New Results

Characterization of the grape powdery mildew genetic resistance loci in Muscadinia rotundifolia Trayshed

Mélanie Massonnet, Amanda M. Vondras, Noé Cochetel, Summaira Riaz, Dániel Pap, Andrea Minio, Rosa Figueroa-Balderas, M. Andrew Walker, Dario Cantu
doi: https://doi.org/10.1101/2021.11.23.469029

Abstract

Muscadinia rotundifolia cv. Trayshed is a valuable source of resistance to grape powdery mildew (PM). It carries two PM resistance (R) loci, Run1.2 on chromosome 12 and Run2.2 on chromosome 18. This study identified the nucleotide-binding leucine-rich repeat (NLR) genes composing each R locus and their associated defense mechanisms. Evaluation of PM disease development showed that introgression of both loci confers resistance to PM in a V. vinifera background, but with varying speed and intensity of the response. To better understand the effect of NLR composition on PM resistance, both haplotypes of each R locus were reconstructed and the gene models within each haplotype were manually refined. We found that the number and classes of NLR genes differed between Run1.2 and Run2.2 loci and between the haplotypes of each R locus. In addition, NLR genes composing Run1.2b or Run2.2 loci exhibited different levels of gene expression, pointing to candidate NLR genes responsible for PM resistance in Trayshed. Finally, a transcriptomic analysis that included six additional R loci showed differences in the defense mechanisms associated with Run1.2b and Run2.2 in response to PM and at constitutive level. Altogether, our results reveal that Trayshed’s R loci are composed of distinct NLRs that trigger different plant defense mechanisms in response to PM and at constitutive level, which would explain the variation of pathogen restriction between the two loci.

Introduction

Grapevine powdery mildew (PM) is a devastating fungal disease caused by Erysiphe necator Schwein. (syn. Uncinula necator), an obligate biotrophic ascomycete that can infect all green organs of a grapevine (Gadoury et al., 2012). Cultivated grapevines that belong to Vitis vinifera (ssp. vinifera) are highly susceptible to PM. Fungicide sprays are applied prophylactically to control the disease but are costly (Sambucci et al., 2019). Natural resistance to PM exists in several wild grapes. Thirteen PM resistance-associated loci were identified in the last two decades (Dry et al., 2019; Karn et al., 2021). Vitis includes several PM-resistant species, including V. romanetii (Ramming et al., 2010; Riaz et al., 2011) and V. piasezkii (Pap et al., 2016), which are native to China, V. vinifera ssp. sylvestris from Central Asia (Riaz et al., 2020), the North American V. cinerea (Dalbó et al., 2001), and the muscadine grape, Muscadinia rotundifolia (Pauquet et al., 2001; Feechan et al., 2013; Riaz et al., 2011).

Muscadinia rotundifolia is closely related to Vitis (Small, 1913) and is resistant to several diseases in addition to PM (Olmo, 1971; Olmo, 1986), including downy mildew (Plasmopara viticola), Pierce’s disease (Xylella fastidiosa), and phylloxera (Daktulosphaira vitifolia). Two major loci associated with PM resistance were found in M. rotundifolia. Resistance to Uncinula necator 1 (Run1), located on chromosome 12, and its alternative form, Run1.2, were identified in M. rotundifolia G52 and Trayshed, respectively (Pauquet et al. 2001; Riaz et al., 2011). Run2.1 and Run2.2 were identified on chromosome 18 of M. rotundifolia Magnolia and Trayshed, respectively (Riaz et al., 2011). Both haplotypes of Trayshed’s Run1.2 were associated with PM resistance and designated Run1.2a and Run1.2b (Feechan et al., 2013).

M. rotundifolia is an ideal partner for breeding PM-resistant grapevines that are durably resistant and require fewer fungicidal applications. This can be facilitated by functionally characterizing the mechanisms of PM resistance in wild grapes and by introgressing functionally diverse PM resistance-associated genes into V. vinifera (Michelmore et al., 2013). In wild grapes, PM resistance is associated with a programmed cell death (PCD)-mediated response in infected epidermal cells. This suggests that PM resistance is based on an intracellular recognition of E. necator’s effectors by disease resistance (R) proteins that activate effector-triggered immunity (Qiu et al., 2015; Dry et al., 2019).

Most R genes encode nucleotide-binding leucine-rich repeat (NLR) proteins (Dubey and Singh, 2018). NLRs are intracellular receptors that recognize and interact directly with pathogen-derived effectors, detect modifications in host cellular targets, or detect molecular decoys triggered by effectors (Dangl et al., 2013). NLR activation leads to the induction of immune responses that can restrict pathogen spread (Jones and Dangl, 2006). These include calcium oscillations, a rapid burst of reactive oxygen species (ROS), extensive transcriptional reprogramming that leads to cell wall modifications, and the synthesis of pathogenesis-related (PR) proteins and antimicrobial compounds (Jones and Dangl, 2006; Dangl et al., 2013; Kretschmer et al., 2019). Effector-triggered immunity is often associated with a hypersensitive response and PCD of infected plant cells that restrict further pathogen development (Jones and Dangl, 2006). NLR intracellular receptors are typically composed of three domains: a C-terminal LRR domain, a central nucleotide-binding site domain (NBS), and a variable N-terminal domain (Meyers et al., 1999; McHale et al., 2006). The variable N-terminal domain distinguishes NLR classes. The three main NLR classes are the TIR-NBS-LRRs, CC-NBS-LRRs, and RPW8-NBS-LRRs; these possess N-terminal toll/interleukin-1 receptor-like (TIR), Coiled-coil (CC), and resistance to powdery mildew 8 (RPW8) domains, respectively (Meyers et al., 1999; McHale et al., 2006; Xiao et al., 2001; Michelmore et al., 2013). Only two TIR-NBS-LRR genes, MrRPV1 and MrRUN1, have been functionally characterized in grapes (Feechan et al., 2013). MrRPV1 and MrRUN1 are at the Run1 locus of M. rotundifolia G52 and confer resistance to downy mildew and PM, respectively. The first diploid chromosome-scale genome assembly of a muscadine grape was recently published and represents a valuable resource for characterizing the genetic basis of other PM resistance loci in M. rotundifolia (Cochetel et al., 2021). A first analysis of the Run1.2 locus suggested an expansion of TIR-NBS-LRR genes in M. rotundifolia Trayshed relative to Cabernet Sauvignon (Cochetel et al., 2021).

In this study, we describe the structure and content of the Run1.2 and Run2.2 loci of M. rotundifolia Trayshed and the mechanisms of defense they provide. We evaluated PM disease development in Run1.2b+ and Run2.2+ genotypes and thirteen additional accessions that included the loci Resistance to E. necator 2 (Ren2), Ren3, Ren4D, Ren4U, Run1, and Run2.1. Haplotypes of Trayshed’s R loci were differentiated and reconstructed by sequencing two backcrossed V. vinifera lines carrying either Run1.2b or Run2.2. Gene models in both loci were manually curated to identify the genes encoding NLRs. To identify NLR genes associated with PM resistance, NLR genes’ expression in Run1.2b and Run2.2 were profiled with and without PM present. Finally, we evaluated the defense mechanisms associated with each of Trayshed’s R loci by comparing them to six additional R loci using RNA-sequencing (RNA-seq).

Results

Run1.2b and Run2.2 loci exhibit different intensity and timing of the PM response

PM disease development was evaluated on detached leaves from e6-23 (Run1.2b+) and 08391-29 (Run2.2+), two V. vinifera lines into which Run1.2b and Run2.2 loci were introgressed by crossing with M. rotundifolia Trayshed and backcrossing with V. vinifera (Supplementary Table S1). Disease development was also assessed on leaves from their PM-susceptible siblings, e5-96 (Run1.2b-) and 08391-28 (Run2.2-), and eleven additional grape accessions, including nine interspecific accessions, and two PM-susceptible V. vinifera, Malaga Rosada and F2-35 (Supplementary Table S1). The nine interspecific hybrids included six siblings derived from backcrosses with one of the following resistant accessions: V. romanetii C166-043 (Ren4D+), V. romanetii C166-026 (Ren4U+), or M. rotundifolia G52 (Run1+) (Ramming et al., 2011; Riaz et al., 2011; Feechan et al., 2013). The remaining three interspecific accessions were 09390-023 (Ren2+), 07712-06 (Ren3+), and 09705-45 (Run2.1+); these inherited PM resistance from V. cinerea B9 (Dalbó et al., 2001), interspecific Villard blanc (Zyprian et al., 2016), and M. rotundifolia Magnolia (Riaz et al., 2011), respectively.

At 5 days post-inoculation (dpi), PM growth was assessed using microscopy (Figure 1a). Little or no mycelia growth was visible on Run1.2b+ leaves and the genotypes carrying Ren4D, Ren4U, Run1 loci, though some secondary hyphae development was observable on Run2.2+, Ren2+, Ren3+, and Run2.1+ genotypes’ leaves (Figure 1a). In contrast, widespread hyphal growth and conidiophore development were observed on leaves from two PM-susceptible V. vinifera cultivars, F2-35 and Malaga Rosada, and all the sibling lines devoid of PM resistance loci (Figure 1a). Necrotic spots were observed beneath appressoria of germinated fungal spores and developing secondary hyphae in all the grape accessions possessing a R locus. This suggests that a hypersensitive, PCD response occurred in the epidermal cells of these genotypes after pathogen penetration, but at different speeds and levels of efficacy. PM biomass on leaves was also estimated by measuring E. necator transcripts. Genotypes carrying a PM resistance locus had significantly lower E. necator transcripts at 5 dpi than genotypes without a PM resistance locus (Figure 1b,c). Significant differences in E. necator transcripts were also observed between Run2.2+, Ren2+, and the other PM-resistant accessions (Kruskal-Wallis test followed by post hoc Dunn’s test; P value ≤ 0.05).

Figure 1:
Figure 1:

Powdery mildew disease development at 5 days post-inoculation. (a) Micrographs of detached leaves stained with Coomassie Brilliant blue to visualize the development of fungal structures on the leaf surface. The ‘brown’ cells below fungal appressoria are epidermal cells that produced a hypersensitive response (HR) to PM. Scale = 100 μm. Total Transcripts per Million (TPM) derived from E. necator transcriptome in PM-resistant (b) and PM-susceptible (PM-S) accessions (c). Significant differences between PM resistance loci are indicated by different letters (Kruskal-Wallis test followed by post hoc Dunn’s test; P value ≤ 0.05). Significant differences between genotypes carrying PM resistance loci and PM-susceptible accessions are indicated by an asterisk (Kruskal-Wallis test, P value = 1.5 × 10−8).

PM growth was also assessed at an advanced disease development stage (14 dpi) using visual scoring (Supplementary Figure S1a). Infection rate at 5 and 14 dpi was similar for all genotypes except Ren2+ and Ren3+, which both had extensive mycelium growth on their leaves at 14 dpi (Kruskal-Wallis test followed by post-hoc Dunn’s test; P value ≤ 0.05). E. necator transcript abundance at 5 dpi was correlated with PM infection scores at 14 dpi (R2 = 0.73; Supplementary Figure S1b), supporting that quantifying E. necator transcript abundance is an effective way to estimate PM susceptibility.

These results show that R loci confer post-penetration resistance to PM in V. vinifera. However, PM resistance level varies between the Run1.2b and Run2.2 loci, suggesting that Trayshed’s R loci differ in the perception of the pathogen and/or in the efficiency of the defense response to the pathogen. This implies differences in terms of intracellular receptors (i.e., NLR composition), NLR gene expression, and/or activated defense-related mechanisms between the Run1.2b and Run2.2 loci.

Structural variants between Trayshed’s Run1.2 haplotypes affect NLR content

The haplotypes of Trayshed’s R loci, Run1.2 and Run2.2, were differentiated to determine the effect of NLR composition on PM resistance. The boundaries of Run1.2 were assigned by aligning the primer sequences of Run1.2-associated SSR markers to both haplotypes on M. rotundifolia Trayshed chromosome 12 (Cochetel et al., 2021). To distinguish Run1.2a and Run1.2b, we sequenced the V. vinifera backcross, e6-23 (Run1.2b+; Supplementary Table S1; Supplementary Table S2). Short sequencing reads from the Run1.2b+ accession covered and aligned perfectly (i.e., with no mismatches) to most of Run1.2 on Haplotype 2 (Supplementary Figure S2), and coverage gaps in Run1.2 on Haplotype 2 were complemented by coverage at Run1.2 on Haplotype 1. This indicates that haplotype switching occurred during Trayshed assembly and phasing. To correct this, Run1.2b was reconstructed using only sequences supported with DNA sequencing reads from e6-23 (Run1.2b+) and Run1.2a was reconstructed using alternative sequences (Figure 2a). The reconstructed Run1.2a and Run1.2b loci were 4.34 Mbp- and 3.38 Mbp-long, respectively. Differences in length between the two haplotypes were associated with several large structural variants (SVs; > 50 bp). For instance, the region of Run1.2b from ∼12 Mbp to 12.3 Mbp corresponds to a ∼800kbp region in the Run1.2a haplotype (Figure 2a). In this case, length difference was due to several inserted sequences and duplication events in Run1.2a compared to Run1.2b. We detected 32,704 SNPs and 7,150 INDELs between the two Run1.2 haplotypes, Run1.2a and Run1.2b.

Figure 2:
Figure 2:

Haplotype comparison and NLR content at Run1.2 and Run2.2 in M. rotundifolia Trayshed. Whole-sequence alignments of the reconstructed haplotypes of Run1.2 (a) and Run2.2 (b) loci. Normalized median DNA-seq coverage per 10 kbp of e6-23 (Run1.2b+) and 08391-29 (Run2.2+) on the diploid genome of M. rotundifolia Trayshed was used to identify Run1.2b and Run2.2 on the Haplotype 2 of chromosome 12 and Haplotype 1 of chromosome 18, respectively. Only DNA-seq reads aligning perfectly on the diploid genome of M. rotundifolia Trayshed were used for base coverage analysis. Chromosomal position of the Run1.2- and Run2.2-associated genetic markers is indicated by black triangles and dashed lines.

We refined the gene models for both Run1.2 haplotypes to determine the effect of SVs and short polymorphisms at the locus. A total of 78 protein-coding genes, including 22 NLR genes, were manually annotated. Run1.2a contained 253 genes and Run1.2b contained 189 genes, indicating that SVs affect gene content. There were 37 and 24 NLR genes in Run1.2a and Run1.2b, respectively, with both composed primarily of CC-NBS-LRR, TIR-NBS-LRR and NBS-LRR genes (Figure 2a; Supplementary Table S3). SVs between haplotypes affect the protein-coding sequences of 22 NLR genes in Run1.2a and 9 NLR genes in Run1.2b. Non-synonymous SNPs were identified in 8 NLR genes in each haplotype.

Run2.2 is mainly composed of TIR-NBS-LRRs

A similar approach was applied to identify and reconstruct Run2.2 in the Haplotype 1 of chromosome 18 in M. rotundifolia Trayshed using short sequencing reads from the genotype 08391-29 (Run2.2+) (Supplementary Tables S1 & S2; Supplementary Figure S3). Reconstructed Run2.2 was 3.45 Mbp long, slightly longer than its alternative on Haplotype 2 chromosome 18 (3.14 Mbp). We manually refined the models of 102 protein-coding genes in the two haplotypes, including 55 NLR genes. More genes were annotated at Run2.2 (207) than at its alternative (179). There were 39 NLR-coding genes at Run2.2 and 29 NLR genes in its alternative. The two haplotypes were mainly composed of TIR-NBS-LRR genes, with 20 genes in Run2.2 and 21 genes in its alternative (Supplementary Table S3). Unlike Run1.2, no NLR genes with a CC or RPW8 N-terminal domain were found at Run2.2. Interestingly, the NLR genes occurred in two clusters in each haplotype (Figure 2b).

Run2.2 and its alternative on Haplotype 2 chromosome 18 contained 456 SVs between them, with an average length of 2.1 ± 3.6 kbp. There were also 24,128 SNPs and 5,773 INDELs. SVs affected 21 and 16 NLR genes in Run2.2 and its alternative, respectively, and non-synonymous SNPs were detected in 16 and 18 NLR genes, respectively.

Run1.2 and Run2.2 loci contain distinct sets of NLRs

NLRs identified in Run1.2 and Run2.2 were compared by constructing a phylogeny using their predicted protein sequences in the four reconstructed haplotypes (Run1.2a, Run1.2b, Run2.2, and its alternative on Haplotype 2 chromosome 18; Figure 3a). In addition, we compared Trayshed’s NLRs with the TIR-NBS-LRRs at Run1/Rpv1 in M. rotundifolia G52 (Barker et al., 2005; Feechan et al., 2013). Run1/Rpv1 is the only R locus characterized in grapes and is an alternative version of Run1.2. Two distinct groups of NLRs were discovered, distinguished by the presence or absence of a TIR domain. A similar clustering pattern was observed when a phylogeny was built using nucleotide-binding domain sequences only (Supplementary Figure S4), as previously observed in other plants (Prigozhin and Krasileva, 2021; Seo et al., 2016). For TIR-containing proteins, NLRs tended to cluster by R locus, with the TIR-NBS-LRRs from Run1/Rpv1 clustering with TIR-NBS-LRR proteins from Run1.2. MrRPV1 from M. rotundifolia G52 clustered with two TIR-NBS-LRRs, one from each Run1.2 haplotype, and MrRUN1 clustered with a TIR-NBS-LRR from Run1.2a (Figure 3a). However, number of LRR motifs in their LRR domain was different (Figure 3b), suggesting that these TIR-NBS-LRRs might be specific to different effectors and/or pathogens.

Figure 3:
Figure 3:

Comparison of the NLRs composing Run1.2 and Run2.2 in M. rotundifolia Trayshed. (a) Neighbor-joining clustering of the predicted NLR protein sequences at Run1.2, Run2.2 and the resistance gene analogs (RGAs) identified at Run1/Rpv1 (Feechan et al., 2013). (b) Domain diagram of Trayshed’s TIR-NBS-LRRs clustering with G52’s MrRUN1 and MrRPV1. Proteins are reported using same number assigned in panel a. LRR motifs were identified using the consensus sequence LxxLxLxx, with L indicating a leucine residue and x indicating any amino acid (Kajava and Kobe, 2002).

Run1.2 and Run2.2 loci are composed of two different sets of NLRs. This may explain why genotypes respond differently to PM. Clustering analyses of TIR-NBS-LRRs in Run1.2 and Run1/Rpv1 support an allelic relationship between them, while differences in LRR domains support some allelic diversity in M. rotundifolia.

NLR genes at Run1.2b and Run2.2 display different levels of transcription

Constitutive NLR gene expression is essential for disease resistance (Michelmore et al., 2013). To further characterize Trayshed’s loci and identify expressed NLR genes that are potentially responsible for PM resistance, we measured gene expression in Run1.2b+ and Run2.2+ leaves 1 and 5 days after inoculation with either E. necator or a mock solution using RNA-seq (Figure 4).

Figure 4:
Figure 4:

Transcript abundance of NLR genes in Run1.2b and Run2.2. Gene expression was monitored in Run1.2b+ and Run2.2+ at 1 and 5 days post inoculation (dpi) with E. necator conidia (PM) or a mock solution (Mock). Transcript abundance is shown as the mean of Transcripts per Million (TPM). n = 3. NLR genes differentially expressed in response to PM are indicated by an asterisk (P value ≤ 0.05).

In Run1.2b+ leaves, expression of almost all NLR genes from the Run1.2b locus (23/24) was detected (Figure 4a). Twelve of them were found with an expression level higher than 1 transcript per million (TPM) in at least one condition (TPM > 1), while 11 NLR genes exhibit a lower expression level (TPM ≤ 1). Three genes at the 5’-end of Run1.2b encoding two TIR-NBS-LRRs and a TIR-NBS, and a CC-NBS-LRR gene towards the 3’-end were the most highly expressed in all conditions (mean TPM > 4 TPM). In addition, the gene with the most elevated expression was the TIR-NBS-LRR gene which predicted protein clustered with MrRPV1 in the phylogenic tree (Figure 3). PM inoculation was found to impact on the gene expression of only one RPW8-NBS-LRR gene at 5 dpi.

In Run2.2+ genotype, we identified 12 NLR genes with a transcript abundance superior to 1 TPM, 14 lowly expressed (TPM ≤ 1) and 3 with no expression (Figure 4b). A TIR-NBS-LRR gene at the 5’-end of Run2.2 was the most highly expressed across conditions. Seven other TIR-NBS-LRR genes in the locus had moderate expression levels. Few NLR genes of the Run2.2 locus were modulated in response to PM. One TIR-NBS-LRR gene was detected as up-regulated in response to PM at 1 dpi, while a TIR-NBS and two TIR-NBS-LRR genes were up-regulated at 5 dpi.

These RNA-seq data show that most of the NLR genes composing Trayshed’s loci are expressed independently of the condition, but they exhibit different levels of gene expression in Run1.2b+ and Run2.2+ genotypes. Expressed NLR genes are candidate genes involved in PM resistance associated with Run1.2b and Run2.2.

Run1.2b and Run2.2 trigger different defense mechanisms in response to PM and at constitutive level

To determine whether the differences in PM development between Run1.2b+ and Run2.2+ were due to differences in the pathways induced, we measured the expression of defense-related genes in response to PM in Run1.2b+, Run2.2+, Run1.2b-, Run2.2-, and five other PM-susceptible genotypes at 1 and 5 dpi. Six additional PM-resistant genotypes were included to evaluate the functional overlap between Trayshed’s Run1.2b and Run2.2 and other sources of resistance that provide variable level of protection to PM (Figure 1b). Because these genotypes are diverse, we constructed comparable reference transcriptomes for each by incorporating variant information and de novo assembled transcripts that are not found in PN40024. We also refined the functional annotation of defense genes in the reference transcriptome. We identified 2,694 grape genes involved in the following defense responses: pathogen recognition by receptor-like kinases (RLKs) and intracellular receptors (NLRs), ROS production and scavenging, nitric oxide (NO) production, calcium, MAPK, salicylic acid (SA), jasmonic acid (JA), ethylene (ET), and abscisic acid (ABA) signaling, pathogenesis-related (PR) protein and phytoalexin biosynthesis, and cell wall reinforcement (Supplementary Table S4).

Comparison between PM- and mock-inoculated leaf transcriptomes showed a similar number of defense-related genes up-regulated in Run1.2b+ and Run2.2+ in response to the pathogen at 1 dpi, with 165 and 135 genes, respectively (Supplementary Figure S5). Number of up-regulated genes decreased at 5 dpi in Run1.2b+ (69) related to 1 dpi, whereas a higher number of up-regulated genes (449) was identified in Run2.2+.

To identify the transcriptional modulation associated with PM resistance, genes up-regulated in response to PM in e6-23 (Run1.2b+) and 08391-29 (Run2.2+) were then compared with the ones detected in their PM-susceptible siblings, e5-96 (Run1.2b-) and 08391-28 (Run2.2-), and the five additional PM-susceptible genotypes (Figure 5a). This comparison impacted substantially the number of up-regulated genes in the genotypes possessing Trayshed’s R loci, especially Run2.2+, in which only 13 of the 449 up-regulated genes at 5 dpi were associated with resistance to PM. The number of up-regulated resistance- and defense-related genes were similar in Run1.2b+ and Run2.2+ at both time points.

Figure 5:
Figure 5:

PM resistance-associated gene expression in defense signaling pathways. (a) Defense-related genes uniquely up-regulated by E. necator in PM-resistant accessions and (c) genes with higher constitutive expression in PM-resistant genotypes than seven PM-susceptible genotypes. For each comparison, gene overlap between e6-23 (Run1.2b+) and 08391-29 (Run2.2+) and the other PM-resistant accessions is detailed in panels (b) and (d).

Among the 68 genes up-regulated in Run1.2b+ at 1 dpi, two encode Respiratory Burst Oxidase Homologs, which participate in ROS production (Suzuki et al., 2011), three are associated with calcium signaling, three encode PR proteins, 13 are involved in phytoalexin biosynthesis, and one is a homolog of SENESCENCE-ASSOCIATED GENE 101 (SAG101). In Arabidopsis thaliana, SAG101 contributes to the SA-mediated response and is required for PCD (Feys et al., 2005). Interestingly, this SAG101-like gene was up-regulated in response to PM at 5 dpi in Run2.2+ and in five PM-susceptible accessions. Of the 41 genes identified as up-regulated in Run2.2+ at 1 dpi and associated with PM resistance, 5 genes encode PR proteins, 5 are involved in terpenoid biosynthesis, 4 in ABA-signaling, and 3 in JA-mediated signaling pathways. Almost half (47.3%) of the up-regulated genes associated with PM resistance in Run1.2b+ and Run2.2+ were also up- regulated by another R locus (Figure 5a,b), indicating some functional overlap among them.

Transcriptomes of mock-inoculated leaves from Run1.2b+, Run2.2+, and six additional PM-resistant genotypes were compared to seven PM-susceptible genotypes to check for constitutive PM resistance. Run1.2b+ had one of the greatest number of highly constitutively expressed defense genes (121 genes at 1 dpi, 133 genes at 5 dpi; Figure 5c,d). Among these genes, we found several genes encoding RLKs and NLRs and genes involved in ROS production and scavenging, SA-mediated signaling, phenylpropanoid biosynthesis, and cell wall reinforcement. Many of these, including the SAG101-like gene, were more highly expressed specifically in Run1.2b+ versus other PM-resistant accessions. In Run2.2+, 6 genes involved in flavonoid biosynthesis were associated with PM resistance at 1 dpi. At 1 dpi and 5 dpi, 5 genes coding for laccases were most highly expressed in Run2.2+. Laccases are enzymes involved in lignin polymerization, an important component of plant cell wall structural strength (Zhao et al., 2013). Constitutively elevated laccase gene expression might thicken the secondary cell wall in leaves, thus providing a physical barrier to pathogens (Miedes et al., 2014) in Run2.2+.

Discussion

Trayshed’s Run1.2b and Run2.2 R loci provide resistance to PM in V. vinifera via different defense mechanisms

Introgression of Trayshed’s Run1.2b and Run2.2 loci into two V. vinifera lines confers post-penetration resistance to PM, but at different speeds and intensities (Figure 1). Similar disease phenotypes have been described in previous studies (Qiu et al., 2015; Dry et al., 2019). This suggests that the intracellular perception of E. necator and/or the efficiency of the defense mechanisms activated by Trayshed’s NLRs consequentially differ. To test the latter hypothesis, we profiled the expression of selected defense-related genes in PM- and mock-inoculated leaves from Run1.2b+, Run2.2+, six other R locus-containing accessions, and seven PM-susceptible accessions with diverse genetic backgrounds. The genes with an expression associated with PM resistance, i.e. the genes up-regulated by PM only in PM-resistant accessions and genes constitutively higher expressed in PM-resistant vs. PM-susceptible genotypes, differed in Run1.2b+ and Run2.2+. In particular, the difference of timing of the up-regulation of a SAG101-like gene suggests that SA-associated response and thus PCD might be delayed in the Run2.2+ genotype. This hypothesis could be tested by profiling defense-related gene expression in additional accessions carrying Trayshed’s R loci and by measuring SA content in both mock- and PM-inoculated leaves. Differences in defense mechanisms associated with Run1.2b and Run2.2 loci also suggest that Trayshed’s PM resistance could be due to the synergic effect of its R loci. Previous studies have showed that stacking R loci could enhance PM resistance (Feechan et al., 2015; Agurto et al., 2017). It would be interesting to evaluate the effect of introgressing both Trayshed’s R loci. Furthermore, comparison of the R loci showed some functional overlap between the eight PM-resistant accessions, but it also highlighted defense mechanisms specific to each R locus. The generated data could serve to estimate the best combination of functionally diverse PM resistance-associated loci to stack and introgress into wine grapes for durable resistance to PM (Michelmore et al., 2013).

Haplotype resolution of Run1.2 and Run2.2 loci identifies distinct NLR composition

By combining a diploid assembly of Trayshed’s genome and DNA sequencing data generated from two backcrossed V. vinifera genotypes, we distinguished and reconstructed both haplotypes of Run1.2 and Run2.2 loci. To our knowledge, this is the first report of complete, haplotype-resolved R loci for grape. The Run1/Rpv1 locus was sequenced prior, but its assembly was fragmented and haploid (Feechan et al., 2013). Deep sequencing Run1.2a+ genotypes and accessions possessing the alternative Run2.2 haplotype would help to validate the NLR composition of these two haplotypes. Trayshed was previously defined as homozygous at Run2.2 locus based on amplicon size (Riaz et al., 2011). However, in silico PCR on Trayshed’s genome showed two amplicon sizes for the UDV108 marker: 225 bp on chromosome 18 Haplotype 1 and 323 bp on chromosome 18 Haplotype 2. Sequencing additional Run2.2+ backcrossed individuals would help determine whether the region on chromosome 18 Haplotype 2 is associated with PM resistance.

Whole-sequence comparison and manual gene model refinement of the two reconstructed haplotypes of each Trayshed’s R locus revealed numerous SVs and short polymorphisms affecting the sequences of multiple NLR genes for both Run1.2b and Run2.2 loci. A total of 77 NLR genes required manual annotation, representing ∼60% of the NLR genes identified among the four haplotypes. This highlights the necessity of generating phased haplotypes and meticulously dissecting complex genomic regions if trait-associated candidate genes are sought (Massonnet et al., 2020).

The NLRs in Trayshed’s Run1.2 and Run2.2 loci were different. All three classes of NLRs, CC-NBS-LRRs, RPW8-NBS-LRRs and TIR-NBS-LRRs were found in Run1.2 haplotypes, but no CC or RPW8 domains were identified among NLRs at Run2.2. The only characterized NLR gene associated with grape PM resistance, MrRUN1, is a TIR-NBS-LRR (Feechan et al., 2013). Functional characterization of the NLR genes composing Trayshed’s R loci would help identify the NLR(s) responsible for PM resistance and to determine whether the NLR class is a decisive factor for grape PM resistance.

Finally, the approach used in this study could be applied to other R loci, giving new opportunities for functional genomics. However, fine mapping or generation of sequencing data (DNA-seq and RNA-seq) from recombinants would be necessary to narrow down candidate NLR genes associated with PM resistance.

Materials and methods

Plant material

Fifteen grape accessions were used in this study, including eight carrying a powdery mildew (PM) resistance (R) locus. The genotypes comprised ten sister lines derived from crosses between a susceptible parent and a parent with an R locus. Information about the lineage of each genotype is provided in Supplementary Table S1.

For each accession, three plants were mock-inoculated and three plants were PM-inoculated in separate Conviron PGR15 growth chambers (UC Davis Controlled Environment Facility) maintained at identical environmental conditions (60% humidity, 23°C during the day for 16 hours, 15°C during the night). E. necator isolate C-strain conidia were amplified on in vitro cultures of detached V. vinifera Carignan leaves, suspended in 0.001 % (v/v) Tween solution, and adjusted to 5 × 105 conidia mL-1. Conidia or a 0.001% Tween solution were applied to leaves using a Preval Sprayer unit (Chicago Aerosol, Coal City, Illinois). Two leaves from each plant were collected 1 and 5 dpi, at the same time of day, and immediately frozen in liquid nitrogen. Leaves from an individual plant were pooled together and constitute a biological replicate. Three biological replications were obtained for each treatment.

Assessment of PM susceptibility

PM susceptibility was evaluated with a detached leaf assay as described in Pap et al. (2016). Leaves of each accession were surface-sterilized in 0.3% sodium hypochlorite solution and inoculated with two-week-old E. necator C-strain conidia using a custom-made settling tower (Reifschneider and Boiteux, 1988). Disease severity was assessed by visually scoring infection rate under a microscope Leica EZ4 D at 14 dpi (Supplementary Figure S1a). Two people independently rated PM growth using a 0–4 scale as described in Pap et al. (2016): 0 = no hypha; 1 = few germinated conidia per leaf with only primary hyphae; 2 = several conidia with primary and, in some cases, secondary hyphae; 3 = mycelium is visible on the entire leaf surface with limited conidiophores; 4 = mycelia coverage is extensive with extensive conidiation, clearly visible with the naked eye. Visual scores per genotype (Supplementary Figure S1a) were compared using a Kruskal-Wallis test followed by a post hoc Dunn’s test (P-value ≤ 0.05). Detached leaves were stained with Coomassie Brilliant Blue R-250 at 5 dpi using the procedure described in Riaz et al. (2013).

DNA and RNA extraction, library preparation, and sequencing

Genomic DNA were isolated from e6-23 (Run1.2b+) and 08391-029 (Run2.2+) mock-inoculated leaves as described in Chin et al. (2016). DNA purity was evaluated using a Nanodrop 2000 spectrophotometer (Thermo Scientific, IL, USA). DNA quantity was estimated with Qubit 2.0 Fluorometer (Thermo Scientific, IL, USA). One microgram of high-quality DNA was fragmented by sonication using a E220 ultrasonicator (Covaris, Woburn, MA, US). Fragmented DNA was used as a template for library preparation using the Kapa LTP library prep kit (Kapa Biosystems, Wilmington, MA, US). Size selection after ligating adapters was done with the eGel system (Invitrogen, Carlsbad, CA, USA). Final libraries were evaluated for quantity and quality using a Bioanalyzer 2100 (Agilent Technologies, CA) and sequenced on the Illumina HiSeqX Ten system in paired-end 150-bp reads (IDseq Inc) (Supplementary Table S2).

Total RNA was extracted using a Cetyltrimethyl Ammonium Bromide (CTAB)-based extraction protocol as described in Blanco-Ulate et al. (2013). RNA concentration and purity were evaluated using a Qubit fluorometer (Thermo Scientific) and a NanoDrop 2000c Spectrophotometer (Thermo Scientific), respectively. Sequencing libraries were prepared using the Illumina TruSeq RNA sample preparation kit v.2 (Illumina, CA, USA). Quality control of size and purity of the final libraries was assessed using the High Sensitivity DNA kit on a Bioanalyzer 2100 (Agilent Technologies, CA). cDNA libraries were sequenced using Illumina HiSeq2500 and HiSeq4000 sequencers (DNA Technologies Core, University of California, Davis, CA, USA) as 50-bp single-end reads (Supplementary Table S5).

Locus reconstruction

Run1.2 and Run2.2 were identified by aligning VMC4f3.1 and VMC8g9 marker primers to Run1.2 and VMC7f2 and UDV108 marker primers to Run2.2 locus (Riaz et al., 2011). Whole-genome DNA sequencing reads from e6-23 (Run1.2b+) and 08391-29 (Run2.2+) were then used to identify Run1.2b and Run2.2 sequences. Low-quality DNA sequencing reads were removed and adapter sequences were trimmed using Trimmomatic v.0.36 (Bolger et al., 2014) with the following settings: LEADING:3 TRAILING:3 SLIDINGWINDOW:10:20 MINLEN:36 CROP:150. High-quality paired-end reads were aligned onto the diploid M. rotundifolia Trayshed genome (Cochetel et al., 2021) using BWA v.01.17 (Li and Durbin, 2009) and default parameters. Reads aligning on the reference genome with no edit distance (0 mismatch), were selected using bamtools filter v.2.5.1 (Barnett et al., 2011) and the tag “NM:0”. These reads were used as input for evaluating base coverage with genomecov (BEDTools v2.29.1; Quinlan, 2014). Coverage from bases located in repetitive elements were removed using BEDTools intersect v2.29.1 (Quinlan, 2014). Median coverage per 10 kbp was calculated using an in-house R script and normalized by dividing by the sequencing coverage. Sequences were removed from the locus and labeled “unplaced” if DNA sequencing reads did not cover a primary contig nor its alternative haplotigs. Each haplotype was fragmented into 1 kbp sequences using seqkit sliding v.0.16.1 (Shen et al., 2016) and aligned to itself using Minimap2 v.2.12-r847-dirty (Li, 2018) to find sequence overlaps between contigs and remove them from the locus. DNA sequencing coverage along the four haplotypes was manually inspected by visualizing alignments using Integrative Genomics Viewer (IGV) v.2.4.14 (Robinson et al., 2011). Loci were reconstructed using the script HaploMake.py from the tool suite HaploSync v1.0 (https://github.com/andreaminio/HaploSync).

Haplotype sequence comparison

Pairwise alignments were performed using NUCmer from MUMmer v.4.0.0 (Marçais et al., 2018) with the option --mum. Alignments with at least 90% identity are shown in Figure 2. Structural variants (SVs; >50 bp) and short INDELs (<50 bp) were called using show-diff and show-snps, respectively from MUMmer v.4.0.0 (Marçais et al., 2018).

Annotation of NLR genes

The genome was scanned with NLR-annotator using default parameters (Steuernagel et al., 2020). Gene models within the R loci were manually refined by visualizing alignments of RNA-seq reads from Trayshed (Cochetel et al., 2021), e6-23 (Run1.2b+), and 08391-29 (Run2.2+) leaves using Integrative Genomics Viewer (IGV) v.2.4.14 (Robinson et al., 2011). RNA-seq reads were aligned onto the diploid genome of M. rotundifolia Trayshed using HISAT2 v.2.1.0 (Kim et al., 2015) and the following settings: --end-to-end --sensitive -k 50.

Predicted proteins were scanned with hmmsearch from HMMER v.3.3.1 (http://hmmer.org/) and the Pfam-A Hidden Markov Models (HMM) database (El-Gebali et al., 2019; downloaded on January 29th, 2021). Protein domains corresponding to the following Pfam domains: NB-ARC (PF00931.23), LRR (PF00560.34, PF07725.13, PF12799.8, PF13306.7, PF13516.7, PF13855.7), TIR (PF01582.21, PF13676.7), RPW8 (PF05659.12), with an independent E-value less than 1.0, and an alignment covering at least 50% of the HMM were selected (Supplementary Table S3). Coiled-coil (CC) domain-containing proteins were identified by COILS (Lupas et al., 1991). Proteins were divided into eight protein classes according to their domain composition: NBS, CC-NBS, NBS-LRR, TIR-NBS, RPW8-NBS, CC-NBS-LRR, TIR-NBS-LRR, and RPW8-NBS-LRR.

Phylogenetic analysis

Predicted NLR protein sequences from Trayshed’s Run1.2 and Run2.2 and G52’s Run1/Rpv1 (Feechan et al., 2013) were aligned using MUSCLE (Edgar, 2004) in MEGAX (Kumar et al., 2018). Resistance gene analogs (RGAs) from Run1/Rpv1 (Feechan et al., 2013) were retrieved on GenBank using the following accession numbers: RGA1, AGC24025; RGA2, AGC24026; RGA4, AGC24027; MrRPV1 (RGA8), AGC24028; RGA9, AGC24029; MrRUN1 (RGA10), AGC24030; RGA11, AGC24031. Phylogenetic analysis of the proteins was performed with MEGAX (Kumar et al., 2018) using the Neighbor-Joining method (Saitou and Nei, 1987) and 1,000 replicates. Evolutionary distances were computed using the Poisson correction method (Zuckerkandl and Pauling, 1965) and are expressed as the number of amino acids or base substitutions per site. Positions with less than 5% site coverage were eliminated.

Reference transcriptome reconstruction

For each accession, we constructed a reference transcriptome composed of a diploid synthetic transcriptome and de novo assembled transcripts. The diploid synthetic transcriptome was reconstructed using variant information. First, adapter sequences were removed and RNA-seq reads were filtered based on their quality using Trimmomatic v.0.36 (Bolger et al., 2014) and these settings: LEADING:3 TRAILING:3 SLIDINGWINDOW:10:20 MINLEN:20. Quality-trimmed reads of the 12 samples from each accession were concatenated into a single file and mapped onto a combined reference genome composed of V. vinifera PN40024 V1 (Jaillon et al., 2007) and E. necator C-strain genome (Jones et al., 2014) following the STAR 2-pass mapping protocol (v.2.5.3a; Dobin et al., 2013; Engstrom et al., 2013). PCR and optical duplicates were removed with Picard tools (v.2.0.1 http://broadinstitute.github.io/picard/), and reads were split into exon segments using SplitNCigarReads from GATK v.3.5-0-g36282e4 (McKenna et al., 2010). GATK HaplotypeCaller was used to call sequence variants with the following parameters: -ploidy 2 - stand_call_conf 20.0 -stand_emit_conf 20.0 -dontUseSoftClippedBases. Variants were filtered using GATK VariantFiltration with these settings: -window 35 -cluster 3 -filterName FS -filter “FS > 30.0” -filterName QD -filter “QD < 2.0”. Variants passing all filters were selected using GATK SelectVariants with “--excludeFiltered” parameter. On average, 265,721 ± 38,043 variants were detected per genotype (Supplementary Table S6). Variants in grape protein-coding regions were selected using bedtools intersect v.2.19.1 (Quinlan, 2014). Two separate genomes were reconstructed for each genotype using the vcf-consensus tool from vcftools (Danecek et al., 2011). The first genome was reconstructed by incorporating both homozygous and heterozygous alternative 2 (ALT2) variants in PN40024 V1, while the second genome was reconstructed using both homozygous and heterozygous alternative 1 (ALT1) variants. A new annotation file (gff3) was created for each genome from its corresponding variant information. CDS were extracted using gffread from Cufflinks v.2.2.1 (Trapnell et al., 2010).

For each genotype, quality-trimmed reads were mapped onto the combined reference transcriptome consisting of its diploid synthetic grape transcriptome and E. necator C-strain CDS (Jones et al., 2014) using Bowtie2 v.2.3.4.1 (Langmead and Salzberg, 2012) and these parameters: --end-to-end --sensitive --un. For each genotype, de novo assembly was performed using unmapped reads from the 12 RNA-seq libraries and TRINITY v.2.4.0 (Grabherr et al., 2011). A total of 550,369 sequences were reconstructed, corresponding to 36,691 ± 2,694 sequences per accession (Supplementary Table S7). To reduce sequence redundancy, reconstructed transcripts from all 15 genotypes were clustered using CD-HIT-EST v.4.6.8 (Li and Godzik, 2006) and an identity threshold of 90%. The longest representative sequence of each of the 102,391 CD-HIT clusters was used as input for TransDecoder v.3.0.1. CDS redundancy was again reduced by clustering using CD-HIT-EST v.4.6.8 (Li and Godzik, 2006), with coverage and identity thresholds of 100%. In total, 2,137 CDS were retained (Supplementary Data S1). Taxonomic analysis of the predicted proteins was performed using Megan v.6.12.5 (Huson et al., 2016) with default parameters after aligning predicted peptides against the RefSeq protein database (ftp://ftp.ncbi.nlm.nih.gov/refseq, retrieved January 17th, 2017). Peptides assigned to proteobacteria, opisthokonts, and viruses were considered microbial contaminants (Supplementary Table S8).

Gene expression analysis

Transcript abundance was evaluated using Salmon v.1.5.1 (Patro et al., 2017) and these parameters: --gcBias --seqBias --validateMappings. For assessing the NLR gene expression in e6-23 (Run1.2b+) and 08391-29 (Run2.2+), transcriptomes of M. rotundifolia Trayshed and E. necator C-strain (Jones et al., 2014) were combined, and their genomes were used as decoy. To study the expression of defense-related genes, the diploid synthetic transcriptomes of each grape accession was combined with the de novo assembled transcripts and the E. necator C-strain transcriptome. The PN40024 V1 and E. necator C-strain genomes were used as decoys. Transcriptome index files were built with a k-mer size of 13. Quantification files were imported using the R package tximport v.1.20.0 (Soneson et al., 2015). Read counts of the two haplotypes of each locus of the diploid synthetic transcriptome were combined. Read-count normalization of the grape transcripts and statistical testing of differential expression were performed using DESeq2 v.1.16.1 (Love et al., 2014). One sample (14305-001_H_5dpi_1) was removed from the RNA-seq analysis because of its low mapping rate (Supplementary Table S5).

Functional annotation of the defense-related genes

Grape and Arabidopsis thaliana predicted proteins were scanned with hmmsearch from HMMER v.3.3.1 (http://hmmer.org/) and the Pfam-A Hidden Markov Models (HMM) database (El-Gebali et al., 2019; downloaded on January 29th, 2021). Protein domains with an independent E-value less than 1.0 and an alignment covering at least 50% of the HMM were selected. Grape proteins were aligned onto A. thaliana proteins (Araport11_genes.201606.pep.fasta; https://www.arabidopsis.org/download/index.jsp) using BLASTP v.2.6.0. Alignments with an identity greater than 30% and a reciprocal target:query coverage between 75% and 125% were kept. For each grape protein, the best hit in the A. thaliana proteome was determined using the highest product of identity, query coverage, and reference coverage. Genes involved in each defense-related category was manually curated based on literature. The functional annotation of defense-related genes used in this study can be found in Supplementary Table S5.

Data availability

Sequencing data are accessible through NCBI under the BioProject ID PRJNA780568. New genome assembly of M. rotundifolia Trayshed and annotation files are available at Zenodo under the DOI 10.5281/zenodo.5703495 and at www.grapegenomics.com.

Conflict of interests

The authors declare no conflict of interest.

Contributions

D.C, M.A.W, S.R, and M.M. designed the project. S.R and D.P performed sample inoculation, collection, microscopy and visual scoring. R.F.-B. extracted DNA and RNA and prepared sequencing libraries. M.M., N.C., A.M performed data analyses. M.M, A.M.V, and D.C wrote the manuscript.

Supplementary information accompanies the manuscript on the Horticulture Research website http://www.nature.com/hortres

Acknowledgements

This work was partially funded by the American Vineyard Foundation grant #2017–1657 and the US Department of Agriculture (USDA)-National Institute of Food and Agriculture (NIFA) Specialty Crop Research Initiative award #2017-51181-26829, and partially supported by funds to D.C. from Louis P. Martini Endowment in Viticulture.

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Posted November 24, 2021.
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Characterization of the grape powdery mildew genetic resistance loci in Muscadinia rotundifolia Trayshed
Mélanie Massonnet, Amanda M. Vondras, Noé Cochetel, Summaira Riaz, Dániel Pap, Andrea Minio, Rosa Figueroa-Balderas, M. Andrew Walker, Dario Cantu
bioRxiv 2021.11.23.469029; doi: https://doi.org/10.1101/2021.11.23.469029
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