Plant-associated microbes play vital roles in promoting plant growth and health, with plants secreting root exudates into the rhizosphere to attract beneficial microbes. Exudate composition defines the nature of microbial recruitment, with different plant species attracting distinct microbiota to enable optimal adaptation to the soil environment. To more closely examine the relationship between plant genotype and microbial recruitment, we analysed the rhizosphere microbiomes of landrace (Chevallier) and modern (NFC Tipple) barley (Hordeum vulgare) cultivars. Distinct differences were observed between the plant-associated microbiomes of the 2 cultivars, with the plant-growth promoting rhizobacterial genus Pseudomonas substantially more abundant in the Tipple rhizosphere. Striking differences were also observed between the phenotypes of recruited Pseudomonas populations, alongside distinct genotypic clustering by cultivar. Cultivar-driven Pseudomonas selection was driven by root exudate composition, with the greater abundance of hexose sugars secreted from Tipple roots attracting microbes better adapted to growth on these metabolites and vice versa. Cultivar-driven selection also operates at the molecular level, with both gene expression and the abundance of ecologically relevant loci differing between Tipple and Chevallier Pseudomonas isolates. Finally, cultivar-driven selection is important for plant health, with both cultivars showing a distinct preference for microbes selected by their genetic siblings in rhizosphere transplantation assays.

SUMMARYBrassica crops are susceptible to diseases which can be mitigated by breeding for resistance. MAMPs (microbe‐associated molecular patterns) are conserved molecules of pathogens that elicit host defences known as pattern‐triggered immunity (PTI). Necrosis and Ethylene‐inducing peptide 1‐like proteins (NLPs) are MAMPs found in a wide range of phytopathogens. We studied the response to BcNEP2, a representative NLP from Botrytis cinerea, and showed that it contributes to disease resistance in Brassica napus. To map regions conferring NLP response, we used the production of reactive oxygen species (ROS) induced during PTI across a population of diverse B. napus accessions for associative transcriptomics (AT), and bulk segregant analysis (BSA) on DNA pools created from a cross of NLP‐responsive and non‐responsive lines. In silico mapping with AT identified two peaks for NLP responsiveness on chromosomes A04 and C05 whereas the BSA identified one peak on A04. BSA delimited the region for NLP‐responsiveness to 3 Mbp, containing ~245 genes on the Darmor‐bzh reference genome and four co‐segregating KASP markers were identified. The same pipeline with the ZS11 genome confirmed the highest‐associated region on chromosome A04. Comparative BLAST analysis revealed unannotated clusters of receptor‐like protein (RLP) homologues on ZS11 chromosome A04. However, no specific RLP homologue conferring NLP response could be identified. Our results also suggest that BR‐SIGNALLING KINASE1 may be involved with modulating the NLP response. Overall, we demonstrate that responsiveness to NLP contributes to disease resistance in B. napus and define the associated genomic location. These results can have practical application in crop improvement.

Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus) Abstract Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species. Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with Brassica napus constitutive QDR to four contrasting fungal pathogens: Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum. We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to V. longisporum and potentially broad-spectrum QDR. This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in B. napus and is a significant resource for the scientific community. Description of data files Full dataset for input into AT analysis Full datasets (infection phenotypes for A. brassicicola, B. cinerea, or V.longisporum, ROS measurements for chitin, flg22, or elf18) and link to original P. brassicae dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075). Table S1 Mean, normalized phenotype data for resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis. Table S2 Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit. Table S3 Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). Table S4 184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted Arabidopsis thaliana orthologs and corresponding descriptions are shown where possible. Table S5 Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. Table S6 Results from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively. Table S7 Shared gene expression markers (GEMs) associated with resistance to different pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted Arabidopsis thaliana orthologs and corresponding descriptions are shown where possible. Table S8 List of genes in linkage disequilibrium with the top marker for Verticillium longisporum resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in Brassica napus reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the B. napus pantranscriptome is indicated. Predicted Arabidopsis thaliana orthologs and corresponding descriptions are shown where possible.

Quantitative disease resistance (QDR) controls the association of the light leaf spot pathogen with Brassica napus; four QDR loci that were in linkage disequilibrium and eight gene expression markers were identified. Quantitative disease resistance (QDR) can provide durable control of pathogens in crops in contrast to resistance (R) gene-mediated resistance which can break down due to pathogen evolution. QDR is therefore a desirable trait in crop improvement, but little is known about the causative genes, and so it is difficult to incorporate into breeding programmes. Light leaf spot, caused by Pyrenopeziza brassicae, is an important disease of oilseed rape (canola, Brassica napus). To identify new QDR gene loci, we used a high-throughput screening pathosystem with P. brassicae on 195 lines of B. napus combined with an association transcriptomics platform. We show that all resistance against P. brassicae was associated with QDR and not R gene-mediated. We used genome-wide association analysis with an improved B. napus population structure to reveal four gene loci significantly (P = 0.0001) associated with QDR in regions showing linkage disequilibrium. On chromosome A09, enhanced resistance was associated with heterozygosity for a cytochrome P450 gene co-localising with a previously described locus for seed glucosinolate content. In addition, eight significant gene expression markers with a false discovery rate of 0.001 were associated with QDR against P. brassicae. For seven of these, expression was positively correlated with resistance, whereas for one, a HXXXD-type acyl-transferase, negative correlation indicated a potential susceptibility gene. The study identifies novel QDR loci for susceptibility and resistance, including novel cryptic QDR genes associated with heterozygosity, that will inform future crop improvement.

UV-B radiation regulates numerous morphogenic, biochemical and physiological responses in plants, and can stimulate some responses typically associated with other abiotic and biotic stimuli, including invertebrate herbivory. Removal of UV-B from the growing environment of various plant species has been found to increase their susceptibility to consumption by invertebrate pests, however, to date, little research has been conducted to investigate the effects of UV-B on crop susceptibility to field pests. Here, we report findings from a multi-omic and genetic-based study investigating the mechanisms of UV-B-stimulated resistance of the crop, Brassica napus (oilseed rape), to herbivory from an economically important lepidopteran specialist of the Brassicaceae , Plutella xylostella (diamondback moth). The UV-B photoreceptor, UV RESISTANCE LOCUS 8 (UVR8), was not found to mediate resistance to this pest. RNA-Seq and untargeted metabolomics identified components of the sinapate/lignin biosynthetic pathway that were similarly regulated by UV-B and herbivory. Arabidopsis mutants in genes encoding two enzymes in the sinapate/lignin biosynthetic pathway, CAFFEATE O-METHYLTRANSFERASE 1 (COMT1) and ELICITOR-ACTIVATED GENE 3–2 (ELI3-2), retained UV-B-mediated resistance to P. xylostella herbivory. However, the overexpression of B. napus COMT1 in Arabidopsis further reduced plant susceptibility to P. xylostella herbivory in a UV-B-dependent manner. These findings demonstrate that overexpression of a component of the sinapate/lignin biosynthetic pathway in a member of the Brassicaceae can enhance UV-B-stimulated resistance to herbivory from P. xylostella. Graphical abstract

Plant associated microbes play vital roles in promoting plant growth and health, with plants secreting root exudates into the rhizosphere to attract beneficial microbes. Exudate composition defines the nature of microbial recruitment, with different plant species attracting distinct microbiota to enable optimal adaptation to the soil environment. To more closely examine the relationship between plant genotype and microbial recruitment, we analysed the rhizosphere microbiomes of landrace (Chevallier) and modern (NFC Tipple) barley cultivars. Distinct differences were observed between the plant-associated microbiomes of the two cultivars, with the plant-growth promoting rhizobacterial genus Pseudomonas substantially more abundant in the Tipple rhizosphere. Striking differences were also observed between the phenotypes of recruited Pseudomonas populations, alongside distinct genotypic clustering by cultivar. Cultivar-driven Pseudomonas selection was driven by root exudate composition, with the greater abundance of hexose sugars secreted from Tipple roots attracting microbes better adapted to growth on these metabolites, and vice versa. Cultivar-driven selection also operates at the molecular level, with both gene expression and the abundance of ecologically relevant loci differing between Tipple and Chevallier Pseudomonas isolates. Finally, cultivar-driven selection is important for plant health, with both cultivars showing a distinct preference for microbes selected by their genetic siblings in rhizosphere transplantation assays.

Brassicas are important crops susceptible to significant losses caused by disease: thus, breeding resistant lines can mitigate the effects of pathogens. MAMPs (microbe-associated molecular patterns) are conserved molecules of pathogens that elicit host defence responses known as pattern-triggered immunity (PTI). Necrosis & Ethylene-inducing peptide 1-like proteins (NLPs) are MAMPs found in a wide range of phytopathogens, including major disease-causing fungal species. We studied the response to the BcNEP2 from Botrytis cinerea as a representative NLP in Brassica napus to improve our understanding of recognition mechanisms that could enable the development of disease-resistant crops.To genetically map regions responsible for NLP recognition, we used an associative transcriptomics (AT) approach using diverse B. napus accessions and bulk segregant analysis (BSA) on DNA pools created from a bi-parental cross of NLP-responsive (Ningyou1) and non-responsive (Ningyou7) lines . In silico mapping with AT identified two peaks associated with NLP recognition on chromosomes A04 and C05 whereas the BSA narrowed it down to a main peak on A04. BSA delimited the region associated with NLP-responsiveness to 3 Mbp, containing ∼245 genes on the Darmor -bzh reference genome. Variants detected in the region were used for KASP marker design and four KASP markers were identified co-segregating with the phenotype. The same pipeline was performed with the ZS11 genome, and the highest associated region was confirmed on chromosome A04. Comparative BLAST analysis revealed there were unannotated clusters of RLP homologs on ZS11 chromosome A04. To reduce the number of candidate genes responsible for NLP recognition, RNA-Seq data was used to detect the unannotated expressed putative genes. Screening the BSA Ning1×7 population demonstrated a highly significant association between NLP-recognition and resistance to Botrytis cinerea . Also, the lines non-responsive to NLP had significantly greater response to the bacterial MAMP flg22. Additionally, BnaA01g02190D , a homologue of Arabidopsis AtBSK1 ( At4g35230 ) BR-SIGNALLING KINASE1, was associated with a high BcNEP2-induced ROS response phenotype. We show that in Arabidopsis, Atbsk1 mutants had significantly lower response to BcNEP2 and increased susceptibility to B. cinerea ( p-value =1.12e-14\***|). Overall, the results define the genomic location for NLP-recognition on the B. napus genome and demonstrate that NLP recognition has a positive contribution to disease resistance which can have practical application in crop improvement.### Competing Interest StatementThe authors have declared no competing interest.

Translational research is required to advance fundamental knowledge on plant immunity towards application in crop improvement. Recognition of microbe/pathogen-associated molecular patterns (MAMPs/PAMPs) triggers a first layer of immunity in plants. The broadly occurring family of necrosis- and ethylene-inducing peptide 1 (NEP1)-like proteins (NLPs) contains immunogenic peptide patterns that are recognized by a number of plant species. Arabidopsis can recognize NLPs by the pattern recognition receptor AtRLP23 and its co-receptors SOBIR1, BAK1, and BKK1, leading to induction of defence responses including the production of reactive oxygen species (ROS) and elevation of intracellular [Ca2+]. However, little is known about NLP perception in Brassica crop species. Within 12 diverse accessions for each of six Brassica crop species, we demonstrate variation in response to Botrytis cinerea NLP BcNEP2, with Brassica oleracea (CC genome) being nonresponsive and only two Brassica napus cultivars responding to BcNEP2. Peptides derived from four fungal pathogens of these crop species elicited responses similar to BcNEP2 in B. napus and Arabidopsis. Induction of ROS by NLP peptides was strongly reduced in Atrlp23, Atsobir1 and Atbak1-5 Atbkk1-1 mutants, confirming that recognition of Brassica pathogen NLPs occurs in a similar manner to that of HaNLP3 from Hyaloperonospora arabidopsidis in Arabidopsis. In silico analysis of the genomes of two B. napus accessions showed similar presence of homologues for AtBAK1, AtBKK1 and AtSOBIR1 but variation in the organization of AtRLP23 homologues. We could not detect a strong correlation between the ability to respond to NLP peptides and resistance to B. cinerea.

Loss of Mildew Resistance Locus O (MLO) in barley confers durable resistance to powdery mildew fungi, which has led to its wide deployment in agriculture. Although MLO is a susceptibility factor, it has become nearly synonymous with powdery mildew resistance. However, MLO has been recently implicated in colonization by arbuscular mycorrhizal fungi and a fungal endophyte, confirming its importance for biotrophic interactions and in promoting symbiosis. Other MLO proteins are involved in essential sensory processes, particularly fertilization and thigmotropism. We propose external stimulus perception as a common theme in these interactions and consider a unified biochemical role, potentially relating to reactive oxygen species (ROS) and calcium regulation, for MLOs across tissues and processes.

Disease resistance genes encoding nucleotide-binding and leucine-rich repeat (NLR) intracellular immune receptor proteins detect pathogens by the presence of pathogen effectors. Plant genomes typically contain hundreds of NLR-encoding genes. The availability of the hexaploid wheat (Triticum aestivum) cultivar Chinese Spring reference genome allows a detailed study of its NLR complement. However, low NLR expression and high intrafamily sequence homology hinder their accurate annotation. Here, we developed NLR-Annotator, a software tool for in silico NLR identification independent of transcript support. Although developed for wheat, we demonstrate the universal applicability of NLR-Annotator across diverse plant taxa. We applied our tool to wheat and combined it with a transcript-validated subset of genes from the reference gene annotation to characterize the structure, phylogeny, and expression profile of the NLR gene family. We detected 3,400 full-length NLR loci, of which 1,560 were confirmed as expressed genes with intact open reading frames. NLRs with integrated domains mostly group in specific subclades. Members of another subclade predominantly locate in close physical proximity to NLRs carrying integrated domains, suggesting a paired helper function. Most NLRs (88%) display low basal expression (in the lower 10 percentile of transcripts). In young leaves subjected to biotic stress, we found up-regulation of 266 of the NLRs To illustrate the utility of our tool for the positional cloning of resistance genes, we estimated the number of NLR genes within the intervals of mapped rust resistance genes. Our study will support the identification of functional resistance genes in wheat to accelerate the breeding and engineering of disease-resistant varieties.

Summary Loss of barley Mildew Resistance Locus O (MLO) is known to confer durable and robust resistance to powdery mildew (Blumeria graminis), a biotrophic fungal leaf pathogen. Based on the increased expression of MLO in mycorrhizal roots and its presence in a clade of the MLO family that is specific to mycorrhizal‐host species, we investigated the potential role of MLO in arbuscular mycorrhizal interactions. Using mutants from barley (Hordeum vulgare), wheat (Triticum aestivum), and Medicago truncatula, we demonstrate a role for MLO in colonization by the arbuscular mycorrhizal fungus Rhizophagus irregularis. Early mycorrhizal colonization was reduced in mlo mutants of barley, wheat, and M. truncatula, and this was accompanied by a pronounced decrease in the expression of many of the key genes required for intracellular accommodation of arbuscular mycorrhizal fungi. These findings show that clade IV MLOs are involved in the establishment of symbiotic associations with beneficial fungi, a role that has been appropriated by powdery mildew.

Wide crosses between genetically diverged parents may reveal novel loci for crop improvement that are not apparent in crosses between elite cultivars. The landrace Chevallier was a noted malting barley first grown in 1820. To identify potentially novel alleles for agronomic traits, Chevallier was crossed with the modern malting cultivar NFC Tipple generating two genetically diverse recombinant inbred line populations. Genetic maps were produced using genotyping-by-sequencing and 384-SNP genotyping, and the populations were phenotyped for agronomic traits to allow the identification of quantitative trait loci (QTL). Within the semi-dwarf 1 (sdw1) region on chromosome 3H Chevallier conferred increased plant height and reduced tiller number, with QTL for these traits explaining 79.4% and 35.2% of the phenotypic variance observed, respectively. Chevallier was also associated with powdery mildew susceptibility, with a QTL on 1H accounting for up to 19.1% of the variance and resistance at this locus most likely resulting from an Mla variant from Tipple. Two novel QTL for physiological leaf spotting were identified on 3H and 7H, explaining up to 17.1% of the variance and with the Chevallier allele reducing symptom severity on 7H. Preliminary micromalting analysis was also undertaken to compare the malting characteristics of Chevallier and Tipple. Chevallier malt contained significantly lower levels of both α-amylase and wort β-glucan than Tipple malt, however no significant differences were observed for the remaining malting parameters measured. This suggests that the most obvious improvements in barley since the introduction of Chevallier are for agronomic traits such as height, yield and lodging resistance rather than for malting characteristics. Overall, our results demonstrate that this wide cross between Chevallier and Tipple may provide a source of novel QTL for barley breeding.

Disease resistance genes encoding intracellular immune receptors of the nucleotide-binding and leucine-rich repeat (NLR) class of proteins detect pathogens by the presence of pathogen effectors. Plant genomes typically contain hundreds of NLR encoding genes. The availability of the hexaploid wheat cultivar Chinese Spring reference genome now allows a detailed study of its NLR complement. However, low NLR expression as well as high intra-family sequence homology hinders their accurate gene annotation. Here we developed NLR-Annotator for in silico NLR identification independent of transcript support. Although developed for wheat, we demonstrate the universal applicability of NLR-Annotator across diverse plant taxa. Applying our tool to wheat and combining it with a transcript-validated subset of genes from the reference gene annotation, we characterized the structure, phylogeny and expression profile of the NLR gene family. We detected 3,400 full-length NLR loci of which 1,540 were confirmed as complete genes. NLRs with integrated domains mostly group in specific sub-clades. Members of another subclade predominantly locate in close physical proximity to NLRs carrying integrated domains suggesting a paired helper-function. Most NLRs (88%) display low basal expression (in the lower 10 percentile of transcripts), which may be tissue-specific and/or induced by biotic stress. As a case study for applying our tool to the positional cloning of resistance genes, we estimated the number of NLR genes within the intervals of mapped rust resistance genes. Our study will support the identification of functional resistance genes in wheat to accelerate the breeding and engineering of disease resistant varieties.

The coordinated expression of highly related homoeologous genes in polyploid species underlies the phenotypes of many of the world's major crops. Here we combine extensive gene expression datasets to produce a comprehensive, genome-wide analysis of homoeolog expression patterns in hexaploid bread wheat. Bias in homoeolog expression varies between tissues, with ~30% of wheat homoeologs showing nonbalanced expression. We found expression asymmetries along wheat chromosomes, with homoeologs showing the largest inter-tissue, inter-cultivar, and coding sequence variation, most often located in high-recombination distal ends of chromosomes. These transcriptionally dynamic genes potentially represent the first steps toward neo- or subfunctionalization of wheat homoeologs. Coexpression networks reveal extensive coordination of homoeologs throughout development and, alongside a detailed expression atlas, provide a framework to target candidate genes underpinning agronomic traits in wheat.