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Zymoseptoria tritici is the causal agent of Septoria tritici blotch, a major pathogen of wheat globally and the most damaging pathogen of wheat in Europe. A gene-for-gene (GFG) interaction between Z. tritici and wheat cultivars carrying the Stb6 resistance gene has been postulated for many years, but the genes have not been identified. We identified AvrStb6 by combining quantitative trait locus mapping in a cross between two Swiss strains with a genome-wide association study using a natural population of c. 100 strains from France. We functionally validated AvrStb6 using ectopic transformations. AvrStb6 encodes a small, cysteine-rich, secreted protein that produces an avirulence phenotype on wheat cultivars carrying the Stb6 resistance gene. We found 16 nonsynonymous single nucleotide polymorphisms among the tested strains, indicating that AvrStb6 is evolving very rapidly. AvrStb6 is located in a highly polymorphic subtelomeric region and is surrounded by transposable elements, which may facilitate its rapid evolution to overcome Stb6 resistance. AvrStb6 is the first avirulence gene to be functionally validated in Z. tritici, contributing to our understanding of avirulence in apoplastic pathogens and the mechanisms underlying GFG interactions between Z. tritici and wheat.

Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG). GCNG encodes the spatial information as a graph and combines it with expression data using supervised training. GCNG improves upon prior methods used to analyze spatial transcriptomics data and can propose novel pairs of extracellular interacting genes. The output of GCNG can also be used for downstream analysis including functional gene assignment. Supporting website with software and data: https://github.com/xiaoyeye/GCNG .

• The control of stem canker disease of Brassica napus (rapeseed), caused by the fungus Leptosphaeria maculans is based largely on plant genetic resistance: single-gene specific resistance (Rlm genes) or quantitative, polygenic, adult-stage resistance. Our working hypothesis was that quantitative resistance partly obeys the gene-for-gene model, with resistance genes ‘recognizing’ fungal effectors expressed during late systemic colonization. • Five LmSTEE (stem-expressed effector) genes were selected and placed under the control of the AvrLm4-7 promoter, an effector gene highly expressed at the cotyledon stage of infection, for miniaturized cotyledon inoculation test screening of a gene pool of 204 rapeseed genotypes. • We identified a rapeseed genotype, ‘Yudal’, expressing hypersensitive response to LmSTEE98. The LmSTEE98–RlmSTEE98 interaction was further validated by inactivation of the LmSTEE98 gene with a CRISPR-Cas9 approach. Isolates with mutated versions of LmSTEE98 induced more severe stem symptoms than the wild-type isolate in ‘Yudal’. This single-gene resistance was mapped in a 0.6 cM interval of the ‘Darmor_bzh’ × ‘Yudal’ genetic map. • One typical gene-for-gene interaction contributes partly to quantitative resistance when L. maculans colonizes the stems of rapeseed. With numerous other effectors specific to stem colonization, our study provides a new route for resistance gene discovery, elucidation of quantitative resistance mechanisms and selection for durable resistance.

Cultivar-strain specificity in the wheat-Zymoseptoria tritici pathosystem determines the infection outcome and is controlled by resistance genes on the host side, many of which have been identified. On the pathogen side, however, the molecular determinants of specificity remain largely unknown. We used genetic mapping, targeted gene disruption and allele swapping to characterise the recognition of the new avirulence factor Avr3D1. We then combined population genetic and comparative genomic analyses to characterise the evolutionary trajectory of Avr3D1. Avr3D1 is specifically recognised by wheat cultivars harbouring the Stb7 resistance gene, triggering a strong defence response without preventing pathogen infection and reproduction. Avr3D1 resides in a cluster of putative effector genes located in a genome region populated by independent transposable element insertions. The gene was present in all 132 investigated strains and is highly polymorphic, with 30 different protein variants identified. We demonstrated that specific amino acid substitutions in Avr3D1 led to evasion of recognition. These results demonstrate that quantitative resistance and gene-for-gene interactions are not mutually exclusive. Localising avirulence genes in highly plastic genomic regions probably facilitates accelerated evolution that enables escape from recognition by resistance proteins.

Development of resistant crops is the most effective way to control plant diseases to safeguard food and feed production. Disease resistance is commonly based on resistance genes, which generally mediate the recognition of small proteins secreted by invading pathogens. These proteins secreted by pathogens are called ‘avirulence’ proteins. Their identification is important for being able to assess the usefulness and durability of resistance genes in agricultural settings. We have used genome sequencing of a set of strains of the melon wilt fungus Fusarium oxysporum f. sp. melonis (Fom), bioinformatics-based genome comparison and genetic transformation of the fungus to identify AVRFOM2, the gene that encodes the avirulence protein recognized by the melon Fom-2 gene. Both an unbiased and a candidate gene approach identified a single candidate for the AVRFOM2 gene. Genetic complementation of AVRFOM2 in three different race 2 isolates resulted in resistance of Fom-2-harbouring melon cultivars. AvrFom2 is a small, secreted protein with two cysteine residues and weak similarity to secreted proteins of other fungi. The identification of AVRFOM2 will not only be helpful to select melon cultivars to avoid melon Fusarium wilt, but also to monitor how quickly a Fom population can adapt to deployment of Fom-2-containing cultivars in the field.

Background The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice ( Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. Results Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC 5 F 2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8 , were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to ‘oxidation-reduction process’ and ‘response to stress’ differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to ‘response to stress’) decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729 , and the genotype of BC 5 F 2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to ‘oxidation-reduction process’, ‘response to stress’ and ‘protein phosphorylation’ interacted with LOC_Os12g18729 . Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. Conclusions In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12 . Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12 . These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.

Background: Allergic rhinitis (AR) is a nasal inflammatory disease resulting from a complex interplay between genetic and environmental factors. The association between Toll-like receptor (TLR) signaling pathway and environmental factors in AR pathogenesis remains to be explored. This study aims to assess the genetic association of AR with single nucleotide polymorphisms (SNPs) in TLR signaling pathway, and investigate the roles of gene-gene and gene-environment interactions in AR. Methods: A total of 452 AR patients and 495 healthy controls from eastern China were enrolled in this hospital-based case-control study. We evaluated putatively functional genetic polymorphisms in TLR2, TLR4 and CD14 genes for their association with susceptibility to AR and related clinical phenotypes. Interactions between environmental factors (such as traffic pollution, residence, pet keeping) and polymorphisms with AR were examined using logistic regression. Models were stratified by genotype and interaction terms, and tested for the significance of gene-gene and gene-environment interactions. Results: In the single-locus analysis, two SNPs in CD14, rs2563298 (A/C) and rs2569191 (CAT) were associated with a significantly decreased risk of AR. Compared with the GG genotype, the GT and GT/TT genotypes of TLR2 rs7656411 (GAT) were associated with a significantly increased risk of AR. Gene-gene interactions (eg, TLR2 rs7656411, TLR4 rsl927914, and CD14 rs2563298) was associated with AR. Gene-environment interactions (eg, TLR4 or CD14 polymorphisms and certain environmental exposures) were found in AR cases, but they were not significant after Bonferroni correction. Conclusion: The genetic polymorphisms of TLR2 and CD14 and gene-gene interactions in TLR signaling pathway were associated with susceptibility to AR in this Han Chinese population. However, the present results were limited to support the association between gene-environment interactions and AR. Keywords: allergic rhinitis, toll-like receptors, CD14, single nucleotide polymorphism, gene-gene interaction, gene-environment interaction

Summary The importance of rhizomicrobiome in plant development, nutrition acquisition and stress tolerance is unquestionable. Relevant plant genes corresponding to the above functions also regulate rhizomicrobiome construction. Deciphering the molecular regulatory network of plant‐microbe interactions could substantially contribute to improving crop yield and quality. Here, the plant gene‐related nutrient uptake, biotic and abiotic stress resistance, which may influence the composition and function of microbial communities, are discussed in this review. In turn, the influence of microbes on the expression of functional plant genes, and thereby plant growth and immunity, is also reviewed. Moreover, we have specifically paid attention to techniques and methods used to link plant functional genes and rhizomicrobiome. Finally, we propose to further explore the molecular mechanisms and signalling pathways of microbe‐host gene interactions, which could potentially be used for managing plant health in agricultural systems.

This article is a Commentary on Kariyawasam et al. (2022), 233: 409–426 and Richards et al. (2022), 233: 427–442.