Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa

doi:10.21203/rs.3.rs-1107347/v1

Download PDF

Research Article

Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa

https://doi.org/10.21203/rs.3.rs-1107347/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 24 Mar, 2022

Read the published version in BMC Plant Biology →

You are reading this latest preprint version

Background

Cabbage white butterflies (Pieris spp.) can be severe pests of Brassica crops such as Chinese cabbage, Pak choi (Brassica rapa) or cabbages (B. oleracea). Eggs of Pieris spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (B. nigra). Unravelling the genetic basis of this egg-killing trait in Brassica crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by P. brassicae eggs in B. rapa.

Results

A germplasm screening of B. rapa 56 accessions, representing the genetic and geographical diversity of a B. rapa core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for Pieris brassicae-induced cell death on chromosomes A02 (Pbc1), A03 (Pbc2), and A06 (Pbc3). The three QTLs Pbc1-3 contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with A. thaliana suggested that Pbc1 and Pbc2 are novel QTLs associated with this trait, while Pbc3 contains also LecRK-I.1, a gene of A. thaliana previously associated with cell death induced by a P. brassicae egg extract.

Conclusions

This study provides the first genomic regions associated with the Pieris egg-induced HR-like cell death in a Brassica crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.

Plant Molecular Biology and Genetics

Plant immunity

insect eggs

HR-like cell death

germplasm screening

QTL mapping

image-based phenotyping

oviposition-induced defence

Pieridae

Brassicaceae

Plant-insect interactions often start with herbivore egg deposition on plant tissues. Through millions of years of co-evolution with insects, plants have evolved mechanisms to recognize insect eggs as non-self to induce defence responses [1]. Different egg-killing traits have been described, such as neoplasm formation [2–4], secretion of toxic chemicals [5], tissue crushing [6], and hypersensitive response (HR)-like cell death [7–9]. Such defenses represent an additional component in the plant-insect arms race, but their potential for sustainable crop protection has so far largely been overlooked and under-utilized [10].

Eggs of Pieris spp. butterflies (Lepidoptera: Pieridae) induce a HR-like cell death on their natural host plants belonging to the Brassicaceae family [11]. The large cabbage white (P. brassicae L.) and the small cabbage white (P. rapae L.) represent major pests of Brassica crops worldwide [12, 13]. Despite available knowledge on plant defences against them, Pieris spp. are specialists well equipped for feeding on Brassicaceae [14–17]. Their caterpillars effectively detoxify the secondary metabolites produced by their host plants, so-called glucosinolates or “mustard oils” [18, 19]. Considering this, Pieris egg-induced HR-like cell death may represent a genuine and unexplored defense response of Brassicaceae against adapted specialist herbivores.

The HR-like cell death response was initially observed underneath eggs of P. rapae and P. napi deposited on leaves of wild populations of black mustard (Brassica nigra L.), on which it caused egg-killing by desiccating or dropping off [7]. Later, this egg-killing trait was also observed underneath eggs of P. brassicae and, interestingly, it was found to work in concert with the attraction of egg parasitoid wasps through the release of oviposition-induced plant volatiles [20, 21]. Under field conditions, the synergistic effect of HR-like cell death and egg parasitism reduced up to 80% of Pieris egg survival on B. nigra [21]. The direct egg-killing effect of the cell death seems to work mainly against singly laid eggs, irrespective of whether they are from solitary species such as P. rapae and P. napi, or from the gregarious species P. brassicae [11, 22]. These studies suggest that HR-like cell death may be an effective egg-killing trait, for which the plant molecular and genetic mechanisms are still poorly understood.

Most of the initial knowledge on the molecular aspects of the Pieris egg-plant interaction was obtained using the model plant A. thaliana [23]. Upon P. brassicae oviposition, A. thaliana responds with reactive oxygen species (ROS) and cell death, further accompanied by the accumulation of phytohormone salicylic acid (SA) and the induction of the SA-responsive gene PATHOGENESIS-RELATED PROTEIN 1 (PR1) [24, 25]. These responses were shown to be dependent on EDS1, ISOCHORISMATE SYNTHASE 1/SALICYLIC ACID INDUCTION DEFICIENT 2 (ICS1/SID2) and, partially, (NONEXPRESSER OF PR GENES 1) (NPR1), which are known signaling components of plant defense responses against biotrophic pathogens [26]. Transcriptomics studies in different plants species have confirmed that insect oviposition induces genes associated with SA- and ROS-mediated immune responses and PR1 gene expression [15, 24, 27–33], including in B. nigra and B. rapa [21, 34, 35]. It is suggested that there is a conserved transcriptional response amongst different plant-insect eggs interactions [36].

The similarities between the plant defenses induced against insect eggs and biotrophic pathogens suggest that insect eggs are also recognized by the plant immune system [23, 37], but it is yet not known how. The induction of plant defenses partly relies on the specific recognition of non-self molecules released by biotic attackers that are detected by plasma membrane pattern recognition receptors (PRRs) [38, 39] or intracellular nucleotide-binding leucin-rich receptors (NLRs) [40]. Feeding of herbivorous insects induce plant immunity through the release of herbivore-associated molecular patterns (HAMPs) contained in oral secretions of insect larvae and/or damage-associated molecular patterns (DAMPs) resulting in damaged plant tissues [41]. Both signals have been associated with the perception by different types of PRRs [16, 42].

Contrary to cues of larval feeding, only a few insects egg-associated molecular patterns (EAMPs) have been identified [2, 27, 43, 44]. In A. thaliana, some candidate PRRs involved in perception of P. brassicae eggs were recently discovered. Several L-type lectin receptor-like kinases (LecRKs), a class of PRRs, were upregulated upon P. brassicae oviposition [24]. One of them, LecRK-I.8, was found to be required for the induction of downstream reactive oxygen species (ROS) production, cell death and PR1 expression [26, 45]. More recently, a genome-wide association study (GWAS) in A. thaliana identified another one, LecRK-I.1, as a candidate gene underlying one of two loci involved in the induction of cell death upon treatment with P. brassicae egg extract [46].

To date, only a few studies have attempted to map genetic loci associated with insect oviposition-induced responses [46–49]. A strong HR-like cell death that eventually leads to egg-killing has been mainly shown for plant species of the tribe Brassiceae (Lineage II), which includes wild species such as Brassica nigra, Sinapis spp., Crambe spp., as well as diverse Brassica crops such as B. napus, B. oleracea and B. rapa but not A. thaliana [11, 21, 50]. Interestingly, species belonging to the tribe Brassiceae are known host plants for Pieris spp. while A. thaliana is not [51].

Next to interspecific variation between Brassicaceae species we also identified intraspecific variation in HR-like cell death among accessions of several species [11, 21], suggesting that genetic analysis to identify casual loci should be feasible. Up to now, classical forward genetics, such a linkage mapping and/or GWAS, helped to identify quantitative trait loci (QTLs) involved in both upstream (perception) and downstream mechanisms associated with plant resistance to insect feeding [15, 52–56]. Currently, genetic mapping efforts in Brassica species are increasingly made possible given a growing availability of high quality genomic and genetic resources [57–60]. It is thus timely to use genetic approaches to unravel the genetics underlying plant-insect egg interactions in non-model species. Genetic mapping of insect egg-induced defenses in Brassica crops can help both the fundamental understanding of HR-like cell death and its applied use as novel defense trait in plant breeding.

Here we present the genetic analysis of P. brassicae butterfly egg-induced HR-like cell death in Brassica rapa by QTL mapping. First, we investigated the phenotypic variation for HR-like cell death within B. rapa germplasm using a core collection previously assembled and curated [61, 62]. Then, we assessed the robustness of the phenotype and we quantitatively measured cell death size with a novel image-based phenotyping protocol. We identified two accessions with a significant difference in size of HR-like cell death and we screened a recombinant inbred line (RIL) population resulting in the identification of three novel QTLs. This study provides the first QTLs and candidate genes associated with butterfly egg-induced cell death in B. rapa, an important crop species and natural host plant of Pieris spp.

Screening of a B. rapa core collection

As a first objective, we investigated whether there was intraspecific variation for HR-like cell death in our B. rapa core collection. Out of the whole collection, we screened a subset of 56 accessions representing the collection geographical and morphological diversity (Additional file 2: Supplementary Table S1). Plants were treated with P. brassicae egg wash, which was previously reported as a reliable egg-mimicking treatment in B. nigra [35]. The main phenotypic diversity in HR-like response among the B. rapa accessions was limited to variation in cell death size (Additional file 1: Supplementary Fig. S1, Additional file 2: Supplementary Table S2). Egg wash induced a cell death on most of the accessions which appeared as necrotic black/dark spots of varying size on the leaf abaxial side (score 1-2). However, the spots never developed into the fully expanded and brown necrotic tissue, also visible on the adaxial side (score 3) (Additional file 1: Supplementary Fig. S1a). Such a strong necrosis was only observed in the B. nigra accession included as positive control (Additional file 1: Supplementary Fig. S1b). Nevertheless, we found differences in HR-like cell death between B. rapa accessions (Kruskal-Wallis: χ²₅₈ = 141.71, P <0.001). Six accessions, i.e. CC-106, FT-086, MIZ-019, R500, R-o-18 and VT-089, showed no cell death (score 0) in all biological replicates (Additional file 1: Supplementary Fig. S1b). Most of the accessions developed only a weak response, with a within-accession variation between individual plants ranging from no cell death (score 0) to small dark necrotic spots (score 1). At the other end of the phenotypic distribution, twelve accessions developed an HR-like cell death of score 2 in most of the biological replicates (i.e. BRO-127, ZCT, PC-184, IMB211, L58, PC-078, CC-114, CC-048, CC-168, CC-050, CC-Z16, CC-058). A specific morphotype was not associated with HR-like cell death as most of the major crop types (Pak choi, turnip, oil types) were found at both extremes of the phenotypic distribution (Additional file 1: Supplementary Fig. S1b). The only exception were the Chinese cabbage (CC) accessions, of which 8 out of 14 developed an HR-like cell death with large black/dark spots (score 2) on most of the biological replicates. Genetic heterogeneity of accessions appeared to be not associated with cell death variation as heterogeneous accessions and homogenous inbred lines and DH lines were found on both side of the phenotypic distribution.

Overall, we found statistical differences in HR-like cell death (Dunn Test, P <0.01) between the accessions that showed no cell death (score 0) and the accessions that developed large dark necrotic spots (score 2) upon egg wash treatment (Additional file 2: Supplementary Table S2). We then selected ten accessions either showing no response (CC-106, R-o-18, R500, SO-040) or an HR-like cell death of score 2 (BRO-030, BRO-127, CC-AO3, IMB211, CC-168, L58) for a further evaluation of their cell death phenotype. These accessions were selected based on specific criteria (see Material and Methods), and also because they were available as homozygous lines; being either inbred due to repeated selfing (self-compatible accessions) or previously used to generate homozygous DH lines (self-incompatible accessions).

Image-based phenotyping of HR-like cell death size on selected B. rapa homozygous lines

The selected B. rapa homozygous lines (inbred and DH lines) were re-evaluated to assess the robustness of their HR-like cell death phenotype with the aim to identify ideal parental lines to generate biparental mapping populations. Plants were treated with both P. brassicae egg clutches (10-20 eggs) and egg wash droplets to compare to which extent the egg wash could mimic the HR-like cell death induced by eggs. Further, we measured HR-like cell death size as quantitative trait using an image-based phenotyping protocol (Additional file 1: Supplementary Fig. S2-S3). Image analysis confirmed the differences in HR-like cell death between the selected accessions (Fig. 1, Additional file 2: Supplementary Table S3). Overall, we found differences in mean cell death sizes in response to both eggs (ANOVA: F₇₄ = 8.55, P < 0.001) and egg wash (ANOVA: F₇₄ = 15.88, P < 0.001). Accessions that developed the smallest HR-like response (CC-106, R-o-18, R500) were statistically different in cell death size from the ones with largest HR-like response (IMB211, L58) for both eggs and egg wash (Tukey`s HSD, P < 0.01). Overall, accessions IMB211 and L58 showed the largest cell death size for both treatments (Fig. 1). In fact, mean cell death size induced by either eggs or egg wash were similar for IMB211 (1.15 and 1.24 mm², respectively) and L58 (1.26 and 1.33 mm², respectively). In contrast, accessions CC-AO3, CC-168 and SO-040 showed a cell death size induced by eggs that was two to three times larger than the response induced by egg wash. CC-106 and R-o-18 showed the smallest mean cell death underneath the eggs (0.08 and 0.24 mm², respectively), in contrast to the total absence of cell death upon egg wash treatment observed in the germplasm screening. Overall, the two treatments showed limited correlation across the ten accessions (Pearson’s r = 0.55, P < 0.001). The broad-sense heritability (H²) was slightly lower for cell death size induced by eggs (0.47) than by egg wash (0.64) (Additional file 2: Supplementary Table S3).

In summary, accessions with a small/intermediate HR-like response showed a larger cell death size under eggs compared to egg wash, while the overall ranking was similar. Thus, we concluded that using egg deposition worked better than egg wash to screen for HR-like cell death in order to not underestimate the cell death induced by low responsive B. rapa accessions. Overall, IMB211 and L58 were confirmed as lines with a strong HR-like cell death while CC-106, R-o-18, R500 confirmed to be lines with a weak cell death, validating the results of the germplasm screening. Out of these accessions, L58 and R-o-18 represented ideal candidates for crossings because of their self-compatibility, similar flowering time, and leaf size/shape. Thus, we used the L58 (♀) x R-o-18 (♂) RIL population that was previously generated by Bagheri et al. [63] to pursue QTL linkage mapping.

Phenotypic analysis and QTL mapping in a RIL population

The RIL population L58 x R-o-18 consisting of 160 lines (F10) was used to identify QTLs underlying P. brassicae egg-induced HR-like cell death. We generated a new linkage map combining markers from previous studies (Additional file 2: Supplementary Table S4-S5) [63, 64]. The final genetic map consisted of 485 loci and covered a total of 1154.4 cM, with a mean density of 2.38 cM (Additional file 1: Supplementary Fig. S4, Additional file 2: Supplementary Table S6-S7). Image-based phenotyping of egg-induced cell death from three experiments was used to estimate best linear unbiased estimators (BLUEs) of cell death size for each parental and RIL genotype. Overall, the parents R-o-18 and L58 showed BLUE values of 0.49 (SD = 0.4) and 1.53 (SD = 0.42) mm², respectively (Fig. 2, Table 1). Their within-accession variation in HR-like cell death size, i.e. their phenotypic range, was larger than what we observed in previous germplasm evaluations, thus resulting in a smaller difference in mean cell death size between the two parents. The RILs showed an approximate normal distribution of cell death size with a mean BLUE value of 0.77 (SD = 0.51) mm² (Fig. 2, Table 1). The RILs phenotypic distribution was skewed towards the R-o-18 phenotypic value and only seven RILs developed a cell death size larger than L58. The broad-sense heritability across the three experiments was similar to what was previously observed for egg-induced cell death size (H² = 0.49).

A total of three QTLs associated with HR-like cell death size were identified on three B. rapa chromosomes and they were named P. brassicae egg-induced cell death (Pbc) (Fig. 3a, Table 2, Additional file 1: Supplementary Fig. S5). First, phenotypic data (BLUEs) were analyzed using single-QTL models, resulting in the identification of two QTLs, i.e. Pbc1 on chromosome A02 (LOD 5.63) and Pbc3 on chromosome A06 (LOD 4.15). Additionally, multi-QTL mapping (MQM) detected another QTL, Pbc2, on chromosome A03 (LOD 3.33). Two-QTL models revealed absence of epistatic interactions from any pairwise comparison among Pbc1-3, and weak additive effects between Pbc1:Pbc2 and Pbc1:Pbc3 (Additional file 1: Supplementary Fig. S6). Pbc1 explained 17.9% of the additive phenotypic variance, with BrID11121 as top marker (85.4 cM) and a 1.5-LOD confidence interval spanning about 27cM between markers 899118|9904922 and BrID11907 (Table 2). The minor QTLs Pbc2 and Pbc3 explained a smaller proportion of the additive phenotypic variance, 6.35% and 7.32% respectively, with BrID90099 (129.2 cM) and BrID90095 (63.9 cM) as respective top markers (Table 2). Pbc1 was the only QTL with a 1.5 LOD confidence interval lying entirely above the LOD significance threshold (Fig. 3a). As the RIL phenotypic distribution was skewed toward the R-o-18 values, we expected L58 alleles contributing to a larger cell death size for all QTLs. Interestingly, this was true only for Pbc1 which showed opposite effect size compared to Pbc2 and Pbc3 (Fig. 3b). In fact, the allele of L58 contributed to an increase in HR-like size of 0.45 mm² for Pbc1, while the allele of R-o-18 determined an increase in HR-like of 0.27 mm² for Pbc2 and 0.28 mm² Pbc3 (Table 2).

Identification of candidate genes underlying the QTLs

We investigated the genomic locations of the three QTLs for potential candidate genes associated with HR-like cell death using the B. rapa reference genome cv. Chiifu (v3.0). We searched for annotated genes that encode for cell surface receptors (PRRs), intracellular receptors (NLRs), or that are involved in general plant defense mechanisms, such as ROS production and cell death (Additional file 2: Supplementary Table S8-10). The QTL Pbc1 showed the largest effect with the allele of the L58 parent contributing to a large cell death and it was located at the interval 8.65-25.29 Mb (± 1.5 LOD) on chromosome A02. This region contains 2012 annotated genes, of which 69 are related to the plant immunity and defense (Additional file 2: Supplementary Table S8). Among them, we found 14 cell surface receptors (of both the RLK and RLP type) and 19 intracellular TIR-NBS-LRR (TNL) receptors. Sixteen of the TNLs are closely located in three clusters, at the intervals 12.47–12.55 Mb, 21.64–21.73 Mb and 22.68–22.99 Mb. Moreover, Pbc1 also includes B. rapa homologs to three RLKs previously found to be upregulated upon oviposition in A. thaliana [24]: i.e., BraA02g017190.3C homolog of a LRKL domain-kinase protein (AT1G66880), BraA02g022170.3C and BraA02g022180.3C homologs of NEMATODE-INDUCED LRR-RLK 1 (NILR1, AT1G74360) and BraA02g033550.3C and BraA02g033570.3C, homologs of PBS1-LIKE 19 (AT5G47070). Further, Pbc1 region includes genes involved in mediating cell death processes, such as BraA02g032910.3C and BraA02g032940.3C that are both homologs of A. thaliana ACCELERATED CELL DEATH 11 (ACD11), and BraA02g033670.3C, a homolog of BAX-INHIBITOR 1 (BI-1, AT5G47120).

Regarding the two minor QTLs, Pbc2 was located in the interval 25.34-38.15 Mb on A03 (± 1.5 LOD) and included 1594 genes in total, of which 49 being plant immunity-related genes (Additional file 2: Supplementary Table S9). Within Pbc2 we found different types of PRRs such as a cluster of 15 cysteine-rich RLKs (CRKs) at the interval 25.91-26.32 Mb and BraA03g059300.3C, homolog of L-TYPE LECTIN RECEPTOR KINASE I.9 (LecRK-I.9). In this region we found also NLRs, specifically a cluster of four TIR-NBS-LRR at the interval 25.52-25.56 Mb. Further, this region included also BraA03g053480.3C and BraA03g057870.3C, homologs of two known regulators of plant immunity, i.e. SUPPRESSOR-OF-NPR1 CONSTITUTIVE 4 (SNC4) and BRI1-ASSOCIATED RECEPTOR KINASE (BAK1), respectively, and two homologs of putative RESPIRATORY BURST OXIDASE HOMOLOGUE G (RbohG) genes. The third QTL, Pbc3, was located between 6.77 and 16.13 Mb on A06 (± 1.5 LOD). This region included a total of 2292 genes, of which 28 plant defense-related genes (Additional file 2: Supplementary Table S10). Within Pbc3 we found homologs of RbohD and RbohJ, different types of RLKs, i.e. homologs to two WALL-ASSOCIATED RECEPTOR KINASES 1 (WAK1, AT1G21250) and 2 (WAK2, AT1G21270), and, interestingly, a cluster of four L-type LecRKs including LecRK-I.1, that was recently associated to P. brassicae egg extract-induced cell death in A. thaliana [46].

Given that Pbc3 appeared to overlap with one of the two loci identified in A. thaliana by Groux et al. [46], we investigated the syntenic relationship between Pbc1-3 regions and A. thaliana genome. Pbc1 was syntenic to regions on A. thaliana chromosomes 1, on the top of chromosome 4 and on middle of chromosome 5 (Additional file 1: Supplementary Fig. S7). Pbc2, which was located to the bottom of chromosome A03, showed synteny to the bottom half of A. thaliana chromosome 4. Pbc3, which is located on the center of chromosome A06, was syntenic to regions on both A. thaliana chromosomes 3 and 5. Indeed, Pbc3 was syntenic to the region of A. thaliana chromosome 3 that included LecRK-I.1, candidate gene associated with egg extract-induced cell death [46]. Overall, Pbc1 and Pbc2 represented novel loci mediating HR-like cell death as they did not show synteny to the two loci previously identified in A. thaliana.

Here, we report the first genetic analysis of a butterfly egg-induced defence trait in an economically important crop, B. rapa, after initial studies in the model plant A. thaliana. We showed intraspecific variation for P. brassicae egg-induced HR-like cell death within a B. rapa germplasm collection. By developing an automated image-based phenotyping protocol for the first time, we could accurately measure HR-like cell death size in a RIL population and identified three new QTLs associated with this trait. The three QTLs include many candidate genes that are involved in plant immunity processes such as extra- and intra-cellular receptors, ROS production, and cell death.

Genetic mapping of egg-induced cell death identified three QTLs, Pbc1 (on chromosome A02), Pbc2 (A03) and Pbc3 (A06) that together explained about 31.5% of the phenotypic variance. Thus, in B. rapa, HR-like cell death size appears to be a polygenic trait similar to whitebacked planthopper egg-induced lesions in rice [47] or other pathogen-induced leaf necrotic symptoms [65, 66]. None of the three QTLs reported here have been validated yet, for example by using alternative segregating populations. Nevertheless, Pbc1 may represent a stable QTL as it explained the larger proportion of variance (17.9%), its confidence interval was entirely above the LOD threshold and it contributed to larger cell death with the allele of L58, the parent showing a stronger HR-like cell death. On the contrary, Pbc2 and Pbc3 represented minor QTLs, their LOD peaks were just above the LOD threshold, and their positive effect was due to alleles of R-o-18, the parent showing a smaller HR-like cell death. The unexplained phenotypic variance may be due to other undetected minor QTLs for which we expect that L58 alleles contribute to a larger cell death. In fact, only few RILs showed transgressive segregation beyond the mean value of L58 while the phenotypic distribution of the whole population was skewed towards the value of R-o-18, the parent showing a small cell death. Future research should validate the stability of the QTLs identified in this study, their (epistatic) interactions, and the effect of the plant genetic background by using other genetic populations/association panels and/or testing different environments.

In this study, we implemented the first image-based phenotyping method to assess insect egg-induced cell death on plant tissues and to perform QTL mapping. So far, image-based methods were used for genetics studies of plant disease symptoms [65–68] or insect feeding damage [54, 69, 70], but never for egg-induced responses. Nevertheless, our experiments showed an intermediate broad-sense heritability (H²=0.47-0.49) which points at a considerable environmental effect on the trait that our current bioassays and/or phenotyping protocol could not disentangle. Insect genetic variation is known to contribute to low heritability of genetic studies of plant defense traits to insect [71]. Unfortunately, at the moment it is difficult to control for the genetic variation of the P. brassicae butterflies used in our bioassay, beyond using a large number of adults and refreshing them regularly during experiments. Our imaged-based phenotyping protocol allowed us to measure necrosis size with increased precision (highly repeatable measurements) but it can possibly be improved in accuracy (measuring the true size of necrosis). Alternatively, the measurement of necrosis size could be combined with other cell death-related traits, e.g. necrosis severity (variation in lesion colour), to possibly determine traits with higher heritability and increase the power of QTL detection.

The three QTLs Pbc1-3 provide a new source of candidate genes that will help to understand the molecular mechanisms underlying the interaction between P. brassicae eggs and Brassica host plants. Within these QTLs regions, we identified different type of genes that are commonly involved in plant immunity processes, such as signalling/stress perception, ROS accumulation and cell death formation. Perception of pathogen-associated molecular patterns (PAMPs) by PRRs surface receptors triggers plant immunity [38]. Many PRRs belonging to different classes were found within the QTLs Pbc1-3, although involvement in plant immunity has been experimentally validated only for a few. For example, the Pbc1 region includes LecRK-V.5 which was previously found to negatively modulate plant immunity against biotrophic pathogens [72–74]. Further, Pbc1 included also orthologs of three (predicted) RLKs (AT1G66880, AT1G74360, AT5G47070), which were previously found to be upregulated upon P. brassicae oviposition in A. thaliana [24]. One of these three, AT1G74360, encodes the LRR-RLK NILR1 that was found to be required for plant immunity to nematodes in a BAK1-dependent manner [75]. BAK1 is a known central regulator of pathogen-triggered immunity which works as a co-receptor of PRRs mediating the perception of many different biotic stresses [76], including feeding by insects, such as aphids and caterpillars [77, 78]. Interestingly, one B. rapa ortholog of BAK1, BraA03g057870.3C, is present within our QTL Pbc2. Whether BAK1 is also involved in the regulation of defences against Pieris eggs is an intriguing question that awaits future experimental validation. Pbc2 includes also many cysteine-rich RLKs (CRKs), of which CRK5 was experimentally shown to mediate pathogen-induced cell death [79] and CRK11 was upregulated upon P. brassicae egg deposition (Little 2007). Finally, QTL Pbc3 included a cluster of B. rapa orthologs of LecRK-I genes, among which LecRK-I.1, which was recently identified as one of the two A. thaliana loci associated with Pieris egg extract-induced cell death [46].

Despite the many different RLKs present within the QTLs Pbc1-3, it is still possible that variation in cell death size is dependent on other genetic mechanisms. Within our QTLs, we identified clusters of intracellular TIR-NBS-LRRs (TNLs) and a B. rapa homolog N REQUIREMENT GENE 1 (NRG1). NRG1 was shown to interact with ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) and SENESCE-ASSOCIATED GENE 101 (SAG101) to form a protein complex that is required for TNL cell death signalling [80, 81]. It is interesting to recall that EDS1 and PHYTOALEXIN DEFICIENT 4 (PAD4), which forms with EDS1 another complex that is also required for TNL-mediated cell death [81, 82], were upregulated in A. thaliana upon P. brassicae egg deposition [24]. Despite all this, it is still largely speculative to state that insect eggs may be perceived by intracellular TNLs in absence of evidences showing that egg-associated molecular patterns can get into contact with plant cells.

Some plant species have been shown to accumulate ROS underneath insect eggs, such as A. thaliana, Pinus sylvestris or Brassica spp. [24, 35, 83], while others use ROS accumulation to directly kill them such as in Solanum dulcamara [4]. Hence, it is suggested that ROS signalling in response to insect eggs is conserved in different plant species [36]. We identified different NADPH oxidases (Rboh proteins) which are known to be involved in production of ROS [84]. Further, we found genes involved in cell death regulation such as the B. rapa orthologs to BI-1 and ACD11 within QTL Pbc1. While BI-1 is a known suppressor of H₂O₂-dependent cell death [85] and has been associated with the cell death regulation in plant-powdery mildew interaction [86], ACD11 is involved in autoimmunity [87] and activation of cell death and defence responses [88]. Finally, we found within Pbc1 also an homolog of ICS1/SID2, which is the enzyme producing the precursor of SA [89], the main phytohormone so far associated with plant defenses to insect eggs. Overall, the QTLs here presented include relatively large regions containing thousands of genes. Thus, future fine-mapping efforts are necessary to increase the resolution on the QTLs here reported and determine the exact genetic mechanisms of P. brassicae egg-induced cell death in B. rapa.

The identification of QTLs involved in HR-like cell death size in B. rapa also allows comparisons with the model plant A. thaliana. QTLs Pbc1 and Pbc2 showed no synteny to the two loci recently identified in A. thaliana [46], thus representing novel genomic regions associated with P. brassicae egg-induced cell death. Interestingly, the QTL Pbc3 is syntenic to the A. thaliana locus on chromosome 3 containing a cluster of clade I L-type LecRKs. The comparison in genetic architectures of HR-like cell death between the B. rapa and A. thaliana is intriguing but too premature to point at communalities and specificities. In fact, there are crucial differences in the plants' responses, experimental setup and plant genetic material employed by the different studies. HR-like cell death in A. thaliana was visually scored in discrete categories which accounted also for light/strong chlorosis and light/strong cell death [46] On the contrary, in our study no chlorosis was observed and only cell death size was measured. Different phenotyping methods/parameters can affect genetic mapping results and indeed it was previously shown that shape and size of leaf disease symptoms can be genetically unliked in different pathosystems [66, 90]. Moreover, bioassays on A. thaliana were conducted by treating plants with P. brassicae egg extract, which was shown to also induce cell death in B. nigra [91]. Nonetheless, egg extract likely contains lipidic compounds from the inner egg tissues [44] and it is still unclear whether and how they are able to diffuse through the egg and reach the leaf surface. Clearly, more research is needed to further disentangle the genetic architectures of egg-induced cell death in A. thaliana and B. rapa.

It is remarkable that B. rapa showed a phenotypic variation in HR-like cell death that was limited to necrotic black spots varying in size. A similar mild cell death appearing as black spots was also observed underneath P. brassicae eggs on a limited number of B. oleracea accessions [11, 23]. This mild cell death contrasts sharply with the strong and severe cell death that we regularly observe on leaves of wild species of the tribe Brassiceae (Brassicaceae Lineage II), such as in Brassica nigra, Sinapis spp., and Crambe spp., which leads to reduced egg survival [11, 22]. The differences in HR-like cell death between wild Brassicaceae, e.g. Brassica nigra, and other Brassica crops raise questions on the role of crop domestication on this defence trait. Brassica crops are characterized by an extraordinary intraspecific diversity in morphotypes which differ significantly from the progenitor wild relatives as result of domestication [92–94]. The selection for specific crop morphotypes has often targeted leaf morphological and/or biochemical traits which often show trade-offs with overall plant defense traits [95, 96]. Whether similar trade-offs also impacted the HR-like cell death expressed by current Brassica crop types should be tested. Certainly, we cannot yet conclude to have captured the full extent of intraspecific phenotypic variation as we screened only 56 accessions from one representative B. rapa core collection [61, 62]. Our choice aimed at encompassing the overall genetic diversity of the core collection while representing all species morphotypes. Nevertheless, we may have missed accessions with a stronger HR-like phenotype and/or alternative variants at the identified QTLs. In summary, future germplasm screenings should not only include more accessions, but also include Brassica wild material. What is the genetic basis for the differences in HR-like cell death severity between wild Brassicaceae, e.g. Brassica nigra, and other Brassica crops is a fascinating question that deserves to be addressed in future research.

The mild HR-like cell death observed in our B. rapa germplasm was shown to not affect egg survival [11]. This is expected to have certain implications regarding its deployment as egg-killing defense trait in plant breeding. In future, screening of more germplasms within Brassica crops, including crop eild relatives, could still identify strong HR-like cell death. An alternative possibility for deploying it as crop defense trait could be via introgression from B. nigra. Interspecific introgression of other disease resistance traits is already being pursued within the Brassica genus using interspecific crosses, embryo rescue, and marker-assisted selection [97, 98].

We report the identification of the first QTLs associated with a HR-like cell death induced by Pieris butterfly eggs in the economically important crop B. rapa. Our study confirms that plant genetic factors are involved in the elicitation of a HR-like cell death, a plant defense response against insect eggs. This work provides the basis for further identification of genes mediating the interaction between butterfly eggs and plants. Future studies should validate the QTLs by screening other genetic populations and/or association panels. Fine-mapping of the identified QTLs would then help to increase the resolution of the loci and further elucidate the genetic regulation of the egg-induced HR-like cell death.

Plant material

For germplasm screening, 56 Brassica rapa L. (Brassicaceae) accessions were selected representing all major B. rapa crop types (e.g., Chinese cabbage, Pak choi, turnip, oil types), and to include different levels of genetic heterogeneity, such as feral populations, landraces, breeding material, and doubled haploid (DH) lines (Additional file 1: Supplementary Fig. S1, Table S1). Most B. rapa accessions and all DH lines were obtained from the core collection of Dr. Bonnema at Plant Breeding, Wageningen University and Research [61, 62] with a few additional accessions obtained from the Centre for Genetic Resources (CGN, The Netherlands). A B. nigra accession previously reported to induce a strong HR-like cell death was used as positive control [22]. After the germplasm screening, ten accessions, considered suitable as potential parents of biparental mapping population, were selected for a second HR-like cell death evaluation. Criteria for the selection were: the accession i) displays an HR-like cell death score at the extremes of the phenotypic distribution and is consistent across individual plants; ii) is fast flowering (< 1 year); iii) is self-compatible; iv) was multiplied by selfings or was used to generate a DH line (in order to have homozygous material to repeat experiments); and, v) it was preferably showing comparable leaf phenotypes to minimize the segregation of leaf morphological traits after crossing. Finally, a mapping population of 160 recombinant inbred lines (RILs), previously generated from a cross between the B. rapa DH lines L58 (caixin type, ssp. parachinensis) and R-o-18 (yellow sarson type, spp. tricolaris) was used for QTL mapping of HR-like cell death size [63].

Plant growing conditions

Plants were grown in a greenhouse compartment under standardized conditions (21/18°C day/night minimum temperature, 16/8 h light/dark photoperiod; and 50-70% relative humidity). The daily maximum temperature was not controlled and subjected to some variation (max +5°C). Seeds were vernalized at 4°C for two days and then sown in small trays with sowing soil (Lensli, Bleiswijk, The Netherlands). Seedlings were transplanted one week after germination to 17 cm diameter pots with potting soil (Lensli, Bleiswijk, The Netherlands). Plants were grown for five weeks before being subjected to P. brassicae oviposition or treatment with egg wash.

Insect rearing

Pieris brassicae L. butterflies were obtained from a rearing facility of the Laboratory of Entomology, Wageningen University. Insects were kept in a greenhouse compartment (21°C, 16/8 h dark photoperiod, 60-80% relative humidity). Larvae were reared on Brussel sprouts (Brassica oleracea var. gemmifera cv. Cyrus), while the adults were fed with a 10% honey solution and allowed to oviposit on the same plant.

Oviposition and egg wash treatment

Freshly eclosed P. brassicae female butterflies were allowed to mate, subsequently separated from the males, kept without plants for two days, and then used for no-choice oviposition experiments. Butterflies were allowed to freely oviposit on one B. rapa plant at the time in small cages each containing 2-4 female butterflies. A maximum of two egg clutches consisting of 10-20 eggs were laid on the last two fully developed leaves of each plant. Then a new plant was placed into the cage. After every 4-5 egg-laden plants, mated female butterflies in each of the small cages were replaced to randomize the effect of insect genetic diversity.

For the egg wash treatment, P. brassicae egg clutches were collected from Brussel sprouts leaves within 24 h after oviposition. Eggs were carefully removed with a stainless-steel lab spatula and collected in an Eppendorf tube in a ratio of ~1000 eggs per 1 ml demineralized water. Eggs were incubated overnight at room temperature, after which the liquid phase was directly used or stored at -20°C.

Experimental design

Germplasm screening was carried out in September 2017 by application of egg wash. Two 5 µl droplets of egg wash were applied on the last two fully developed leaves of each plant. Droplets of an equivalent amount of demineralised water were applied as negative control. Each genotype/accession was represented by 3 - 5 replicates (individual plants). Plants were arranged in a randomized complete block design with five blocks and one plant per accession within each block.

Re-evaluation of ten homozygous lines, that were either inbred (CC-106, SO-040, BRO-127, IMB211, CC-168 BRO-030, CC-AO3, L58) or DH lines (R-o-18, R500, BRO-030, CC-AO3, L58), was carried out in February 2018 using both no-choice oviposition and egg wash treatment. Plants were arranged in a randomized complete block design with two blocks and five plants per accession within each block.

The QTL mapping experiment was carried out in August/September 2018 using eggs deposited by P. brassicae females. The whole RIL population was grown three times over three consecutive weeks, each time with one replicate per RIL and three replicates for the two parents L58 and R-o-18. Plants were subjected to no-choice oviposition as described above.

Assessment of HR-like cell death

Egg wash-induced HR-like cell death was scored in the germplasm screening on a scale from 0 to 3 with: 0, no visible symptoms; 1, a grey/dark spot smaller than droplet size; 2, a black necrotic spot covering the whole treated area; 3, strong cell death visible also on the adaxial side (Additional file 1: Supplementary Fig. S1a). For the re-evaluation of homozygous (inbred and DH) lines from ten selected accessions and for the QTL experiment, egg and egg wash-induced HR-like cell death size (area) was measured with an image-based phenotyping protocol (see next section). For all experiments, individual plants were assigned the highest HR-like cell death score or size out of all egg- and egg wash-treated spots. To account for variability in egg clutches size, cell death size measured underneath egg clutches was normalized to 10 eggs (cell death _clutch / # eggs _clutch * 10).

Development of image-based phenotyping protocol

To obtain a reliable and reproducible quantification of egg- and egg wash-induced HR-like cell death size (area), we developed a custom image-based phenotyping protocol (Additional file 1: Supplementary Fig. S2). Leaf discs containing egg clutches- or egg wash-treated spots were sampled with a cork borer of 6 mm diameter and placed in Petri dishes with 1% phytoagar (Duchefa Biochemie, Haarlem, The Netherlands) or wet filter paper (Additional file 1: Supplementary Fig. S2a). Just prior to the sampling of the leaf disks with deposited egg clutches, eggs were counted and then gently removed with adhesive tape to prevent leaf damaging. Spots treated with egg wash were sampled directly. Leaf discs were imaged with a Dino-Lite Edge Digital microscope (AnMo Electronics Corporation, Hsinchu, Taiwan) connected to a laptop (Additional file 1: Supplementary Fig. S2b). Each leaf disc was imaged with the light polarizer filter “fully open” using the following settings: LED zone 2 and 4: ON; LED zone 1 and 3: OFF; autoexposure: ON; white balance: STANDARD; output file format: PNG; resolution: 2592x1944 pixel.

Image analysis was performed on Fiji with ImageJ v1.52 software [99] using the image segmentation plugin Trainable WEKA Segmentation v3.2.28 [100]. Image analysis was executed through a custom Fiji macro script. In WEKA, the image segmentation was performed using the training features Minimum, Maximum, Mean, Variance, Median and the classifier algorithm FastRandomForest. For each leaf disc, first a classifier model was trained using a training set composed by representative image pixels that were labelled either as “healthy leaf tissue” or “HR-like cell death”. The trained classifier was then applied to the whole image to generate an 8-bit segmentation of HR-like cell death spots (Additional file 1: Supplementary Fig. S2d). The 8-bit segmented HR-like area was finally measured in Fiji using the command Analyze particles with Area as measurement (Additional file 1: Supplementary Fig. S2e). The use of the WEKA automated segmentation resulted in more reproducible measurements of cell death size (Additional file 1: Supplementary Fig. S3).

Phenotypic data analysis

All data analyses were performed in R 3.5.3 [101]. Raw data were firstly checked for assumptions of normality (Shapiro-Wilk normality test) and homogeneity of variances (Fligner-Killeen test). Non-normal data were analyzed after data transformation (root square on cell death size) or with a non-parametric test (cell death score). Phenotypic data obtained from germplasm screening were not normally distributed and thus analyzed with the non-parametric Kruskall-Wallis test. Post-hoc analysis was conducted with the Dunn test using Benjamini–Hochberg correction as implemented by the dunnTest function from FSA package [102]. Phenotypic data from re-evaluation of ten B. rapa accessions were analyzed on square root-transformed data. HR-like cell death size was analyzed by using the following model:

y = µ + Block + Block:Row + Block:Col + G + r _error (1)

in which µ is the overall trait mean, Block is the blocking factor of the experimental design, Row and Col represents the spatial location of plant within a Block, G represents the genotype. Parsimonious models were explored by stepwise removal of each factor and comparison of the full versus reduced model with a Likelihood Ratio Test. The most parsimonious model retained only G as factor. Finally, data were analyzed using one-way ANOVA, followed by Tukey`s HSD post hoc test with Benjamini–Hochberg correction with α < 0.01 as implemented in multcomp package[103].

Estimation of variance components

Variance components for genetic and experimental residual error were estimated with equation (1) fitted as mixed model with all factors included as random [104]. The model was analyzed by REML procedure using the function lmer from lme4 package [105]. Classic broad-sense heritability (H²) was calculated by using the estimated variance components with the formula σ_G / (σ_G + σ_E) as previously described (Hollander 2003). Genetic coefficient of variation (CV) was calculated according to the equation: CV = \(\sqrt{{{{\sigma }}^{2}}_{\text{X}}}/n\) * 100, where n is the grand mean of the population, and σ²_X is a variance component (σ²_G or σ²_E).

Linkage map construction and QTL analysis

A combined and denser genetic map for the RIL population L58 x R-o-18 was created using marker data previously generated in two separate studies, that is AFLP, SSR and SNP markers [63], and InDel PCR markers [64] (Additional file 2: Supplementary Tables S4-S5). We used Haldane’s mapping function with default setting as implemented in JoinMap 4.0 [106], to convert recombination frequencies to centiMorgan (cM). The final linkage map constituted of 485 markers and 10 linkage groups corresponding to the 10 chromosomes of the B. rapa A genome for a total of 1154.44 cM (Additional file 1: Supplementary Fig. S6-S7).

QTL analysis was performed using the R/qtl package in R 3.5.3 [107]. Genotype probabilities at positions not covered by the linkage map were estimated every 1 cM with the calc.genoprob function (step size = 1). First, single QTL models were searched with the scanone function using the Haley-Knott regression method. Subsequently, multiple-QTL mapping (MQM) was applied to investigate additional small effect QTLs with the mqmscan function. As separate analysis, MQM was also implemented with the stepwiseqtl function which gave similar results. Finally, epistatic additive and interactions were investigated with pairwise two-QTLs models as implemented in the scantwo command. For all analysis, LOD score significance threshold at 5% was estimated with a 1000 permutations test.

Identification of candidate genes withing the QTLs regions

In order to investigate the gene content underlying the identified QTLs, the linkage map was anchored to the B. rapa reference genome Chiifu v3.0 (downloaded from BRAD Brassica database, accessed in August 2021) [108]. Sequences of the QTL-flanking markers were aligned to the reference genome using Geneious Prime v8 [109] to extract their physical location and all B. rapa genes contained within them. B. rapa gene functional annotation was assigned as the best match to A. thaliana protein database (genome TAIR 10) using BLAST+ v2.12.0 (E-value = 1e⁻⁵). Candidate genes associated with plant defense, biotic stress and cell death, including cell surface and intracellular receptors, were manually searched within the description of the A. thaliana orthologs to each B. rapa gene.

Analysis of syntenic relationships between B. rapa and Arabidopsis thaliana genomes was performed using the comparative genomic tool SynMap on the CoGe web platform [110–112]. SynMap legacy version was used with the following settings: DAGChainer Options “Maximum distance between two matches (-D): 20 genes”; “Minimum number of aligned pairs (-A): 5 genes”; Merge Syntenic Blocks “Algorithm: Quota align merge”; Syntenic Depth “Algorithm: Quota Align”, “Ratio of coverage depth (A. thaliana) 1 -to- 3 (B. rapa) ”, “Overlap distance 40”; Fractionation Bias “Run OFF”; CodeML “Calculate syntenic CDS pairs: Synonymous (Ks) substitution rate; “Color scheme: Rainbow 2”, “Max Value: 2”, “Log10 Transform: OFF”; Advance Options “Tandem duplication distance: 10”.

QTL: quantitative trait locus

HR: hypersensitive response

ROS: reactive oxygen species

SA: salicylic acid

PRR: pattern recognition receptor

RLP: receptor-like protein

RLK: receptor-like kinase

LecRK: L-type lectin receptor kinase

CRK: cysteine-rich receptor kinase

WAK: wall-associated receptor kinase

NLR: nucleotide-binding leucin-rich receptor

TIR-NBS-LRR (or TNL): Toll-interleukin 1 receptor domain NLRs

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article (Additional file 3).

Author`s contribution

NB, LC, KB, BJZ, MES, GB and NEF designed the experiments. NB, LC and GBK performed the experiments. NB and JV developed the image-based phenotyping method. ME-S prepared the genetic map. NB analysed the data. NB wrote a first draft of manuscript. LC, ME-S, KB, BJZ, MES, GB and NEF critically commented and revised it. All authors read and approved the manuscript.

Acknowledgements

We are thankful to: Johan Bucher (WUR Laboratory of Plant Breeding) for providing us seeds of the B. rapa core collection and the Centre for Genetic Resources the Netherlands (CGN) for providing additional accessions; the employees of Unifarm (WUR) for taking care of the plants used in our experiments; Pieter Rouweler and André Gidding for rearing of Pieris brassicae; Eline Ampt for the introduction to ImageJ. Oviposition on the RIL population was possible thanks the help of Thijmen Breeschoten, Frank Becker, Marco Busscher, Nynke Groendijk-Wilders, Dêêdi Sogbohossou and Xiuji Wang. We finally thank Marcos Malosetti and Roel van Bezouw for help with statistical analysis and QTL mapping.

Funding

This research was made possible by support of the Dutch Technology Foundation TTW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by Ministry of Economic Affairs (NWO/TTW VIDI grant 14854 to N.E.F.).

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Hilker M, Fatouros NE. Plant responses to insect egg deposition. Annu Rev Entomol 2015; 60: 493–515.
Doss RP, Oliver JE, Proebsting WM, Potter SW, Kuy S, Clement SL et al. Bruchins: Insect-derived plant regulators that stimulate neoplasm formation. Proc Natl Acad Sci 2000; 97: 6218–6223.
Petzold-Maxwell J, Wong S, Arellano C, Gould F. Host plant direct defence against eggs of its specialist herbivore, Heliothis subflexa. Ecol Entomol 2011; 36: 700–708.
Geuss D, Stelzer S, Lortzing T, Steppuhn A. Solanum dulcamara’s response to eggs of an insect herbivore comprises ovicidal hydrogen peroxide production. Plant Cell Environ 2017; 40: 2663–2677.
Seino Y, Suzuki Y, Sogawa K. An Ovicidal substance produced by rice plants in response to oviposition by the whitebacked planthopper, Sogatella furcifera (HORVÁTH) (Homoptera: Delphacidae). Appl Entomol Zool 1996; 31: 467–473.
Desurmont GA, Weston PA. Aggregative oviposition of a phytophagous beetle overcomes egg-crushing plant defences. Ecol Entomol 2011; 36: 335–343.
Shapiro AM, DeVay JE. Hypersensitivity reaction of Brassica nigra L. (Cruciferae) kills eggs of Pieris butterflies (Lepidoptera: Pieridae). Oecologia 1987; 71: 631–632.
Balbyshev NF, Lorenzen JH. Hypersensitivity and egg drop: a novel mechanism of host plant resistance to colorado potato beetle (Coleoptera: Chrysomelidae). J Econ Entomol 1997; 90: 652–657.
Garza R, Vera J, Cardona C, Barcenas N, Singh SP. Hypersensitive response of beans to Apion godmani (Coleoptera: Curculionidae). J Econ Entomol 2001; 94: 958–962.
Fatouros NE, Cusumano A, Danchin EGJ, Colazza S. Prospects of herbivore egg-killing plant defenses for sustainable crop protection. Ecol Evol 2016; 6: 6906–6918.
Griese E, Caarls L, Bassetti N, Mohammadin S, Verbaarschot P, Bukovinszkine’Kiss G et al. Insect egg-killing: a new front on the evolutionary arms-race between brassicaceous plants and pierid butterflies. New Phytol 2021; 230: 341–353.
Kumar S, Sarwan Kumar C. Assessment of avoidable yield losses in crop brassicas by insect-pests. J Entomol Zool Stud 2017; 5: 1814–1818.
Ryan SF, Lombaert E, Espeset A, Vila R, Talavera G, Dinca V et al. Global invasion history of the agricultural pest butterfly Pieris rapae revealed with genomics and citizen science. Proc Natl Acad Sci 2019; 116: 20015–20024.
Kourelis J, Van Der Hoorn RAL. Defended to the nines: 25 years of resistance gene cloning identifies nine mechanisms for R protein function. Plant Cell 2018; 30: 285–299.
Nallu S, Hill JA, Don K, Sahagun C, Zhang W, Meslin C et al. The molecular genetic basis of herbivory between butterflies and their host plants. Nat Ecol Evol 2018; 2: 1418–1427.
Erb M, Reymond P. Molecular interactions between plants and insect herbivores. Annu. Rev. Plant Biol. 2019; 70: 527–557.
De-la-Cruz IM, Velázquez-Márquez S, Núñez-Farfán J. What do we know about the genetic basis of plant defensive responses to herbivores? A Minireview. In: Evolutionary Ecology of Plant-Herbivore Interaction. Springer, Cham, 2020, pp 295–314.
Wheat CW, Vogel H, Wittstock U, Braby MF, Underwood D, Mitchell-Olds T. The genetic basis of a plant-insect coevolutionary key innovation. Proc Natl Acad Sci 2007; 104: 20427–20431.
Kliebenstein DJ, Kroymann J, Mitchell-Olds T. The glucosinolate-myrosinase system in an ecological and evolutionary context. Curr Opin Plant Biol 2005; 8: 264–271.
Fatouros NE, Lucas-Barbosa D, Weldegergis BT, Pashalidou FG, van Loon JJA, Dicke M et al. Plant volatiles induced by herbivore egg deposition affect insects of different trophic levels. PLoS One 2012; 7: e43607.
Fatouros NE, Pineda A, Huigens ME, Broekgaarden C, Shimwela MM, Figueroa Candia IA et al. Synergistic effects of direct and indirect defences on herbivore egg survival in a wild crucifer. Proc R Soc 2014; 281: 20141254.
Griese E, Dicke M, Hilker M, Fatouros NE. Plant response to butterfly eggs: inducibility, severity and success of egg-killing leaf necrosis depends on plant genotype and egg clustering. Sci Rep 2017; 7: 7316.
Reymond P. Perception, signaling and molecular basis of oviposition-mediated plant responses. Planta 2013; 238: 247–258.
Little D, Gouhier-Darimont C, Bruessow F, Reymond P. Oviposition by pierid butterflies triggers defense responses in Arabidopsis. Plant Physiol 2007; 143: 784–800.
Bruessow F, Gouhier-Darimont C, Buchala A, Metraux JP, Reymond P. Insect eggs suppress plant defence against chewing herbivores. Plant J 2010; 62: 876–885.
Gouhier-Darimont C, Schmiesing A, Bonnet C, Lassueur S, Reymond P. Signalling of Arabidopsis thaliana response to Pieris brassicae eggs shares similarities with PAMP-triggered immunity. J Exp Bot 2013; 64: 665–674.
Fatouros NE, Broekgaarden C, Bukovinszkine’Kiss G, Van Loon JJA, Mumm R, Huigens ME et al. Male-derived butterfly anti-aphrodisiac mediates induced indirect plant defense. Proc Natl Acad Sci 2008; 105: 10033–10038.
Firtzlaff V, Oberländer J, Geiselhardt S, Hilker M, Kunze R. Pre-exposure of Arabidopsis to the abiotic or biotic environmental stimuli ‘chilling’ or ‘insect eggs’ exhibits different transcriptomic responses to herbivory. Sci Rep 2016; 6: 1–13.
Baruah IK, Panda D, Jagadale M V., Das DJ, Acharjee S, Sen P et al. Bruchid egg induced transcript dynamics in developing seeds of black gram (Vigna mungo). PLoS One 2017; 12: e0176337.
Drok S, Bandoly M, Stelzer S, Lortzing T, Steppuhn A. Moth oviposition shapes the species-specific transcriptional and phytohormonal response of Nicotiana attenuata to larval feeding. Sci Rep 2018; 8: 1–14.
Lortzing V, Oberländer J, Lortzing T, Tohge T, Steppuhn A, Kunze R et al. Insect egg deposition renders plant defence against hatching larvae more effective in a salicylic acid-dependent manner. Plant Cell Environ 2019; 42: 1019–1032.
Valsamakis G, Bittner N, Fatouros NE, Kunze R, Hilker M, Lortzing V. Priming by timing: Arabidopsis thaliana adjusts its priming response to Lepidoptera eggs to the time of larval hatching. Front Plant Sci 2020; 11: 1969.
Das D, Baruah IK, Panda D, Paswan RR, Acharjee S, Sarmah BK. Bruchid beetle ovipositioning mediated defense responses in black gram pods. BMC Plant Biol 2021; 21: 1–22.
Fatouros NE, Voirol LRP, Drizou F, Doan QT, Pineda A, Frago E et al. Role of Large Cabbage White butterfly male-derived compounds in elicitation of direct and indirect egg-killing defenses in the black mustard. Front Plant Sci 2015; 6: 794.
Caarls L, Bassetti N, Doesburg F van, Verbaarschot P, Loon JJA van, Schranz ME et al. Deciphering Brassica plant defence responses to cabbage white butterfly egg-associated molecular patterns. Preprint at https://www.biorxiv.org/content/10.1101/2021.03.29.437462v1.
Lortzing T, Kunze R, Steppuhn A, Hilker M, Lortzing V. Arabidopsis, tobacco, nightshade and elm take insect eggs as herbivore alarm and show similar transcriptomic alarm responses. Sci Rep 2020; 10: 1–16.
Erb M, Meldau S, Howe GA. Role of phytohormones in insect-specific plant reactions. Trends Plant Sci. 2012; 17: 250–259.
Couto D, Zipfel C. Regulation of pattern recognition receptor signalling in plants. Nat. Rev. Immunol. 2016; 16: 537–552.
Tang D, Wang G, Zhou JM. Receptor kinases in plant-pathogen interactions: More than pattern recognition. Plant Cell 2017; 29: 618–637.
van der Burgh AM, Joosten MHAJ. Plant immunity: thinking outside and inside the box. Trends Plant Sci. 2019; 24: 587–601.
Acevedo FE, Peiffer M, Tan CW, Stanley BA, Stanley A, Wang J et al. Fall armyworm-associated gut bacteria modulate plant defense responses. Mol Plant-Microbe Interact 2017; 30: 127–137.
Gust AA, Pruitt R, Nürnberger T. Sensing danger: key to activating plant immunity. Trends Plant Sci 2017; 22: 779–791.
Hilker M, Stein C, Schröder R, Varama M, Mumm R. Insect egg deposition induces defence responses in Pinus sylvestris: characterisation of the elicitor. J Exp Biol 2005; 208: 1849–1854.
Stahl E, Brillatz T, Queiroz EF, Marcourt L, Schmiesing A, Hilfiker O et al. Phosphatidylcholines from Pieris brassicae eggs activate an immune response in Arabidopsis. eLife 2020; 9: 1–21.
Gouhier-Darimont C, Stahl E, Glauser G, Reymond P. The Arabidopsis lectin receptor kinase Lecrk-I.8 is involved in insect egg perception. Front Plant Sci 2019; 10: .
Groux R, Stahl E, Gouhier-Darimont C, Kerdaffrec E, Jimenez-Sandoval P, Santiago J et al. Arabidopsis natural variation in insect egg-induced cell death reveals a role for LECTIN RECEPTOR KINASE-I.1. Plant Physiol 2021; 185: 240–255.
Yang Y, Xu J, Leng Y, Xiong G, Hu J, Zhang G et al. Quantitative trait loci identification, fine mapping and gene expression profiling for ovicidal response to whitebacked planthopper (Sogatella furcifera Horvath) in rice (Oryza sativa L.). BMC Plant Biol 2014; 14: 145.
Mariyammal I, Seram D, Samyuktha SM, Karthikeyan A, Dhasarathan M, Murukarthick J et al. QTL mapping in Vigna radiata × Vigna umbellata population uncovers major genomic regions associated with bruchid resistance. Mol Breed 2019; 39: 1–13.
Tamiru A, Paliwal R, Manthi SJ, Odeny DA, Midega CAO, Khan ZR et al. Genome wide association analysis of a stemborer egg induced “call-for-help” defence trait in maize. Sci Rep 2020; 10: 1–12.
Pashalidou FG, Fatouros NE, van Loon JJA, Dicke M, Gols R. Plant-mediated effects of butterfly egg deposition on subsequent caterpillar and pupal development, across different species of wild Brassicaceae. Ecol Entomol 2015; 40: 444–450.
Harvey JA, Witjes LMA, Benkirane M, Duyts H, Wagenaar R. Nutritional suitability and ecological relevance of Arabidopsis thaliana and Brassica oleracea as foodplants for the cabbage butterfly, Pieris rapae. Plant Ecol 2007; 189: 117–126.
Dogimont C, Chovelon V, Pauquet J, Boualem A, Bendahmane A. The Vat locus encodes for a CC-NBS-LRR protein that confers resistance to Aphis gossypii infestation and A. gossypii -mediated virus resistance. Plant J 2014; 80: 993–1004.
Liu Y, Wu H, Chen H, Liu Y, He J, Kang H et al. A gene cluster encoding lectin receptor kinases confers broad-spectrum and durable insect resistance in rice. Nat Biotechnol 2015; 33: 301–307.
Thoen MPM, Davila Olivas NH, Kloth KJ, Coolen S, Huang PP, Aarts MGM et al. Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping. New Phytol 2017; 213: 1346–1362.
Gust AA, Nürnberger T. A plant surface receptor for sensing insect herbivory. Proc Natl Acad Sci 2020; 117: 32839–32841.
Sun M, Voorrips RE, van’t Westende W, van Kaauwen M, Visser RGF, Vosman B. Aphid resistance in Capsicum maps to a locus containing LRR-RLK gene analogues. Theor Appl Genet 2020; 133: 227–237.
Zhang L, Cai X, Wu J, Liu M, Grob S, Cheng F et al. Improved Brassica rapa reference genome by single-molecule sequencing and chromosome conformation capture technologies. Hortic Res 2018 51 2018; 5: 1–11.
Belser C, Istace B, Denis E, Dubarry M, Baurens FC, Falentin C et al. Chromosome-scale assemblies of plant genomes using nanopore long reads and optical maps. Nat. Plants. 2018; 4: 879–887.
Lou P, Woody S, Greenham K, VanBuren R, Colle M, Edger PP et al. Genetic and genomic resources to study natural variation in Brassica rapa. Plant Direct 2020; 4.: e00285.
Li Y, Liu GF, Ma LM, Liu TK, Zhang CW, Xiao D et al. A chromosome-level reference genome of non-heading Chinese cabbage [Brassica campestris (syn. Brassica rapa) ssp. chinensis]. Hortic Res 2020; 7: 1–13.
Zhao J, Artemyeva A, Del Carpio DP, Basnet RK, Zhang N, Gao J et al. Design of a Brassica rapa core collection for association mapping studies. Genome 2010; 53: 884–898.
Del Carpio DP, Basnet RK, de Vos RCH, Maliepaard C, Visser R, Bonnema G. The patterns of population differentiation in a Brassica rapa core collection. Theor Appl Genet 2011; 122: 1105–1118.
Bagheri H, El-Soda M, van Oorschot I, Hanhart C, Bonnema G, den Bosch TJ van et al. Genetic analysis of morphological traits in a new, versatile, rapid-cycling Brassica rapa recombinant inbred line population. Front Plant Sci 2012; 3: 183.
Zhang X, Meng L, Liu B, Hu Y, Cheng F, Liang J et al. A transposon insertion in FLOWERING LOCUS T is associated with delayed flowering in Brassica rapa. Plant Sci 2015; 241: 211–220.
Corwin JA, Copeland D, Feusier J, Subedy A, Eshbaugh R, Palmer C et al. The quantitative basis of the Arabidopsis innate immune system to endemic pathogens depends on pathogen genetics. PLoS Genet 2016; 12: e1005789.
Yates S, Mikaberidze A, Krattinger SG, Abrouk M, Hund A, Yu K et al. Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch. Plant Phenomics 2019; 2019.
Stewart E l., Croll D, Lendenmann MH, Sanchez-Vallet A, Hartmann FE, Palma-Guerrero J et al. Quantitative trait locus mapping reveals complex genetic architecture of quantitative virulence in the wheat pathogen Zymoseptoria tritici. Mol Plant Pathol 2017; 19: 201–216.
Fordyce RF, Soltis NE, Caseys C, Gwinner R, Corwin JA, Atwell S et al. Digital imaging combined with genome-wide association mapping links loci to plant-pathogen interaction traits. Plant Physiol 2018; 178: 1406–1422.
Kloth KJ, Busscher-Lange J, Wiegers GL, Kruijer W, Buijs G, Meyer RC et al. SIEVE ELEMENT-LINING CHAPERONE1 restricts aphid feeding on Arabidopsis during heat stress. Plant Cell 2017; 29: 2450–2464.
Visschers IGS, Peters JL, Timmermans LLH, Edwards E, Ferrater JB, Balatero CH et al. Resistance to three thrips species in Capsicum spp. depends on site conditions and geographic regions. J Appl Entomol 2019; 143: 929–941.
Kliebenstein DJ. Quantitative genetics and genomics of plant resistance to insects. Annu Plant Rev online 2017; 47: 235–262.
Arnaud D, Desclos-Theveniau M, Zimmerli L. Disease resistance to Pectobacterium carotovorum is negatively modulated by the Arabidopsis lectin receptor kinase LecRK-V.5. Plant Signal Behav 2012; 7: 1070–1072.
Desclos-Theveniau M, Arnaud D, Huang TY, Lin GJC, Chen WY, Lin YC et al. The Arabidopsis lectin receptor kinase LecRK-V.5 represses stomatal immunity induced by Pseudomonas syringae pv. tomato DC3000. PLoS Pathog 2012; 8: e1002513.
Wang Y, Bouwmeester K, Beseh P, Shan W, Govers F. Phenotypic analyses of Arabidopsis T-DNA insertion lines and expression profiling reveal that multiple L-type lectin receptor kinases are involved in plant immunity. Mol Plant-Microbe Interact 2014; 27: 1390–1402.
Mendy B, Wang’ombe MW, Radakovic ZS, Holbein J, Ilyas M, Chopra D et al. Arabidopsis leucine-rich repeat receptor–like kinase NILR1 is required for induction of innate immunity to parasitic nematodes. PLoS Pathog 2017; 13: e1006284.
Roux M, Schwessinger B, Albrecht C, Chinchilla D, Jones A, Holton N et al. The Arabidopsis leucine-rich repeat receptor-like kinases BAK1/SERK3 and BKK1/SERK4 are required for innate immunity to hemibiotrophic and biotrophic pathogens. Plant Cell 2011; 23: 2440–2455.
Yang D-H, Hettenhausen C, Baldwin IT, Wu J. BAK1 regulates the accumulation of jasmonic acid and the levels of trypsin proteinase inhibitors in Nicotiana attenuata’s responses to herbivory. J Exp Bot 2011; 62: 641–652.
Vincent TR, Avramova M, Canham J, Higgins P, Bilkey N, Mugford ST et al. Interplay of plasma membrane and vacuolar ion channels, together with BAK1, elicits rapid cytosolic calcium elevations in Arabidopsis during aphid feeding. Plant Cell 2017; 29: 1460–1479.
Chen K, Fan B, Du L, Chen Z. Activation of hypersensitive cell death by pathogen-induced receptor-like protein kinases from Arabidopsis. Plant Mol Biol 2004; 56: 271–283.
Lapin D, Kovacova V, Sun X, Dongus JA, Bhandari D, Von Born P et al. A coevolved EDS1-SAG101-NRG1 module mediates cell death signaling by TIR-domain immune receptors. Plant Cell 2019; 31: 2430–2455.
Sun X, Lapin D, Feehan JM, Stolze SC, Kramer K, Dongus JA et al. Pathogen effector recognition-dependent association of NRG1 with EDS1 and SAG101 in TNL receptor immunity. Nat Commun 2021 121 2021; 12: 1–15.
Wagner S, Stuttmann J, Rietz S, Guerois R, Brunstein E, Bautor J et al. Structural basis for signaling by exclusive EDS1 heteromeric complexes with SAG101 or PAD4 in plant innate immunity. Cell Host Microbe 2013; 14: 619–630.
Bittner N, Trauer-Kizilelma U, Hilker M. Early plant defence against insect attack: involvement of reactive oxygen species in plant responses to insect egg deposition. Planta 2017; 245: 993–1007.
Torres MA. ROS in biotic interactions. Physiol Plant 2010; 138: 414–429.
Ishikawa T, Uchimiya H, Kawai-Yamada M. The role of plant Bax inhibitor-1 in suppressing H₂O₂-induced cell death. In: Methods in Enzymology. Academic Press, 2013, pp 239–256.
Weis C, Pfeilmeier S, Glawischnig E, Isono E, Pachl F, Hahne H et al. Co-immunoprecipitation-based identification of putative BAX INHIBITOR-1-interacting proteins involved in cell death regulation and plant–powdery mildew interactions. Mol Plant Pathol 2013; 14: 791–802.
Brodersen P, Petersen M, Pike HM, Olszak B, Skov S, Ødum N et al. Knockout of Arabidopsis accelerated-cell-death11 encoding a sphingosine transfer protein causes activation of programmed cell death and defense. Genes Dev 2002; 16: 490–502.
Li Q, Ai G, Shen D, Zou F, Wang J, Bai T et al. A Phytophthora capsici effector targets ACD11 binding partners that regulate ROS-mediated defense response in Arabidopsis. Mol Plant 2019; 12: 565–581.
Lefevere H, Bauters L, Gheysen G. Salicylic acid biosynthesis in plants. Front Plant Sci 2020; 11: 338.
Méline V, Caldwell DL, Kim B-S, Baireddy S, Yang C, Sparks EE et al. Image-based assessment of plant disease progression identifies new genetic loci for resistance. Preprint at https://www.biorxiv.org/content/10.1101/2021.07.13.452064v2.
Groux R, Fouillen L, Mongrand S, Reymond P. Sphingolipids are involved in Pieris brassicae egg-induced cell death in Arabidopsis thaliana. Preprint at https://www.biorxiv.org/content/10.1101/2021.07.09.451813v1.
Cheng F, Sun R, Hou X, Zheng H, Zhang F, Zhang Y et al. Subgenome parallel selection is associated with morphotype diversification and convergent crop domestication in Brassica rapa and Brassica oleracea. Nat Genet 2016; 48: 1218–1224.
McAlvay AC, Ragsdale AP, Mabry ME, Qi X, Bird KA, Velasco P et al. Brassica rapa domestication: untangling wild and feral forms and convergence of crop morphotypes. Mol Biol Evol 2021; 38: 3358–3372.
Mabry ME, Turner-Hissong SD, Gallagher EY, McAlvay AC, An H, Edger PP et al. The evolutionary history of wild, domesticated, and feral Brassica oleracea (Brassicaceae). Mol Biol Evol 2021; 38: 4419–4434.
Turcotte MM, Turley NE, Johnson MTJ. The impact of domestication on resistance to two generalist herbivores across 29 independent domestication events. New Phytol 2014; 204: 671–681.
Whitehead SR, Turcotte MM, Poveda K. Domestication impacts on plant-herbivore interactions: a meta-analysis. Philos Trans R Soc B Biol Sci 2017; 372: 20160034.
Diederichsen E, Frauen M, Linders EGA, Hatakeyama K, Hirai M. Status and perspectives of clubroot resistance breeding in crucifer crops. J Plant Growth Regul 2009; 28: 265–281.
Lv H, Fang Z, Yang L, Zhang Y, Wang Y. An update on the arsenal: mining resistance genes for disease management of Brassica crops in the genomic era. Hortic Res 2020; 7: 34.
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T et al. Fiji: an open-source platform for biological-image analysis. Nat Methods 2012; 9: 676–682.
Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A et al. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics 2017; 33: 2424–2426.
R Core Team. R: a language and environment for statistical computing. 2021. https://www.r-project.org/.
Ogle D, Doll J, Wheeler P, Dinno A. FSA: simple fisheries stock assessment methods. 2021. https://github.com/droglenc/FSA.
Hothorn T, Bretz F, Westfall P. multcomp: simultaneous inference in general parametric models. 2021. https://cran.r-project.org/package=multcomp.
Khan MS, Yin X, van der Putten PEL, Jansen HJ, van Eck HJ, van Eeuwijk FA et al. A model-based approach to analyse genetic variation in potato using standard cultivars and a segregating population. II. Tuber bulking and resource use efficiency. F Crop Res 2019; 242: 107582.
Bates D, Maechler M, Bolker B, Walker S. lme4: linear mixed-effects models using eigen and S4. 2021. https://github.com/lme4/lme4/.
Kyazma. JoinMap. https://www.kyazma.nl/index.php/JoinMap/.
Broman KW, Wu H, Sen S, Churchill G. R/qtl: QTL mapping in experimental crosses. Bioinformatics, 19: 889-890.
BRAD Brassica Database. http://brassicadb.cn/#/Download/. Accessed on August 2021.
Biomatters. Geneious Prime. https://www.geneious.com/.
Lyons E, Pedersen B, Kane J, Alam M, Ming R, Tang H et al. Finding and comparing syntenic regions among Arabidopsis and the outgroups papaya, poplar, and grape: CoGe with rosids. Plant Physiol 2008; 148: 1772–1781.
Haug-Baltzell A, Stephens SA, Davey S, Scheidegger CE, Lyons E. SynMap2 and SynMap3D: Web-based whole-genome synteny browsers. Bioinformatics 2017; 33: 2197–2198.
CoGe: Comparative Genomics. https://genomevolution.org/coge/. Accessed on August 2021.

Table 1. Summary statistics of cell death phenotypic data (BLUEs) of the L58 x R-o-18 RIL population.

Genotype	N	Range	Mean	SD ^a	H²^b
L58	7	0.9 - 1.83	1.53	0.42	0.49
R-o-18	7	0 - 0.95	0.49	0.40
RILs	160	0 - 2.94	0.77	0.51

^aSD standard deviation

^bH² broad-sense heritability

Table 2. Quantitative trait loci associated with HR-like cell death size in the L58 x R-o-18 RIL population.

QTL	chr	LOD peak ^a (cM)	Peak marker (cM) (Mb)		1.5-LOD interval (cM) (Mb)	R²^b	Effect ^c (mm²)
*Pbc1*	A02	5.63 (86)	BrID11121 (85.4) (23.52)	899118\|9904922 - BrID11907 (64.35 - 91.07) (8.65 – 25.29)		17.90	0.45
*Pbc2*	A03	3.33 (129)	BrID90099 (129.2) (31.58)	900988\|9961556 - E3552M3 (110.88 – 147.46) (25.33 – 38.15) ^d		6.35	-0.27
*Pbc3*	A06	4.15 (64)	BrID90095 (63.9) ()	BrID10649 - BrID90309 (61.94 - 72.63) ()		7.32	-0.28

^a LOD threshold of MQM estimated after 1000 permutations and 5% error rate was 2.59.

^b R²indicates the percentage of additive phenotypic variance explained by each QTL.

^c Effect size of each QTL, calculated as μ_A- μ_B,where μ_A is the mean of RILs with the L58 allele and μ_B is the mean of RILs with the R-o-18 allele.

^d E3552M3 is an AFLP marker, thus with unknown location. The right border of the 1.5 LOD interval is conservatively set at the end of the chromosome. The closest marker, BrID101283, is ~1 LOD from the LOD peak and it is located at 36.04 Mb.

No competing interests reported.

AdditionalFile1SupplementaryFigures.pptx
Additional file 1: Supplementary figures (.pptx) Supplementary Figure S1. Germplasm screening of 56 B. rapa accessions reveals variation in HR-like cell death. Supplementary Figure S2. Image-based phenotyping protocol developed to quantify HR-like cell death size on leaves. Supplementary Figure S3. Replicability of two image segmentation methods to quantify HR-like cell death size. Supplementary Figure S4. Genetic linkage map for the L58 x R-o-18 RIL population obtained. Supplementary Figure S5. Quantitative trait loci for HR-like cell death size in the L58 x R-o-18 RIL population. Supplementary Figure S6. Heatmap of genome-wide LOD scores of two-QTL models to investigate epistatic interaction and additive effects. Supplementary Fig. S7. Synteny analysis between B. rapa quantitative trait loci for cell death size and A. thaliana.
AdditionalFile2SupplementaryTables.xlsx
Additional file 2: Additional file 2: Supplementary Tables (.xls) Supplementary Table S1. List of B. rapa accessions used for the germplasm screening. Supplementary Table S2. Evaluation of 56 B. rapa accessions for P. brassicae egg wash-induced necrosis. Supplementary Table S3. Summary statistics of B. rapa homozygous lines re-evaluated for egg- and egg wash-induced cell death. Supplementary Table S4. List of SNPs that were used to design PCR markers and construct a genetic map. Supplementary Table S5. List of InDels that were used to design PCR markers and construct a genetic map. Supplementary Table S6. Genetic linkage map constructed with the L58 x R-o-18 RIL population. Supplementary Table S7. Summary of the L58 x R-o-18 RIL population genetic map. Supplementary Table S8. Candidate genes related to plant immunity found within the regions of QTLs Pbc1 (A02). Supplementary Table S9. Candidate genes related to plant immunity found within the regions of QTLs Pbc2 (A03). Supplementary Table S10. Candidate genes related to plant immunity found within the regions of QTLs Pbc3 (A06).
Additionalfile3Originaldatasets.xlsx
Additional file 3: Original datasets (.xls) Supplementary Table S11. Phenotypic data of the 56 B. rapa accessions tested with the germplasm screening. Supplementary Table S12. Phenotypic data of B. rapa homozygous lines re-evaluated for egg- and egg wash-induced cell death. Supplementary Table S13. Phenotypic data of parents and L58 x R-o-18 population across three repeated experiments. Supplementary Table S14. Marker data used to construct the L58 x R-o-18 RIL population genetic map.

Download PDF

Journal Publication

published 24 Mar, 2022

Read the published version in BMC Plant Biology →

Editorial decision: Major revision
10 Jan, 2022
Reviews received at journal
13 Dec, 2021
Reviewers agreed at journal
10 Dec, 2021
Reviewers agreed at journal
05 Dec, 2021
Reviewers invited by journal
29 Nov, 2021
Editor assigned by journal
24 Nov, 2021
Editor invited by journal
24 Nov, 2021
Submission checks completed at journal
24 Nov, 2021
First submitted to journal
23 Nov, 2021

You are reading this latest preprint version

Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa

Status:

Journal Publication

Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Results

Screening of a B. rapa core collection

Image-based phenotyping of HR-like cell death size on selected B. rapa homozygous lines

Phenotypic analysis and QTL mapping in a RIL population

Identification of candidate genes underlying the QTLs

Discussion

Conclusion

Material And Methods

Plant material

Plant growing conditions

Insect rearing

Oviposition and egg wash treatment

Experimental design

Assessment of HR-like cell death

Development of image-based phenotyping protocol

Phenotypic data analysis

Estimation of variance components

Linkage map construction and QTL analysis

Identification of candidate genes withing the QTLs regions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1