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Transcriptomic Studies in Non-Model Plants: Case of Pisum sativum L. and Medicago lupulina L.

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Olga A. Kulaeva, Alexey M. Afonin, Aleksandr I. Zhernakov, Igor A. Tikhonovich and Vladimir A. Zhukov

Submitted: 18 November 2016 Reviewed: 07 April 2017 Published: 13 September 2017

DOI: 10.5772/intechopen.69057

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Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

Edited by Fabio A. Marchi, Priscila D.R. Cirillo and Elvis C. Mateo

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Transcriptomics is a dynamically developing branch of biology highly important for geneticists and molecular ecologists alike. A large number of studies concerning differential gene expression, mapping of genes and quantitative trait loci (QTL), analysis of genotyping variations and so on has been conducted recently on several non‐model plants using next‐generation sequencing techniques. One example of non‐model legumes is garden pea (Pisum sativum L.), a valuable pulse crop capable of forming nitrogen‐fixing nodules and arbuscular mycorrhiza. Adaptation of standardised RNA‐seq approaches and data analysis developed for model plants to P. sativum should facilitate both studying of pea molecular genetics and breeding of new cultivars possessing agriculturally important traits. Another non‐model legume is black medick Medicago lupulina L. (a close relative of model legume plant barrel medick, Medicago truncatula Gaertn.), for which unique genetic lines almost obligatory dependent on arbuscular mycorrhiza symbiosis formation have been obtained. Such lines show promise as the perfect model for studying the genetic bases of arbuscular mycorrhiza development. In this chapter, we give a brief description of the current developments in the field of garden pea and black medick transcriptomics. Our aim is to provide a quick start guide to the non‐expert researchers for next‐generation sequencing (NGS)‐based transcriptome analysis.


  • transcriptomics
  • RNA‐seq
  • non‐model legume plants
  • nitrogen‐fixing symbiosis
  • arbuscular mycorrhiza
  • Pisum sativum L.
  • Medicago lupulina L.

1. Introduction

Transcriptome is defined as the sum of all the messenger RNA molecules expressed from the genes of an organism, tissue, or a cell. Transcriptome analysis is a powerful method for plant biology research since studying expressed genes facilitates investigation into plant development, responses to environmental stresses, plant‐microbe interactions and so on. Transcriptomic analysis of model organisms, such as the classical object of plant genetics, Arabidopsis thaliana (L.) Heyhn., with available full‐genome sequence enables researchers to conduct more precise measurements of gene expression level, including alternative splicing and epigenetic modifications studies, in order to reveal the molecular mechanisms involved in specific biological processes [1]. Undoubtedly, many aspects of plant biology, for example, economically important traits such as specific immunity, pathogen resistance and symbiotic efficiency contributing to high crop productivity, cannot be studied with the use of model plants only, making the investigation of non‐model plants a necessity.

The rapid decrease of per‐base sequencing cost coupled with unprecedented development rates of computational biology practices opened the field of transcriptomics for in‐depth investigation of non‐model plants [1]. In the last few years, a large number of studies concerning differential gene expression, mapping of genes and quantitative trait loci (QTLs), analysis of genotyping variations and so on using next‐generation sequencing (NGS) techniques has been conducted on several non‐model plants including legumes (members of family Fabaceae) [24].

The leguminous plants (chickpea (Cicer arietinum L.), pea (Pisum sativum L.) and lentil (Lens culinaris Medik.)) were among the earliest domesticated plant species [5] and are to this day an integral part of agricultural systems [6]. These and other members of the Fabaceae family are essential for economics as a food, fodder and oil source [3]. A significant feature of most legume species is their capability of forming mutualistic symbioses with soil microorganisms. Root‐nodule symbiosis, the association of the legumes with nodule bacteria collectively called rhizobia, provides the plant with fixed atmospheric nitrogen [7]. This fact makes the legume‐rhizobial inter‐organismal system an essential component of natural and agricultural ecosystems [8]. Arbuscular‐mycorrhizal (AM) symbiosis (association with arbuscular mycorrhizal fungi), inherent to over 80% of land plants including most of legumes [9], facilitates water and mineral (especially phosphorous) uptake of the plant and consequently the nutritional value of the crop. Legumes are also capable of forming symbioses with endophytic plant growth promoting bacteria also contributing to plant productivity [10, 11].

In the early 1990s, two legume species—Medicago truncatula Gaertn. and Lotus japonicus (Regel.) K. Larsen—were introduced as model objects for studying plant genetics of symbiotic nitrogen fixation and AM development [1214]. Both species have small diploid genomes (approx. 500 Mb) [15] and are self‐pollinators with short generation time able to produce hundreds to thousands of seeds per plant. Intensive studies of genetics resulted in high‐quality annotated genomes for both L. japonicus and M. truncatula, accumulation of gene expression microarray datasets and development of several tools and repositories combining the diverse genetic, genomic and transcriptomic data in these model species (the Medicago Gene Expression Atlas [16, 17], the Medicago genome database [18], the Lotus Base information portal [19], etc.).

During the last decade, rapid development of sequencing and bioinformatics technologies significantly improved the state of genomics in non‐model legumes. In the past few years, genomes of important legumes, such as Glycine max (L.) Merr. [20], Phaseolus vulgaris L. and Trifolium pratense L. [21], were sequenced and are currently available at Phytozome website ( and in the integrative bioinformatic platform Legume IP providing information about gene and protein sequences, gene models and annotations, syntenic regions, protein families and phylogenetic trees [22].

Despite all the recent research progress, most of the agriculturally important legumes were considered ‘orphan’ crops for a long time as separated from the intense genomic studies due to large genomes, and their agricultural significance mainly in developing countries lacking funds for large‐scale ‘omics’ studies [3]. Most genome and transcriptome analysis tools were developed for particular model objects [23] and can generally be used for studying ‘orphan’ species [24, 25], although careful fine‐tuning may be necessary for successful deployment of said tools in non‐model organisms (see Figure 1). With the cost of genome assemblies remaining prohibitively high, researchers are forced to work with only transcriptome data, making the analysis strategy all the more important.

Figure 1.

Pipelines of transcriptome assembly in non‐model plants (based on the information from Refs. [23, 24].) Three strategies for RNA‐seq analysis. (A) Using a draft genome. Novel transcript discovery, quantification and functional annotation. (B) De novo transcriptome assembly with no reference. For quantification, reads are mapped back to the novel reference transcriptome followed by the functional annotation of the novel transcripts as in (A). (C) Combination of the two methods. Transcriptomes are first assembled using methods (A) and (B) then merged using CD‐HIT‐EST and cap3. Transcripts are then annotated as in (B).

It is worth noting that one of the most challenging steps of transcriptome analysis pipelines is correct transcript annotation. The simplest approach giving a sufficiently accurate result is BLAST search against annotated sequences of other species. The development of transcriptome annotation pipelines, for example, Trinotate [26], has more or less taken the burden of transcriptome annotation off of the researcher. Trinotate combines the output of a number of annotation tools into an integrated database simplifying the following deeper analysis of acquired data.

One example of an ‘orphan’ legume is garden pea (Pisum sativum L.), a valuable pulse crop capable of forming both nitrogen‐fixing symbiosis and arbuscular mycorrhiza. Global production of green pea in 2014 was 17.4 million tons, harvested from 2.3 million hectares, with an additional 11.2 million tons of dried pea from 6.9 million hectares [6]. The genome of the species is considered to be about 4300 Mb with high percentage of repetitive sequences [27]. Adaptation of RNA‐seq data analysis approaches standardised for model plants to P. sativum should facilitate both studying of pea molecular genetics and breeding of new cultivars possessing agriculturally important traits.

Black medick (Medicago lupulina L.), a close relative of a model legume plant barrel medick (M. truncatula Gaertn.), is another example of an important (but almost not studied in terms of genetics) non‐model legume. It is valuable as a pasture legume component in complex grass mixtures and can also be used as an intermediate culture in crop rotation and as green manure. Black medick is characterised by high protein, vitamin and mineral content, long growing season and ability for improving soil fertility due to nitrogen fixation, therefore being a perfect lawn plant [28]. Black medick is a very promising object for studying AM functioning and development, since a unique genetic line of M. lupulina obligatory dependent on arbuscular mycorrhiza symbiosis formation has been selected from the spring landrace population VIK‐32 of M. lupulina var. vulgaris Koch originating from Kazakhstan [28, 29]. Plants of the line MlS‐1 (for Medicago lupulina Spring) [28] demonstrate dwarfism when grown in the soil with low Pi (inorganic phosphorus) level in the absence of the AM fungi inoculation but can grow normally when inoculated with AM fungus. Therefore, MlS‐1 line is considered highly effective in AM symbiosis formation (as inoculation by fungi dramatically heightens the plant biomass). Apparently, MlS‐1 line is only capable of using the symbiotrophic way of phosphorus uptake from the soil, supposedly due to yet unidentified mutation(s) and, consequently, can serve as a model object for the investigation of arbuscular‐mycorrhizal symbiosis. For instance, this line is suitable for mutagenesis aimed at selection of mutants with defects in arbuscular mycorrhiza development, since plants carrying mutations in genes related to AM formation can be easily identified by visual examination as demonstrating dwarfism under inoculation with AM fungi [29].

High level of genome synteny, similarity of gene sequences and developmental processes provide the opportunity to use the vast amounts of data accumulated on M. truncatula in genetics, genomic and transcriptomics of these non‐model legumes M. lupulina and P. sativum. In this chapter, we give a brief description of the current achievements in the field of transcriptomics of non‐model legumes black medick (M. lupulina) and garden pea (P. sativum).


2. Transcriptome assembly studies

2.1. P. sativum transcriptomics

The genome of P. sativum is as of yet not assembled due to its comparatively large size and numerous repeats, greatly reducing the number of research methods available. Pea transcriptome, unlike genome, is closer in size to transcriptomes of other legumes, including model plant M. truncatula, making it more susceptible to analysis. Due to the existence of tissue‐specific gene expression, different plant tissues possess unique sets of transcripts, making the choice of tissue samples important for further research. Furthermore, transcriptome assemblies from distinct plant organs should be used as reference for analysis of tissue‐specific processes. A high‐quality transcriptome assembly with full tissue representation is therefore crucial for studies associated with gene interactions (differential gene expression, see section 3), gene polymorphism studies and proteome analysis.

In the last 5 years, several pea transcriptome assemblies of distinct organs and tissues were presented by different workgroups. The first publication of pea transcriptome sequencing and assembly was made by Franssen et al. [30]. Total of 20 libraries from flowers, leaves, cotyledons, epicotyls and hypocotyls and etiolated and light‐treated etiolated seedlings were sequenced using the Roche 454 sequencing platform. Several iterations of de novo assembly and merging yielded 81,449 unigenes. Sudheesh et al. [31] sequenced transcriptomes from different parts (leaf, stipule, stem, tendril tissues from multiple nodes, root‐tip tissues, flowers, stamens, pistils, immature pods, immature seeds and nodules) of two pea cultivars (Parafield and Kaspa) differing in both seed and plant morphological characteristics. Read assembly for separate cultivars yielded 126,335 and 145,730 contigs, respectively, with 87% showing significant expression levels in both cultivars. Later on, Liu et al. sequenced samples from pea seeds harvested at the stage of 10 and 25 days after pollination and assembled 77,273 unigenes [32].

Several transcriptome assembly sets were generated for Single Nucleotide Polymorphism (SNP) marker development and genetic mapping in pea (see section 4). Duarte et al. [33] sequenced libraries from eight pea cultivars (six spring sown, one winter sown field pea, one fodder pea cultivar) with Roche 454 technology. A total of 3,826,797 reads were assembled into 68,850 contigs by MIRA transcriptome assembler [34]. Sindhu et al. sequenced 3’‐anchored libraries of eight diverse pea accessions (six P. sativum cultivars (CDC Bronco, Alfetta, Cooper, CDC Striker, Nitouche and Orb) and two wild accessions P651 (P. fulvum), PI 358610 (P. sativum ssp. abyssinicum)) with Roche 454 technology, generating 4,008,648 reads in total. De novo assembly was performed for 520,797 reads from the CDC Bronco by MIRA, resulting in a set of 29,725 reference contigs representing a significant proportion of the 3′ end of genes in pea [35].

Since analysis of inter organismal genetic network between pea and rhizobia is a poorly developed field, assembly of a high‐quality transcriptome provided researchers with the much‐needed data on nodule‐specific transcripts. Transcriptomes of pea nodules and root tips were obtained by Zhukov et al. [36]. Transcriptome sequencing using the Illumina Genome Analyzer IIx platform (Illumina Inc.) generated 52,021,865 reads from the ‘Nodules’ library and 17,684,604 reads from the ‘Root Tips’ library, yielding 58,397 and 37,287 contigs assembled de novo by Trinity, respectively [37]. A total of 13,000 nodule‐specific contigs were annotated by alignment to known plant protein‐coding sequences and by Gene Ontology search. Of these, 581 sequences were found to possess full Coding DNA Sequence (CDSs) and could thus be considered novel nodule‐specific transcripts of pea. Further investigation of those transcripts can potentially lead to the discovery of key regulators of nodule symbiosis, such as identification of pea gene homologous to Nodulation signaling pathway 1 (NSP1) gene of M. truncatula [38]. In this study, pea gene Sym34 was shown to be homologous to the M. truncatulaNSP1 gene, based on preliminary stop codons detected in an open reading frame of NSP1 homologous sequence in two sym34 allelic mutants (RisNod1 and RisNod23) and full co‐segregation of the alleles of the hypothetical pea Nsp1 gene with the nodulation phenotype in F2 generation.

Alves‐Carvalho et al. [39] sequenced transcriptomes of roots, nodules, shoots, leaves, flowers, seeds, tendrils and pods harvested at different developmental stages of pea cultivar ‘Caméor’. Sequencing of 20 cDNA libraries produced one billion reads. After de novo assembly and several steps of redundancy reduction, 46,099 contigs were obtained. The main objective of their study was to obtain the most complete transcriptome and to filter out all the artefacts and chimeric contigs so a rigorous filtration pipeline was developed and implemented. The accumulated transcriptome data was used for the development of the Pea RNA‐Seq gene atlas containing expression profiles of thousands of genes in several organs, including symbiotic nodules. It is worth noting that the pipeline used in this work filtered out a large proportion of short protein‐coding transcripts, including a number of NCR peptide‐coding transcripts [40], making the Pea RNA‐Seq gene atlas less useful than tissue‐specific transcriptomes in some cases.

Pea RNA‐Seq gene atlas is also lacking information regarding mycorrhiza‐specific transcripts. Genetic framework of mycorrhizal symbiosis is as of yet not fully understood in either model or non‐model legumes [38]. In order to discover symbiotically active genes both in plant roots and arbuscular‐mycorrhizal fungus, a transcriptome of Frisson pea cultivar roots colonised by Rhizophagus irregularis isolate BEG144 was assembled by our workgroup. Sequencing was performed on an Illumina HiSeq2000 sequencing platform yielding 120 million pair end reads. In order to separate the transcriptomes of two organisms present in the samples, all the reads were mapped using the HISAT2 mapper [41] to the genome of R. irregularis [42]. Over 5 million successfully mapped reads were assembled by Trinity with default parameters yielding 30,000 transcripts, in good correlation with 28,000 of known genes for the fungus [42, 43].

All the transcripts not mapped to the R. irregularis genome were then assembled with the Trinity pipeline with standard assembly parameters and quality trimming parameters. This resulted in more than 200,000 contigs, of which more than 100,000 were similar to genes of pea and other plants of the Fabaceae family.

An assessment of transcriptome assembly and annotation completeness with single‐copy orthologs for all available pea transcriptomes was carried out using BUSCO V.2 software with OrthoDB v9.1 ‘embryophyta’ base as a reference [44]. The lowest number of present groups in the transcriptome published by Franssen et al. [30] named ‘Franssen’ is due to low transcriptome coverage. High number of missing groups in ‘Kaspa’, ‘Parafield’ and ‘SGE’ assemblies are most likely the result of limited tissue representation (see Figure 2). Deep sequencing of mycorrhized roots yielded similar results in regard to transcriptome completeness as a combined transcriptome from 20 tissues, indicative of assembly of low‐copy transcripts due to high transcriptome coverage.

Figure 2.

The results of BUSCO analysis of pea transcriptomes. Light-blue: complete and single-copy genes; dark-blue: complete and duplicated genes; yellow: fragmented genes; red: missing genes.

2.2. M. lupulina transcriptomics

M. lupulina is a plant of the Fabaceae family, a close relative to the M. truncatula, for which a unique genetic line MlS‐1 characterised by obligate mycotrophic lifestyle was obtained [28]. This line may potentially be extremely useful as a model for investigation of genetic foundations of mycorrhizal symbiosis. M. lupulina is a novel object for genomic studies, so to kick‐start its analysis the transcriptome of the mycorrhized roots of M. lupulina was sequenced using the Illumina 2500 platform. Plants of MlS‐1 line were grown in soil under inoculation with R. irregularis strain RCAM00320, followed by total RNA extraction from the mycorrhized root system and appropriate preparation of cDNA libraries for Illumina sequencing. Using Trinity assembly pipeline, 41 million paired reads were assembled yielding over 138,000 contigs, of which 19,022 showed resemblance to genes of R. irregularis. Further analysis revealed over 70,000 contigs similar to known genes of M. truncatula. The assembled transcriptome can be used as reference for differential gene expression analysis.


3. Differential gene expression (DGE)

Analysis of alterations in gene expression between conditions or genotypes is the most significant part of transcriptomic data analysis. The differences in expression levels can help determine the important genes and elucidate the processes taking place in the investigated samples.

Extensive analysis of gene expression can be carried out by microarray analysis or RNA sequencing technology. Microarray technology requires prior knowledge of gene sequences and is more suitable for objects with available genome sequence. In the case of model object M. truncatula, combination of microarray data resulted in development of atlas of gene expression profiles (Medicago truncatula Gene Expression Atlas (MtGEA)) ( MtGEA contains information about gene expression in roots, nodules, stems, petioles, leaves, vegetative buds, flowers, seeds, pods and is potentially helpful for studying other legumes. Despite the fact that pea genome is not sequenced yet, several studies of pea gene expression have been carried out by microarray technology. Analysis of gene expression during Mycosphaerella pinodes infection was carried out using a microarray [45] containing 16,470 different 70‐mer oligonucleotides from M. truncatula and only 25 did not show a detectable signal [46]. In another study, microarray transcriptome profiling based on known pea Expressed Sequence Tags (ESTs) revealed altered expression of genes associated with programmed cell death, oxidative stress and protein ubiquitylation during seed aging [47].

In spite of many advantages of microarrays, this technique is not effective for quantification of transcript splice variants and, furthermore, cannot provide information about novel genes not included in the array. The development of NGS technology made analysis of full transcriptome gene expression possible. To date, there were several studies of pea gene expression based on RNA‐seq technology. Comparative analysis of transcriptional control of pea seed development conducted by RNA‐seq revealed significant differences in gene expression between vegetable and grain pea. Genes associated with sugar and starch biosynthesis were significantly activated during seed maturation. Analysis of differential expression of these genes revealed a negative correlation between soluble sugar and starch flux in vegetable and grain pea seeds [32]. Alves‐Carvalho et al. [39] developed the Pea RNA‐Seq gene atlas containing expression profiles of thousands of genes in different pea tissues harvested at distinct developmental stages [48].

Although RNA‐seq technology is indispensable for exhaustive transcriptome studies, it is not the most cost‐efficient tool for gene expression analysis due to substantial sequencing depth required for rare transcript detection. There are RNA‐seq modifications, for example, Massive Analysis of cDNA Ends (MACE) developed by GenXPro GmbH (Frankfurt am Main, Germany) ( that increase the sequencing depth (number of reads per‐transcript) by sequencing only a 50–500 bp fragment (adjacent to the 5’ or 3’‐end of the transcript, dependent on the version) [49]. As each read originates from a distinct copy of mRNA, MACE technology is free of duplications and similar artefacts, leading to much more accurate transcript quantification. Even though MACE data cannot be used to distinguish expression of splice‐variants of genes, it can be successfully applied in a number of scenarios even with species not possessing a high‐quality transcriptome.

In our opinion, 5’MACE is a technology possessing potential for simultaneous analysis of gene expression in prokaryotic and eukaryotic organisms; therefore, this technology is practically tailor‐made for the analysis of plant‐microbe interaction, particularly for studying the process of root nodule development in the plants of the Fabaceae family.

One of the many challenges in analysing the onset of nodule symbiosis is the small amounts of tissue available. Enclosed environments of symbiotic compartments complicate direct measurements. Implementation of 5’MACE technology made it possible to analyse the gene expression patterns of both organisms simultaneously in a developing nodule and at a fraction of the cost of a full RNA‐seq study.

In our group, 5’MACE was implemented in a study investigating the expression changes in pea nodules caused by a mutation in the Sym31 gene with unknown function. This gene is responsible for the unique fix mutant phenotype (non‐nitrogen‐fixing nodules) with halted bacteroid development [50]. Two plant genotypes Sprint‐2Fix (carrying a mutation in the Sym31 gene) and parental wild‐type line Sprint‐2 were inoculated with an efficient Rhizobium leguminosarum bv. viciae RCAM1026 [51]. All the obtained reads were sequentially mapped to the RCAM1026 genome (about 8% mapped reads), then to the pea transcriptome assembly from Alves‐Carvalho et al. [39] (about 60% mapped reads) resulting in two sets of differential transcriptome data. The transcript quantification was carried out using the edgeR package [52]. Differentially expressed genes were then visualised on a metabolic map using KOBAS 2.0 annotation server [53]. Analysis resulted in the discovery of a coordinated shift in sulphur metabolism in both organisms. These preliminary data show the great potential of the 5’MACE technology in furthering our understanding of inter‐organismal gene regulatory networks in plant‐microbe interactions.


4. Transcript‐based markers and their usage

The application of NGS for massive genetic polymorphism discovery is widely used due to being much more labour and time efficient than previously used methods such as microarray hybridisation [54] or denaturing high‐performance liquid chromatography (HPLC) [55]. Originally, the main challenge in using NGS methods for massive polymorphism screening was obtaining sequences of a particular genomic locus for multiple lines due to complexity of plant genomes and the relatively low productivity of the first‐generation NGS‐sequencing platforms, leading to the development of several methods for sequencing optimisation.

For example, Restriction site Associated DNA‐sequencing method (RAD‐Seq) consists of genome cleavage and selection of fragments of appropriate size flanked by specific restriction sites (as with RFLP and AFLP analyses) [56]. RAD‐Seq yields fragments distributed randomly over a genome and is suitable for discovering indels (insertion‐deletion polymorphisms), SNVs (single nucleotide variations) and microsatellites simple sequence repeats (SSR). Using RAD‐Seq, Boutet et al. [57] discovered a total of 419,024 SNVs between at least two of the four pea lines analysed in their work. Pea genetic map constructed by genotyping a subset of 64,754 SNVs on a subpopulation of 48 RILs (recombinant inbred lines) was collinear with previous pea consensus maps and therefore with the M. truncatula genome. Yang et al. [58] using Illumina HiSeq 2500 platform uncovered 8899 putative SSR‐containing sequences. Reliable amplifications of detectable polymorphic fragments among 24 genotypes of pea were obtained for about a half of randomly selected SSR, 820 in total.

Another way of data complexity reduction is transcriptome sequencing. It makes the discovery of polymorphic sites in open reading frames (ORFs) and 5′‐ and 3′‐untranslated regions (UTR) of a gene possible. Moreover, polymorphic sites associated with individual genes may have special meaning for evolutionary studies and QTL analyses. Even though the transcriptome sequencing omits introns and intergenic regions, it can successfully be used for SSR site detection.

Several polymorphism‐screening studies aimed on SNVs and SSR sites discovering in transcriptomic data were performed on pea (see Table 1). SNVs detection may be executed by mapping NGS reads to an existing reference transcriptome assembly [59] or by de novo assembly of those reads [33, 35, 60]. In the case of existing assembly, the additional data complexity reduction is achievable by limiting sequenced mRNA regions. Since UTRs are generally more polymorphic than ORFs using sequences from the 3’ and 5’ mRNA, ends in SNV analysis should yield comparable results to those obtained with RNA‐seq. 3’MACE protocol for cDNA‐libraries preparation was used by Zhernakov et al. [59] to discover SNVs distinguishing six pea lines. Mapping MACE reads to the reference nodule transcriptome assembly of the pea line SGE [36] resulted in characterisation of over 34,000 polymorphic sites in more than 9700 contigs. Several of these SNVs were located within recognition sites of restriction endonucleases which allowed the design of co‐dominant Cleaved Amplified Polymorphic Sequences (CAPS) markers for the particular transcript.

SNVs are markers of choice now due to their abundance and the availability of high‐throughput screening techniques. SNV genotyping systems are now available, varying in the number of samples and markers to be genotyped, such as GoldenGate® and Infinium from Illumina Inc., SNPStream from Beckman Coulter and GeneChip from Affymetrix [61]. Illumina GoldenGate® oligonucleotide pool assay (OPA) designed for transcriptome‐discovered SNVs was used for pea salinity tolerance QTLs search [60].

As the pea genome is not sequenced yet, the genetic linkage maps are still relevant, since determination of loci responsible for target traits requires their fine mapping and subsequent search for candidate genes in the already sequenced genome of the model legume plant M. truncatula. Transcriptome‐discovered SNVs and high‐throughput genotyping systems made the construction of several highly saturated genetic maps of pea possible (see Table 1) [33, 35, 60].

Year Plant material Platform, technique Number of putative discovered SNVs Number of putative discovered SSR‐sites Number of created and mapped markers References
2013 Parafield, Yarrum, Kaspa, 96–286 454 Roche, GS‐FLX 36,188 2932 705 Leonforte et al. [60]
2014 Six spring sown: Lumina, Hardy, Panache, Rocket, Kayanne, Terese Roche 454, GS‐FLX 35,455 2397 1340 Duarte et al. [33]
One winter sown: Cherokee
One fodder: Champagne
2014 Pisum sativum: CDC Bronco, Alfetta, Cooper, CDC Striker, Nitouche, Orb. Roche 454, Titanium over 20,000 406 1536 Sindhu et al. [35]
P. fulvum: P651
P. sativum ssp. abyssinicum: PI 358610
2017 SGE = JI3023 Illumina HiSeq 2000, MACE 34,711 Zhernakov et al. [59]
Finale = JI2678
Frisson = JI2491
NGB1238 = JI0073
Sparkle = JI0427
Sprint‐2 = JI2612

Table 1.

Studies aimed at gene polymorphism detection in pea (Pisum sativum L.) using transcriptome NGS‐sequencing.


5. Conclusion

Next‐generation sequencing techniques make the analysis of differential gene expression and molecular marker development by transcriptome sequencing possible even in species lacking genomic information. Further development of sequencing and bioinformatics should substantially promote the investigation into genetics of non‐model plants. It is worth noting that numerous traits like effectiveness of symbioses development [62] or specific resistance to pathogens can only be studied in each particular cultivated plant species, most having limited genomic data available. In addition, the decline in biodiversity makes the investigation of unique secondary metabolites inherent to non‐model medicinal plants a pressing matter.

Leguminous plants capable of improving the soil quality due to the formation of the mutualistic symbioses with nodule bacteria and arbuscular mycorrhizal fungi are an integral part of agricultural systems. The genetics of most crop legumes lags behind that of model plants, and some are even considered ‘orphan’ crops, separated from the intense genomic studies due to a number of factors. Fortunately, the similarity of genome organisation, or ‘genome synteny’, characteristic for most related species, can help ‘translate’ the genomic data from the model legumes to their pulse crop relatives [63].

Using RNA‐seq technologies for de novo transcriptome assembly provides opportunities for finding novel genes and isoforms in non‐model species and investigation of their differential expression. Comparison to genomes and transcriptomes of closely related species can help determine the level of evolutionary distance between the two species and discover possible evolutionary pressures shaping contemporary species. Technologies for determining gene expression levels using transcript ends (like 3’ and 5’ MACE) can be used to conduct large‐scale gene expression studies on a smaller budget. 5’ MACE, a technology for simultaneous analysis of prokaryotic and eukaryotic transcript abundancies, is particularly useful for studying plant‐bacteria interactions. Using transcriptome‐sequencing data in genetic marker development streamlines the construction of high‐quality genomic maps, crucial for routine gene identification tasks as well as potentially for refining genome assemblies for non‐model organisms. All the methods are useful in investigation of the unique phenotypes not present in the model plants, for example, M. lupulina MlS‐1 genetic line, uniquely dependent on the AM formation. Adaptation of standardised RNA‐seq approaches and data analysis developed for model plants to an important crop culture P. sativum should facilitate the breeding of new cultivars that meet the requirements of the present‐day agriculture and possess the complex of beneficial traits, including increased efficiency of interactions with nodule bacteria and arbuscular‐mycorrhizal fungi.



The work was supported by Russian Foundation for Basic Research (grant # 16‐34‐60132 for O.A. Kulaeva), by grant of the President of the Russian Federation (project NSh‐6759.2016.4 for A.M. Afonin), by Russian Science Foundation (grant # 14‐24‐00135 for I.A. Tikhonovich and grant # 16‐16‐00118 for A.I. Zhernakov and V.A. Zhukov). The authors thank Dr A.P. Yurkov (ARRIAM, St. Petersburg, Russia) for providing the Medicago lupulina L. MlS‐1 line and A.S. Sulima (ARRIAM, St. Petersburg, Russia) for critical reading of the manuscript.


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Written By

Olga A. Kulaeva, Alexey M. Afonin, Aleksandr I. Zhernakov, Igor A. Tikhonovich and Vladimir A. Zhukov

Submitted: 18 November 2016 Reviewed: 07 April 2017 Published: 13 September 2017