Expressed sequence-tag analysis in Casuarina glauca actinorhizal nodule and root

Authors

  • Valérie Hocher,

    1. UMR 1098, Institut de Recherche pour le Développement (IRD), BP 64501, 911 avenue Agropolis, 34394 Montpellier cedex 5, France; *Current address: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 montpellier, France
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  • Florence Auguy,

    1. UMR 1098, Institut de Recherche pour le Développement (IRD), BP 64501, 911 avenue Agropolis, 34394 Montpellier cedex 5, France; *Current address: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 montpellier, France
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  • Xavier Argout,

    1. UMR 1098, Institut de Recherche pour le Développement (IRD), BP 64501, 911 avenue Agropolis, 34394 Montpellier cedex 5, France; *Current address: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 montpellier, France
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  • * Laurent Laplaze,

    1. UMR 1098, Institut de Recherche pour le Développement (IRD), BP 64501, 911 avenue Agropolis, 34394 Montpellier cedex 5, France; *Current address: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 montpellier, France
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  • Claudine Franche,

    1. UMR 1098, Institut de Recherche pour le Développement (IRD), BP 64501, 911 avenue Agropolis, 34394 Montpellier cedex 5, France; *Current address: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 montpellier, France
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  • Didier Bogusz

    1. UMR 1098, Institut de Recherche pour le Développement (IRD), BP 64501, 911 avenue Agropolis, 34394 Montpellier cedex 5, France; *Current address: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 montpellier, France
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Author for correspondence:Valérie Hocher Tel: +33-467-41-61-96 Fax: +33-467-41-62-22 Email: hocher@mpl.ird.fr

Summary

  • • The present study aimed to identify and assess the frequency and tissue specificity of plant genes in the actinorhizal Casuarina glaucaFrankia symbiosis through expressed sequence tag (EST) analysis.
  • • Using a custom analysis pipeline for raw sequences of C. glauca uninfected roots and nodules, we obtained an EST databank web interface. Gene expression was studied in nodules vs roots using comparative quantitative real-time reverse transcription–polymerase chain reaction (qRT–PCR).
  • • From roots and nodules, 2028 ESTs were created and clustered in 242 contigs and 1429 singletons, giving a total of 1616 unique genes. Half the nodule transcripts showed no similarity to previously identified genes. Genes of primary metabolism, protein synthesis, cell division and defence were highly represented in the nodule library. Differential expression was observed between roots and nodules for several genes linked to primary metabolism and flavonoid biosynthesis.
  • • This comparative EST-based study provides the first picture of the set of genes expressed during actinorhizal symbiosis. We consider our database to be a flexible tool that can be used for the management of EST data from other actinorhizal symbioses.

Introduction

Two nitrogen-fixing root-nodule endosymbioses between soil bacteria and plants have been described: one between Rhizobium and legumes; the other between Frankia and actinorhizal plants. The Rhizobium–legume symbiosis involves 20 000 plant species of the Leguminoseae (Fabeaceae) family, while actinorhizal plants comprise approx. 260 species belonging to eight angiosperm families. Unlike legume nodules, actinorhizal nodules are structurally and developmentally similar to lateral roots, with a central vascular bundle and peripheral infected cortical tissue (Pawlowski & Bisseling, 1996; Franche et al., 1998). Mature actinorhizal nodules are indeterminate multilobed structures. Phylogenetic analysis using rbcl chloroplast gene sequences showed that legumes and actinorhizal plants belong to the Rosid I clade, suggesting that a genetic predisposition to form root-nodule symbioses originated in a common ancestor (Soltis et al., 1995; Doyle, 1998). Furthermore, legume–rhizobial and –actinorhizal symbioses are suggested to have originated from the more ancient arbuscular mycorrhizal symbioses (Duc et al., 1989; Kistner & Parniske, 2002).

The mechanisms of the symbiotic association between Frankia and actinorhizal plants are still poorly understood. The molecular understanding of regulatory events in actinorhizal symbiosis is mainly limited by the lack of a genetic transformation system for the microsymbiont Frankia. However, the current sequencing of three Frankia genomes should allow the identification of genes homologous to Rhizobium symbiotic genes and of global changes in Frankia gene expression within the context of actinorhizal nodule development (P. Normand, pers. comm.). In the past decade, some progress has been made in understanding the plant genes that are expressed at different stages of actinorhizal nodule differentiation. Differential screening of nodule cDNA libraries with root and nodule cDNAs has resulted in the isolation of a number of nodule-specific or nodule-enhanced plant genes in several actinorhizal plants including Alnus, Datisca, Eleagnus and Casuarina (Laplaze et al., 2006).

While the construction of large-scale expressed sequence tag (EST) databases led to the description of the global gene expression patterns of the host plant in Rhizobium–legume and mycorrhizal symbioses (Shoemaker et al., 2001; Asamizu et al., 2004; Grunwald et al., 2004; Cannon et al., 2005; Duplessis et al., 2005), no EST resource has been reported for actinorhizal plants.

Our group is currently working on the Frankia–Casuarinaceae symbiosis as a model system to study actinorhizal nodule development, as Casuarina glauca and its close relative Allocasuarina verticillata can be transformed genetically using Agrobacterium tumefasciens (Franche et al., 1998).

In this study we report the in silico analyses and comparison of ESTs from nonnodulated roots and nodules of C. glauca. In addition, several ESTs were selected for quantitative real-time reverse transcription–polymerase chain reaction (qRT–PCR) analysis to examine changes in gene expression induced by the symbiotic interaction with Frankia.

Materials and Methods

Plant and bacterial growth conditions

Casuarina glauca Sieb. ex Spreng seeds were provided by the Desert Development Center (Cairo, Egypt). Plants were grown and inoculated with Frankia Thr strains (Girgis et al., 1990) as previously described (Gherbi et al., 1997). Nodules and noninoculated roots (controls) were harvested 3 wk after inoculation.

cDNA library construction and sequencing

Total RNA was extracted from C. glauca noninoculated roots and nodules using the Invisorb Spin Plant-RNA Mini kit (Invitek, San Carlos, CA, USA) according to the manufacturer's instructions. Poly(A+) RNA was obtained from total RNA using the mRNA purification kit (Amersham Pharmacia, Freiburg, Germany) for roots and the Oligotex mRNA Spin Column (Qiagen, Hilden, Germany) for nodules. Root cDNA was prepared using the Zap–cDNA synthesis and Zap-cDNA gigapackIII gold cloning kit (Stratagene, La Jolla, CA, USA); nodule cDNA was generated using the Smart cDNA library construction kit (Clontech, Palo Alto, CA, USA).

2878 clones were randomly selected from both libraries and processed through the robotic and genomic Languedoc-Roussillon Génopole platform (http://www.genopole-montpellier-lr.org/PF/seq/index.htm). Single-pass sequencing from the 5′ end was done using the universal T3 primer for roots and the 5′ Lambda TriplEx2 sequencing primer (5′-CTCCGAGATCTGGACGAGC-3′) for nodules.

Sequence processing and EST database construction

Each set of sequence data (root and nodule) was first processed individually using a multimodule custom pipeline which linked sequence backup, base calling, elimination of sequences shorter than 50 bp and low-quality sequences, vector trimming and sequence assembling, as described by Jouannic et al. (2005). To assign functions, the valid ESTs and the assembled consensus sequences were locally aligned using blastall (NCBI, ftp://ftp.ncbi.nlm.nih.gov/blast) to accessions in a local nonredundant protein sequence database with entries from GenPept, Swissprot, PIR, PDF, PDB and NCBI RefSeq, using the blastx algorithm with an E value cut-off at 10−5. If the EST sequences did not match any database sequences, the blastn algorithm was used in conjunction with a nucleotide sequence database, with entries from all traditional divisions of GenBank, EMBL and DDBJ. Identified bacterial sequences were removed from the database.

Sequences were classified into three categories. ‘Annotated’ corresponds to sequences showing significant matches with protein sequences with an identified function in databanks. ‘Unknown function’ corresponds to sequences showing significant matches (E < 10−5) and homology to a protein with no identified function. ‘No homology’ groups sequences for which the E value was > 10−5, or for which no match was observed in databanks.

Finally, ESTs/clusters were grouped in functional categories according to the classification developed for the Medicago truncatula EST databank (Covitz et al., 1998; Journet et al., 2002). All resulting data (sequences, clustering results and blast results) were automatically integrated in a relational database (ESTDB), searchable via a local web browser-based interface. The nucleotide sequence data reported here are available in the DDBJ/EMBL/GenBank databases under accession numbers CO 036851–CO 038878. Contig sets and singletons are available to the public at http://www.mpl.ird.fr/rhizo/ (seeResources). Custom-made pipeline requests should be submitted to bogusz@mpl.ird.fr.

In a second set of analyses, root and nodule ESTs were then grouped and submitted to stackpack (http://www.sanbi.ac.za/Dbases.html) to compare root and nodule ESTs and to sort nodule-specific sequences. Results were stored in the database.

Quantitative real-time RT–PCR

Gene expression was performed using a two-step qRT–PCR procedure. Poly(A)+ RNA was (1) purified from root and nodule samples (each from 40 plants) using the Oligotex mRNA Spin-Column kit (Qiagen) to eliminate bacterial RNA present in nodules; (2) quantified with Quant-iT Ribogreen RNA Reagent (Molecular Probes, Invitrogen, Eugene, OR, USA); and (3) reverse-transcribed (49.5 ng per reaction) using the Reverse Transcription System (Promega, Madison, WI, USA). To minimize potential heterogeneity in RT reaction yield, each cDNA sample was derived from 5 independent RT reactions. cDNAs were used as templates in qRT–PCR reactions with gene-specific primers designed using beacon designer software (Premier Biosoft International, Palo Alto, CA, USA) (see Table 2). qRT–PCR was conducted using the FullVelocity SYBR Green QPCR Master Mix in a total volume of 25 µl and the FullVelocity cycling PCR program on an MX 3005P (Stratagene). A melting curve was recorded at the end of every run to assess product specificity. For each target gene, PCR conditions (primer concentrations, cDNA quantity) were optimized and PCR efficiency was determined. qRT–PCR reactions were run in three replicates/plates, and experiments were repeated four times. Quantification using the comparative threshold-cycle method was performed. Target gene expression was normalized to ubiquitin and corrected according to the PCR efficiency value (Pfaffl, 2001). Data were processed by two-way anova to determine the significance of gene-expression differences between roots and nodules. The products of qRT–PCR were run on agarose gel electrophoresis and showed an equal sized band as predicted in the template sequence.

Table 2. Casuarina glauca genes selected for qRT–PCR analysis on the basis of their putative involvement in nodule development and/or functioning
Sequence name in EST databaseAccession numberDescriptionPrimers
ForwardReverse
  • Expression was studied using a comparative qRT–PCR method. The primers used for each gene are indicated.

  • *

    This sequence is the most representative of the CL1 group, and was used for primer design.

  • **

    This gene was not found in the EST database, but was used as a control in qRT–PCR experiments.

Casuarina glauca genes selected for qRT–PCR analysis
CG-N01_013_A07CO038723Putative peptide transporter (CgPPT)GGAGTCGGAAACTTTATGAGCAGAAGATGGGAAGCATTCAGGTTG
CG-N01_006_A05CO038323Carbonic anhydrase (CgCA)AAAACCGGGTTCGCTCACTTGCAAACACGAGAGTCTGAGCAT
CG-N01_008_F06CO038106Sucrose synthase (CgSS)CTGGAAGCACGTGTCCAACCGACGGCAAGAGGAACGGACT
CG-N01_006_C04CO038303Dihydroflavonol 4-reductase (CgDFR)CTGCGTCACTGGTGCTTCTGCCTTCTTCATATTTCCTGGGTCTCTC
CG-N01_002_A10CO038460Flavonol synthase (CgFS)CCACAGGAGACCATCATCGGCAGCAGTTTACCACCCTTTCG
CG-N01_002_B10CO038451Flavonoid 3′,5′-hydroxylase (CgF3′5′H)CAACCACACATGCGAAGTGACATCAGGGTCTCGCCCAAT
CG-N01_012_A03CO038801Laccase (CgLAC)GCAATCAGATTCAAGGCAGATAACCGGTGGTAGTAGAGTCTCATTCAAGC
CG-N01_011_B09*CO038855No hit found (N11B9)GGGCGGGGGCCCTGCCTCAGTGGAGGGGAGGGACGGGATTCG
Genes already described in C. glauca
CG-N01_012_E09CO038761Chalcone synthase (CgCHS1) (Laplaze et al., 1999)CTTCGCCCATCCGTCAAAAGTCTCCGAGCACACGACAAGC
CG-N01_002_C11CO038438Subtilase (Cg12) (Laplaze et al., 2000)ATGCCACGCTTGATACCACCCGACTTGACAAATTCCTTTCC
CgENOD40** Homologous to leguminous ENOD40 (Santi et al., 2003)GATTCTTAACTCTGCTGATGCTTGCCGTTCTGTGACTTG
CG-N01_002_E06CO038421Haemoglobin (CgHb) (Gherbi et al., 1997)TGGGAGGTACTGAAGCAAAACAAGGAGAACACATACTTGGATTC
CG-N01_002_F01CO038414Metallothionein (CgMT1) (Laplaze et al., 2002)TGTCTTCCTGTGGCTGTGTCCTCCTTCAACGCTCATC
Genes used for normalization of real-time PCR results
CG-R01_010_F8CO037049Poly ubiquitin (CgUBI)CCAGGATAAGGAGGGCATTCGGAGACGGAGCACAAGATG

Results

Casuarina glauca root and nodule EST analysis

Sequencing produced 2878 ESTs with an average length of 447 and 357 bp for C. glauca root and nodule libraries, respectively (Table 1). Of these sequences, 70% were considered to be of high quality; 948 of these derived from nodules and 1080 from roots. Then 30% of the sequences were eliminated because of low quality, small size (<50 bp) or insert problems. A total of 1616 different genes in the form of 187 clusters (242 contigs), assembled from two or more ESTs and 1429 singletons, were formed from root and nodule data sets (Table 1). Many ESTs that produced identical blast hits were grouped in the same contigs. We also found few clusters containing ESTs with similar but not identical DNA sequences that may encode different isoforms. Sequence redundancy (ESTs in clusters/total ESTs) reached 26% for roots and 34% for nodules, suggesting that continued sequencing has the potential to uncover other novel sequences from the constructed libraries.

Table 1. Casuarina glauca root and nodule expressed sequence tags (ESTs) and cluster collection statistics
 RootNoduleTotal
Number of cDNAs sequenced153413442878
EST summary:
Number of high-quality ESTs1080 (70%)948 (71%)2028 (70%)
Mean EST length (bp) 447357 402
EST size range (bp)  50–882 50–825  50–880
Nonvalid sequences 454 (30%)396 (29%) 850 (30%)
 Small size sequence (< 50 bp) 213 (47%) 84 (21%) 297 (35%)
 Low-quality sequences 137 (30%)274 (69%) 411 (48%)
 Sequences with multi-inserts   7 (2%)  1 (0.2%)   8 (0.9%)
 Sequences with no insert  97 (21%) 37 (9%) 134 (16%)
Cluster summary:
Number of ESTs assembled10809482028
Number of clusters 103 84 187
Number of singletons 8006291429
Number of contig sequences 126116 242
Mean contig length (bp) 620500 
Contig size range (bp)  50–1374 83–919 
Redundancy (%)  26 34  30

Functional annotation of sequences and clustering

Examination of the initial blast matches resulted in classification of the nodule and root sequences (singletons and clusters) in three categories (Fig. 1a). A large proportion of nodule sequences (48% compared with 26% for root sequences) showed no significant match with any protein sequence in the public databases, and 18% of the root and 12% of the nodule sequences match to protein sequences classified as ‘unknown function’. Around 60% of root and 40% of nodule sequences were annotated, and the sequences identified were subsequently assigned to 14 functional categories on the basis of the classification developed for the M. truncatula EST databank (Covitz et al., 1998; Journet et al., 2002) (Fig. 1b). All categories were represented, and for both nodule and root ESTs the major categories were ‘protein synthesis’ and ‘primary metabolism’. These two categories, plus ‘cell-division cycle’ and ‘defence and cell rescue’, were more common in nodules than in roots.

Figure 1.

Classification of Casuarina glauca clusters and singletons. (a) Distribution of clusters and singletons based on E value and top 10 results of blast. (b) Distribution of annotated C. glauca clusters and singletons according to the functional categories developed for annotation of Medicago truncatula expressed sequence tags (ESTs).

A number of nodule EST/cluster sequences showed similarity to previously described proteins such as actinorhizal nodulins: haemoglobin (Gherbi et al., 1997); metallothionein (Laplaze et al., 2002); subtilisin (Laplaze et al., 2000); rubisco activase (Okubara et al., 1999); saccharose synthase (Van Ghelue et al., 1996); glycine and histidine-rich proteins (Pawlowski et al., 1997). Furthermore, several ESTs showed some similarity to nodulin genes identified in legume–Rhizobium symbioses, such as carbonic anhydrase (Table 2), which is thought to be involved in oxygen regulation in some legume nodules (Galvez et al., 2000; Flemetakis et al., 2003). However, EST homologues to early nodulin genes in legumes such as ENOD2, ENOD12 and ENOD11 (Vijn et al., 1995; Journet et al., 2001) were not detected.

Comparison of C. glauca root and nodule sequences

To compare root and nodule sequences, an assembling method using stackpack software was used for the complete set of ESTs. This analysis enabled sorting of nodule- and root-specific sequences, and revealed a number of sequences that were present in both organs. This provided insights into the level of expression of genes in roots and nodules (data not shown). The majority of nodule-specific sequences (70.5%) were composed of unique sequences or groups of two ESTs, which were considered to be relatively low-copy gene transcripts. Twenty per cent of the sequences grouped three to four ESTs. The largest and unique nodule-specific group of sequences consisted of a total of 33 ESTs with homology to haemoglobin. It should be noted that the largest group of sequences (82 ESTs, named CL1) encoded a class of protein that did not match any known protein sequence. Of these 82 ESTs, 78 occurred in nodules and only four in roots, suggesting that CL1 expression is induced on nodule formation.

Comparison of expression pattern in C. glauca roots and nodules for selected ESTs by qRT–PCR

In order to test the usefulness of our EST database to identify symbiotic genes, 13 genes were selected to verify nodule-specific and/or nodule-enhanced expression by qRT–PCR on the basis of their putative involvement in nodule development and/or functioning (Table 2). CL1, which groups 82 ESTs, was also selected, as in silico analysis had shown it to be overexpressed in nodules.

Using the ubiquitin (CgUBI) gene, which is equally expressed in roots and nodules (Fig. 2a), as internal control, we first analysed the level of expression of previously described actinorhizal symbiotic genes that were present in the database. Figure 2b shows that metallothionein (CgMT1) was equally expressed in roots and nodules. We also evaluated transcript abundance of the C. glauca ENOD40 gene (Santi et al., 2003), which was not expressed in nodules. Conversely, haemoglobin (CgHb) and Cg12, encoding a subtilase, appeared to be expressed only in nodules. The transcripts of a putative peptide transporter (CgPPT) and of sucrose synthase (CgSS) were less abundant in nodules, while carbonic anhydrase (CgCA) was more expressed in roots and nodules (Fig. 2c).

Figure 2.

Analysis by qRT–PCR of the expression of selected genes in nodules of Casuarina glauca. Relative quantification was calculated by comparing levels of gene expression in nodule samples with root samples (control sample) after gene normalization using ubiquitin (CgUBI) as reference gene. Data are expressed as fold-difference of gene expression with respect to the control sample, expression of which is set at 1 (dashed line). Values > 1 correspond to overexpression in nodules, while values < 1 correspond to underexpression. Values are the mean of four experiments. (a) Expression of CgUBI in roots and nodules. Expression was similar in both organs and this gene was used for qRT–PCR normalization. (b) Expression of CgMT1 and CgENOD40 previously characterized in C. glauca. These were used as controls for qRT–PCR validation. (c) Expression of target genes. CgUBI, ubiquitin; CgMT1, metallothionein; CgENOD40, ENOD 40; CgPPT, putative peptide transporter; CgCA, carbonic anhydrase; CgSS, sucrose synthase; CgLAC, laccase; CgCHS1, chalcone synthase; CgFS, flavonol synthase; CgF3′5′H, flavonoid 3′,5′-hydroxylase; CgDFR, dihydroflavonol 4-reductase; N11B9, unknown gene.

In previous studies we showed that C. glauca produces large amounts of polyphenols in response to Frankia infection, and in mature nodules (Laplaze et al., 1999). We therefore studied the expression of five genes present in the database encoding enzymes involved in polyphenol metabolism. Chalcone synthase (CgCHS1); flavonoid 3′,5′-hydroxylase (CgF3′5′H); dihydroflavonol 4-reductase (CgDFR); and flavonol synthase (CgFS) were more highly expressed in nodules than in roots (3.49, 3.6, 1.6 and 4.5 times higher, respectively). A laccase (CgLAC) was also shown to be expressed slightly more in nodules.

The transcript annotated as having no homology to any known genes in available databases (CL1) was studied on the basis of the expression of its most representative sequence (N11B9; Table 2): it was found to be up to 5.6 times more abundant in nodules, and may be a new, symbiotic, enhanced C. glauca gene (Fig. 2c). For each gene, anova revealed a significant difference in expression between root and nodule (P = 0.000 at the 5% level) excepted for CgUBI (P = 0.155) and for CgMT1 (P = 0.432).

Discussion

Here we provide the first genomic platform for the study of plant gene expression in actinorhizal symbiosis. Our C. glauca root and nodule EST database contains 1616 different unique transcripts (clusters and singletons) assembled from over 2000 valid sequences. A low level of redundancy was observed (26 and 34% for roots and nodules, respectively), indicating that the two libraries still have considerable potential to uncover novel sequences. Database similarity searches with our ESTs showed that 48% of the total nodule ESTs did not match any protein sequence, suggesting that these transcripts represent important genes that are specific to actinorhizal symbiosis. A majority of annotated root and nodule sequences were classified as ‘primary metabolism’ or ‘protein synthesis and processing’; the percentages of these subgroups are similar to those noted for M. truncatula and Lotus japonicus (Journet et al., 2002; Poulsen & Podenphant, 2002). Together with the two latter functional subgroups, transcripts involved in the ‘cell-division cycle’ are more abundant in nodules than in roots, a finding consistent with the increase in protein metabolism, cell division and cell growth observed in developing organs. We also found that, similarly to M. truncatula reported data, the ‘defence and cell rescue’ functional category is represented more often in nodules than in roots, suggesting that responses induced by symbionts and pathogens overlap (Journet et al., 2002).

We used qRT–PCR to assess differential expression of several genes in nodules vs roots. The validity of this approach was confirmed because, as previously shown by Gherbi et al. (1997) and Laplaze et al. (2000), we observed nodule-specific expression of haemoglobin (CgHB) and serine protease (Cg12). The expression profiles of CgENOD40 and CgMT1 were also in agreement with earlier studies by Santi et al. (2003) and Laplaze et al. (2002). Surprisingly, all selected nodule genes linked to primary metabolism (CgSS, CgCA, CgPPT), and predicted to be upregulated during nodule development as shown in legumes (Journet et al., 2002; Colebatch et al., 2004; El Yahyaoui et al., 2004), appeared to be under- or only slightly overexpressed in C. glauca nodules. Similar observations were reported for M. truncatula, and could be linked to the fact that these genes are members of multigene families, with alternative expression during nodule development (Journet et al., 2002; El Yahyaoui et al., 2004).

Enhanced transcript expression in nodules of a laccase (CgLAC), a polyphenoloxydase involved in lignification process and plant–pathogen responses (Mayer & Staples, 2002), and of several enzymes related to flavonoid synthesis (Winkel-Shirley, 2001) support previous data showing that the amount of phenolic compounds increases dramatically in C. glauca nodules compared with uninfected roots (Laplaze et al., 1999). The involvement of flavonoids in the establishment of legume–Rhizobium interactions is well documented (Perret et al., 2000), and they could also be involved in the morphogenesis of legume nodules (Stafford, 1997; Mathesius, 2001). The role of flavonoids in actinorhizal species is still unknown but, together with previous work (Laplaze et al., 1999; 2006), our results suggest that, similarly to legume–Rhizobium symbioses, polyphenols play a significant role in actinorhizal symbiosis.

In conclusion, a collection of 2028 ESTs of an actinorhizal species has been generated for the first time. Obviously this EST database needs to be amplified, but it already represents a resource for further functional analysis of specific genes and for comparative genomics with legume–Rhizobium and mycorrhizal symbioses. This should provide clues to the identification of genetic factors, thus enabling successful root-nodule symbioses to be established.

Acknowledgements

We thank B. Piegu and R. Cook for discussion on the ESTs analysis pipeline design, C. Montavon for helpful discussion on qRT–PCR, and S. Dussert for statistical analysis of our results.

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