and PCR screening of Tnt1 insertion mutagenesis populations or fast-neutron generated
deletion populations are reverse genetic approaches presently possible.As noted above, the efficacy of RNAi inM. truncatula has been documented [48] including use in whole plants
and in transformed roots. The combination of RNAi constructs and hairy root
transformation is useful for large scale screening projects to identify genes
of interest for further analysis. A large-scale project to identify gene
function by silencing in M. truncatula is underway (http://www.cbs.umn.edu/labs/ganttlab/rnai.html).
Initial results from this project include identification of a calcium-dependent
protein kinase involved in nodule development, a gene that had not been
identified through classical mutational analysis [117].TILLING (Targeting Induced Local Lesions in Genomes) has
proven useful inArabidopsis and other plants (reviewed in [118]). Briefly, the sequence of a
gene of interest is analyzed with a computer program that determines the consequences of all
possible EMS mutations (primarily G to A transitions) on the amino acid sequence of the
deduced protein. Regions are chosen for PCR amplification based on the concept
that those regions most likely to result in altered protein function are highly
conserved domains in proteins, and PCR primers are designed to amplify these
regions of DNA from each plant. The PCR products are analyzed for single-base
pair changes in a high throughput sequencing gel system using an enzyme that
detects and cleaves single-base mismatches in DNA. The use of high throughput
methodology and a well-characterized and curated population of mutagenized
plants allows a plant containing a lesion in the gene of interest to be
identified in days. The benefits of TILLING are not only the
rapid identification of lesions, but the nature of the lesions themselves. The
point mutations generated by EMS treatment
allow the use of TILLING to generate an allelic series that includes both
missense and nonsense mutations.InM. truncatula, the Cook lab. at UC Davis developed a population of
∼4000 curated EMS mutagenized plants for
purpose of TILLING.  This resource is
currently unavailable as a community resource due to the absence of
funding.  To date genotypic screens for
mutations in 15 genes of interest to the Cook lab or collaborators have been
undertaken, and 143 mutants identified, with recovery rates of 9.89 alleles per
kbp screened.  Phenotypic
characterization of one (of 23 unique) allele identified from one of the early
genotypic screens for mutations in the M.
truncatula arbuscule specific phosphate transporter MtPT4 is described by Javot et al. [119].  Characterization of other mutants in this
collection is currently ongoing or advanced to the stage where manuscripts are
in preparation for submission (Douglas R. Cook and Varma Penmetsa, personal
communication).A reverse-genetics platform has been established inMedicago truncatula exploiting fast
neutron (FN) mutagenesis and a highly sensitive PCR-based detection first documented
in Arabidopsis [120]. The FN-based
screening platform produces complete loss of function mutants by identifying
large deletions in the targeted region. Central to this platform is the development
of a detection strategy which allows a mutant amplicon, possessing an internal
deletion, to be preferentially amplified in pools where genomic target sequence
is present at a 20 000-fold excess. This detection sensitivity has been
achieved through a combination of techniques for suppressing the amplification
of the wild-type sequence and preferentially amplifying the mutant product. The
population has been arrayed such that 12 000 M2 plants can be analyzed in 4 PCR
reactions. These megapools can then be dissected using 25 PCR reactions on 3D
pools, allowing identification of the individual seed lot containing the
mutant. In comparison with the well-established TILLING method [121–123], which utilizes 8-fold
PCR-based screening, FN alleles can be isolated at a fraction of the cost and
avoid the problems associated with EMS mutagenesis of targeting small genes and
the very high number of background mutations in isolated mutants. An initial
characterization of the FN system analyzed 10 genes in a subpopulation of 60 000 M2 plants. Mutants were recovered for 4 target genes. A population of 180 000 M2 plants has now been established and should allow the recovery of mutants
from a majority of targeted loci. Information for accessing this resource can
be found at www.jicgenomelab.co.uk. (C. Rodgers and G. E. D. Oldroyd,
personal communication).Recently, researchers have identified a tobacco
retrotransposon,Tnt1, that moves
randomly in M. truncatula but only
upon passage through tissue culture [39]. This retrotransposon can be
used to generate a large population of plants with tagged mutation sites in
tissue culture that become stable upon regeneration of whole plants, an
important resource for both forward and reverse genetics. A population
mutagenized by Tnt1 can be used for
reverse genetic screens by sequencing of tagged sites and forward genetic
screens by observation of phenotypes. The isolation of the M. truncatula pim gene through this reverse genetics approach
demonstrates the utility of the system for identifying mutants by sequence [40].


## 6. Translational Genomics from Model to Crop Legumes

The value of the model systems will be enhanced by the
ability to connect model systems to crops at the structural and functional
genome levels. For example, conserved genome structure (synteny) between model
and crop species could allow the use of model species as a surrogate genome for
map-based cloning of agronomically important genes in crops with complex
genomes. Moreover,
detailed knowledge of the molecular
basis of conserved phenotypes in model species can be translated to great
advantage for gene discovery in related species. Working withM. truncatula as a reference system,
researchers have used comparative genomics tools to bridge model and crop
legumes through comparative mapping of orthologous genes [54, 124, 125]. Alignment of linkage maps
and sequenced orthologous regions reveals an extensive network of macro- and
microsynteny between legume species [125–127]. In fact, the conserved
genome organization between M. truncatula and crop legumes has allowed for cross-species prediction and isolation of
several genes required for root symbiosis using M. truncatula as a surrogate [128, 129] and reviewed in [125]. Despite the emerging picture
of substantial synteny between legumes, the level of conservation decreases as
the evolutionary distance increases [124, 125]. Thus, comparisons within
Galegoid or Phaseolid legumes tend to reveal chromosome-level synteny, while
comparisons between the two clades tend to reveal large-segment synteny, which
is also reflected in the differences in chromosome number between Galegoid and
Phaseolid legumes [125, 127].The broad taxonomic distance separating the two clades warrants
the development of one or two reference systems within each clade, M. truncatula, and L. japonicus for the cool-season legumes and soybean for
the tropical-season legumes [130].A significant effort has been
undertaken in comparative genomic analysis of legume resistance gene homologs
(RGHs). Most plant disease
resistance genes identified to date belong to the nucleotide binding site (NBS)
leucine rich repeat (LRR) family [131]. NBS-LRR genes can be further
classified by the presence or absence of a toll/interleukin receptor (TIR)
homology domain. In previous studies, researchers investigated the genomic
architecture of RGHs in M. truncatula [21], and used phylogenetic
methods to assess evolutionary trends in this large gene family in legumes and
across the angiosperms [21, 132]. The results from these
studies revealed several important insights into RGH gene evolution in plants.
Despite the presence of the two major lineages of RGHs (i.e., TIR and non-TIR
NBS-LRR genes) in all dicots, each of these lineages is populated by numerous
family-specific or family-predominant clades [132]. For example, the major RGH
clades that define legumes are absent from the Brassicaceae and Solanaceae, and
vice versa. Thus, there are likely to be aspects of RGHs (including both
structural and functional attributes) that are peculiar to individual plant
families. When phylogenetic analyses were conducted within the legume family [21], it was found that all known
major clades in legumes are represented by sequences from M. truncatula, providing evidence that the major RGH radiations
predate the respective speciation events. There are also cases that cophyletic
RGHs occupy syntenic positions between legumes. The availability of a nearly
complete catalog of M. truncatula NBS-LRR genes is expected to greatly enable rapid and efficient
characterization of RGHs in other closely related legumes. A legume genome
project towards this effort has recently been funded by the NSF Plant Genome
Research Program. The goal of this funded project was to develop genomic tools
for five less-studied legume species (i.e., chickpea, pigeon pea, cowpea,
peanut, and lupine), which are economically important in the developing
countries of Africa and Asia (D. R. Cook, personal communication).Forage legumes, such as alfalfa,
red clover, and white clover, are an important component of animal and
sustainable agriculture throughout the world. In addition to providing superior
forage quality for animal production and improving soil fertility through
nitrogen fixation, forage legumes also contribute to
the improvement of soil structure and control of soil erosion. Alfalfa (Medicago sativa), for example, is grown on over 26 million
acres and ranks third in acreage planted and dollar value in the US (USDA Crop
Values Summary 2005). The
true clovers (Trifolium spp.), which
are often grown together with forage grasses, are also widely distributed.
Despite serving as a major source of meat and milk
products via animals, the economic importance of forages to food production and
the agricultural economy of the US
are not fully appreciated. Consequently, forage legumes suffer from
poorly developed genetic and genomic infrastructure due to both limited federal
funding and their intractable genetic system (e.g., polyploidy and
self-incompatibility). The lack of such infrastructure limits the application
of genomics-enabled technologies in the genetic improvement of forage legumes.
Nevertheless, all these forage legumes are closely related to the model legume M. truncatula, a cool-season legume
within the tribe Trifolieae. Therefore, forage legumes could be an immediate
beneficiary of the study of M. truncatula genomics. As many of the pathogens of M.
truncatula are also pathogens of closely related forage legumes, it should
be possible to clone resistance genes that are active against pathogens of crop
legume species in M. truncatula. In
addition, due to the close relationship of resistance gene sequences between
these species, it is likely that functional resistance genes can be moved
across species boundaries by transgenic approaches.Thus  the genetic, genomic, and molecular tools available inM. truncatula allow not only
investigation of basic processes important to legumes, but also transfer of that information to important crop species.



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