Transcriptome profiling of lentil in response to Ascochyta lentis infection

Pedro García-García (García-García, P)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.

Francisca Vaquero (Vaquero, F)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.

F. Javier Vences (Vences, FJ)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.

Luis E. Sáenz de Miera (Sáenz de Miera, LE)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.

Carlos Polanco (Polanco, C)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.

Ana I. González (González, AI)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.

Ralf Horres (Horres, R)

GenXPro GmbH, Altenhöferallee 3, 60438, Frankfurt am Main, Germany.

Nicolas Krezdorn (Krezdorn, N)

GenXPro GmbH, Altenhöferallee 3, 60438, Frankfurt am Main, Germany.

Björn Rotter (Rotter, B)

GenXPro GmbH, Altenhöferallee 3, 60438, Frankfurt am Main, Germany.

Peter Winter (Winter, P)

GenXPro GmbH, Altenhöferallee 3, 60438, Frankfurt am Main, Germany.

Marcelino Pérez de la Vega (Pérez de la Vega, M)

Universidad de León, Area de Genética, Dpto. de Biología Molecular, 24071 León, Spain.



Aim of study: The purpose was to identify some general and genotypic-specific defense responses, in order to obtain a set of candidate genes presumably involved in the resistance.

Area of study: The experiment was carried out in León, Spain.

Material and methods: We have analyzed the response of three lentil genotypes to Ascochyta lentis (isolate AL 84) at transcriptomic level using the Massive Analysis of cDNA Ends (MACE) technique: the susceptible cultivar 'Lupa', the moderately resistant 'ILL5588' and the resistant wild accession 'BG 16880' (L. culinaris subsp. orientalis).

Main results: MACE results yielded a total of 50,935 contigs. The average number of detected contigs in each of the six samples was approximately of 40,000. In response to Ascochyta infection, the jasmonic acid pathway and the lignin biosynthesis were up-regulated in resistant genotypes, while they were down-regulated in the susceptible one. The response to chitin, the salicylic pathway and the auxin response were activated only in the resistant L. c. culinaris genotype, while the giberellin synthesis was only induced in the susceptible L. c. culinaris cv. 'Lupa'. A set of 18 lentil gene sequences putatively involved in the response to the pathogen were validated by RT-qPCR.

Research highlights: It can be concluded that in response to the infection by Ascochyta, the lignin biosynthesis and the JA pathway were critical for the resistance, while the giberellin synthesis seems to be related with susceptibility to the pathogen.

Additional keywords: Lens culinaris; Lens orientalis; transcriptomic; MACE.

Abbreviations used: ABA (abscisic acid); ET (ethylene); FDR (false discovery rate); GA (gibberellic acid); GO (gene ontology); JA (jasmonic acid); MACE (massive analysis of cDNA ends); NLR (nucleotide-binding domain and leucine rich repeat containing protein); RT-qPCR (quantitative reverse transcription PCR); SA (salicylic acid).

Authors' contributions: PGG, FV, FJV, LESM, CP, AIG and MPV designed the study and participated in the data interpretation. PGG, FV, FJV and AIG carried out the Ascochyta inoculations and RNA extraction. RH, NK, BR and PW carried out the MACE experiment and the general bioinformatics analyses. LESM and CP performed specific bioinformatics analyses and made substantial contributions to the manuscript. PGG and MPV drafted the manuscript. All authors read and approved the final manuscript.

Supplementary material (files S1 to S10) accompanies the paper on SJAR’s website.

Citation: García-García, P; Vaquero, F; Vences, FJ; Sáenz de Miera, LE; Polanco, C; González, AI; Horres, R; Krezdorn, N; Rotter, B; Winter, P; Pérez de la Vega, M (2019). Transcriptome profiling of lentil in response to Ascochyta lentis infection. Spanish Journal of Agricultural Research, Volume 17, Issue 4, e0703.

Received: 11 Apr 2019. Accepted: 14 Nov 2019.

Copyright © 2019 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC-by 4.0) License.

Funding agencies/Institutions Project / Grant
Spanish Ministerio de Economía y Competitividad (co-financed with FEDER funds) AGL2013-44714-R
Consejería de Educación, Junta de Castilla y León, Spain. LE113G18

Competing interests: The authors have declared that no competing interests exist.

Correspondence should be addressed to Pedro García-García:





Material and methods






Ascochyta blight is one of the main lentil diseases in most of the countries producing this crop. This disease, caused by the fungus Ascochyta lentis (syn A. fabae f. sp. lentis, teleomorph Didymella lentis), affects all above ground parts of the plant and is characterized by necrotic lesions. On susceptible cultivars under favorable conditions, it can lead to breakage of the stems and severe yield reduction. Seed quality may also be reduced through seed discoloration or retardation of seed development. Chemicals can control Ascochyta blight, but besides the environmental problems and the inputs increase related with the use of fungicides, the development of resistant cultivars is considered a more efficient and sustainable approach (Davidson & Kimber, 2007).

Ascochyta lentis is a host-specific pathogen (Pee­ver, 2007) and it is considered as a necrotrophic fungus, although a short biotrophic period cannot be completely excluded (Tivoli & Banniza, 2007; Sa­ri et al., 2017). The pathogenicity of this type of fungus and the resistance of the plant could be related to the production of specific fungal toxins and plant receptors or detoxifying molecules. Whatever the me­chanism is, it should include the recognition of the pathogen in order to trigger the defense response. The recognition can be based on interactions between pathogen-associated molecular patterns (PAMPs) and the corresponding plant pattern recognition receptors (PRRs) in the pattern-triggered immunity (PTI), or alternatively it could be based on pathogen effectors and plant receptors, in special nucleotide-binding domain and leucine rich repeat containing (NLR) proteins, in the effector-triggered immunity (ETI). Both ways result in the activation of the plant immune response, mainly by the jasmonic acid (JA) and ethylene (ET) dependent pathways, which act in a synergistic way (JA/ET pathway). The salicylic acid (SA) signaling pathway, and probably the abscisic acid (ABA) and gibberellic acid (GA) signaling pathways, also have a role in the final output (Mengiste, 2012; Abdullah & Akhtar, 2016).

Gene expression analyses in lentil revealed that the JA and SA signaling pathways play a central role in determining the resistance/susceptibility to A. len­tis, although the ET, SA, GA and ABA pathways might also be involved (Mustafa et al., 2009; Ford et al., 2017). Nevertheless, at least for some genotypes, only the JA pathway genes could explain the different reaction against Ascochyta in resistant vs susceptible lentil cultivars (Sari et al., 2017). These observations generally agree with the results obtained in other legumes after their host-specific Ascochyta infection, as in Pisum (Fondevilla et al., 2014, 2018), Lathyrus (Almeida et al., 2015), Vicia (Ocaña et al., 2015) or Cicer (Madrid et al., 2012).

Recently, Khorramdelazad et al. (2018) and Sari et al. (2018) have utilized the RNA-seq approach for the study of transcriptome changes in lentil after the infection by A. lentis. In this way, a set of candidate genes involved in the different stages of the defen­se response has been identified, from the patho­gen recognition to the systemic acquired resistance. Although the response was genotype-specific, showing up the existence of several resistance mechanisms in lentil (Sari et al., 2018), some common characteristics are found in the resistant/tolerant genotypes, such as an earlier and faster detection and signalling response to the infection, and a higher expression level of structu­ral defense-related genes, in special those coding for proteins involved in the reinforcement of cell walls (Khorramdelazad et al., 2018).

An alternative technology to analyze gene ex­pression is the Massive Analysis of cDNA Ends (MACE) because it is considered one the most cost-effective methodologies for the studies on changes in gene expression (Zajac et al., 2015). This technique has proved its usefulness in other plant species and it yielded data on candidate genes further validated by qPCR (Yakovlev et al., 2014; Zajac et al., 2015; Weiꞵ et al., 2017; Weiꞵ & Winkelmann, 2017). Our main objective was to obtain a general overview of the changes in gene expression that occur in other interesting lentil genotypes showing different levels of resistance when they are infected by Ascochyta using the MACE technique. The purpose was to identify some general and genotypic-specific defense responses, in special the common gene expression changes showed by the resistant genotypes, in order to obtain a set of candidate genes presumably involved in the resistance. This group of genes will provide the basis for the development of genic markers useful in breeding programs, and for further studies on mapping and expression changes of the candidate genes in other lentil genotypes.

Material and methodsTop

Three samples of Lens culinaris have been used, two of them belong to the cultivated species L. c. subsp. culinaris (accession 'ILL5588' and cv. 'Lupa') and one to the wild ancestor L. c. subsp. orientalis (accession 'BG 16880'). 'Lupa' is a Spanish cultivar, 'ILL5588' is an accession from the International Center for Agricultural Research in the Dry Areas (ICARDA) lentil collection and 'BG 16880' is an accession from the Spanish Centro Nacional de Recursos Fitogenéticos (CRF). The response of the three genotypes to the A. lentis infection was different: L. c. orientalis showed resistance to the fungus, 'ILL5588' could be considered as moderately resistant, and 'Lupa' was highly susceptible to the pathogen.

Thirty plants of each accession were grown in sterile conditions until inoculation (15 days after germination) with a conidial suspension of the isolate AL 84 of A. lentis (ATCC® 46981™) following the method described in Ford et al. (1999). Other 30 plants per accession were grown in the same conditions and similarly treated, but without the presence of the pathogen, to use them as controls. Twenty four hours after inoculation, the aerial parts of the corresponding plants were pooled, frozen in liquid nitrogen, grinded and stored at -80ºC until total RNA extraction was carried out. According to Roundhill et al. (1995), it is around 24 hours after infection when the first signs of disturbance in the plant cell cytoplasm in reaction to the hyphae penetration are detected. Also at this time the magnitude of oxidative burst seems to be related with the level of the plant resistance (Sambasivam et al., 2017) and the differences in expression changes among lentil genotypes are more apparent (Khorramdelazad et al., 2018; Sari et al., 2018). Total RNA was extracted using the method described in Chang et al. (1993).

The RNA of each corresponding genotype and treatment were pooled separately to produce six different MACE cDNA libraries. The libraries were constructed using the “MACE-Kit” (GenXPro GmbH, Frankfurt am Main, Germany) as described in Zawada et al. (2014). In short, cDNA was obtained from ≈5 μg of total RNA, then randomly fragmented and biotinylated 3′ ends were captured after binding to a streptavidin matrix. A library ready for high-throughput sequencing was prepared using TrueQuant adapters included in the kit. The library consisted of 50–

1000 bp-long fragments derived from the 3′-end of the cDNAs. The 3′ ends of the libraries were sequenced on a HiSeq 2000 (Illumina) with 100 cycles to generate the MACE tags, each tag representing one single transcript molecule. The Illumina-derived sequence reads were processed with GenXPro’s in-house MACE analysis pipeline. Briefly, the elimination of duplicate PCR-derived tags, identified by TrueQuant technology, was carried out. Then, low-quality sequences were deleted, and finally the removal of sequencing adapter primers and poly(A)-tails was carried out using an in-house Python script.

Reads from all samples were assembled using the Trinity RNA-Seq de novo Assembly (Grabherr et al., 2011). The resulting contigs of the assemblies longer than 100 bp were annotated by BLAST (BLAST 2.2.25+) to different sources, using in all cases the default parameters implemented in the NCBI BLAST tool and a threshold e-value of 10-5. The contigs were compared by BLASTX, first to all Fabaceae Swissprot and afterwards to all Fabaceae Trembl protein sequences from Uniprot ( Contigs that did not have a match to one of these databases were annotated by BLASTN to all Lens mRNA sequences from NCBI. Contigs were hereafter again used as reference for annotation and quantification of the MACE tags. Only uniquely mapped reads were taken into account. In order to compare the different samples, a normalization of the raw counts was performed according to Anders & Huber (2010).

The p-values for differential gene expression be­tween the samples were calculated using the DEGSeq R/Bioconductor package (Wang et al., 2010). The values for controlling the False Discovery Rate (FDR) were estimated according to Benjamini & Hochberg (1995). A change in a contig expression was considered statistically significant when the FDR value was ≤ 0.05.

The functional annotation of the sequences was carried out using the MACE2GO software, available at GenXPro's server at http:\\ The enrichment of the gene ontology (GO) categories was based on the genes that showed differential expression at p ≤ 0.05, and the statistical significance was calculated using the Fisher's exact test. The results of the GO enrichment analysis were summarized and plotted using the REVIGO (Reduce plus Visualize Gene Ontology) software that clusters the terms by their relationship and avoids the redundancy, making easier the results interpretation (Supek et al., 2011).

In order to test the reliability of the MACE results, RT-qPCR was carried out for 18 genes. Three biological and three technical replicates for each genotype and treatment (control and infected) were obtained in the same conditions as in the MACE experiment and used for this analysis. Total RNA was extracted using the same method as in the MACE experiment, and after a DNase digestion, RNA quality and quanti­ty was evaluated by electrophoresis and NanoDrop spectrophotometry. To be sure of the complete absence of DNA contamination, we followed the method described in Vaghefi et al. (2013). For each sample, 1 μg of RNA was reverse transcribed using the High-Capacity RNA-o-cDNA™ kit (Applied Biosystems™) following the manufacturer's instructions, and the cDNA obtained was diluted 1:10 to be used in the RT-qPCR reactions. Three technical replicates per sample were prepared and each one contained 7.5 μL

of Power SYBR™ Green PCR Master Mix (Applied Biosystems™), 2 μL of cDNA and 0.4 μM of each primer in a total volume of 15 μL. The RT-qPCR was performed in a StepOnePlus Real-Time PCR instrument (Applied Biosystems™) using the follo­wing program: 95 ºC for 10 min, 40 cycles of 95 ºC for 15 s and 60 ºC for 1 min and finally a melting stage from 60 ºC to 95 ºC with increasing steps of

0.3 ºC. The amplification efficiency for each primer pair was estimated by the software LingRegPCR v2017.1 (Ruijter et al., 2009). The analysis of gene expression changes was carried out with the Comparative CT (ΔΔCT) method implemented in StepOne™ software v2.3 (Life Technologies).

Most of the primers for RT-qPCR were designed from specific MACE contig sequences using the soft­ware Primer3Plus (Untergasser et al., 2007). For the reference gene, we used the primer pair LcActin-257 described in Sari et al. (2017) because it has been validated in RT-qPCR experiments dealing with Asco­chyta infection in lentil. The information about the primer sequences, PCR efficiencies and foldchanges can be found in the File S1 [suppl.].


General characteristics of the MACE experiment

The sequencing data generated by MACE were deposited in the NCBI BioProject repository under accession ID PRJNA356810. A summary of the general numbers of reads and contigs obtained is shown in Table 1. The experiment yielded 11,416,403 reads that were assembled de novo in 50,935 contigs. The contig sequences are presented in the File S2 [suppl.] and the data for each sample in the File S3 [suppl.]. For each of the six samples, the number of detected contigs was similar (~ 40,000), only the sample “Lupa control” yielded a slightly lower number of contigs (36,023). The final transcriptome was made of 43,970 different contigs when considering as different only those contigs that matched different genes or sequences in the BLAST analysis. Contigs that matched the same sequence in the BLAST analysis were considered as coming from the same transcript, and the reads were summed up for the differential gene expression analysis.

Table 1. Summary of the results obtained in the MACE experiments for the six Lens samples.

Most of the contigs (70 %) were shared between the three genotypes (Fig. 1A), and only a small proportion (~ 7 %, ranging from 784 to 1,303 contigs) was ex­clusive of a single genotype. The obtained data for control and infected plants from the three genotypes are shown in the Fig. 1B and 1C, respectively. When comparing the contigs detected in a specific genotype, a high proportion was common between the control and infected sample (Fig. 1D), and only a small percentage (between 2.5 % to 4.5 %) presented a significant change in expression, always with a greater number for the up-regulated contigs.

Figure 1. Number of different contigs detected in total (control + infected) (A), control (B) and infected (C) samples, and comparison control vs. infected within samples (D). The total number of different contigs in each sample is presented in the rectangles. The number of contigs with a significant change in the expression (↑, up-regulated, ↓, down-regulated, FDR ≤ 0.05) when comparing controls vs. infected with A. lentis is shown (D).

Functional annotation of the contigs

The results of the functional annotation are shown in the File S4 [suppl.]. Approximately 16 % of the contigs matched a well-characterized sequence, 13 % matched genes coding for putative proteins with GO term, while the remaining 71 % were uncharacterized RNAs or related to genes coding for uncharacterized proteins.

The BLAST hits mainly matched well-characteri­zed gene sequence from legume species, reflecting the phylogenetic proximity with lentil, although it was noticeable that 193 contigs without a BLAST hit in the plant databases, matched sequences from Ascochyta.

The 20 most abundant GO terms for the three categories (biological process, molecular function and cellular component) are represented in the Fig. 2. For the category "biological process", the most represented GO term was the very general one "metabolic process". Some more specific terms, possibly related with the response of the plants to the pathogen infection can be found within the “biological processes” (oxidation-reduction process, phosphorylation, defense response, etc.). In the "molecular function" category, the terms related with "binding" of ATP, nucleotides and metal ions were the most abundant, although other terms related with oxidoreductase and kinase activities were also represented. The "cellular component" category showed a high abundance of terms related with the cellular membranes.

Figure 2. Number of contigs included in the 20 most frequent GO terms for the three main categories: (A) biological process, (B) molecular function and (C) cellular component.

Contigs showing differential expression after the A. lentis infection

Contigs were considered differentially expressed when a statistically significant change in the number of normalized reads after the A. lentis infection was observed (FDR ≤ 0.05), and were clustered in groups showing similar changes of expression. In all groups, only contigs with a full description were considered and when several contigs matched the same sequence, only one is shown (File S5 [suppl.]).

Among the groups, those constituted by contigs which are exclusively up-regulated in the resistant genotypes are especially interesting because they would include the genes directly involved in an effective defense response (File S5 [suppl.], sheets "up in R, down in S", "up in R, no change in S"). Several contigs found in this group seem to be directly involved in the different levels of plant defense. Thus, some sequences seem to be related with the protection against the oxidative stress caused by the ROS species (peroxidase, aldose reductase or superoxide dismutase). Contigs for WRKY transcription factors, which in Arabidopsis are directly involved in the resistance response to necrotrophic fungi (Buscaill & Rivas, 2014), were also found. In addition, there were sequences presumably coding for enzymes involved in the synthesis of monoterpernes, which protect the plant against pathogens when accumulated in leaves or roots (geranyl-diphosphate synthase), for enzymes that degrade the fungal cell walls (glucan 1,3-beta-glucosidase, glucosylceramidase), or for enzymes in­volved in the lignification and defense against fungal pathogens (laccases).

It is also worth mentioning the contigs related with hormone pathways involved in defense. The cysteine-rich receptors-like protein kinases might trigger the ABA and SA signaling pathways (Burdiak et al., 2015; Lu et al., 2016). The activation of the ET pathway was reflected in the up-regulation of the contig c7470_g1_s1, which would code for an ethylene response factor. The up-regulation of the contig c9517_g1_s1, probably involved in the synthesis of a patatin-related phospholipase A, suggests a defense through the JA pathway (Canonne et al., 2011).

The resistance to A. lentis could also be related to the capacity of the plant of maintaining the expression of some key genes. Thus, the contigs showing down-regulation in the susceptible 'Lupa' but without ex­pression changes in the more resistant genotypes could provide valuable information. In this group (File S5 [suppl.], sheet "no change in R, down in S"), some may be related with the lack of an effective defense response against Ascochyta. Examples of these functions are CC-NBS-LRR resistance protein, serine/threonine pro­tein kinase, calcium and calcium/calmodulin-de­pen­dent serine/threonine-protein kinase, calmodulin pro­tein kinase or auxin-induced protein AUX28. The role of calmodulin in the defense may be related to the MLO gene family response which has an important role in the resistant defense against fungus in several plant species (Kim et al., 2002). Likewise, the down-regulation of some genes of the auxin pathway in the susceptible genotype has shown that a correct auxin signaling is needed against necrotrophic pathogens (Fu & Wang, 2011).

The contigs showing an identical change of expression in the three genotypes (File S5 [suppl.], sheets "up in R and S" and "down in R and S") are supposed to be involved in general mechanisms of response against the pathogen. Among the common up-regulated sequences, at least 17 were related with genes described in other plant species as part of the defense mechanisms. Thus, four contigs have similarity with genes coding for pathogenesis related proteins (PR) or disease resistance response proteins. Among them, it would be worth remarking the contigs c112_g1_s1 and c24313_g1_s1, which matched with the genes coding for the L. culinaris pathogenesis-related protein 4 and pathogenesis-related protein 10.

GO enrichment analysis

In order to get a wider picture of the affected processes by the fungus infection, a GO enrichment analysis was carried out. For this study, we have used the p-value ≤ 0.05 as indication of a change in the gene expression instead the FDR value to get a higher number of data for each GO term.

The significant enriched GO terms in each genotype are shown in File S6 [suppl.]. A long list of GO terms was obtained, but many terms were closely related. Therefore the enriched GO terms were plotted using REVIGO software (Supek et al., 2011), which clusters similar GO terms based in their semantic similarity. The enriched GO terms for the "biological process" category in the three genotypes are presented in the Fig. 3, the other two categories are shown in the File S7 [suppl.].

Figure 3. REVIGO plots of the enriched GO terms associated to contigs showing a significant expression change after Ascochyta lentis infection the category "biological process". (A) Enriched up-regulated GO terms in the genotype 'ILL5588', (B) enriched down-regulated terms in 'ILL5588', (C) enriched up-regulated terms in the genotype Lens culinaris orientalis, (D) enriched down-regulated terms in the genotype L. c. orientalis, (E) enriched up-regulated terms in the genotype 'Lupa', (F) enriched down-regulated terms in 'Lupa'.

Ascochyta lentis sequences

Some contigs did not match any sequence in the plant databases and were virtually undetected in control plants, while they showed a significant increase in infected plants. In order to explore if they had a fungal origin, these contigs were BLAST compared against the A. lentis genome (INSDC RZGK00000000.1, assembly Alentis_Al4) and the Ascochyta rabiei genome database (AscRab1.0, INSDC Assembly GCA_001630375.1, Verma et al., 2016) and the sequences from this species published by Fondevilla et al. (2015). A total of 193 contigs matched to fungal sequences (File S8 [suppl.]).

Besides some sequences related with genes involved in the general functions of the fungus (ribosomal, membrane or mitochondrial proteins), some other could be implicated in the pathogenicity since several matched sequences in the PHI database (Pathogen-Host Interaction database, According to this database the corresponding genes are involved in the virulence phenotype. For example, Contig292 was related with the major protein of the Woronin body, a structure derived from the peroxisomes that is initially required for a proper development and functioning of appressoria, and subsequently necessary for the survival of hyphae during invasive growth and host colonization (Soundarajan et al., 2004), and contig c34174_g1_s1 was related with an alcohol oxidase that has been described as a pathogenicity factor in Cladosporium fulvum (Segers et al., 2001).

RT-qPCR validation

In order to test the reliability of the expression changes observed in the MACE experiment, 18 sequences were analyzed with RT-qPCR in three biological and three technical replicates of each genotype and condition (control and infected). The genes were chosen because of their putative role in defense and/or their abundance in the MACE results. In all cases, the amplification efficiency was at least a 90 % (File S1 [suppl.]). The reference gene (LcActin-257), validated by Sari et al. (2017), also showed a stable expression in the MACE contigs that matched it. For the analyzed genes, the correlation between the observed foldchanges after the infection in MACE (File S9 [suppl.]) and RT-qPCR was positive and statistically significant in the three genotypes (0.69 for 'Lupa', 0.80 for 'ILL5588' and 0.62 for L. c. orientalis) (File S10 [suppl.]).


Because one of the causes of the limited production of lentil worldwide is the fungal disease caused by A. lentis, we were interested in the analysis of the differences in response to the infection in susceptible and resistant cultivars.

In this study, the changes in the lentil transcriptome after the A. lentis infection were estimated in new lentil materials by using the MACE technique for the first time in this species. This approach has allowed us to obtain high-quality information, not only on the expressed genes in the plant genotypes, but also on their expression levels.

The significant and positive correlation between the expression changes observed in the MACE and RT-qPCR experiments in the three genotypes supports the reliability of the obtained results in the transcriptome profiling using the MACE technique. Moreover, the resolution of the MACE technique is highlighted by the fact that also sequences from the pathogen were identified, although they were in a small proportion in the total RNA sampled from infected plants.

The length of the MACE contigs, mainly between 100 and 1000 bp, was enough to allow the identification of homologous genes in the databases. It is remarkable that a 48 % of the sequences did not match any characterized sequence, maybe due to the fact that MACE mainly covers the 3' end of a transcript that often only contains the 3' UTR, which makes difficult to get a protein annotation.

Even with a limited functional annotation, these data yielded a number of candidate genes involved in the resistance response based on the type of identified genes and the expression changes in the three genotypes analyzed. The key resistance genes are probably distinct in different genotypes, as illustrated by the different genes used in the lentil breeding programs (Pérez de la Vega et al., 2011; Sudheesh et al., 2016), although some common mechanisms seemed to have a role, such as the activation of the JA pathway (Sari et al., 2018).

The way in which A. lentis is recognized by the plant and how this fact triggers the defense response is not yet resolved. Although a change in a gene expression after the infection does not necessarily imply a direct role of that gene in the resistance response, when the changes are similar in several resistant genotypes and different from the changes in susceptible ones, it seems logical to consider the gene as involved in the resistance. Based on this rationale this study has pointed out several genes probably involved in the response between the fungus recognition by the plant until the accumulation of antifungal compounds in plant cells.

Similarly, a change in gene expression in the same direction for all genotypes (resistant or susceptible) can be the signature of those genes involved in general mechanisms of response against pathogens, but not the ultimate responsible for the resistance, or alternatively by-products due to the stress. This set of genes included candidate genes previously described as responsible for the resistance, such as some genes coding for PR proteins (PR4 and PR10a). The RT-qPCR data did not show a significant change of expression for these genes with the exception of the up-regulation of PR4 in the susceptible genotype (File S9 [suppl.]).

Although these results suggest that these genes are not main candidates for the differences in resistance response among the three genotypes analyzed in this work, it would be possible that the resistance depended not only in the magnitude of the expression change, but also in the absolute quantity of a gene product inside the cell. Besides, we cannot rule out that the resistance response would be determined by the different alleles borne in the three genotypes or by post-transcriptional modifications no detected in the sequences obtained with the MACE technique.

The recognition of the pathogen is usually mediated by the interaction of a pathogen molecule (i.e., chitin) by a plant receptor, usually a wall associated receptor-like kinase (Mengiste, 2012). At least 10 contigs in our work belonged to this kind of receptors, but they were in low copy number and their expression changes were not significant. On the other hand, the recognition can also be mediated by interactions between fungal effectors and R-proteins, many of which belong to the NBS-LRR gene family. We found 42 contigs related to NBS-LRR genes, and most of them did not show significant changes after the infection.

After the pathogen recognition, several hormones pathways, mainly SA, ABA and the synergistic JA and ET, would mediate the defense response. Our data point to a high number of contigs matching genes related with those signalling pathways. For instance, 33 contigs in 'ILL5588' contain the GO term 0009751 (response to salicylic acid) and four out of the 33 were down-regulated and 11 up-regulated after infection, or 142 contigs contain the GO term 0006952 (defense response) among which seven were down-regulated and 34 up-regulated (File S6 [suppl.]).

A general view of the processes that change sig­nificantly after the infection was obtained by sum­marizing the enriched GO terms using the software REVIGO (Fig. 3 and File S7 [suppl.]). In 'ILL5588' (moderately resistant), many up-regulated enriched GO terms were related with the plant defense, some were general terms such as "defense response", "response to biotic stimulus" or "response to stress", but others were more specifically related in the response to Ascochyta (defense response to fungus, response to chitin, response to SA, response to JA, response to auxin). Other up-regulated terms seemed to suggest the way in which the plant is controlling the fungus invasion, such as reinforcing the cell wall (lignin biosynthesis) or storing toxic antifungal compounds, most of them derived from flavonoids, isoprenoids, phenylpropanoids or fatty acids (Amil-Ruiz et al., 2011). One of the highest up-regula­ted enriched terms was "oxidation-reduction process", probably related with the accumulation of reactive oxygen species (ROS) in the early stages of the fungus infection. The role the oxidative burst and the associated cell death is not clear in the necrotrophic pathogenicity, although the general idea is that the early activation of ROS production induces the defense mechanisms, but a sustained production and the associated cell death promotes susceptibility to necrotrophs and resistance to biotrophs (Mengiste, 2012). In lentil, the resistance has been associated to both responses: the inhibition, such as in the cultivar 'CDC Robin' (Sari et al., 2017) and the promotion, such as in 'ILL7537' (Khorramdelazad et al., 2018), of the programmed cell death.

In 'ILL5588', the down-regulated enriched terms are mainly related with photosynthesis functions, a usual response after a pathogen invasion (Huot et al., 2014).

L. c. orientalis shared several up-regulated enriched GO terms with 'ILL5588', and although they were in a lower number and belonged to a more general GO level, some coincidences suggest a common mechanism of defense with 'ILL5588' (lignin biosynthesis, response to JA, flavonoid biosynthesis, or phenylpropanoid bio­synthesis). Many down-regulated terms were related with photosynthesis as found in 'ILL5588'. One of the down-regulated terms (cutin biosynthesis) seems to be directly related with the defense, since cutin is supposed to protect all the aerial parts of the plants. However, although the relationships of cutin with the response mechanisms is proved, its role its controversial: while in tomato the level of cutin in the cuticle is correlated with the resistance to Botrytis cinerea, in Arabidopsis mutants with lower levels of cutin in the leaves showed an increase in the resistance to B. cinerea (Fich et al., 2016).

A remarkable difference between the susceptible 'Lupa' and the resistant genotypes was the down-regulation of the response to JA and the up-regulation of the genes for gibberellin biosynthesis. Gibberellin promotes the degradation of DELLA proteins, which strengths the SA signaling pathway and weakens the JA pathway, thus increasing the susceptibility to necrotrophs (Navarro et al., 2008). The role of DELLA proteins in the resistance to A. lentis is supported by Khorramdelazad et al. (2018), who found an over-expression of the coding genes in the resistant genotype ILL7537, suggesting a main role in the systemic acquired resistance signaling.

Summarizing the results for these three Lens geno­types, it can be concluded that in response to Ascochyta, the reaction by the host plant was multiple and although it presents genotype-associated peculiarities, some com­mon points can be observed. Thus, the JA pathway was activated in resistant genotypes, while it was down-regulated in the susceptible one, and the processes related with the lignin biosynthesis are also induced exclusively in the two resistant genotypes. The response to chitin, the salicylic pathway and the auxin response were activated only in the resistant L. c. culinaris genotype 'ILL5588', while the giberellin synthesis was only induced in the susceptible L. c. culinaris cv. 'Lupa'. Finally, the general defense responses and the metabolism of ROS products were activated in all the three genotypes. Because the hormone pathways are interconnected, either synergism (JA and auxin pathways) or antagonism (JA and SA pathways, SA and auxin pathways) among them, the balance of all signals would be the determinant of the level of resistance.

Since similar results have been previously published in other lentil genotypes (Khorramdelazad et al., 2018; Sari et al. 2018), we can conclude that our results agree with the up-regulation of JA signaling as a general response of the resistance to Ascochyta blight infection in lentil.


The authors would like to thank Dra. Ana Llorente for helping in the greenhouse and laboratory work.


Abdullah SNA, Akhtar MS, 2016. Plant and necrotrophic fungal pathogen interaction: Mechanism and mode of action. In: Plant, soil and microbes; Hakeem KR, Akhtar MS, Abdullah SNA (eds). pp: 29-53. Springer Int Publ Switzerland.

Almeida NF, Krezdorn N, Rotter B, Winter P, Rubiales D, Vaz Patto MC, 2015. Lathyrus sativus transcriptome resistance response to Ascochyta lathyri investigated by deepSuperSAGE analysis. Front Plant Sci 6: 178.

Amil-Ruiz F, Blanco-Portales R, Muñoz-Blanco J, Caballero JL, 2011. The strawberry plant defense mechanism: A molecular review. Plant Cell Physiol 52: 1873-1903.

Anders S, Huber H, 2010. Differential expression analysis for sequence count data. Genome Biol 11: R106.

Benjamini Y, Hochberg Y, 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Roy Stat Soc B 57: 289-300.

Burdiak P, Rusaczonek A, Witon D, Glów D, Karpinski S, 2015. Cysteine-rich receptor-like kinase CRK5 as a regulator of growth, development, and ultraviolet radiation responses in Arabidopsis thaliana. J Exp Bot 66: 3325-3337.

Buscaill P, Rivas S, 2014. Transcriptional control of plant defence responses. Curr Opin Plant Biol 20: 35-46.

Canonne J, Froidure-Nicolas S, Rivas S, 2011. Phospholipases in action during plant defense signaling. Plant Signal Behav 6: 13-18.

Chang S, Puryear J, Cairney J, 1993. A simple and efficient method for isolating RNA from pine trees. Plant Mol Biol Rep 11: 113-116.

Davidson JA, Kimber RBE, 2007. Integrated disease management of Ascochyta blight in pulse crops. Eur J Plant Pathol 119: 99-110.

Fich EA, Segerson NA, Rose JA, 2016. The plant polyester cutin: Biosynthesis, structure and biological roles. Annu Rev Plant Biol 67: 207-233.

Fondevilla S, Rotter B, Krezdorn N, Jüngling R, Winter P, Rubiales D, 2014. Identification of genes involved in resistance to Didymella pinodes in pea by deepSuperSAGE transcriptome profiling. Plant Mol Biol Rep 32: 258-269.

Fondevilla S, Krezdorn N, Rotter B, Kahl G, Winter P, 2015. In planta identification of putative pathogenicity factors from the chickpea pathogen Ascochyta rabiei by de novo transcriptome sequencing using RNA-Seq and Massive Analysis of cDNA Ends. Front Microbiol 6: 1329.

Fondevilla S, Fernández-Romero MD, Satovic Z, Rubiales D, 2018. Expressional and positional candidate genes for resistance to Peyronellaea pinodes in pea. Euphytica 214: 236.

Ford R, Pang ECK, Taylor PWJ, 1999. Genetics of resistance to ascochyta blight (Ascochyta lentis) of lentil and the identification of closely linked RAPD markers. Theor Appl Genet 98: 93-98.

Ford R, Tan D, Vaghefi N, Mustafa B, 2017. Abscisic acid activates pathogenesis-related defense gene signaling in lentils. In: Mechanism of plant hormone signaling under stress, 1st edn, Vol. 1; Pandey G (ed). pp: 243-270. John Wiley & Sons.

Fu J, Wang S, 2011. Insights into auxin signaling in plant-pathogen interactions. Front Plant Sci 2: 74.

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al., 2011. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol 29: 644-652.

Huot B, Yao J, Montgomery BL, He SY, 2014. Growth-defense tradeoffs in plants: A balancing act to optimize fitness. Mol Plant 7: 1267-1287.

Khorramdelazad M, Bar I, Whatmore P, Smetham G, Bhaaskaria V, Yang Y, Bai1 SH, Mantri N, Zhou Y, Ford R, 2018. Transcriptome profiling of lentil (Lens culinaris) through the first 24 hours of Ascochyta lentis infection reveals key defence response genes. BMC Genomics 19: 108.

Kim MC, Panstruga R, Elliott C, Muller J, Devoto A, Yoon HW, Park HC, Cho MJ, Schulze-Lefert P, 2002. Calmodulin interacts with MLO protein to regulate defence against mildew in barley. Nature 416: 447-451.

Lu K, Liang S, Wu Z, Bi C, Yu Y, Wang X, Zhang D, 2016. Overexpression of an Arabidopsis cysteine-rich receptor-like protein kinase, CRK5, enhances abscisic acid sensitivity and confers drought tolerance. J Exp Bot 67: 5009-5027.

Madrid E, Rajesh PN, Rubio J, Gil J, Millán T, Chen W, 2012. Characterization and genetic analysis of an EIN4-like sequence (CaETR-1) located in QTLAR1 implicated in ascochyta blight resistance in chickpea. Plant Cell Rep 31: 1033-1042.

Mengiste T, 2012. Plant immunity to necrotrophs. Annu Rev Phytopathol 50: 267-294.

Mustafa BM, Coram TE, Pang ECK, Taylor PWJ, Ford RA, 2009. cDNA microarray approach to decipher lentil (Lens culinaris) responses to Ascochyta lentis. Australas Plant Pathol 38: 617-631.

Navarro L, Bari R, Achard P, Lisón P, Nemri A, Harberd NP, Jones JD, 2008. DELLAs control plant immune responses by modulating the balance of jasmonic acid and salicylic acid signaling. Curr Biol 18: 650-655.

Ocaña S, Seoane P, Bautista R, Palomino C, Claros GM, Torres AM, Madrid E, 2015. Large-scale transcriptome analysis in faba bean (Vicia faba L.) under Ascochyta fabae infection. PLoS ONE 10: e0135143.

Peever TL, 2007. Role of host specificity in the speciation of Ascochyta pathogens of cool season food legumes. Eur J Plant Pathol 119: 119-126.

Pérez de la Vega M, Fratini R, Muehlbauer FJ, 2011. Lentil. In: Genetics, genomics and breeding of cool season grain legumes; Pérez de la Vega M, Torres AM, Cubero JI, Kole C (eds). pp: 98-150. Science Publishers-CRC Press.

Roundhill SJ, Fineran BA, Cole ALJ, Ingerfeld M, 1995. Structural aspects of Ascochyta blight of lentil. Can J Bot 73: 485-497.

Ruijter JM, Ramakers C, Hoogaars WMH, Karlen Y, Bakker O, van den Hoff MJB, Moorman AFM, 2009. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res 37: e45.

Sambasivam P, Taylor PWJ, Ford R, 2017. Pathogenic variation and virulence related responses of Ascochyta lentis on lentil. Eur J Plant Pathol 147: 265-277.

Sari E, Bhadauria V, Vandenberg A, Banniza S, 2017. Genotype-dependent interaction of lentil lines with Ascochyta lentis. Front Plant Sci 8: 764.

Sari E, Bhadauria V, Ramsay L, Borhan MH, Lichtenzveig J, Bett KE, Vandenberg A, Banniza S, 2018. Defense responses of lentil (Lens culinaris) genotypes carrying non-allelic ascochyta blight resistance genes to Ascochyta lentis infection. PLoS ONE 13: e0204124.

Segers G, Bradshaw N, Archer D, Blissett K, Oliver P, 2001. Alcohol oxidase is a novel pathogenicity factor for Cladosporium fulvum, but aldehyde dehydrogenase is dispensable. MPMI 3: 367-377.

Soundarajan S, Jedd G, Li X, Ramos-Pamploña M, Chua NH, Naqvi NI, 2004. Woronin body function in Magnaporthe grisea is essential for efficient pathogenesis and for survival during nitrogen starvation stress. Plant Cell 16: 1564-1574.

Sudheesh S, Rodda MS, Davidson J, Javid M, Stephens A, Slater AT, Cogan NOI, Forster JW, Kaur S, 2016. SNP-based linkage mapping for validation of QTLs for resistance to ascochyta blight in lentil. Front Plant Sci 7: 1604.

Supek F, Bošnjak M, Škunca N, Šmuc T, 2011. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6: e21800.

Tivoli B, Banniza S, 2007. Comparison of the epidemiology of ascochyta blights on grain legumes. Eur J Plant Pathol 119: 59-76.

Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, Leunissen JAM, 2007. Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res 35: W71-W74.

Vaghefi N, Mustafa BM, Dulal N, Selby-Pham J, Taylor PWJ, Ford R, 2013. A novel pathogenesis-related protein (LcPR4a) from lentil, and its involvement in defence against Ascochyta lentis. Phytopathol Mediterr 52: 192-201.

Verma S, Gazara RK, Nizam S, Parween S, Chattopadhyay D, Verma PK, 2016. Draft genome sequencing and secretome analysis of fungal phytopathogen Ascochyta rabiei provides insight into the necrotrophic effector repertoire. Sci Rep 6: 24638.

Wang L, Feng Z, Wang X, Wang X, Zhang X, 2010. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26: 136-138.

Weiꞵ S, Winkelmann T, 2017. Transcriptomic profiling in leaves representing aboveground parts of apple replant disease affected Malus domestica 'M26' plants. Sci Hortic 222: 111-125.

Weiꞵ S, Bartsch M, Winkelmann T, 2017. Transcriptomic analysis of molecular responses in Malus domestica 'M26' roots affected by apple replant disease. Plant Mol Biol 94: 303-318.

Yakovlev IA, Lee Y, Rotter B, Olsen JE, Skrøppa T, Johnsen Ø, Fossdal CG, 2014. Temperature-dependent differential transcriptomes during formation of an epigenetic memory in Norway spruce embryogenesis. Tree Genet Genomes 10: 355.

Zajac BK, Amendt J, Horres R, Verhoff MA, Zehner R, 2015. De novo transcriptome analysis and highly sensitive digital gene expression profiling of Calliphora vicina (Diptera: Calliphoridae) pupae using MACE (Massive Analysis of cDNA Ends). Forensic Sci Int Genet 15: 137-146.

Zawada AM, Rogacev KS, Müller S, Rotter B, Winter P, Fliser D, Heine GH, 2014. Massive analysis of cDNA ends (MACE) and miRNA expression profiling identifies proatherogenic pathways in chronic kidney disease. Epigenetics 9: 161-172.