Open Access

Genetic mapping of Pinus flexilis major gene (Cr4) for resistance to white pine blister rust using transcriptome-based SNP genotyping

  • Jun-Jun Liu1Email author,
  • Anna W. Schoettle2,
  • Richard A. Sniezko3,
  • Rona N. Sturrock1,
  • Arezoo Zamany1,
  • Holly Williams1,
  • Amanda Ha1,
  • Danelle Chan1,
  • Bob Danchok3,
  • Douglas P. Savin3 and
  • Angelia Kegley3
BMC Genomics201617:753

https://doi.org/10.1186/s12864-016-3079-2

Received: 2 February 2016

Accepted: 8 September 2016

Published: 23 September 2016

Abstract

Background

Linkage of DNA markers with phenotypic traits provides essential information to dissect clustered genes with potential phenotypic contributions in a target genome region. Pinus flexilis E. James (limber pine) is a keystone five-needle pine species in mountain-top ecosystems of North America. White pine blister rust (WPBR), caused by a non-native fungal pathogen Cronartium ribicola (J.C. Fisch.), has resulted in mortality in this conifer species and is still spreading through the distribution. The objective of this research was to develop P. flexilis transcriptome-wide single nucleotide polymorphism (SNP) markers using RNA-seq analysis for genetic mapping of the major gene (Cr4) that confers complete resistance to C. ribicola.

Results

Needle tissues of one resistant and two susceptible seedling families were subjected to RNA-seq analysis. In silico SNP markers were uncovered by mapping the RNA-seq reads back to the de novo assembled transcriptomes. A total of 110,573 in silico SNPs and 2,870 indels were identified with an average of 3.7 SNPs per Kb. These SNPs were distributed in 17,041 unigenes. Of these polymorphic P. flexilis unigenes, 6,584 were highly conserved as compared to the genome sequence of P. taeda L (loblolly pine). High-throughput genotyping arrays were designed and were used to search for Cr4-linked genic SNPs in megagametophyte populations of four maternal trees by haploid-segregation analysis. A total of 32 SNP markers in 25 genes were localized on the Cr4 linkage group (LG). Syntenic relationships of this Cr4-LG map with the model conifer species P. taeda anchored Cr4 on Pinus consensus LG8, indicating that R genes against C. ribicola have evolved independently in different five-needle pines. Functional genes close to Cr4 were annotated and their potential roles in Cr4-mediated resistance were further discussed.

Conclusions

We demonstrated a very effective, low-cost strategy for developing a SNP genetic map of a phenotypic trait of interest. SNP discovery through transcriptome comparison was integrated with high-throughput genotyping of a small set of in silico SNPs. This strategy may be applied to mapping any trait in non-model plant species that have complex genomes. Whole transcriptome sequencing provides a powerful tool for SNP discovery in conifers and other species with complex genomes, for which sequencing and annotation of complex genomes is still challenging. The genic SNP map for the consensus Cr4-LG may help future molecular breeding efforts by enabling both Cr4 positional characterization and selection of this gene against WPBR.

Keywords

Disease resistance Genetic map Limber pine Transcriptome de novo assembly White pine blister rust

Background

Pinus flexilis E. James (limber pine) is a keystone five-needle pine species of the subgenus Strobus in mountain-top ecosystems of North America. Its high elevation distribution ranges ~1,600 m to > 3,300 m, much wider than any co-occurring conifer tree species. Populations dominate in dry, rocky, exposed windswept slopes; and its latitudinal range extends from 33°N to 51°N [1]. P. flexilis stands show slow growth in diverse environments across the landscape with longevity of some trees surpassing 1500 years, indicating that adaptation to different habitats has equipped this conifer species with a high capacity for physiological plasticity or broad physiological tolerances [2]. The ability of P. flexilis to colonize extreme environments and withstand climate variability makes it ecologically important in high elevation ecosystems. However, white pine blister rust (WPBR), caused by a non-native fungal pathogen Cronartium ribicola (J.C. Fisch.), threatens the sustainability of this conifer species and other five-needle pines in North America [3]. WPBR, in combination with mountain pine beetle (Dendroctonus ponderosae), limber pine dwarf mistletoe (Arceuthobium cyanocarpum), and climate change have caused widespread mortality in P. flexilis and reduced capacity for forest recovery throughout a significant portion of its range [46]; a 40 % loss in basal area of P. flexilis is projected by 2030 in the absence of intervention [7].

All native five-needle pines are highly susceptible to WPBR and their wild populations have been impacted to various degrees and at increasing rates [3]. To date, resistance breeding has been the main strategy used for WPBR management. Genetic resistance to WPBR is determined by dominant major resistance (R) genes or multiple genes with minor effects in the five-needle pine hosts [8]. Screening for major gene resistance (MGR) in breeding programs has identified four major R genes against C. ribicola, named Cr1, Cr2, Cr3, and Cr4 in P. lambertiana Dougl. (sugar pine) [9], P. monticola Dougl. ex D. Don (western white pine) [10], P. strobiformis Engelm. (southwestern white pine) [11], and P. flexilis [12], respectively. These R genes trigger a hypersensitive reaction (HR)-like defense that usually limits infection to the needles, precluding stem infection. Infection by different virulent inocula indicated that Cr1, Cr2, and Cr3 target at different avirulent (avcr) alleles [11]. Cr4 shows resistance to the virulent (vcr2) inoculum that overcomes Cr2 [12]. Although the classic gene-for-gene interaction [13] has been demonstrated in these WPBR pathosystems; neither R genes nor their corresponding avcr genes have been molecularly characterized.

Genetic maps of Cr1 and Cr2 were developed previously [1416], but we still do not know whether five-needle pine R genes are different alleles of the same R gene, or different R genes. The majority of characterized plant R genes belong to the super families encoding proteins with nucleotide binding site/leucine-rich repeat (NBS-LRR) or encoding receptor-like kinases (RLKs), which confer hosts complete resistance against various pathogens/pests, including biotrophic fungal pathogens [17]. For development of R gene-targeted DNA markers, NBS-LRR genes have been isolated from P. monticola [18] and P. lambertiana [19]. DNA markers (including single nucleotide polymorphism-SNPs) of the NBS-LRR genes were used as functional candidates for R gene characterization in the WPBR pathosystem [20]. In the two past decades, various types of DNA markers have been developed and used for genetic studies of WPBR resistance or for construction of genetic maps of five-needle pines [16, 2125]. However, DNA markers have not been developed in P. flexilis, and genetic diversity and molecular mechanisms underlying P. flexilis resistance to WPBR remain poorly understood.

Like other Pinus species, P. flexilis has a huge genome with 1 C of 31.2 pg [26]; the estimate of total genome length is about 30.5Gbp. Although the P. taeda draft genome has been assembled [27] and the cost of next generation sequencing (NGS) has decreased, sequencing the P. flexilis genome is still a challenging task due to its huge size. Identification of genetic determinants is critical for the development of disease resistant P. flexilis. Compared to agricultural plants, conifer breeding is a slow process, because identifying and pyramiding disease tolerance traits is far more challenging in these species with much longer life cycle, and larger but less well characterized genomes. DNA markers closely linked to R genes have the potential to strengthen the efficiency of breeding [28, 29]. In P. flexilis and other five-needle pines, resistant parents can be identified relatively quickly if a marker-assisted selection (MAS) tool is available.

This research was undertaken to identify a large number of SNPs in the P. flexilis transcriptome de novo assembled by RNA-seq analysis. SNPs were then applied to develop high-throughput genotyping arrays for genetic mapping of a Cr4 linkage group (LG) using populations selected from current breeding and conservation programs in North America. Cr4 was anchored on a Pinus consensus genetic map by comparison with P. taeda genetic maps.

Results

Phenotypic analysis of genetic resistance to C. ribicola

Four seedling families were assessed for phenotypic segregation of Cr4-mediated resistance for Cr4 genetic mapping. Their seed germination rates varied from 38-75 %, and seedling family GE-213, GE-214, LJ-112, and PHA-106 yielded 146, 145, 122, and 156 seedlings, respectively. The inoculation spore density delivered to the seedlings was measured at 11,670 spores/cm2 and C. ribicola basidiospores showed a germination rate of 97 %. Each seedling was phenotyped via inspections for signs and symptoms of disease and resistance; early inspections revealed C. ribicola needle infection spots (lesions) on all seedlings and 99 % of them presented with more than 10 needle infection spots, indicating a successful inoculation that challenged every seedling (Fig. 1). Cankered seedlings with production of spermatia were considered as a confirmed susceptible genotype, its occurrence ranging from 35.9 % (seedling family GE-213) to 61.8 % (seedling family PHA-106) with an average of 52.9 %. Phenotypic segregation for stem-cankered and stem-canker-free was consistent with previous tests [12].
Fig. 1

Disease phenology on Pinus flexilis seedlings with rust inoculation at 5-months old. a Needle lesions (spots) at 3 months post-inoculation (MPI); b Spermatia (pycnia) evident at 10 MPI, with arrows indicating pycnian drops; c Susceptible seedling killed by rust; d Resistant seedling growing in the 2nd year

P. flexilis needle transcriptome

RNA-seq analysis on needle tissues generated approximately 68, 72, and 70 million 100 bp PE reads for seedling families MRO-3501, UT-3359A, and NR-3647, respectively. Quality filtering by the read trim procedure removed about 0.3 % of total reads due to low quality. Our de novo assembled needle transcriptome using Trinity contained 163,075 transcripts with N50 at 1,861-bp and total transcriptome length of 158.5 Mbp (Additional file 1: Table S1). The mRNA sequences were predicted as expressed from 98,996 unigene sequences. A BLASTx search using the P. taeda proteome (84,522 proteins) revealed the transcriptome assembly contained 10,886 unique gene sequence highly conserved between P. flexilis and P. taeda (E value ≤ 10e-100). A total of 37,294 unique gene sequences showed significantly homology hits (BLASTx E value ≤ 10e-6) to the P. taeda proteome. These 37,294 Pinus conserved unigene sequences covered a total P. flexilis exome sequence length of 42.9 Mbp, which accounts for 0.14 % of the 30.5 Gbp genome of P. flexilis. A tBLASTn search of the P. taeda protein database (including 84,552 putative proteins) against our P. flexilis transcripts revealed that 94.3 % of them had significant homology hits (E < 10e-6) (Additional file 1: Table S2), suggesting that our de novo assembled P. flexilis transcriptome may have a relatively high coverage.

P. flexilis NBS-LRR genes and defense-related genes in response to C. ribicola infection were predicted using a BLAST search against the corresponding sequence sets from P. monticola. BLASTx analysis (E value ≤ 10e-10) identified 2,654 transcripts encoding NBS-LRR proteins expressed from 792 unigenes, and BLASTn analysis (E value ≤ 10e-100) identified 2,219 transcripts encoding putative WPBR-responsive proteins expressed from 827 unigenes in the P. flexilis needles (Additional file 1: Table S2).

Transcriptome comparison analysis identified 415 differentially expressed genes (DEGs) between resistant seedling family (NR-3647) and susceptible seedling families (MRO-3501 and UT-3359A). A total of 527 transcripts were expressed from these DEGs, and 142 DEGs for 152 transcripts were commonly detected in both comparisons of transcriptomes: NR-3647 vs. MRO-3501, or NR-3647 vs. UT-3359A (Fig. 2a). As revealed by GO annotation analysis on DEGs, the three top subcategories under the biological process category were oxidation-reduction process (score 64.3), cellular protein metabolic process (score 53.7), and response to stress (score 43.7). Fifty-three and 25 DEG sequences encoded for putative R-homologs (NBS-LRR and RLK) and pathogenesis-related proteins, respectively. This evidence suggests that DEGs may be involved in disease resistance and/or adaptation to local habitats.
Fig. 2

Transcriptome analysis of gene expression levels using RNA-seq. a Venn diagrams of differentially expressed genes (DEGs) as detected by comparisons of transcriptomes between resistant (NR-3647) and susceptible (UT-3359A and MRO-3501) seedling families. Numbers of transcripts expressed from DEGs are shown in the parentheses. b Expression values (FPKM) of the genes with SNPs selected for design of genotyping arrays. FPKM values were mapped to the genes in order from the largest to the smallest values, using the transcriptome of the resistant seedling family NR-3647 as a reference

In silico SNP calling

Using our de novo assembled transcriptome as a reference, a total of 110,573 SNPs and 2,870 indels were called in the three seedling families, and these polymorphisms were distributed among 21,561 transcripts expressed from 17,041 unique genes. On average, we detected 6.5 SNPs per unigene and 3.7 SNPs per Kb. Of 10,886 P. flexilis unigene sequences highly conserved between P. flexilis and P. taeda (BLASTx E value ≤ 10e-100), 6,584 (~60 % of total) were polymorphic with SNP distribution. A BLASTn search against P. taeda mapped gene sequences [30], yielded 2,165 P. flexilis polymorphic genes with identical hits (E values < e-100), providing anchored conserved genes for comparative mapping between P. taeda and P. flexilis.

Nucleotide variation counts detected 31,740, 34,622, 37,959 SNPs; as well as 234, 273, and 515 indels in the seedling families MRO-3501, UT-3359A, NR-3647, respectively. The majority of nucleotide variations (63.9 % to 67.2 % of total SNPs and 76.9 % to 85.0 % of total indels) were seed family-specific. Nucleotide variations shared among seed families were limited. Pair-wise comparison among the three seedling families revealed that only about 20 % of total SNPs from one seedling family were shared with another family (Fig. 3); i.e. most nucleotide variations were localized within individual seedling families. Those MGR seedling family-specific SNPs were potential sites for searching for Cr4-linked DNA markers at a next step.
Fig. 3

Number of single nucleotide polymorphisms detected in three seedling families of Pinus flexilis. The intersecting portions of the Venn diagram illustrate the number of shared loci in pair-wise comparison of seedling families

Cr4 genetic mapping by high-throughput SNP genotyping

A total of 324 in silico SNPs were selected in candidate gene groups (NBS-LRR or RLK gene families and putative WPBR-responsive genes) with high conservation among Pinus, and used to design Sequenom genotyping arrays. In addition to SNP coverages, gene expression was also considered in SNP selection. As shown in Fig. 2b, a majority of genes with selected SNPs (>83 %) shows good transcript expression (FPKM >1 at least in one seedling family), 21 genes were expressed differentially between resistant and susceptible seed families (p < 0.05 with FDR correction). Others were included due to their high homologies with candidate groups. These SNP markers were screened in the seedlings with identified phenotypes following C. ribicola infection. Seventy-two SNPs were selected to genotype all megagametophyte samples from four maternal trees. After manually checking SNPs, we constructed individual linkage maps for each maternal tree separately (Fig. 4). Linkage of SNP markers to Cr4-mediated phenotypes was significant in all four maternal trees (logarithm of odds-LOD ≤ 6 for GE-213, LOD ≤ 9 for GE-214, and LOD ≤ 10 for LJ-112 and PHA-106). The SNP markers shared among the four maternal trees were aligned manually and a total of 32 SNP markers were found to reside in 25 unique genes (Additional file 1: Table S3).
Fig. 4

Genetic linkage map of SNP markers in seedling families GE-213, GE-214, LJ-112, and PHA-106. Three SNP markers that segregated in all four genotyped seedling families are shown by red lines

Among the 32 Cr4-linked SNP markers, only three (snp296815Y, snp204077R1, and snp438219K) were shared across all four maternal trees. The same allele at each of these three shared SNP loci was linked in coupling to the resistant allele at the Cr4 locus in all four families used in the mapping experiments described here. Nine SNP loci (snp160798Y1, anp160798Y2, snp297341Y, snp355530R, snp382214M, snp416463K, snp444092M, snp463406Y, and snp86819R) were mapped in repulsion on the Cr4-LGs in different maternal trees. These results indicated high genetic divergence among the maternal trees from different geographical regions.

The order of the SNP markers was highly consistent across the maternal trees. Multiple SNPs from the same gene sequence (for example, snp463406Y and snp463406R) were usually mapped at the same position on the LG. A consensus Cr4-LG was constructed by merging four individual maternal maps with a total length of 47 cM and an average density of 1.5 cM per SNP marker. The Cr4 region was highly saturated; and SNP markers snp290363Y and snp296815Y were closest to Cr4 with genetic distances at 0.35 cM and 1.00 cM respectively (Fig. 4).

Comparative genetic map of Cr4 linkage group

We compared the Cr4-LG to P. taeda consensus maps reported recently [30]. All 25 genes mapped on the Cr4-LG showed identical hits to P. taeda genes, with BLASTn e-values ranging from 7.66E-109 to 0 (Additional file 1: Table S4). All 25 Pinus highly conserved genes were localized on the P. taeda consensus LG8; and 21 of them were mapped with relative positions on LG8. The Cr4-LG shows high collinearity when the SNP markers are compared according to the corresponding functional genes they tag (Additional file 2: Figure S1). The relative order of the mapped genes is highly correlated among two Pinus species with Pearson correlation R 2 = 0.886 (p < 0.00001) (Fig. 5). The comparative mapping anchored Cr4 on the Pinus consensus LG8 at a position around 146.74 cM.
Fig. 5

Scatter plot analysis to evaluate the correlation of marker positions of Pinus highly conserved genes between Pinus flexilis and P. taeda. P. taeda gene positions on Pinus consensus linkage group 8 were reported previously by Westbrook et al. [30]. The symbol "+" indicates the predicted position of the Cr4 locus

Functional genes close to Cr4

The 25 functional genes localized on the Cr4-LG map were annotated, and 21 of them encode proteins with putative biological functions (Additional file 1: Table S4). Several genes close to Cr4 were revealed with potential roles in genetic resistance against pathogens/pests, and they encoded putative proteins with significant homologies to class IV chitinase, NOTUM homolog isoform x1, zinc finger CCCH domain-containing (C3H-type ZF) protein, benzyl alcohol o-benzoyl transferase (EC: 2.3.1.196), aleurain-like thiol protease (EC: 3.4.22.16), nodulation-signaling pathway 1 (NSP1), and galactinol synthase 1 (GOLS1, EC: 2.4.1.123) (Additional file 1: Table S4). Other annotated genes that were mapped on the Cr4-LG included those encoding proteins with homologies to NBS-LRR protein, expansin, SHISA-5, endoglucanase, and protein sensitive to proton rhizotoxicity, suggesting their functions in plant defense responses to environmental stresses.

Discussion

Transcriptome SNP development by RNA-seq analysis

In the present study, Cr4 was mapped using a research strategy that integrated SNP development using RNA-seq analysis and subsequent high-throughput genotyping with only a few hundred in silico genic SNP markers. Recently we used this strategy for genetic identification of functional genes in significant linkage disequilibrium with P. monticola Cr2 for major gene resistance to C. ribicola [20]. As the cost is low and affordable for most research labs, this strategy may be useful for similar research on non-model conifer species. The P. flexilis de novo assembled transcriptomes and large number of genic SNPs identified in this study may provide useful genomic resources for further investigation of resistance mechanisms and selective adaptation to biotic/abiotic stresses and climate change in other native North American five-needle pines.

Molecular breeding of five-needle pines for resistance to WPBR would benefit from recent advance in conifer genomics studies, including the P. taeda genome sequence, the recent release of the P. lambertiana draft genome assembly [27] (also see www.pinegenome.org/pinerefseq/), and de novo assembled transcriptomes and related NGS data from other five-needle pines [25, 31, 32]. Genome annotation for protein-coding genes in Pinus species [27, 33] provides a valuable source of genic DNA markers for comparative analysis among related conifer species, with a high potential to determine candidates for map-based gene cloning and identification of functional markers for marker assisted selection in the WPBR pathosystems. Research approaches for this study have integrated genomic information previously reported in the genus Pinus to generate a consensus genetic map for Cr4-LG, an important step towards P. flexilis molecular breeding and further genomics research on resistance mechanisms.

Construction of Cr4-LG consensus map using functional SNP markers

Traditional DNA markers (such as RAPD, AFLP, and SSR) are developed on random genomic sequences without knowledge of the causal genes. Marker discovery has been greatly improved as NGS technologies have advanced and whole genome or transcriptome sequences in non-model plants have become available. The majority of SNP markers discovered through a strategy of genotyping by sequencing were localized in the non-genic regions [25]. Recently RNA-seq-based transcriptome profiling has been widely used for SNP discovery in forest conifer species [20, 32, 3437]. SNP markers derived from transcriptomes represent alleles of functional genes with transcript expression. The functional SNPs targeted at exomes may cause amino acid changes in the encoded proteins, with differentiation of protein structures and biochemical properties. This type of DNA marker thus has a potential to provide direct insight into the biological contribution of alleles underlying physiological traits of interest [38].

We constructed a consensus map by genetically localizing Cr4 using 32 SNP markers that represented 25 unique genes. These Cr4-linked genic SNP markers were selected from in silico SNPs by transcriptome comparison between Cr4-resistant and cr4-susceptible seedling families. Common SNP loci among four maternal trees that originated in three different geographical regions were used to build a consensus map for Cr4-LG. Comparison of the Cr4-LG maps among the four maternal trees revealed highly consistent marker orders for the 25 mapped genes, by which the consensus Cr4-LG map was constructed. We observed small discrepancies for the Cr4 position in four individual Cr4-maps, which may not suggest real chromosome rearrangements because different numbers of susceptible samples were calculated in each mapping population. As more recombination events are expected in the larger population resulting from the merging of four maternal trees, the Cr4 position on the consensus map is theoretically more reliable.

Cr4 is anchored on the Pinus consensus LG8

Localization of 25 Pinus conserved genes in the consensus Cr4-LG map gives us a solid framework for further comparative analysis of P. flexilis resistance with other pine species. By comparison with recently updated P. taeda consensus genetic maps [30], we anchored Cr4 to the Pinus consensus LG8 with a predicted location at 146.74 cM. The highly syntenous relationship between Cr4-LG and the Pinus consensus LG8 (R 2 = 0.886) will guide fine mapping using larger populations to increase marker density in the genomic region of Cr4 in a future study. Application of consensus maps with syntenic markers localized P. lambertiana Cr1 on the Pinus consensus LG2 [16], the same LG where P. taeda Fr1 for resistance to fusiform rust pathogen Cronartium quercuum sp. fusiforme (Cqf) was localized [27, 39]. P. monticola Cr2 was localized on Pinus consensus LG1 (Liu et al. unpublished data).

Localization of Cr1, Cr2, and Cr4 on different LGs (or chromosomes) indicates that R genes against C. ribicola have evolved independently in five-needle pines. C. ribicola invaded into North America only about one hundred years ago, suggesting that occurrence of the five-needle pine R genes is not caused by pathogen selection pressure and that they may have already been present in wild populations at rare frequencies in North America before the arrival of C. ribicola. Furthermore, the maternal trees sampled in this study, except PHA106, are from sites not yet invaded by C. ribicola. A further genomic comparison of Pinus R genes against C. ribicola and C. quercuum f. sp. fusiforme would generate new insights into the evolution of the innate immune response within the genus [27].

Cr4 functional candidates

Localization of functional genes as well as their SNPs on the Cr4-LG facilitates Cr4 characterization at the molecular level. Genetic mapping of the R gene families is an effective strategy for identification of positional R-gene candidates, development of high-density R-gene genetic maps, and design of diagnostic markers for breeding selection of resistance genotypes and QTL [40]. Several genes with tight linkage to Cr4 were identified as functional R candidate genes and defence-related genes in the P. flexilis immune system.

Contig_463406, encoding a NBS-LRR protein as a homolog of the tobacco N protein against tobacco mosaic virus (TMV), was mapped at a position of ~25 cM from Cr4, which indicates that it is not the Cr4 candidate. However, >60 % of NBS-LRR genes are organized in clusters in angiosperms, and R-genes families have been expanded by lineage-specific tandem duplications with high sequence similarity between duplicated gene copies [41]. Therefore, searching LP-463406 homologs is probably useful in finding other NBS-LRR genes as Cr4 positional candidates. P. flexilis contigs LP-296815, LP-290363, and LP-362068 were localized within about one cM from Cr4, providing landmark genes for searching for Cr4 candidates on the Pinus consensus maps. One cM genetic distance probably covers about 10 Mbp of genomic DNA sequence on the Pinus physical map [30]. As P. taeda and P. lambertiana genome draft assemblies are updated with improved completeness, this length of DNA sequence can be scrutinized for members of R gene families (such as NBS-LRR and RLK), which may include the targeted Cr4 gene itself.

Six functional genes were uncovered in a range of 3 cM from Cr4 encoding proteins with putative physiological roles in disease resistance, including class IV chitinase, NOTUM homolog, C3H-type ZF protein, benzyl alcohol o-benzoyltransferase, aleurain-like thiol protease, and NSP1. Class IV endochitinaes are a group of pathogenesis-related (PR) proteins functioning in plant defence against pathogens [42]. As glycosyl hydrolases, they catalyze the degradation of chitin, a β-1,4-linked polymer of N-acetylglucosamine (GlcNAc), a major structural component of fungal cell walls. P. monticola class IV endochitinases were up-regulated in both resistant and susceptible seedlings post-C. ribicola infection [31], and an association study demonstrated their significant contribution to partial resistance to C. ribicola [24].

C3H-type ZF proteins may have regulatory functions in mRNA processing during a series of plant developmental and adaptive processes, including plant defense responses to (a)biotic stresses [43]. Most C3H-type ZF genes are regulated by biotic or abiotic stresses in Arabidopsis and rice, suggesting that they may be involved in plant tolerance to stresses [44]. In the P. monticola C3H-type ZF family, expression of two members are upregulated in response to C. ribicola infection, one responsive in both resistant and susceptible genotypes and another responsive only in Cr2-resistant seedlings [20]. A loss-of-function mutant of an Arabidopsis C3H-type ZF protein showed an increased local susceptibility to a fungal pathogen and sensitivity to seed germination in the presence of ABA [45]. Overexpression of a cotton C3H-type ZF gene (GhZFP1) in transgenic tobacco increased resistance to a pathogenic fungus (Rhizoctonia solani) and enhanced tolerance to salt stress [43].

NOTUM-homologs encode pectinacetylesterase (PAE) in plants and fungi, catalyzing the deacetylation of pectin, a major compound of primary cell walls. Decreased pectin acetylation resulted in increased Arabidopsis resistance to microbial pathogens [4648], suggesting that pectin acetylation may play an important role in plant resistance to pathogens. Transgenic Arabidopsis plants with over-expression of an Aspergillus nidulans PAE gene (AnRAE) showed reduction of pectin and xyloglucan acetylation and enhanced resistance to Botrytis cinerea, which probably is achieved by H2O2 accumulation, up-regulation of defense-related genes, and callose deposition [47]. Deacetylated pectin is more easily degraded by endogenous and microbial polygalacturonases (PGs), leading to the accumulation of active oligogalacturonide (OG), and the latter functions as damage associated molecular patterns (DAMPs) in plant immunity system for constitutive and pathogen-induced resistance against pathogens [48].

A benzyl alcohol O-benzoyltransferase gene was up-regulated in tobacco leaves during a HR response to a pathogen infection [49]. This enzyme is probably involved in the formation of volatile ester benzylbenzoate, and at least some of these volatile esters may serve as antimicrobial or antifungal agents to prevent further spread of disease [50]. The biotrophic fungal pathogen Cladosporium fulvum contains a virulence factor (Avr2) that inhibits several host proteases (including Cys proteases, thiol proteases Aleurain, and Aleurain-like) required for plant basal defense [51].

NSP1 is a member of the GRAS transcription factor family. GRAS transcription factors are proposed to have high functional diversity and may act as integrators of multiple growth regulatory and environmental signals in non-legume plants. GRAS transcripts accumulated in tomato during incompatible interactions and silencing one of them impaired host resistance to bacterial speck disease [52]. The presence of NBS-LRR type R genes is possibly connected to repression of GRAS transcriptional regulators during interactions of potato (Solanum tuberosum) with Phytophthora infestans [53]. All of these results suggest that the Cr4-linked candidate genes may have putative roles in the plant immune system at different levels. However, none of these candidates may encode for an R protein directly involved in recognition of the C. ribicola avirulence factor (termed as avcr4), and the physiological roles of these genes in Cr4-mediated resistance still has to be investigated.

Potential application of Cr4-LG maps in MGR breeding

SNP markers of the Cr4-LG provide targeted genomic DNA sequences to develop MAS tools for P. flexilis breeding and conservation programs. The match rate of SNP genotypes with the phenotypic traits is an important factor for considering application of SNP markers in practical selection of desired plant traits and prediction of related phenotypic development [29], which depends on genetic distances between diagnostic markers and targeted genes that control the phenotypic trait. Ideal DNA markers should be valid at the species level across all geographic regions the plant species is distributed. A perfect match between genotypes of SNPs and the Cr4-mediated resistant phenotype was not detected consistently in all four mapped seedling families in the present study. The numbers of populations and SNPs were still limited for discovery of NBS-LRR or RLK genes with tight linkage to Cr4. Due to complex genomes, long life cycles, and difficulty in phenotyping, similar limitations were encountered in genetic mapping of other forest species [1416, 25, 30, 39]. Therefore, more research is needed to achieve complete accuracy of phenotype prediction within a molecular breeding program of a forest species. Functional SNPs within the Cr4 gene itself would be an ideal genomic tool for MGR selecting in P. flexilis, but positional cloning and functional determination of a conifer gene still requires considerable research effort. Nonetheless, following molecular characterization of R genes, R allele-specific sequences have been used to create marker-assisted tools for plant breeders to select resistance genotypes [54].

The present work identified SNP marker snp290363Y closest to Cr4 in the seed family GE-213 (with distance of ~ 0.3 cM), but it did not segregate in other three mapped seedling families. Searching additional SNPs from the gene contig_290363, or other Pinus conserved genes with physical linkage to this gene in the P. taeda genome sequence, may lead to identification of DNA markers for Cr4 selection with application in a wider range of P. flexilis populations.

Markers snp296815Y and snp204077R1 segregated in all four mapped seedling families and placed at loci to Cr4 at genetic distance of 1.12 and 3.21 cM on the consensus Cr4-LG. The same allele at these two SNP loci was linked in coupling to the resistant allele at the Cr4 locus in all four mapped seedling families, suggesting there may be a potential to use the SNP markers in selection of resistant progeny in related seedling families. It waits further confirmation if these two SNP markers are valid or not in more geographic regions. Using genome-wide genetic markers, genomic selection has the potential to improve P. taeda breeding by shortening the current breeding cycle from 12 to 20 years to less than 7 years [55]. Currently it takes 1 ~ 2 years for MGR screening in five-needle pines by artificial rust inoculation in greenhouses. Application of DNA markers will decrease selection time to one or two months as MGR prediction can be performed at the seed germination stage using a segment of cotyledons before limber pine seedlings are planted.

For many of the high elevation five-needle pines, including P. flexilis, little breeding work is done. There will be selection, identification of resistant parents (MGR and partial resistance); then management strategies will be developed accordingly [56]. In many cases seed is collected from wild populations, or from resistant parents to use for restoration or reforestation. Therefore, a technique like MAS which requires only needle collections, rather than cone collections followed by progeny tests, can greatly accelerate the identification of resistant seed trees across the landscape. For P. flexilis, genomic-based tools can assess the Cr4 status of younger non-reproductive trees in the field currently untestable with progeny tests, enabling timely assessments of the presence and frequency of Cr4 in populations that have sustained high mortality of mature trees as a result of disturbance such as the recent mountain pine beetle (Dendroctonus ponderosae) epidemic. Estimates of the frequency of Cr4 in stands and knowing how many MGRs there are in the species would help guide management actions [56]. In addition, the ability to confirm the Cr4 status of a formerly disease-free tree with recent WPBR symptoms allows for rapid detection of the evolution of virulence (vcr4) in C. ribicola.

The SNP marker snp204077R1 results in an amino acid change (Lys/His) of galactinol synthase 1, suggesting it may be functional marker affecting enzymatic activity. Arabidopsis galactinol synthase is stress-inducible and plays a key role in the accumulation of galactinol and raffinose as osmoprotectants in drought-stress tolerance of plants [57]. Vogan and Schoettle [58] found that Cr4 seedling families constitutively exhibited greater cold hardiness and lower stomatal conductance than susceptible seedling families during moderate drought. These results suggest that Cr4 and drought-tolerance genes may be co-inherited and there is a potential to select P. flexilis genotypes for better fitness to both biotic and abiotic stresses under a warming climate.

In addition to host markers for marker-assisted selection, pathogen effectors are now applied as molecular markers that accelerate and improve plant breeding of genetic resistance to various pathogens/pests [28]. As the C. ribicola reference transcriptome has been assembled and putative pathogenic effectors were identified [59], the avcr candidates can be used as pathogenic baits for biochemical screening of their corresponding R products in the five-needle pine. Molecular elucidation of the gene-for-gene model during five-needle pine-blister rust interactions is another strategy that could lead to the identification of the R genes in the WPBR pathosystems.

Conclusions

The present study represents the first research on genetic mapping of the P. flexilis major gene (Cr4) for resistance against C. ribicola by genotyping SNPs of candidate genes discovered by RNA-seq analysis. The first SNP dataset with more than 100,000 novel SNPs were uncovered in the P. flexilis exomes from three seedling families. A large number of non synonymous SNPs for amino acid changes in the encoded proteins provide novel insight into the mechanisms underlying the genetic variability of pest/pathogen resistance and other adaptive traits.

Methods

Plant materials and fungus inoculation

For Cr4 genetic mapping, seeds of four maternal trees GE-213, GE-214, LJ-112, and PHA-106, which have shown segregation of the Cr4-controlled canker-free trait in previous tests [12], were stratified in April and sown in May 2014 at Dorena Genetic Resource Center (Dorena-GRC). Megagametophyte samples were collected from each seedling during the seed germination stage, and subject to genomic DNA extraction for Cr4 genetic mapping based on haploid segregation analysis. Seedlings were inoculated with C. ribicola September 8-15, 2014 at Dorena-GRC following a previously reported protocol [12]. After inoculation, seedlings were moved to the greenhouse. WPBR disease symptoms were assessed for each inoculated seedling four times on January 12, February 18, April 22, and November 15, 2015.

For RNA-seq analysis, two susceptible seedling families (MRO-3501 and UT-3359A) and one MGR seedling family (NR-3647) were sown in April, 2010. The MGR seedling family was inoculated in Sept. 2010 and susceptible seedling families were not inoculated. Foliage from at least ten canker-free MGR-survivors or ten non-inoculated susceptible seedlings per seedling family was sampled using liquid nitrogen on July 21–22, 2014 at Dorena-GRC, and kept at -80 °C until RNA extraction.

Transcriptome assembly by RNA-Seq analysis

Needle samples of the three seedling families were used for total RNA extraction following a manual protocol as described previously [31]. After removal of genomic DNA by DNase (RNase-free) treatment for 30 min at 37 °C, total RNA was cleaned using an RNeasy Plant Mini Kit (Qiagen, Toronto, ON) and quantified using a NanoDrop Spectrophotometer ND-1000 (NanoDrop Technologies, Inc.). RNA integrity was assessed using a 2100 Bioanalyzer (Agilent Technologies). Total RNAs were pooled from 10 seedlings per seedling family. Messenger RNA was separated from 250 ng of total RNAs and cDNA libraries were constructed using a TruSeq stranded mRNA Sample Preparation Kit as per the manufacturer’s recommendations (Illumina). Libraries were quantified using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (D-Mark). Average size of cDNA fragment was estimated using a 2100 Bioanalyzer (Agilent Technologies). Each library was indexed by a sample-specific bar-coding tag and cDNA libraries were pooled in equal ratios for one lane run with 100-bp paired-ends (PE) using Illumina HiSeq2000 at the Génome Québec Innovation Centre, McGill University (Quebec, Canada).

Raw reads were first trimmed using Trimmomatic with default settings at ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 [60]. Trimmed reads from the three seedling families were used to generate a preliminary assembly by de novo assembly using Trinity (version: trinityrnaseq_r2013-02–25) with default k-mer length of 25 [61].

De novo assembled transcripts were annotated using the BLAST2GO program [62]. The P. taeda proteome (high quality protein sequences-9,024 and low quality protein sequences-75,528) derived from genome v1.01 [27] was used as a local database in BLAST programs. Pinus highly conserved genes were identified in BLASTx at homology E values ≤ 10e-100. P. monticola resistance gene analogs of the NBS-LRR and RLK families and defense responsive genes to C. ribicola infection [31] were used in BLASTp programs to search for corresponding homologs in the P. flexilis transcriptome.

Global gene expression analysis

Transcriptome profiles were compared among three seedling families. Trimmed reads from each seedling family were mapped to the de novo assembled transcriptome and only paired reads (fragments) were counted in read mapping with a minimum length fraction of 0.9 and a minimum similarity fraction of 0.9. Expression values were calculated as FPKM (Fragments Per Kilobase of exon per Million fragments mapped) using CLC Genomics Workbench 5.5 (CLC bio, QIAgen, Aarhus, Denmark). A resistant seedling family was used as reference group in comparisons. A Z-test was used for statistical analysis with FDR correction to identify differentially expressed genes (DEGs) with fold change > ±2.

SNP discovery and validation by high-throughput genotyping

In silico SNP detection was performed in each seedling family by mapping trimmed RNA-seq reads back to the de novo-assembled transcriptome using CLC Genomics Workbench (v5.5) with the following parameters: a mismatch cost of 1, indel cost of 3, length fraction of 0.95, and similarity fraction of 0.95. SNPs were detected using quality-based variation detection at the following parameters: neighborhood radius = 5, maximum gap and mismatch count = 1, minimum neighborhood quality = 20, minimum central quality = 20, ignore non-specific matches = yes, ignore broken pairs = no, minimum coverage = 10, minimum variant frequency (%) = 35, maximum expected alleles = 2, require presence in both forward and reverse reads = yes. The number of shared and unique SNPs was calculated based on pairwise comparisons among three seedling families.

A subset of SNPs were selected from the in silico data set generated above for high-throughput genotyping based on a few criteria related to putative gene functions, transcript expression, and SNP types as outlined in a previous report with a little modification [20]. In general, candidate genes with putative functions in disease resistance (the NBS-LRR and RLK gene families) and plant defense as related to WPBR infection, identified by BLAST searches as above, were the targets for a selection of SNPs. Among candidate genes, those Pinus highly conserved genes were included with a high priority for a comparative genetic mapping between different Pinus species. Of SNPs within candidate genes, we focused on those non-synonymous SNPs, which resulting in dramatic changes of amino acids (for example, changes between neutral amino acids and acidic or basic ones) for a potential to include putative functional SNPs in the genotyping arrays.

Megagametophyte tissues of individual seedlings were homogenized in liquid nitrogen using a FastPre-24 instrument (MP Biomedicals, Santa Ana, CA, USA). Genomic DNA was extracted using a DNeasy Plant Mini kit (Qiagen). SNP genotyping was performed using Sequenom iPLEX MassARRAY (Sequenom, Diego, CA, USA) at the Génome Québec Innovation Centre, McGill University as described previously [20]. A total of 324 in silico SNPs were selected for array design and screened for marker segregation analysis using 96 megagametophyte samples from three MGR seedling families, each seedling family with 16 susceptible and 16 resistant samples. Genes and their SNPs with the expected Mendelian segregation ratio (1:1) and potential association with the susceptible phenotype were selected and genotyped in all megagametophyte samples of four seedling families using Sequenom iPLEX SNP genotyping technology.

Genetic map construction

Haploid segregation analysis was performed to map SNP markers for construction of Cr4-LG as described previously [15]. Each SNP marker was tested for Mendelian segregation in each mapping population by X 2 (α = 0.05). Markers showing significant (P < 0.05) segregation distortion were initially eliminated from the map construction and were then added later as accessory markers. The polymorphic SNP markers were mapped to Cr4 using JoinMap version 3.0 software [63]. A LOD threshold of 6 and a distance threshold of 30 cM were used to define a LG. The Kosambi mapping function was used to calculate genetic distances. The consensus map was constructed directly from the marker names and genetic distances in the input maps using MergeMap [64]. The latest P. taeda consensus maps [30] were used as references to anchor Cr4 in Pinus LGs. The Pearson correlation coefficient was used to measure the strength of the genetic map relationship between two Pinus species. The sequence and nucleotide variation has been submitted to GenBank dbSTS and dbSNP databases.

Declarations

Acknowledgements

We would like to thank Gary Zhang, Hao Chen, and Aimin Guan for help on bioinformatic analyses. This research was supported by US-FS Special Technology Development Program (R2-2014-01) and the CFS-GRDI fund.

Authors’ contributions

J-JL, AWS, RAS, and RNS designed the study and performed the interpretation. AZ, HW, AH, and DC contributed to RNA-seq and genotyping analyses. BD, DPS, and AK performed phenotyping analysis. J-JL drafted the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Data accessibility

The Illumina 100-bp PE RNA-seq raw reads were deposited in the NCBI Sequence Read Archive (SRA) under accession numbers SRR3273741-SRR3273743. DNA sequences: Primers used for PCR and sequence data surrounding the SNPs are given in Additional file 1: Table S3.

Open AccessThis 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.

Authors’ Affiliations

(1)
Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada
(2)
USDA Forest Service, Rocky Mountain Research Station
(3)
USDA Forest Service, Dorena Genetic Resource Center

References

  1. Rebertus AJ, Burns AJ, Veblen TT. Stand dynamics of Pinus flexilis-dominated subalpine forests in the Colorado Front Range. J Veg Sci. 1991;2:445–58.View ArticleGoogle Scholar
  2. Schoettle AW, Rochelle SG. Morphological variation of Pinus flexilis (Pinaceae), a bird-dispersed pine, across a range of elevations. Am J Bot. 2000;87:1797–806.View ArticlePubMedGoogle Scholar
  3. Tomback DF, Achuff P. Blister rust and western forest biodiversity: ecology, values and outlook for white pines. Forest Pathol. 2010;40:186–225.View ArticleGoogle Scholar
  4. Smith CM, Langor DW, Myrholm C, Weber J, Gillies C, Stuart-Smith J. Changes in white pine blister rust infection and mortality in limber pine over time. Can J For Res. 2013;43:919–28.View ArticleGoogle Scholar
  5. Smith JM, Paritsis J, Veblen TT, Chapman TB. Permanent forest plots show accelerating tree mortality in subalpine forests of the Colorado Front Range from 1982 to 2013. For Ecol Manage. 2015;341:8–17.View ArticleGoogle Scholar
  6. Cleaver CM, Jacobi WR, Burns KS, Means RE. Limber pine in the central and southern Rocky Mountains: Stand conditions and interactions with blister rust, mistletoe, and bark beetles. For Ecol Manage. 2015;358:139–53.View ArticleGoogle Scholar
  7. Krist FJ, Ellenwood JR, Woods ME, McMahan AJ, Cowardin JP, Ryerson DE, Sapio FJ, Zweifler MO, Romero SA. 2013-2027 National insect and disease forest risk assessment. U.S. Department of Agriculture, Forest Service, Forest health technology enterprise team. 2014.Google Scholar
  8. Sniezko RA, Smith J, Liu J-J, Hamelin RC. Genetic resistance to fusiform rust in southern pines and white pine blister rust in white pines—a contrasting tale of two rust pathosystems—current status and future prospects. Forests. 2014;5:2050–83.View ArticleGoogle Scholar
  9. Kinloch Jr BB, Parks GK, Fowler CW. White pine blister rust: Simply inherited resistance in sugar pine. Can J Bot. 1970;58:1912–4.View ArticleGoogle Scholar
  10. Kinloch Jr BB, Sniezko RA, Barnes GD, Greathouse TE. A major gene for resistance to white pine blister rust in western white pine from the western Cascade Range. Phytopathol. 1999;89:861–7.View ArticleGoogle Scholar
  11. Kinloch Jr BB, Dupper GE. Genetic specificity in the white pine-blister rust pathosystem. Phytopathol. 2002;92:278–80.View ArticleGoogle Scholar
  12. Schoettle AW, Sniezko RA, Kegley A, Burns KS. White pine blister rust resistance in limber pine: evidence for a major gene. Phytopathol. 2014;104:163–73.View ArticleGoogle Scholar
  13. Flor HH. Current status of the gene-for-gene concept. Annu Rev Phytopathol. 1971;9:275–96.View ArticleGoogle Scholar
  14. Devey ME, Mix AD, Kinloch Jr BB, Neale DB. Random amplified polymorphic DNA markers tightly linked to a gene for resistance to white pine blister rust in sugar pine. Proc Natl Acad Sci U S A. 1995;92:2066–70.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Liu J-J, Ekramoddoullah AK, Hunt RS, Zamani A. Identification and characterization of random amplified polymorphic DNA markers linked to a major gene (Cr2) for resistance to Cronartium ribicola in Pinus monticola. Phytopathol. 2006;96:395–9.View ArticleGoogle Scholar
  16. Jermstad KD, Eckert AJ, Wegrzyn JL, Delfino-Mix A, Davis DA, Burton DC, Neale DB. Comparative mapping in Pinus: sugar pine (Pinus lambertiana Dougl.) and loblolly pine (Pinus taeda L.). Tree Genet Genomes. 2011;7:457–68.View ArticleGoogle Scholar
  17. Dangl JL, Horvath DM, Staskawicz BJ. Pivoting the plant immune system from dissection to deployment. Science. 2013;341:746–51.View ArticlePubMedGoogle Scholar
  18. Liu J-J, Ekramoddoullah AK. Isolation, genetic variation and expression of TIR-NBS-LRR resistance gene analogs from limber pine (Pinus monticola Dougl. ex. D. Don.). Mol Genet Genomics. 2004;270:432–41.View ArticleGoogle Scholar
  19. Jermstad KD, Sheppard LA, Kinloch BB, Delfino-Mix A, Ersoz ES, Krutovsky KV, Neale DB. Isolation of a full-length CC–NBS–LRR resistance gene analog candidate from sugar pine showing low nucleotide diversity. Tree Genet Genomes. 2006;2:76–85.View ArticleGoogle Scholar
  20. Liu J-J, Sniezko RA, Sturrock RN, Chen H. Western white pine SNP discovery and high-throughput genotyping for breeding and conservation applications. BMC Plant Biol. 2014;14:1586.Google Scholar
  21. Echt CS, Nelson CD. Linkage mapping and genome length in eastern white pine (Pinus strobus L.). Theor Appl Genet. 1997;94:1031–7.View ArticleGoogle Scholar
  22. Richardson BA, Rehfeldt GE, Kim M-S. Congruent climate related genecological responses from molecular markers and quantitative traits for western white pine (Pinus monticola). Int J Plant Sci. 2009;170:1120–31.View ArticleGoogle Scholar
  23. Liu J-J, Ekramoddoullah AK. Development of leucine-rich repeat polymorphism, amplified fragment length polymorphism, and sequence characterized amplified region markers to the Cronartium ribicola resistance gene Cr2 in western white pine (Pinus monticola). Tree Genet Genomes. 2008;4:601–10.View ArticleGoogle Scholar
  24. Liu J-J, Sniezko RA, Ekramoddoullah AK. Association of a novel Pinus monticola chitinase gene (PmCh4B) with quantitative resistance to Cronartium ribicola. Phytopathol. 2011;101:904–11.View ArticleGoogle Scholar
  25. Friedline CJ, Lind BM, Hobson EM, Harwood DE, Mix AD, Maloney PE, Eckert AJ. The genetic architecture of local adaptation I: The genomic landscape of foxtail pine (Pinus balfouriana Grev. & Balf.) as revealed from a high-density linkage map. Tree Genet Genomes. 2015;11:49.View ArticleGoogle Scholar
  26. Zonneveld BJM. Genome sizes of 172 species, covering 64 out of the 67 genera, range from 8 to 72 picogram. Nordic J Bot. 2012;30:490–502.View ArticleGoogle Scholar
  27. Neale DB, Wegrzyn JL, Stevens KA, Zimin AV, Puiu D, Crepeau MW, Cardeno C, Koriabine M, Holtz-Morris AE, Liechty JD, Martínez-García PJ, Vasquez-Gross HA, Lin BY, Zieve JJ, Dougherty WM, Fuentes-Soriano S, Wu L-S, Gilbert D, Marçais G, Roberts M, Holt C, Yandell M, Davis JM, Smith KE, Dean JF, Lorenz WW, Whetten RW, Sederoff R, Wheeler N, McGuire PE, Main D, Loopstra CA, Mockaitis K, deJong PJ, Yorke JA, Salzberg SL, Langley CH. Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies. Genome Biol. 2014;15:R59.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Vleeshouwers VG, Oliver RP. Effectors as tools in disease resistance breeding against biotrophic, hemibiotrophic, and necrotrophic plant pathogens. Mol Plant-Microbe Interact. 2014;27:196–206.View ArticlePubMedGoogle Scholar
  29. Ragimekula N, Varadarajula NN, Mallapuram SP, Gangimeni G, Reddy RK, Kondreddy HR. Marker assisted selection in disease resistance breeding. J Plant Breed Genet. 2013;1:90–109.Google Scholar
  30. Westbrook JW, Chhatre VE, Wu L-S, Chamala S, Neves LG, Muñoz P, Martínez-García PJ, Neale DB, Kirst M, Mockaitis K, Nelson CD, Peter GF, Davis JM, Echt CS. A consensus genetic map for Pinus taeda and Pinus elliottii and extent of linkage disequilibrium in two genotype-phenotype discovery populations of Pinus taeda. G3: Genes Genomes. Genetics. 2015;5:1685–94.Google Scholar
  31. Liu J-J, Sturrock RN, Benton R. Transcriptome analysis of Pinus monticola primary needles by RNA-seq provides novel insight into host resistance to Cronartium ribicola. BMC Genomics. 2013;14:884.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Wachowiak W, Trivedi U, Perry A, Cavers S. Comparative transcriptomics of a complex of four European pine species. BMC Genomics. 2015;16:234.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Wegrzyn JL, Liechty JD, Stevens KA, Wu L-S, Loopstra CA, Vasquez-Gross HA, Dougherty WM, Lin BY, Zieve JJ, Martínez-García PJ, Holt C, Yandell M, Zimin AV, Yorke JA, Crepeau MW, Puiu D, Salzberg SL, Dejong PJ, Mockaitis K, Main D, Langley CH, Neale DB. Unique features of the loblolly pine (Pinus taeda L.) megagenome revealed through sequence annotation. Genetics. 2014;196:891–909.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Varshney RK, Nayak SN, May GD, Jackson SA. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol. 2009;27:522–30.View ArticlePubMedGoogle Scholar
  35. Chen J, Uebbing S, Gyllenstrand N, Lagercrantz U, Lascoux M, Kallman T. Sequencing of the needle transcriptome from Norway spruce (Picea abies Karst L.) reveals lower substitution rates, but similar selective constraints in gymnosperms and angiosperms. BMC Genomics. 2012;13:589.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Canales J, Bautista R, Label P, Gómez-Maldonado J, Lesur I, Fernández‐Pozo N, Rueda-López M, Guerrero-Fernández D, Castro-Rodríguez V, Benzekri H, Cañas RA, Guevara MA, Rodrigues A, Seoane P, Teyssier C, Morel A, Ehrenmann F, Le Provost G, Lalanne C, Noirot C, Klopp C, Reymond I, García-Gutiérrez A, Trontin JF, Lelu-Walter MA, Miguel C, Cervera MT, Cantón FR, Plomion C, Harvengt L, Avila C, Gonzalo Claros M, Cánovas FM. De novo assembly of maritime pine transcriptome: implications for forest breeding and biotechnology. Plant Biotechnol J. 2014;12:286–99.View ArticlePubMedGoogle Scholar
  37. Pinosio S, González-Martínez SC, Bagnoli F, Cattonaro F, Grivet D, Marroni F, Lorenzo Z, Pausas JG, Verdú M, Vendramin GG. First insights into the transcriptome and development of new genomic tools of a widespread circum-Mediterranean tree species, Pinus halepensis Mill. Mol Ecol Resour. 2014;14:846–56.View ArticlePubMedGoogle Scholar
  38. Poczai P, Varga I, Laos M, Cseh A, Bell N, Valkonen JP, Hyvönen J. Advances in plant gene-targeted and functional markers: a review. Plant Methods. 2013;9:6.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Quesada T, Resende Jr MFR, Munoz P, Wegrzyn JL, Neale DB, Kirst M, Peter GF, Gezan SA, Nelson CD, Davis JM. Mapping fusiform rust resistance genes within a complex mating design of loblolly pine. Forests. 2014;5:347–62.View ArticleGoogle Scholar
  40. Sekhwal MK, Li P, Lam I, Wang X, Cloutier S, You FM. Disease resistance gene analogs (RGAs) in plants. Int J Mol Sci. 2015;16:19248–90.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Zhang R, Murat F, Pont C, Langin T, Salse J. Paleo-evolutionary plasticity of plant disease resistance genes. BMC Genomics. 2014;15:187.View ArticlePubMedPubMed CentralGoogle Scholar
  42. van Loon LC, Rep M, Pieterse CM. Significance of inducible defense-related proteins in infected plants. Annu Rev Phytopathol. 2006;44:135–62.View ArticlePubMedGoogle Scholar
  43. Guo YH, Yu YP, Wang D, Wu CA, Yang GD, Huang JG, Zheng CC. GhZFP1, a novel CCCH-type zinc finger protein from cotton, enhances salt stress tolerance and fungal disease resistance in transgenic tobacco by interacting with GZIRD21A and GZIPR5. New Phytol. 2009;183:62–75.View ArticlePubMedGoogle Scholar
  44. Wang D, Guo Y, Wu C, Yang G, Li Y, Zheng C. Genome-wide analysis of CCCH zinc finger family in Arabidopsis and rice. BMC Genomics. 2008;9:44.View ArticlePubMedPubMed CentralGoogle Scholar
  45. AbuQamar S, Chen X, Dhawan R, Bluhm B, Salmeron J, Lam S, Dietrich RA, Mengiste T. Expression profiling and mutant analysis reveals complex regulatory networks involved in Arabidopsis response to Botrytis infection. Plant J. 2006;48:28–44.View ArticlePubMedGoogle Scholar
  46. Manabe Y, Nafisi M, Verhertbruggen Y, Orfila C, Gille S, Rautengarten C, Cherk C, Marcus SE, Somerville S, Pauly M, Knox JP, Sakuragi Y, Scheller HV. Loss-of-function mutation of REDUCED WALL ACETYLATION2 in Arabidopsis leads to reduced cell wall acetylation and increased resistance to Botrytis cinerea. Plant Physiol. 2011;155:1068–78.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Pogorelko G, Lionetti V, Fursova O, Sundaram RM, Qi M, Whitham SA, Bogdanove AJ, Bellincampi D, Zabotina OA. Arabidopsis and Brachypodium transgenic plants expressing A. nidulans acetylesterases have decreased degree of polysaccharide acetylation and increased resistance to pathogens. Plant Physiol. 2013;162:9–23.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Pogorelko G, Lionetti V, Bellincampi D, Zabotina O. Cell wall integrity: Targeted post-synthetic modifications to reveal its role in plant growth and defense against pathogens. Plant Signal Behav. 2013;8:e25435.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Czernic P, Huang HC, Marco Y. Characterization of hsr201 and hsr515, two tobacco genes preferentially expressed during the hypersensitive reaction provoked by phytopathogenic bacteria. Pant Mol Biol. 1996;31:255–65.View ArticleGoogle Scholar
  50. D’Auria JC, Chen F, Pichersky E. Characterization of an acyltransferase capable of synthesizing benzylbenzoate and other volatile esters in flowers and damaged leaves of Clarkia breweri. Plant Physiol. 2002;130:466–76.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Van Esse P, van’t Klooster JW, Bolton MD, Yadeta KA, van Baarlen P, Boeren S, Vervoort J, de Wit PJGM, Thomma BPHJ. The Cladosporium fulvum virulence protein Avr2 inhibits host proteases required for basal defense. Plant Cell. 2008;20:1948–63.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Mayrose M, Ekengren SK, Melech-Bonfil S, Martin GB, Sessa G. A novel link between tomato GRAS genes, plant disease resistance and mechanical stress response. Mol Plant Pathol. 2006;7:593–604.View ArticlePubMedGoogle Scholar
  53. Gyetvai G, Sønderkær M, Göbel U, Basekow R, Ballvora A, Imhoff M, Kersten B, Nielsen K-L, Gebhardt C. The transcriptome of compatible and incompatible interactions of potato (Solanum tuberosum) with Phytophthora infestans revealed by DeepSAGE analysis. PLoS ONE. 2012;7(2):e31526.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Vleeshouwers VG, Raffaele S, Vossen JH, Champouret N, Oliva R, Segretin ME, Rietman H, Cano LM, Lokossou A, Kessel G, Pel MA, Kamoun S. Understanding and exploiting late blight resistance in the age of effectors. Annu Rev Phytopathol. 2011;49:507–31.View ArticlePubMedGoogle Scholar
  55. Resende Jr MF, Muñoz P, Acosta JJ, Peter GF, Davis JM, Grattapaglia D, Resende MD, Kirst M. Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. New Phytol. 2012;193:617–24.View ArticlePubMedGoogle Scholar
  56. Schoettle AW, Cleaver CM, Burns KS, Connor JJ. Limber pine conservation strategy for the greater Rocky Mountain National Park area. General Technical Report RMRS-GTR-xxx. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 2016; (in press).Google Scholar
  57. Taji T, Ohsumi C, Iuchi S, Seki M, Kasuga M, Kobayashi M, Yamaguchi-Shinozaki K, Shinozaki K. Important roles of drought- and cold-inducible genes for galactinol synthase in stress tolerance in Arabidopsis thaliana. Plant J. 2002;29:417–26.View ArticlePubMedGoogle Scholar
  58. Vogan PJ, Schoettle AW. Selection for resistance to white pine blister rust affects the abiotic stress tolerances of limber pine. For Ecol Manage. 2015;344:110–9.View ArticleGoogle Scholar
  59. Liu J-J, Sturrock RN, Sniezko RA, Williams H, Benton R, Zamany A. Transcriptome analysis of the white pine blister rust pathogen Cronartium ribicola: de novo assembly, expression profiling, and identification of candidate effectors. BMC Genomics. 2015;16:678.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics. 2014;30(15):2114–20.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, Macmanes MD, Ott M, Orvis J, Pochet N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R, Leduc RD, Friedman N, Regev A. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.View ArticlePubMedGoogle Scholar
  62. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.View ArticlePubMedGoogle Scholar
  63. Van Ooijen JW, Voorrips RE. JoinMap® version 3.0: software for the calculation of genetic linkage maps. Wageningen: Plant Research International; 2001.Google Scholar
  64. Wu Y, Close TJ, Lonardi S. Accurate construction of consensus genetic maps via integer linear programming. IEEE/ACM Tran Comput Biol Bioinform. 2011;8:381–94.View ArticleGoogle Scholar

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