Skip to main content

Advertisement

Volume 17 Supplement 2

"Selected articles from Belyaev Conference 2017: plant biology

Differential expression of NBS-LRR-encoding genes in the root transcriptomes of two Solanum phureja genotypes with contrasting resistance to Globodera rostochiensis

BMC Plant Biology volume 17, Article number: 251 (2017) Cite this article

Abstract

Background

The characterization of major resistance genes (R genes) in the potato remains an important task for molecular breeding. However, R genes are rapidly evolving and frequently occur in genomes as clusters with complex structures, and their precise mapping and identification are complicated and time consuming.

Results

Comparative analysis of root transcriptomes of Solanum phureja genotypes with contrasting resistance to Globodera rostochiensis revealed a number of differentially expressed genes. However, compiling a list of candidate R genes for further segregation analysis was hampered by their scarce annotation. Nevertheless, combination of transcriptomic analysis with data on predicted potato NBS-LRR-encoding genes considerably improved the quality of the results and provided a reasonable number of candidate genes that provide S. phureja with strong resistance to the potato golden cyst nematode.

Conclusion

Combination of comparative analyses of tissue-specific transcriptomes in resistant and susceptible genotypes may be used as an approach for the rapid identification of candidate potato R genes for co-segregation analysis and may be used in parallel with more sophisticated studies based on genome resequencing.

Background

New disease resistance genes (R genes) have been commonly introduced into crop plants through intra- and/or interspecific introgressive hybridization. Both cultivated and closely related wild species have been used for this purpose for a long time. Marker-assisted selection is efficiently exploited to facilitate the successful breeding of new resistant cultivars and to combine several R genes into a single genotype [1, 2]. Mapping resistance loci is commonly performed by phenotyping segregating populations and genotyping them with a large number of genetic markers. Some R genes have been cloned and characterized. It was revealed that nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes compose the largest plant resistance gene family, accounting for ∼80% of more than 140 cloned R genes [3]. However, the search for R gene variants providing plant varieties with resistance against a specific pathogen or new pathogen races is still complicated and time consuming. In many cases, the responsible R gene remains unidentified, and genetic markers (if available) are associated with qualitative trait loci (QTLs) containing several (or many) candidate genes.

Recently, new approaches in this field were developed on the basis of genomic data (some recently published examples are presented below). Genome-wide resequencing and comparison with reference genomes revealed a number of NBS-LRR candidate genes in common wild rice [4], Medicago truncatula [5], Arachis duranensis and A. hypogaea [6]. Comparison of syntenic genomic regions of related species containing NBS-LRR genes was found to be a promising way to locate candidate resistance genes in the genomes of various crops (e.g., [7, 8]). Sometimes, R genes in resistant cultivars were not found in reference genomes; e.g., quantitative trait loci in Spanish barley landrace on the long arm of chromosome 7H provided resistance against powdery mildew and contained a cluster of NBS-LRR genes absent in the reference barley genome [9]. Many R gene analogs (RGAs) have conservative domains and may be predicted by bioinformatic tools [10] that facilitate their identification.

Combination of genomic and transcriptomic approaches provides an efficient way to identify candidate R genes for further verification. For example, a search for ascochyta blight resistance genes located close to nine QTLs in the chickpea genome revealed approximately 30 NBS-LRR candidate genes. Further comparison of their transcription patterns in resistant and susceptible genotypes revealed five candidate genes with genotype–specific expression [11]. The investigation of QTLs associated with willow resistance against leaf rust revealed a candidate TIR-NBS-LRR gene whose constitutive expression was considerably lower in the susceptible genotype before and after inoculation with Melampsora larici-epitea [12]. Comparative transcriptome analysis of Gossypium hirsutum genotypes resistant and susceptible to reniform nematodes revealed a number of candidate RGAs located close to quantitative trait loci [13]. RNA-seq of resistant recombinant inbred lines of Arachis hypogaea at different time points after inoculation with the nematode Meloidogyne arenaria revealed the molecular mechanisms of pathogenesis and plant defenses as well as a constitutively expressed TIR-NBS-LRR gene that potentially activates an effector-induced immune response [14].

Analysis of the genomes and transcriptomes of resistant plant genotypes commonly results in a list of candidate R genes that should be further tested by co-segregation analysis or other tools of reverse genetics. Various experimental approaches were developed to identify the candidate NBS-LRR genes responsible for the recognition of specific pathogens (effectoromics (defined as a high-throughput, functional genomics approach that uses effectors for probing the plant germplasm to detect R genes [15]), dsRNA-mediated suppression of a candidate gene in a resistant plant [16], overexpression of NBS-LRR genes in susceptible plants [17, 18], etc.). However, the search for NBS-LRR genes of interest is hampered by their natural variability; commonly, genomes of cultivated plants contain clusters with dozens of duplicated and reorganized RGAs with highly similar structures [19, 20].

One of the potential methods of rapid target gene identification may be the combination of comparative transcriptome analysis of resistant and susceptible plant genotypes with bioinformatic predictions of NBS-LRR-related transcripts (the predictions could be based on their conservative NBS domain (e.g., [21, 22]) or other computational techniques [23]). We applied this approach to evaluate the number of differentially expressed NBS-LRR genes on the model of root transcriptomes of two Solanum phureja accessions of different origins from the VIR collection. These accessions are likely to be characterized by different sets of evolved R genes, and it was found earlier that these genotypes were at least different in their resistance to the potato wart Synchytrium endobioticum [24] and to the golden potato cyst nematode Globodera rostochiensis (Wollenweber) Behrens (GPCN) [25]. The resistant genotype contains no genetic markers to the known GPCN strong resistance genes Gro1–4 and H1 [25] and possibly bears new R gene variants. We hypothesize that the usage of tissue-specific transcriptomes for the prediction of NBS-LRR-related transcripts results in the rapid identification of candidate R genes for further experimental verification.

Potato cyst nematodes originated in Andean regions of South America [26]. At present, GPCN is found worldwide and is one of the most economically important potato pathogens [27]. Currently, G. rostochiensis occurs locally in some regions of the European part of Russia, southern Siberia, and the Far East of Russia [25, 28]. Depending on the potato cultivar, yield losses can range from 19% to 90% [29], and GPCN eggs can remain dormant and viable within the cyst for 30 years [30]. Most chemical nematicides are not efficient [31, 32] or are prohibited in Europe, and the control of GPCN is mainly based on the deployment of single resistance genes (R-genes). However, only a few R genes are available, and their efficacy is threatened by the capacity of nematodes to evolve. R genes conferring strong resistance to the pathotype Ro1 of G. rostochiensis were introgressed into commercial potato varieties from Andean potato species: the H1 gene from the cultivated species Solanum tuberosum subsp. andigenum [33] and the Gro1–4 gene from the Bolivian wild species S. spegazzinii [34, 35]. Since the S. phureja genotypes used in this investigation contained no markers for H1 and Gro1–4 genes [25], the resistant genotype likely contains a new R gene variant. One of the aims of this study was to compile a set of new candidate R genes against GPCN for further investigation and inclusion in potato breeding programs or other biotechnological approaches for the improvement of plant resistance to pathogens (e.g., [18, 36,37,38,39]).

Methods

Plant material

Two accessions of diploid cultivated species S. phureja k-11,291 (collected in Peru) and k-9836 (from Bolivia) were selected from the VIR potato collection. Each accession was represented by one clone (genotype) with the VIR introduction numbers i-0144787 (k-11,291) and i-0144786 (k-9836), respectively. These accessions were characterized by nuclear SSRs, chromosome counts, and morphological features [40]. According to plastid SSRs data, these accessions have unequal haplotypes, indicating different maternal origins [41]. It was previously found that these genotypes differed in their resistance to GPCN (pathotype Ro1): i-0144786 is susceptible, whereas i-0144787 is highly resistant but contains no DNA markers of Gro1–4 and H1 (TG689, 239E4 left/Alu I, and Gro1–4) [25]. S. tuberosum cultivars ‘Nevsky’ and ‘Red Scarlett’ (susceptible and resistant to GPCN, respectively) were used as controls.

Evaluation of S. phureja resistance to GPCN

A population of G. rostochiensis (pathotype Ro1) from an infested plot in the Leningrad Region, Russia (Belogorka), was characterized previously [25] with appropriate molecular markers [42]. The nematode population was propagated on the susceptible cultivar ‘Nevsky’ under greenhouse conditions. Cysts were extracted from soil by the flotation technique and stored for 4 months at 4 °C.

To stimulate root formation, potato tubers were placed on sterile watered sand in trays within 2 weeks, and each tuber was further transferred to 10-cm-diameter plastic pots (500 ml) half filled with sterile soil and used for inoculation by GPCN. Before inoculation, in order to estimate the nematode population densities, cysts were crushed, and the contents of nematode eggs and juveniles were calculated. Inoculation by GPCN was performed by spraying 1 ml of water suspension with approximately 1500 eggs and juveniles on the roots of one potato tuber. After inoculation, the tubers were covered with sterile soil, and plants were incubated at 4000 lx, 16 h of light, and 22 °C [25]. Infected roots, stained with acid fuchsin were scanned for the presence of nematodes under an AxioScope A1 light microscope (Carl Zeiss, Germany).

For evaluation of plant resistance to GPCN, cysts were extracted from the roots by the flotation technique 3 months after inoculation and crushed, and the numbers of juveniles and eggs were calculated. Then, using the following standard scoring system (OEPP/EPPO, 2006), the degree of resistance to GPCN was recorded: scores of 9–7, highly resistant; scores of 6–4, moderately resistant; and scores of 3–1, susceptible.

RNA extraction

For RNA-seq, roots were collected 72 h after inoculation. For each genotype, three infected and three control (water-inoculated) plants were used. The roots of these plants were thoroughly rinsed with sterile distilled water, fixed in liquid nitrogen and used for RNA extraction. Total RNA was extracted with an RNeasy Plant Mini Kit (Qiagen).

RNA-seq analysis

The quality of RNA samples was evaluated using a Bioanalyzer 2100 (Agilent). ERCC Spike-In Mix2 was added to each RNA sample prior to poly-A mRNA extraction using a Dynabeads mRNA Purification Kit (Ambion). RNA-seq library preparations were carried out using an Ion Total RNA-Seq Kit v2 (Life Technologies) according with the manufacturer’s instructions with modifications. Chemical 5-min-long RNA fragmentation was used instead of an enzymatic treatment to increase the reproducibility and proportion of long fragments. Size selection using Caliper LabChip XT (Perkin-Elmer) was carried out to obtain library inserts 250–300 bp long. E-PCR, enrichment and quantification for Ion Torrent sequencing were performed with One-Touch 2 and One-Touch ES systems (Life Technologies). Sequencing was carried out on the Ion PGM (Life Technologies) using Hi-Q View sequencing kits and 318v2 chips. ERCC analysis demonstrated the absence of significant misrepresentation (R-squared values, 0.93–0.97).

qRT-PCR

For qPCR, RNA was treated with DNAse (Qiagen RNase-Free DNase Set). A 0.7 μg aliquot of RNA was used to prepare single-stranded cDNA by reverse transcription based on a RevertAid™ kit (Thermo Fisher Scientific Inc., Waltham, MA, USA) and a (dT)15 primer.

Primers were designed using IDT PrimerQuest software (http://eu.idtdna.com/PrimerQuest/Home/) for ten DEGs.

The β-tubulin gene sequence (Accession number: 609,267) was used as a reference. The following primer sequences were designed using OLIGO software: Forward, 5`-AGCTTCTGGTGGACGTTATG-3`, and Reverse, 5`-ACCAAGTTATCAGGACGGAAGA-3`. The subsequent qRT-PCR was based on a SYNTOL SYBR Green I kit (Syntol, Moscow, Russia). Three technical replicates of each reaction were run.

Bioinformatic analysis of RNA-seq data

Library preprocessing

The Prinseq tool [43] was used to assess sequence quality and filter the libraries. Nucleotide sequences larger than 50 nt and with a mean Phred quality score greater than 20 were used for further analysis.

Library mapping

We used the S. tuberosum group Phureja clone DM1–3516 R44 (genome version 3.0.34, European Nucleotide Archive ID GCA_000226075.1 [44] as a reference. Nucleotide sequences and their annotations were downloaded from the Ensembl Plants database [45]. In addition, the locations of 755 predicted NB-LRR loci [46] were mapped on the reference genome by aligning their sequences with the aid of the Gmap tool [47] (positions of potential R genes are listed in Additional file 1).

To map the filtered libraries in the genome, the TopHat2 [48] tool was implemented after constructing genome indexes with Bowtie2 software [49]. Read alignments were processed with the Cufflinks pipeline [50]. Numbers of read counts mapped to each genome segment, either expressed or annotated in the genome assembly (‘transcripts’), and corresponding RPKM (reads per kilobase per million mapped reads) values [51] were used to detect differentially expressed genes (DEGs) between the S. phureja accessions studied.

DEGs prediction

Analysis of differential expression of S. phureja genes was performed using Cuffdiff utility of Cufflinks pipeline. Transcripts with total RPKM values lesser than 12 were discarded. Transcript was considered differentially expressed in two libraries if it had two-fold or higher difference in abundance (|logFC| > 1, significance level q < 0.05). For functional analysis, up- and down-regulated transcripts were analyzed separately.

Data on characteristic peptides (peptide IDs) were taken from annotation in Spud database (http://solanaceae.plantbiology.msu.edu/data/PGSC_DM_v3.4_g2t2c2p2func_nonredundant.txt.zip) [52] that provides the links between the gene and corresponding transcripts, CDS and peptides. Lists of peptide IDs for significantly up- and down-regulated genes were processed with AgriGO database [53] to evaluate the enriched gene ontology terms for these DEGs.

Results

Verification of resistance levels of S. phureja accessions i-01444786 and i-01444787 to GPCN

Roots of S. phureja accessions i-0144787, i-0144786, S. tuberosum susceptible cultivar ‘Nevsky’ (10 tubers) and resistant cultivar ‘Red Scarlett’ (10 tubers) were inoculated with GPCN and analyzed at several time points. Penetration of roots of both S. phureja genotypes by GPCN juveniles were detected starting from 3 h after inoculation (Fig. 1). It was detected that GPCN formed a large number of cysts after 3 months of cultivation on the roots of both S. phureja i-0144786 and susceptible control ‘Nevsky’ (Fig. 2) but not on the roots of S. phureja i-0144787 or the resistant S. tuberosum cultivar ‘Red Scarlett’ (Table 1). According to the international 9-score scale [44], S. phureja i-0144786 and cultivar ‘Nevsky’ were susceptible (scores of 2 and 1, respectively), whereas S. phureja i-0144787 and cultivar ‘Red Scarlett’ were resistant (scores of 7 and 9, respectively) (Table 1). These data confirmed the previously reported results (i-0144786, score of 2; i-0144787, scores of 7–9 [25]).

Fig. 1
figure1

GPCN juvenile penetration into the root tissues of the susceptible S. phureja accession i-0144786 (a) and resistant S. phureja accession i-0144787 (b) (3 h after inoculation; arrows mark the juveniles)

Full size image
Fig. 2
figure2

Images of roots with cysts of GPCN after 3 months of inoculation of the susceptible S. phureja accession i-0144786 (a), the susceptible S. tuberosum cultivar Nevsky (b), and the resistant S. phureja accession i-0144787 (c)

Full size image
Table 1 Resistance of two S. phureja accessions to pathotype Ro1 G. rostochiensis
Full size table

For transcriptome analysis, samples of roots of S. phureja accessions i-0144786 and i-0144787 were obtained after 72 h of inoculation with either nematode or water (pooled from 3 plants per library). In total, 12 samples were obtained (three technical replicates) for i-0144786/water (Sus_cont), i-0144787/water (Res_cont), i-0144786/GPCN (Sus_nem), and i-0144787/GPCN (Res_nem).

Library preprocessing

Twelve libraries of pooled reads, containing a total of 48,059,222 short reads comprising 7.44 gigabases, were produced as raw sequencing data. Filtering resulted in 47,310,018 short reads that comprised 7.31 gigabases of sequences after the removal of 1.5% of short reads (Table 2).

Table 2 Metrics of S. phureja short-read sequenced librariesa
Full size table

Differential gene expression in the roots of resistant and susceptible S. phureja genotypes

Analysis of S. phureja transcriptomic data with the aid of the Cufflinks pipeline revealed 45,171 genome fragments corresponding to both annotated genes and unannotated genome segments in the reference genome assembly of S. tuberosum [45]. RPKM values were counted, and the amounts of DEGs in S. phureja accessions i-0144786 and i-0144787 are listed in Table 3 (a detailed description is available in Additional file 2).

Table 3 Numbers of DEGs between the root transcriptomes of S. phureja genotypes resistant (Res) or susceptible (Sus) to GPCN infection (cont – inoculation with water; nem – inoculation with GPCN)
Full size table

To verify the RNA-seq results, transcripts of 10 DEGs that were more abundant in the S. phureja-resistant genotype transcriptomes were selected. The log2(FC) values predicted by RNA-seq data and the experimental log2(FC) values for verified genes as well as the sequences of primers and other technical information are shown in Additional file 3. The NBS-LRR-encoding genes were preferentially used for verification. This list included S. phureja DEGs similar to the following genes from the reference genome: late blight resistance protein Rpi-blb2 (PGSC0003DMG400004561), TMV resistance protein N (PGSC0003DMG400020722), Tospovirus resistance protein C (PGSC0003DMG402016602), Rpi protein (PGSC0003DMG400023288), late blight resistance protein (PGSC0003DMG400005970), Cc-nbs-lrr resistance protein (PGSC0003DMG400026666), disease resistance protein R3a (PGSC0003DMG402027402), disease resistance protein (PGSC0003DMG400018464), Nbs-lrr resistance protein (PGSC0003DMG400013308), and HJTR2GH1 protein (PGSC0003DMG400011517). The results of the qPCR supported the RNA-seq data. In all cases, the target transcripts were more abundant in transcriptomes of resistant genotypes, and in 8 cases, the difference was larger than twofold and statistically significant (Additional file 4).

Since these DEGs were revealed by the alignment to the annotated reference potato genome, we carried out a Gene Ontology term search for genes up- and down-regulated in the GPCN-resistant genotypes. For down-regulated genes, the enriched GO terms included ‘translation’ (p = 0.0011), ‘nucleosome assembly’ (p = 9.17·10−4), ‘nucleosome’ (p = 5.6·10−4) and ‘structural constituent of ribosome’ (p = 2.4·10−4). Since the inoculation of plant roots with either water or nematode resulted in tissue wounding, the inhibition of the expression of house-keeping genes likely reflects the response to this stressful condition (Additional file 5). For up-regulated genes, the most enriched GO terms included ‘response to oxidative stress’ (p = 5.72·10−16) and ‘peroxidase activity’ (p = 3.05·10−16) (Additional file 6). These terms reflect the non-specific cellular responses to stressful conditions commonly resulting in generation of ROS (reactive oxygen species) and oxidative stress (sometimes followed by programmed cell death as a hypersensitive response), as well as the synthesis of peroxidases for cell wall modification. In general, GO term enrichments corresponded to the expected transcriptome reprogramming in the frame of a combined non-specific response to the root wounding and the onset of a specific response to the GPCN infestation (72 h after inoculation).

Closer inspection of the DEG list revealed a remarkable difference between the genotypes. One may see that the transcriptomes of the resistant S. phureja genotype is characterized by higher content of the transcripts similar to various potato defense-related genes according to their annotation ([52, 54] ‘description’ field) (Additional file 2). Since the aim of this study concerns the identification of major R genes providing strong resistance to GPCN, the most probable candidates are likely to belong to the NBS-LRR family. However, annotation of S. tuberosum genes is frequently scarce, and only a very few DEGs revealed in this study contained the specific term ‘NBS-LRR’ or a similar term in the ‘description’ field, whereas non-specific terms such as ‘disease resistance’ were more abundant (Additional file 2). Thus, we used additional information on 755 NB-LRR loci predicted in the potato genome [46]. This analysis revealed approximately 330 S. phureja root transcripts potentially coding for NBS-LRR related proteins (Additional file 7). This list of DEGs was ranked on the basis of the following simple description (Additional file 8): Group 1 contained the most probable candidate genes with either no or very little mRNA representation in roots of the susceptible S. phureja genotype but represented in the roots of the resistant i-0144787 genotype (2 genes). Group 2 contained S. phureja mRNAs with either no or little representation in the water-inoculated roots of the susceptible genotype and large presentation in the water inoculated roots of the resistant variety (17 genes). Finally, Group 3 contained mRNAs represented in both resistant and susceptible accessions but several times more abundant in the resistant genotype (11 genes) (Additional file 8).

Discussion

The identification of new major resistance loci in populations of potato and closely related wild species is an important step of breeding. R loci mapping is commonly performed by phenotyping segregating populations and genotyping them with a large number of genetic markers. However, the identification of R genes is frequently hampered by their nature. It was detected that complex clusters of the NBS-LRR genes in plant genomes are rapidly evolving; plant varieties are commonly characterized by both high levels of copy number variation and disproportionately large SNP accumulation in these genes [5, 19].

Recent development of NGS (next-generation sequencing) techniques has resulted in the accumulation of genomic nucleotide sequences and has provided new opportunities in this field. Resequencing of the genomes of resistant plant genotypes facilitates the identification of R genes of interest (e.g., [4,5,6]). Potential NBS-LRR genes may also be predicted in genomes with the aid of bioinformatic tools (e.g., [21,22,23]), and application of these tools for genome analysis may provide large lists of potential RGAs [10].

Despite the application of various NGS-based approaches providing a wide range of new opportunities, the identification of R genes of interest is a complex and time-consuming process. It is likely that a combination of comparative analysis of tissue-specific transcriptomes of susceptible and resistant plant genotypes with bioinformatic predictions of potential NBS-LRR-encoding genes may provide a rapid way to compile a list of candidate RGAs for the genotyping of segregating populations. Since most pathogens commonly infect specific tissues, transcriptome analysis skips both non-specific functional R genes and non-transcribed pseudogenes annotated in the reference genomes. In turn, prediction of NBS-LRR-encoding mRNAs may substantially improve the annotation of related genes in the nucleotide sequence databanks.

To test this approach, we selected two different accessions of S. phureja from the VIR collection that likely bear various sets of functional R genes. It was demonstrated previously that these accessions were contrasted in their resistance to the important pathogen G. rostochiensis (pathotype Ro1) [25], and we evaluated the number of differentially expressed NBS-LRR genes in their root transcriptomes.

GPCN infestation and major resistance genes

The penetration of roots by juveniles of root-knot nematodes and their migration to the vascular bundle to arrange a feeding site were similar during both compatible and incompatible interactions (Fig. 1). It is likely that specific nematode recognition occurs after the nematodes inject their esophageal gland secretions to initiate the formation of the feeding site. If the interaction is incompatible, the hypersensitive response can occur as early as 24 h after inoculation and can be identified by a zone of cell death cutting nematode juveniles from the nutrient supply [55]. However, resistance can also be initiated later. G. rostochiensis or G. pallida can establish the syncytium and become sedentary in resistant tomato and potato plants bearing NBS-LRR resistance genes (Hero and Gpa2, respectively), but surrounding plant cells further become necrotic, which prevents the completion of the nematode lifecycle. Delayed HR may result from either weak recognition or the late appearance of a nematode effector [55]. Another important feature of nematode inoculation is significant tissue damage resulting in a non-specific wounding stress response induced by plant cell wall fragments. This non-specific wounding response may overlap with the specific response to GPCN or be an integral part of it [56].

A number of quantitative trait loci derived from other cultivated species or their wild relatives were previously identified in Solanaceae with partial resistance to potato cyst nematodes [57]. Several major genes suitable for potato breeding were found. The H1 locus confers hypersensitive resistance to GPCN (pathotypes Ro1 and Ro4) and was exploited in breeding very actively [33]. Gro1–4 is a member of the Gro1 locus, which confers nearly absolute resistance to all pathotypes of G. rostochiensis, and it is therefore considered a useful resistance gene [58]. Broad-spectrum resistance to G. rostochiensis and G. pallida is conferred by the Grp1 gene [59]. Only two G. rostochiensis resistance genes were characterized at the molecular level: Gpa2 from S. tuberosum ssp. andigena [60] and Gro1–4 from S. spegazzinii [58]. Gpa2 and Gro1–4 genes and a resistance gene from tomato (Hero) belong to the NBS-LRR family.

S. phureja model

It was known that accession i-0144786 was susceptible, whereas i-0144787 was highly resistant to GPCN [25], and these degrees of resistance were confirmed in the present research (Figs. 1 and 2; Table 1). It may be assumed that the root transcriptome of i-0144787 plants contains mRNAs coding for NBS-LRR genes that are not transcribed (or transcribed at significantly lower levels) in the roots of i-0144786 plants. To test this hypothesis, the root transcriptomes of resistant and susceptible genotypes collected 72 h after inoculation with either G. rostochiensis or water were sequenced. The lists of DEGs were compiled, and GO term analysis revealed the enrichment of house-keeping genes in down-regulated groups and stress-related genes in up-regulated groups (Table 3; Additional file 2). This result reflects the typical response to either tissue wounding alone (inoculation with water) or a combination of tissue wounding and nematode infestation (inoculation with GPCN). It was also found that only one corresponding gene in the reference S. tuberosum genome was annotated as belonging to the NBS-LRR family, which complicates the selection of candidate R genes for further analysis. This finding likely resulted from a stringent significance threshold of a standard pipeline and a scarce annotation. Thus, we used additional information on 755 NB-LRR loci predicted in the S. tuberosum genome [46]. This information revealed approximately 300 transcripts in the root transcriptomes of S. phureja genotypes potentially encoding NBS-LRR-related proteins.

Interestingly, genotypes i-0144786 and i-0144787 were characterized by different subsets of expressed NBS-LRR-like RGAs (Additional file 8). These accessions evolved under different conditions and pathogenic pressure. In our opinion, systemic comparison between the differential expression of RGA subsets with phenotypic screening of the resistance to various pathogens may be considered a prospective source for the identification of new candidate R genes. In this study, four transcriptomes were compared (roots of resistant and susceptible genotypes taken 72 h after inoculation with either water or nematodes). The nematode juveniles significantly damage tissues during the penetration process, resulting in non-specific wounding stress. The procedure of inoculation itself also damages root tissues. Thus, the transcriptome of water-inoculated roots may be considered an appropriate control to reveal the biotic stress response components. To select the potential R genes, the NBS-LRR-like genes were divided into three groups (Additional file 8). The first group contained two transcripts of S. phureja genes present in the roots of the resistant genotype i-0144787 and either absent or present in a very small amount in the susceptible i-0144786. The second group contained 17 transcripts absent or present in small amounts in the root transcriptome of water-inoculated susceptible plants. Strong resistance to G. rostochiensis is commonly based on the rapid hypersensitive response followed by the programmed cell death of the neighboring plant cells, and it is likely that efficient NBS-LRR genes should be expressed before nematode infestation (e.g., [12, 14]). We hypothesized that low expression of NBS-LRR receptor genes in the absence of GPCN infestation results in a delay of the hypersensitive response and may provide time for successful nematode progression. Finally, the third group includes transcripts present in the roots of both genotypes but considerably more abundant in the i-0144787 accession.

Conclusion and perspectives

Comparative analysis of the root transcriptomes of Solanum phureja genotypes with additional computational predictions of mRNAs coding for NBS-LRR-like proteins revealed a reasonable number of candidate R genes for further co-segregation analysis. In our opinion, this approach provides a rapid method of candidate gene selection and may be used in parallel with more sophisticated studies based on genome resequencing. If successful, this approach considerably accelerates the time it takes to identify resistance genes for targeted breeding. It should also be mentioned that in addition to the source of new genes, S. phureja is the donor of fertile-type cytoplasm, which is very promising for the genetic improvement of the common potato S. tuberosum [61].

References

  1. 1.

    Leonova IN. Molecular markers: implementation in crop plant breeding for identification, introgression, and gene pyramiding. Vavilov. J Genet Breed. 2013;17:314–25.

  2. 2.

    Pradhan SK, Nayak DK, Mohanty S, Behera L, Barik SR, Pandit E, Lenka S, Anandan A. Pyramiding of three bacterial blight resistance genes for broad-spectrum resistance in deepwater rice variety, Jalmagna. Rice. 2015;8:19.

  3. 3.

    Shao ZQ, Wang B, Chen JQ. Tracking ancestral lineages and recent expansions of NBS-LRR genes in angiosperms. Plant Signal Behav. 2016;11(7):e1197470.

  4. 4.

    Liu W, Ghouri F, Yu H, Li X, Yu S, Shahid MQ, Liu X. Genome wide re-sequencing of newly developed Rice lines from common wild rice (Oryza rufipogon Griff.) for the identification of NBS-LRR genes. PLoS One. 2017;12(7):e0180662.

  5. 5.

    Zhou P, Silverstein KA, Ramaraj T, Guhlin J, Denny R, Liu J, Farmer AD, Steele KP, Stupar RM, Miller JR, Tiffin P, Mudge J, Young ND. Exploring structural variation and gene family architecture with de novo assemblies of 15 Medicago genomes. BMC Genomics. 2017;18(1):261.

  6. 6.

    Song H, Wang P, Li C, Han S, Zhao C, Xia H, Bi Y, Guo B, Zhang X, Wang X. Comparative analysis of NBS-LRR genes and their response to Aspergillus flavus in Arachis. PLoS One. 2017;12(2):e0171181.

  7. 7.

    Quirin EA, Mann H, Meyer RS, Traini A, Chiusano ML, Litt A, Bradeen JM. Evolutionary meta-analysis of Solanaceous resistance gene and Solanum resistance gene analog sequences and a practical framework for cross-species comparisons. Mol Plant-Microbe Interact. 2012;25(5):603–12.

  8. 8.

    Morata J, Puigdomènech P. Variability among Cucurbitaceae species (melon, cucumber and watermelon) in a genomic region containing a cluster of NBS-LRR genes. BMC Genomics. 2017;18(1):138.

  9. 9.

    Cantalapiedra CP, Contreras-Moreira B, Silvar C, Perovic D, Ordon F, Gracia MP, Igartua E, Casas AM. A cluster of nucleotide-binding site-leucine-rich repeat genes resides in a barley powdery mildew resistance quantitative trait loci on 7HL. Plant Genome. 2016;9(2). doi:10.3835/plantgenome2015.10.0101.

  10. 10.

    Sekhwal MK, Li P, Lam I, Wang X, Cloutier S, You FM. Disease resistance gene analogs (RGAs) in plants. Int J Mol Sci. 2015;16(8):19248–90.

  11. 11.

    Sagi MS, Deokar AA, Tar’an B. Genetic analysis of NBS-LRR gene family in chickpea and their expression profiles in response to Ascochyta blight infection. Front Plant Sci. 2017;8:838.

  12. 12.

    Martin T, Rönnberg-Wästljung AC, Stenlid J, Samils B. Identification of a differentially expressed TIR-NBS-LRR gene in a major QTL associated to leaf rust resistance in Salix. PLoS One. 2016;11(12):e0168776.

  13. 13.

    Li R, Rashotte AM, Singh NK, Lawrence KS, Weaver DB, Locy RD. Transcriptome analysis of cotton (Gossypium hirsutum L.) genotypes that are susceptible, resistant, and hypersensitive to reniform nematode (Rotylenchulus reniformis). PLoS One. 2015;10:e0143261.

  14. 14.

    Clevenger J, Chu Y, Arrais Guimaraes L, Maia T, Bertioli D, Leal-Bertioli S, Timper P, Holbrook CC, Ozias-Akins P. Gene expression profiling describes the genetic regulation of Meloidogyne arenaria resistance in Arachis hypogaea and reveals a candidate gene for resistance. Sci Rep. 2017;7:1317.

  15. 15.

    Vleeshouwers VGAA, Oliver R. Effectors as tools in disease resistance breeding against biotrophic, hemibiotrophic, and necrotrophic plant pathogens. Mol Plant Pathogen Inter. 2014;27:196–206.

  16. 16.

    Brendolise C, Montefiori M, Dinis R, Peeters N, Storey RD, Rikkerink EHA. Novel hairpin library-based approach to identify NBS-LRR genes required for effector-triggered hypersensitive response in Nicotiana benthamiana. Plant Methods. 2017;13:32.

  17. 17.

    Tran PT, Choi H, Kim SB, Lee HA, Choi D, Kim KH. A simple method for screening of plant NBS-LRR genes that confer a hypersensitive response to plant viruses and its application for screening candidate pepper genes against pepper mottle virus. J Virol Methods. 2014;201:57–64.

  18. 18.

    Zhang C, Chen H, Cai T, Deng Y, Zhuang R, Zhang N, Zeng Y, Zheng Y, Tang R, Pan R, Zhuang W. Overexpression of a novel peanut NBS-LRR gene AhRRS5 enhances disease resistance to Ralstonia solanacearum in tobacco. Plant Biotechnol J. 2017;15(1):39–55.

  19. 19.

    Wei C, Chen J, Kuang H. Dramatic number variation of R genes in Solanaceae species accounted for by a few R gene subfamilies. PLoS One. 2016;11(2):e0148708.

  20. 20.

    Qian LH, Zhou GC, Sun XQ, Lei Z, Zhang YM, Xue JY, Hang YY. Distinct patterns of gene gain and loss: diverse evolutionary modes of NBS-encoding genes in three Solanaceae crop species. G3 (Bethesda). 2017;7(5):1577–85.

  21. 21.

    Lozano R, Ponce O, Ramirez M, Mostajo N, Orjeda G. Genome-wide identification and mapping of NBS-encoding resistance genes in Solanum tuberosum group phureja. PLoS One. 2012;7(4):e34775.

  22. 22.

    Zhao Y, Weng Q, Song J, Ma H, Yuan J, Dong Z, Liu Y. Bioinformatics analysis of NBS-LRR encoding resistance genes in Setaria italica. Biochem Genet. 2016;54(3):232–48.

  23. 23.

    Pal T, Jaiswal V, Chauhan RSDRPPP. A machine learning based tool for prediction of disease resistance proteins in plants. Comput Biol Med. 2016;78:42–8.

  24. 24.

    Khiutti A, Afanasenko O, Antonova O, Shuvalov O, Novikova L, Krylova E, Chalaya N, Mironenko N, Spooner DM, Gavrilenko T. Characterization of resistance to Synchytrium endobioticum in cultivated potato accessions from the collection of Vavilov Institute of Plant Industry (VIR) collection. Plant Breed. 2012;131:744–50.

  25. 25.

    Limantseva L, Mironenko N, Shuvalov O, Antonova O, Khiutti A, Novikova L, Afanasenko O, Spooner D, Gavrilenko T. Characterization of resistance to Globodera rostochiensis pathotype Ro1 in cultivated and wild potato species accessions from the Vavilov Institute of Plant Industry. Plant Breed. 2014;133:660–5.

  26. 26.

    Hockland S, Niere B, Grenier E, Blok V, Phillips M, den Nijs L, Anthoine G, Pickup J, Viaene N. An evaluation of the implications of virulence in non-European populations of Globodera pallida and G. rostochiensis for potato cultivation in Europe. Nematology. 2012;14:1–13.

  27. 27.

    Evans K, Trudgill DL. Pest aspects of potato production. Part 1. The nematode pests of potatoes. In: Harris P, editor. The potato crop. London: Chapman & Hall; 1992. ISBN 0 412 29640 3.

  28. 28.

    Gus’kova LA, Bolezni, vyzyvaemye nematodami (nematodozy). Bolezni kul’turnykh rastenii (Diseases caused by nematodes (nematoses) Diseases of cultivated plants), St. Petersburg. 2005, pp. 204–215.

  29. 29.

    Friedman W. Pests not known to occur in the United States or of limited distribution, No. 68: Golden Nematode. United States Department of Agriculture, Animal and Plant Health Inspection Service, Plant Protection and Quarantine. 10 pp. 1985.

  30. 30.

    Winslow RD, Willis RJ. Nematode diseases of potatoes. II. Potato cyst nematode, Heterodera rostochiensis. In: Webster J, editor. Economic Nematology. New York: Acad. Press; 1972. p. 18–34.

  31. 31.

    Trudgill DL, Elliot MJ, Evans K, Phillips MS. The white potato cyst nematode (Globodera pallida) – a critical analysis of the threat in Britain. Ann Appl Biol. 2003;143:73–80.

  32. 32.

    Catalogue of pesticides and agro-chemicals, used in the territory of the Russian Federation. 2017. https://www.agroxxi.ru/upload/pia/handbook64.zip

  33. 33.

    Bakker E, Achenbach U,·Bakker J, van Vliet J,·Peleman J,·Segers B, van der Heijden S,· van der Linde P, Graveland R,·Hutten R,· van Eck H, Coppoolse E,·An der Vossen E, Bakker J, Goverse A A high-resolution map of the H1 locus harboring resistance to the potato cyst nematode Globodera rostochiensis. Theor Appl Genet 2004;109:146-152.

  34. 34.

    Barone A, Ritter E, Schachtschabel U, Debener T, Salamini F, Gebhardt C. Localization by restriction fragment length polymorphism mapping in potato of a major dominant gene conferring resistance to the potato cyst nematode Globodera rostochiensis. Mol Gen Genet. 1990;224:177–82.

  35. 35.

    Ballvora A, Hesselbach J, Niewöhner J, Leiste D, Salamini F, Gebhardt C. Marker enrichment and high-resolution map of the segment of potato chromosome VII harbouring the nematode resistance gene Gro1. Mol Gen Genet. 1995;249:82–90.

  36. 36.

    Kochetov AV, Titov SE, Kolodyazhnaya YS, Komarova ML, Koval VS, Makarova NN, IlYinskyi YY, Trifonova EA, Shumny VK. Tobacco transformants bearing antisense suppressor of proline dehydrogenase gene are characterized by higher proline content and cytoplasm osmotic pressure. Russ J Genet. 2004;40:216–8.

  37. 37.

    Trifonova EA, Sapotsky MV, Komarova ML, Scherban AB, Shumny VK, Polyakova AM, Lapshina LA, Kochetov AV, Malinovsky VI. Protection of transgenic tobacco plants expressing bovine pancreatic ribonuclease against tobacco mosaic virus. Plant Cell Rep. 2007;26:1121–6.

  38. 38.

    Sugawara T, Trifonova EA, Kochetov AV, Kanayama Y. Expression of extracellular ribonuclease gene increases resistance to cucumber mosaic virus in tobacco. BMC Plant Biol. 2016;16(Suppl 3):246.

  39. 39.

    Li X, Zhang Y, Yin L, Lu J. Overexpression of pathogen-induced grapevine TIR-NB-LRR gene VaRGA1 enhances disease resistance and drought and salt tolerance in Nicotiana benthamiana. Protoplasma. 2017;254:957–69.

  40. 40.

    Gavrilenko T, Antonova O, Ovchinnikova A, Novikova L, Krylova E, Mironenko N, Pendinen G, Islamshina A, Shvachko N, Kiru S, Kostina L, Afanasenko O, Spooner DA. Microsatellite and morphological assessment of the Russian National Potato Collection. Genet Res Crop Evol. 2010;57:1151–64.

  41. 41.

    Gavrilenko T, Antonova O, Shuvalova A, Krylova E, Alpatyeva N, Spooner D, Novikova L. Genetic diversity and origin of cultivated potatoes based on plastid microsatellite polymorphism. Genet Res Crop Evol. 2013;60:1997–2015.

  42. 42.

    Bulman SR, Marshall JW. Differentiation of Australasian potato cyst nematode (PCN) populations using the polymerase chain reaction (PCR). N Z J Crop Hor Sci. 1997;25:123–9.

  43. 43.

    Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.

  44. 44.

    OEPP/EPPO. Testing of potato varieties to assess resistance to Globodera rostochiensis and Globodera pallida. OEPP/EPPO Bull. 2006;36:419–20.

  45. 45.

    Potato Genome Sequencing Consortium. Genome sequence and analysis of the tuber crop potato. Nature. 2011;475:189–95.

  46. 46.

    Jupe F, Witek K, Verweij W, Śliwka J, Pritchard L, Etherington GJ, Maclean D, Cock PJ, Leggett TM, Bryan GJ, Cardle L, Hein I, Jones JD. Resistance gene enrichment sequencing (RenSeq) enables reannotation of the NB-LRR gene family from sequenced plant genomes and rapid mapping of resistance loci in segregating populations. Plant J. 2013;76:530–44.

  47. 47.

    Wu TD, Watanabe CK. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005;21(9):1859–75.

  48. 48.

    Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.

  49. 49.

    Langmead B, Salzberg S. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–9.

  50. 50.

    Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SA, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.

  51. 51.

    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;7:621–8.

  52. 52.

    Hirsch CD, Hamilton JP, Childs KL, Cepela J, Crisovan E, Vaillancourt B, Hirsch CN, Habermann M, Neal B, Buell CR, Spud DB. A resource for mining sequences, genotypes, and phenotypes to accelerate potato breeding. Plant Genome. 2014;7(1)

  53. 53.

    Du Z, Zhou X, Ling Y, Zhang Z, Su Z. AgriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010;38:W64–70.

  54. 54.

    Kersey PJ, Allen JE, Armean I, Boddu S, Bolt BJ, Carvalho-Silva D, Christensen M, Davis P, Falin LJ, Grabmueller C, Humphrey J, Kerhornou A, Khobova J, Aranganathan NK, Langridge N, Lowy E, McDowall MD, Maheswari U, Nuhn M, Ong CK, Overduin B, Paulini M, Pedro H, Perry E, Spudich G, Tapanari E, Walts B, Williams G, Tello-Ruiz M, Stein J, Wei S, Ware D, Bolser DM, Howe KL, Kulesha E, Lawson D, Maslen G, Staines DM. Ensembl Genomes 2016: more genomes, more complexity. Nucleic Acids Res. 2016;44:D574–80.

  55. 55.

    Kaloshian I, Desmond OJ, Atamian HS. Disease resistance-genes and defense responses during incompatible interactions. In: Jones J, Gheysen G, Fenoll C, editors. Genomics and molecular genetics of plant–nematode interactions. New York: Springer; 2011. p. 309–24.

  56. 56.

    Holbein J, Grundler FM, Siddique S. Plant basal resistance to nematodes: an update. J Exp Bot. 2016;67:2049–61.

  57. 57.

    Asano K, Kobayashi A, Tsuda S, Nishinaka M, Tamiya S. DNA marker-assisted evaluation of potato genotypes for potential resistance to potato cyst nematode pathotypes not yet invading into Japan. Breed Sci. 2012;62:142–50.

  58. 58.

    Paal J, Henselewski H, Muth J, Meksem K, Menéndez CM, Salamini F, Ballvora A, Gebhardt C. Molecular cloning of the potato Gro1-4 gene conferring resistance to pathotype Ro1 of the root cyst nematode Globodera rostochiensis, based on a candidate gene approach. Plant J. 2004;38(2):285–97.

  59. 59.

    Finkers-Tomczak A, Danan S, van Dijk T, Beyene A, Bouwman L, Overmars H, van Eck H, Goverse A, Bakker J, Bakker EA. High-resolution map of the Grp1 locus on chromosome V of potato harbouring broad-spectrum resistance to the cyst nematode species Globodera pallida and Globodera rostochiensis. Theor Appl Genet. 2009;119:165–73.

  60. 60.

    Van der Vossen EAG, van der Voort JR, Kanyuka K, Bendahmane A, Sandbrink H, Baulcombe DC, Bakker J, Stiekema WJ, Klein-Lankhorst RM. Homologues of a single resistance-gene cluster in potato confer resistance to distinct pathogens: a virus and a nematode. Plant J. 2000;23(5):567–76.

  61. 61.

    Sanetomo R, Gebhardt K. Cytoplasmic genome types of European potatoes and their effects on complex agronomic traits. BMC Plant Biol. 2015;15:162.

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by a grant from the Russian Science Foundation (RSF) No. 16–16-04073 (including the publication costs). Plants were cultivated in the IC&G greenhouse facility (supported by budget project 0324–2016-0001); sequencing was performed in the IC&G Center of Genomic Investigations.

Availability of data and materials

Raw sequenced data are available at NCBI as BioProject PRJNA408434.

About this supplement

This article has been published as part of BMC Plant Biology Volume 17 Supplement 2, 2017: Selected articles from Belyaev Conference 2017: plant biology. The full contents of the supplement are available online at https://bmcplantbiol.biomedcentral.com/articles/supplements/volume-17-supplement-2.

Author information

Affiliations

  1. Institute of Cytology and Genetics, SB RAS, Novosibirsk, 630090, Russia
    • Alex V. Kochetov
    • , Anastasiya Y. Glagoleva
    • , Kseniya V. Strygina
    • , Elena K. Khlestkina
    • , Sophia V. Gerasimova
    • , Salmaz M. Ibragimova
    • , Natalja V. Shatskaya
    • , Gennady V. Vasilyev
    • , Dmitry A. Afonnikov
    •  & Nikolay A. Shmakov
  2. Novosibirsk State University, Novosibirsk, 630090, Russia
    • Alex V. Kochetov
    • , Anastasiya Y. Glagoleva
    •  & Elena K. Khlestkina
  3. Vavilov Institute of Plant Genetic Resources (VIR), Saint Petersburg, 190000, Russia
    • Olga Y. Antonova
    • , Tatyana A. Gavrilenko
    •  & Natalia V. Alpatyeva
  4. St. Petersburg State University, St. Petersburg, 199034, Russia
    • Tatyana A. Gavrilenko
  5. All Russian Research Institute for Plant Protection, Saint Petersburg, 196608, Russia
    • Alexander Khiutti
    •  & Olga S. Afanasenko
  6. Novosibirsk State Agrarian University, Novosibirsk, 630039, Russia
    • Alex V. Kochetov
Authors
  1. Search for Alex V. Kochetov in:
  2. Search for Anastasiya Y. Glagoleva in:
  3. Search for Kseniya V. Strygina in:
  4. Search for Elena K. Khlestkina in:
  5. Search for Sophia V. Gerasimova in:
  6. Search for Salmaz M. Ibragimova in:
  7. Search for Natalja V. Shatskaya in:
  8. Search for Gennady V. Vasilyev in:
  9. Search for Dmitry A. Afonnikov in:
  10. Search for Nikolay A. Shmakov in:
  11. Search for Olga Y. Antonova in:
  12. Search for Tatyana A. Gavrilenko in:
  13. Search for Natalia V. Alpatyeva in:
  14. Search for Alexander Khiutti in:
  15. Search for Olga S. Afanasenko in:

Contributions

AVK designed this research and wrote the manuscript. OSA and AK performed the experimental portions of GPCN inoculation and resistance evaluation. GVV prepared libraries, carried out the sequencing of the libraries on the Ion Torrent platform and participated in data interpretation. NVS participated in the preparation and sequencing of the libraries. KVS, AYG, and SVG participated in the extraction of RNA, preparation of cDNA, design of qRT-PCR reference gene primers, performance of qRT-PCRs and drafting of the manuscript. NAS and DAA performed in silico analysis of the sequencing data and participated in the drafting of the manuscript. EKK and SMI contributed to the design and coordination of the study and to revising the manuscript critically. OYA and NVA prepared the S. phureja plant materials for GPCN inoculation, carried out the extraction of RNA from S. phureja roots and participated in data interpretation. TAG proposed to use the selected S. phureja genotypes in this study, provided this material and participated in the coordination of the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Alex V. Kochetov.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Positions of potential resistance genes (Jupe et al. [46]) in the reference S. tuberosum genome. (XLSX 44 kb)

Additional file 2:

Comparison of root transcriptomes of resistant and susceptible S. phureja genotypes collected 72 h after inoculation with either nematode or water. (XLSX 236 kb)

Additional file 3:

The log2(FC) values and experimental fold changes for DEGs verified with qRT-PCR as well as the primer sequences constructed and used in qRT-PCR. (XLSX 13 kb)

Additional file 4:

qRT-PCR validation of DEGs (relative mRNA levels of 10 genes obtained using gene-specific primers and cDNA of susceptible and resistant S. phureja genotypes (24H = i-0144786 and 36H = i-0144787, respectively)). (PDF 17 kb)

Additional file 5:

GO terms enriched for down-regulated transcripts in the roots of the nematode-resistant S. phureja genotype. (PDF 399 kb)

Additional file 6:

GO terms enriched for up-regulated transcripts in the roots of the nematode-resistant S. phureja genotype. (PDF 240 kb)

Additional file 7:

S. phureja mRNAs corresponding to resistance genes predicted in the reference S. tuberosum genome (Jupe et al. [46]). (XLSX 62 kb)

Additional file 8:

List of candidate S. phureja NBS-LRR-encoding genes providing resistance to G. rostochiensis. (XLSX 18 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kochetov, A.V., Glagoleva, A.Y., Strygina, K.V. et al. Differential expression of NBS-LRR-encoding genes in the root transcriptomes of two Solanum phureja genotypes with contrasting resistance to Globodera rostochiensis . BMC Plant Biol 17, 251 (2017) doi:10.1186/s12870-017-1193-1

Download citation

Keywords

  • NBS-LRR genes
  • Solanum phureja
  • Resistance
  • Globodera rostochiensis

BMC Plant Biology

ISSN: 1471-2229

Contact us